From 410548dabf850cf2e4e5604b352d03de4d03a654 Mon Sep 17 00:00:00 2001
From: pyth0n1c <emcginnis@splunk.com>
Date: Fri, 15 Nov 2024 10:40:10 -0800
Subject: [PATCH] Removed required_fields from, well, everything.

---
 ...e_of_blocked_outbound_traffic_from_aws.yml |  5 --
 ...loud_infrastructure_api_calls_per_user.yml |  6 +-
 .../baseline_of_cloud_instances_destroyed.yml | 14 +---
 .../baseline_of_cloud_instances_launched.yml  | 14 +---
 ...loud_security_group_api_calls_per_user.yml |  7 +-
 ...baseline_of_command_line_length___mltk.yml |  9 +--
 .../baseline_of_dns_query_length___mltk.yml   |  7 +-
 ...ine_of_kubernetes_container_network_io.yml | 60 +++++++-------
 ..._kubernetes_container_network_io_ratio.yml | 63 ++++++++-------
 ...aseline_of_kubernetes_process_resource.yml | 54 +++++++------
 ...e_of_kubernetes_process_resource_ratio.yml | 70 ++++++++--------
 ...aseline_of_network_acl_activity_by_arn.yml |  3 -
 ..._of_s3_bucket_deletion_activity_by_arn.yml |  3 -
 ...line_of_security_group_activity_by_arn.yml |  3 -
 baselines/baseline_of_smb_traffic___mltk.yml  |  8 +-
 baselines/count_of_assets_by_category.yml     |  4 -
 ...ount_of_unique_ips_connecting_to_ports.yml |  4 -
 ..._list_of_approved_aws_service_accounts.yml |  4 -
 ...bited_processes_to_enterprise_security.yml |  4 +-
 .../baseline_of_api_calls_per_user_arn.yml    |  6 +-
 ..._aws_instances_launched_by_user___mltk.yml | 36 ++++-----
 ...ws_instances_terminated_by_user___mltk.yml | 36 ++++-----
 ..._api_call_per_user_roles_in_cloudtrail.yml |  9 +--
 ...seen_aws_provisioning_activity_sources.yml | 10 +--
 .../deprecated/previously_seen_ec2_amis.yml   |  7 +-
 .../previously_seen_ec2_instance_types.yml    |  7 +-
 .../previously_seen_ec2_launches_by_user.yml  |  5 --
 .../previously_seen_users_in_cloudtrail.yml   |  7 +-
 ...te_previously_seen_users_in_cloudtrail.yml |  7 +-
 baselines/discover_dns_records.yml            |  5 --
 baselines/dnstwist_domain_names.yml           |  4 +-
 ...ystems_creating_remote_desktop_traffic.yml |  4 -
 ...stems_receiving_remote_desktop_traffic.yml |  4 -
 .../identify_systems_using_remote_desktop.yml |  4 -
 baselines/monitor_successful_backups.yml      |  2 -
 baselines/monitor_unsuccessful_backups.yml    |  2 -
 ...iously_seen_aws_cross_account_activity.yml |  5 --
 ...n_aws_cross_account_activity___initial.yml |  9 +--
 ...en_aws_cross_account_activity___update.yml |  7 --
 baselines/previously_seen_aws_regions.yml     |  3 -
 ...loud_api_calls_per_user_role___initial.yml |  8 +-
 ...cloud_api_calls_per_user_role___update.yml |  6 --
 ...ud_compute_creations_by_user___initial.yml |  7 +-
 ...oud_compute_creations_by_user___update.yml |  5 --
 ...ly_seen_cloud_compute_images___initial.yml |  6 +-
 ...sly_seen_cloud_compute_images___update.yml |  4 -
 ...cloud_compute_instance_types___initial.yml |  4 -
 ..._cloud_compute_instance_types___update.yml |  4 -
 ...stance_modifications_by_user___initial.yml |  8 +-
 ...nstance_modifications_by_user___update.yml |  6 --
 ...rovisioning_activity_sources___initial.yml |  7 +-
 ...provisioning_activity_sources___update.yml |  5 --
 ...reviously_seen_cloud_regions___initial.yml |  6 +-
 ...previously_seen_cloud_regions___update.yml |  4 -
 ...previously_seen_command_line_arguments.yml |  4 -
 ...viously_seen_ec2_modifications_by_user.yml |  4 -
 ...een_running_windows_services___initial.yml |  6 +-
 ...seen_running_windows_services___update.yml |  6 +-
 ...sly_seen_s3_bucket_access_by_remote_ip.yml |  5 --
 ...sly_seen_users_in_cloudtrail___initial.yml |  7 +-
 ...usly_seen_users_in_cloudtrail___update.yml |  5 --
 ...ly_seen_zoom_child_processes___initial.yml |  7 +-
 ...sly_seen_zoom_child_processes___update.yml |  7 +-
 ...ady_for_spectre_meltdown_windows_patch.yml |  8 --
 .../windows_updates_install_failures.yml      |  4 -
 .../windows_updates_install_successes.yml     |  4 -
 ...rushftp_server_side_template_injection.yml | 51 ++++++++----
 ...ct_distributed_password_spray_attempts.yml | 56 +++++++------
 .../detect_new_login_attempts_to_routers.yml  | 24 ++++--
 .../detect_password_spray_attempts.yml        | 63 ++++++++-------
 .../email_attachments_with_lots_of_spaces.yml | 41 ++++++----
 ...itten_outside_of_the_outlook_directory.yml | 35 +++++---
 ...s_sending_high_volume_traffic_to_hosts.yml | 40 +++++++---
 .../ivanti_vtm_new_account_creation.yml       | 42 ++++++----
 .../monitor_email_for_brand_abuse.yml         | 26 ++++--
 .../no_windows_updates_in_a_time_frame.yml    | 29 +++++--
 ...entication_failed_during_mfa_challenge.yml | 45 +++++++----
 .../okta_idp_lifecycle_modifications.yml      | 48 ++++++-----
 .../application/okta_mfa_exhaustion_hunt.yml  | 38 ++++++---
 ...e_and_response_for_verify_push_request.yml | 51 ++++++++----
 ...a_multi_factor_authentication_disabled.yml | 44 ++++++----
 .../okta_multiple_accounts_locked_out.yml     | 43 ++++++----
 ..._multiple_failed_mfa_requests_for_user.yml | 39 +++++----
 ...failed_requests_to_access_applications.yml | 39 ++++++---
 ..._users_failing_to_authenticate_from_ip.yml | 45 +++++++----
 .../okta_new_api_token_created.yml            | 47 ++++++-----
 .../okta_new_device_enrolled_on_account.yml   | 43 ++++++----
 ...g_detection_with_fastpass_origin_check.yml | 28 ++++---
 .../okta_risk_threshold_exceeded.yml          | 53 ++++++++----
 ...uccessful_single_factor_authentication.yml | 46 ++++++-----
 .../okta_suspicious_activity_reported.yml     | 45 +++++++----
 ...kta_suspicious_use_of_a_session_cookie.yml | 48 +++++++----
 .../okta_threatinsight_threat_detected.yml    | 48 ++++++-----
 ...kta_unauthorized_access_to_application.yml | 46 +++++++----
 .../okta_user_logins_from_multiple_cities.yml | 50 ++++++++----
 ..._auth_source_and_verification_response.yml | 55 +++++++++----
 ..._multiple_failed_mfa_requests_for_user.yml | 41 ++++++----
 ..._new_mfa_method_after_credential_reset.yml | 56 ++++++++-----
 ...gid_new_mfa_method_registered_for_user.yml | 43 ++++++----
 ...suspicious_email_attachment_extensions.yml | 36 ++++++---
 .../application/suspicious_java_classes.yml   | 30 ++++---
 ...servers_executing_suspicious_processes.yml | 36 ++++++---
 .../windows_ad_add_self_to_group.yml          | 28 ++++---
 ...ows_ad_dangerous_deny_acl_modification.yml | 61 ++++++++++----
 ...ws_ad_dangerous_group_acl_modification.yml | 69 ++++++++++++----
 ...ows_ad_dangerous_user_acl_modification.yml | 66 +++++++++++----
 ...ws_ad_dcshadow_privileges_acl_addition.yml | 60 ++++++++++----
 .../windows_ad_domain_root_acl_deletion.yml   | 59 ++++++++++----
 ...indows_ad_domain_root_acl_modification.yml | 59 ++++++++++----
 .../application/windows_ad_gpo_deleted.yml    | 45 +++++++----
 .../application/windows_ad_gpo_disabled.yml   | 43 ++++++----
 .../windows_ad_gpo_new_cse_addition.yml       | 56 ++++++++-----
 .../windows_ad_hidden_ou_creation.yml         | 58 ++++++++++----
 .../windows_ad_object_owner_updated.yml       | 48 +++++++----
 ...ndows_ad_privileged_group_modification.yml | 24 +++---
 .../windows_ad_self_dacl_assignment.yml       | 59 ++++++++++----
 ...s_ad_suspicious_attribute_modification.yml | 50 +++++++-----
 ...windows_ad_suspicious_gpo_modification.yml | 60 ++++++++++----
 ..._group_or_object_modification_activity.yml | 28 +++++--
 ...increase_in_user_modification_activity.yml | 32 ++++++--
 ...mber_of_cloud_infrastructure_api_calls.yml | 36 ++++++---
 ...gh_number_of_cloud_instances_destroyed.yml | 38 ++++++---
 ...igh_number_of_cloud_instances_launched.yml | 37 ++++++---
 ...mber_of_cloud_security_group_api_calls.yml | 38 ++++++---
 ..._eks_kubernetes_cluster_scan_detection.yml | 34 ++++----
 ...azon_eks_kubernetes_pod_scan_detection.yml | 37 +++++----
 ...concurrent_sessions_from_different_ips.yml | 48 +++++++----
 ..._aws_defense_evasion_delete_cloudtrail.yml | 43 ++++++----
 ...se_evasion_delete_cloudwatch_log_group.yml | 44 ++++++----
 ...fense_evasion_impair_security_services.yml | 41 ++++++----
 ...efense_evasion_stop_logging_cloudtrail.yml | 49 ++++++++----
 ..._aws_defense_evasion_update_cloudtrail.yml | 48 +++++++----
 ...ontainer_upload_outside_business_hours.yml | 45 +++++++----
 ..._aws_ecr_container_upload_unknown_user.yml | 43 +++++++---
 .../cloud/asl_aws_iam_delete_policy.yml       | 38 ++++++---
 .../asl_aws_iam_failure_group_deletion.yml    | 50 ++++++++----
 .../asl_aws_iam_successful_group_deletion.yml | 35 +++++---
 ...s_multi_factor_authentication_disabled.yml | 44 ++++++----
 ...aws_new_mfa_method_registered_for_user.yml | 37 ++++++---
 ...ttribute_modification_for_exfiltration.yml | 46 +++++++----
 ...concurrent_sessions_from_different_ips.yml | 42 ++++++----
 ...sole_login_failed_during_mfa_challenge.yml | 46 ++++++-----
 ..._policy_version_to_allow_all_resources.yml | 48 ++++++-----
 detections/cloud/aws_createaccesskey.yml      | 33 +++++---
 detections/cloud/aws_createloginprofile.yml   | 42 +++++++---
 .../aws_credential_access_failed_login.yml    | 38 +++++----
 .../aws_credential_access_getpassworddata.yml | 44 ++++++----
 ...s_credential_access_rds_password_reset.yml | 35 +++++---
 ...ctivity_from_previously_unseen_account.yml | 45 ++++++++---
 .../aws_defense_evasion_delete_cloudtrail.yml | 43 ++++++----
 ...se_evasion_delete_cloudwatch_log_group.yml | 43 ++++++----
 ...fense_evasion_impair_security_services.yml | 38 +++++----
 ...aws_defense_evasion_putbucketlifecycle.yml | 40 ++++++----
 ...efense_evasion_stop_logging_cloudtrail.yml | 43 ++++++----
 .../aws_defense_evasion_update_cloudtrail.yml | 43 ++++++----
 .../aws_detect_attach_to_role_policy.yml      | 24 ++++--
 .../aws_detect_permanent_key_creation.yml     | 30 ++++---
 detections/cloud/aws_detect_role_creation.yml | 39 ++++-----
 .../aws_detect_sts_assume_role_abuse.yml      | 34 ++++----
 ...aws_detect_sts_get_session_token_abuse.yml | 32 ++++----
 ...g_keys_with_encrypt_policy_without_mfa.yml | 43 ++++++----
 ...with_kms_keys_performing_encryption_s3.yml | 41 ++++++----
 .../cloud/aws_disable_bucket_versioning.yml   | 43 ++++++----
 .../aws_ec2_snapshot_shared_externally.yml    | 44 ++++++----
 ...s_ecr_container_scanning_findings_high.yml | 43 ++++++----
 ...ing_findings_low_informational_unknown.yml | 43 ++++++----
 ...ecr_container_scanning_findings_medium.yml | 42 ++++++----
 ...ontainer_upload_outside_business_hours.yml | 42 ++++++----
 .../aws_ecr_container_upload_unknown_user.yml | 38 +++++----
 .../cloud/aws_excessive_security_scanning.yml | 37 ++++++---
 ...n_via_anomalous_getobject_api_activity.yml | 38 +++++----
 .../aws_exfiltration_via_batch_service.yml    | 36 ++++++---
 ...ws_exfiltration_via_bucket_replication.yml | 48 ++++++-----
 .../aws_exfiltration_via_datasync_task.yml    | 44 ++++++----
 .../aws_exfiltration_via_ec2_snapshot.yml     | 49 +++++++-----
 ...ber_of_failed_authentications_for_user.yml | 42 ++++++----
 ...mber_of_failed_authentications_from_ip.yml | 39 ++++++---
 .../aws_iam_accessdenied_discovery_events.yml | 41 ++++++----
 ...aws_iam_assume_role_policy_brute_force.yml | 43 +++++++---
 detections/cloud/aws_iam_delete_policy.yml    | 36 ++++++---
 .../cloud/aws_iam_failure_group_deletion.yml  | 43 +++++++---
 .../aws_iam_successful_group_deletion.yml     | 35 +++++---
 .../cloud/aws_lambda_updatefunctioncode.yml   | 30 ++++---
 ...s_multi_factor_authentication_disabled.yml | 47 ++++++-----
 ..._multiple_failed_mfa_requests_for_user.yml | 44 +++++-----
 ..._users_failing_to_authenticate_from_ip.yml | 36 +++++----
 ...ntrol_list_created_with_all_open_ports.yml | 53 +++++++-----
 ...ws_network_access_control_list_deleted.yml | 41 ++++++----
 ...aws_new_mfa_method_registered_for_user.yml | 42 +++++-----
 .../cloud/aws_password_policy_changes.yml     | 38 +++++----
 ...ws_s3_exfiltration_behavior_identified.yml | 48 +++++++----
 ..._access_by_provider_user_and_principal.yml | 48 +++++++----
 .../aws_saml_update_identity_provider.yml     | 43 ++++++----
 .../cloud/aws_setdefaultpolicyversion.yml     | 42 ++++++----
 ...nsole_authentication_from_multiple_ips.yml | 38 ++++++---
 ...uccessful_single_factor_authentication.yml | 45 ++++++-----
 ...mber_of_failed_authentications_from_ip.yml | 40 +++++++---
 detections/cloud/aws_updateloginprofile.yml   | 42 +++++++---
 ...ure_active_directory_high_risk_sign_in.yml | 41 ++++++----
 ..._consent_bypassed_by_service_principal.yml | 48 ++++++++---
 ...pplication_administrator_role_assigned.yml | 43 +++++++---
 ...entication_failed_during_mfa_challenge.yml | 55 +++++++++----
 ...k_user_consent_for_risky_apps_disabled.yml | 45 ++++++++---
 ...concurrent_sessions_from_different_ips.yml | 48 +++++++----
 .../azure_ad_device_code_authentication.yml   | 43 ++++++----
 .../azure_ad_external_guest_user_invited.yml  | 38 ++++++---
 ...ad_fullaccessasapp_permission_assigned.yml | 42 +++++++---
 ..._ad_global_administrator_role_assigned.yml | 40 +++++++---
 ...ber_of_failed_authentications_for_user.yml | 44 ++++++----
 ...mber_of_failed_authentications_from_ip.yml | 44 ++++++----
 ...d_multi_factor_authentication_disabled.yml | 39 ++++++---
 ...ti_source_failed_authentications_spike.yml | 49 +++++++++---
 ...ds_and_useragents_authentication_spike.yml | 53 +++++++-----
 ..._multiple_denied_mfa_requests_for_user.yml | 43 ++++++----
 ..._multiple_failed_mfa_requests_for_user.yml | 46 +++++++----
 ...tiple_service_principals_created_by_sp.yml | 40 +++++++---
 ...ple_service_principals_created_by_user.yml | 43 +++++++---
 ..._users_failing_to_authenticate_from_ip.yml | 41 +++++++---
 .../azure_ad_new_custom_domain_added.yml      | 41 +++++++---
 .../azure_ad_new_federated_domain_added.yml   | 40 +++++++---
 .../azure_ad_new_mfa_method_registered.yml    | 46 ++++++++---
 ..._ad_new_mfa_method_registered_for_user.yml | 42 ++++++----
 ...th_application_consent_granted_by_user.yml | 44 +++++++---
 .../cloud/azure_ad_pim_role_assigned.yml      | 40 +++++++---
 ...azure_ad_pim_role_assignment_activated.yml | 41 +++++++---
 ...entication_administrator_role_assigned.yml | 43 +++++++---
 ...ivileged_graph_api_permission_assigned.yml | 46 +++++++----
 .../azure_ad_privileged_role_assigned.yml     | 41 +++++++---
 ...ged_role_assigned_to_service_principal.yml | 46 ++++++++---
 ...re_ad_service_principal_authentication.yml | 45 +++++++----
 .../azure_ad_service_principal_created.yml    | 40 +++++++---
 ...rvice_principal_new_client_credentials.yml | 42 ++++++----
 ...azure_ad_service_principal_owner_added.yml | 43 +++++++---
 ...sful_authentication_from_different_ips.yml | 45 +++++++----
 ...d_successful_powershell_authentication.yml | 42 ++++++----
 ...uccessful_single_factor_authentication.yml | 40 ++++++----
 ...e_ad_tenant_wide_admin_consent_granted.yml | 43 +++++++---
 ...mber_of_failed_authentications_from_ip.yml | 43 +++++++---
 ..._consent_blocked_for_risky_application.yml | 48 ++++++++---
 ...r_consent_denied_for_oauth_application.yml | 41 +++++++---
 ...ure_ad_user_enabled_and_password_reset.yml | 44 +++++++---
 ..._ad_user_immutableid_attribute_updated.yml | 41 +++++++---
 .../azure_automation_account_created.yml      | 42 ++++++----
 .../azure_automation_runbook_created.yml      | 42 ++++++----
 .../cloud/azure_runbook_webhook_created.yml   | 42 ++++++----
 .../cloud/circle_ci_disable_security_job.yml  | 32 ++++++--
 .../cloud/circle_ci_disable_security_step.yml | 23 ++++--
 ...alls_from_previously_unseen_user_roles.yml | 41 +++++++---
 ...ance_created_by_previously_unseen_user.yml | 35 +++++---
 ...ce_created_in_previously_unused_region.yml | 41 +++++++---
 ...e_created_with_previously_unseen_image.yml | 42 +++++++---
 ...d_with_previously_unseen_instance_type.yml | 43 +++++++---
 ...nce_modified_by_previously_unseen_user.yml | 38 ++++++---
 ...g_activity_from_previously_unseen_city.yml | 64 +++++++++++----
 ...ctivity_from_previously_unseen_country.yml | 63 +++++++++++----
 ...vity_from_previously_unseen_ip_address.yml | 63 +++++++++++----
 ...activity_from_previously_unseen_region.yml | 64 +++++++++++----
 ..._security_groups_modifications_by_user.yml | 44 +++++++---
 .../detect_aws_console_login_by_new_user.yml  | 35 +++++---
 ...ws_console_login_by_user_from_new_city.yml | 47 ++++++++---
 ...console_login_by_user_from_new_country.yml | 47 ++++++++---
 ..._console_login_by_user_from_new_region.yml | 48 ++++++++---
 ...etect_gcp_storage_access_from_a_new_ip.yml | 44 +++++++---
 .../detect_new_open_gcp_storage_buckets.yml   | 38 +++++----
 .../cloud/detect_new_open_s3_buckets.yml      | 49 ++++++++----
 ...etect_new_open_s3_buckets_over_aws_cli.yml | 56 ++++++++-----
 .../cloud/detect_s3_access_from_a_new_ip.yml  | 33 +++++---
 ...s_security_hub_alerts_for_ec2_instance.yml | 41 ++++++----
 ...ke_in_aws_security_hub_alerts_for_user.yml | 24 ++++--
 ...blocked_outbound_traffic_from_your_aws.yml | 46 ++++++++---
 .../detect_spike_in_s3_bucket_deletion.yml    | 41 ++++++++--
 ...entication_failed_during_mfa_challenge.yml | 37 ++++++---
 .../cloud/gcp_detect_gcploit_framework.yml    | 31 +++----
 ..._kubernetes_cluster_pod_scan_detection.yml | 29 +++----
 ...p_multi_factor_authentication_disabled.yml | 38 ++++++---
 ..._multiple_failed_mfa_requests_for_user.yml | 35 ++++++--
 ..._users_failing_to_authenticate_from_ip.yml | 45 +++++++----
 ...uccessful_single_factor_authentication.yml | 39 ++++++---
 ...mber_of_failed_authentications_from_ip.yml | 45 ++++++++---
 .../cloud/gdrive_suspicious_file_sharing.yml  | 31 ++++---
 ...thub_actions_disable_security_workflow.yml | 46 ++++++-----
 .../cloud/github_commit_changes_in_master.yml | 30 +++++--
 detections/cloud/github_commit_in_develop.yml | 30 +++++--
 detections/cloud/github_dependabot_alert.yml  | 41 +++++-----
 .../github_pull_request_from_unknown_user.yml | 42 +++++-----
 .../gsuite_drive_share_in_external_email.yml  | 39 +++++----
 .../gsuite_email_suspicious_attachment.yml    | 45 +++++++----
 ...ail_suspicious_subject_with_attachment.yml | 41 ++++++++--
 ...mail_with_known_abuse_web_service_link.yml | 36 +++++++--
 ...ail_with_attachment_to_external_domain.yml | 36 ++++++---
 .../gsuite_suspicious_calendar_invite.yml     | 31 ++++---
 .../gsuite_suspicious_shared_file_name.yml    | 48 +++++++----
 ...of_login_failures_from_a_single_source.yml | 45 ++++++-----
 ...es_abuse_of_secret_by_unusual_location.yml | 56 ++++++++-----
 ..._abuse_of_secret_by_unusual_user_agent.yml | 56 ++++++++-----
 ..._abuse_of_secret_by_unusual_user_group.yml | 55 ++++++++-----
 ...s_abuse_of_secret_by_unusual_user_name.yml | 53 +++++++-----
 .../cloud/kubernetes_access_scanning.yml      | 52 ++++++++----
 ..._inbound_network_activity_from_process.yml | 49 ++++++++----
 ..._anomalous_inbound_outbound_network_io.yml | 74 +++++++++--------
 ...s_inbound_to_outbound_network_io_ratio.yml | 77 ++++++++++--------
 ...outbound_network_activity_from_process.yml | 51 ++++++++----
 ...etes_anomalous_traffic_on_network_edge.yml | 48 +++++++----
 ...es_aws_detect_suspicious_kubectl_calls.yml | 46 ++++++-----
 ...rnetes_create_or_update_privileged_pod.yml | 52 +++++++-----
 .../cloud/kubernetes_cron_job_creation.yml    | 52 +++++++-----
 .../cloud/kubernetes_daemonset_deployed.yml   | 49 +++++++-----
 .../cloud/kubernetes_falco_shell_spawned.yml  | 44 +++++++---
 .../cloud/kubernetes_newly_seen_tcp_edge.yml  | 36 ++++++---
 .../cloud/kubernetes_newly_seen_udp_edge.yml  | 36 ++++++---
 .../cloud/kubernetes_nginx_ingress_lfi.yml    | 29 +++++--
 .../cloud/kubernetes_nginx_ingress_rfi.yml    | 28 +++++--
 .../cloud/kubernetes_node_port_creation.yml   | 51 +++++++-----
 ...netes_pod_created_in_default_namespace.yml | 52 +++++++-----
 ...netes_pod_with_host_network_attachment.yml | 52 +++++++-----
 ...previously_unseen_container_image_name.yml | 63 ++++++++-------
 .../kubernetes_previously_unseen_process.yml  | 65 ++++++++-------
 ...bernetes_process_running_from_new_path.yml | 66 ++++++++-------
 ...ss_with_anomalous_resource_utilisation.yml | 68 +++++++++-------
 ..._process_with_resource_ratio_anomalies.yml | 73 ++++++++++-------
 .../kubernetes_scanner_image_pulling.yml      | 34 +++++---
 ...scanning_by_unauthenticated_ip_address.yml | 52 ++++++++----
 ...ubernetes_shell_running_on_worker_node.yml | 66 +++++++--------
 ...nning_on_worker_node_with_cpu_activity.yml | 67 ++++++++--------
 .../kubernetes_suspicious_image_pulling.yml   | 52 +++++++-----
 .../cloud/kubernetes_unauthorized_access.yml  | 49 ++++++++----
 ...365_add_app_role_assignment_grant_user.yml | 43 ++++++----
 .../cloud/o365_added_service_principal.yml    | 41 ++++++----
 ..._consent_bypassed_by_service_principal.yml | 46 +++++++----
 .../cloud/o365_advanced_audit_disabled.yml    | 42 +++++++---
 ...application_available_to_other_tenants.yml | 41 ++++++----
 ...5_application_registration_owner_added.yml | 38 ++++++---
 ...applicationimpersonation_role_assigned.yml | 41 ++++++----
 ...k_user_consent_for_risky_apps_disabled.yml | 43 ++++++----
 .../cloud/o365_bypass_mfa_via_trusted_ip.yml  | 50 +++++++-----
 ...365_compliance_content_search_exported.yml | 38 +++++----
 ...o365_compliance_content_search_started.yml | 38 +++++----
 ...concurrent_sessions_from_different_ips.yml | 37 ++++++---
 .../cloud/o365_cross_tenant_access_change.yml | 39 ++++++---
 detections/cloud/o365_disable_mfa.yml         | 41 ++++++----
 detections/cloud/o365_dlp_rule_triggered.yml  | 47 +++++++----
 ...5_elevated_mailbox_permission_assigned.yml | 39 +++++----
 ...email_access_by_security_administrator.yml | 35 +++++---
 ...mail_reported_by_admin_found_malicious.yml | 35 +++++---
 ...email_reported_by_user_found_malicious.yml | 40 ++++++----
 .../o365_email_security_feature_changed.yml   | 34 +++++---
 .../o365_email_suspicious_behavior_alert.yml  | 38 ++++++---
 ...xcessive_authentication_failures_alert.yml | 42 ++++++----
 .../cloud/o365_excessive_sso_logon_errors.yml | 42 +++++-----
 .../o365_external_guest_user_invited.yml      | 44 ++++++----
 .../o365_external_identity_policy_changed.yml | 48 ++++++++---
 ...ed_application_consent_granted_by_user.yml | 41 ++++++----
 ...65_fullaccessasapp_permission_assigned.yml | 45 +++++++----
 ...ber_of_failed_authentications_for_user.yml | 39 +++++----
 .../o365_high_privilege_role_granted.yml      | 39 ++++++---
 ...ed_application_consent_granted_by_user.yml | 43 ++++++----
 .../o365_mailbox_email_forwarding_enabled.yml | 40 +++++++---
 ...ailbox_folder_read_permission_assigned.yml | 40 +++++++---
 ...mailbox_folder_read_permission_granted.yml | 39 ++++++---
 ...box_inbox_folder_shared_with_all_users.yml | 43 ++++++----
 ...box_read_access_granted_to_application.yml | 37 ++++++---
 ...ti_source_failed_authentications_spike.yml | 41 +++++++---
 ...ds_and_useragents_authentication_spike.yml | 43 ++++++----
 ..._multiple_failed_mfa_requests_for_user.yml | 40 ++++++----
 ...65_multiple_mailboxes_accessed_via_api.yml | 45 +++++++----
 ...tiple_service_principals_created_by_sp.yml | 43 ++++++----
 ...ple_service_principals_created_by_user.yml | 43 ++++++----
 ..._users_failing_to_authenticate_from_ip.yml | 41 ++++++----
 ...o365_new_email_forwarding_rule_created.yml | 40 +++++++---
 ...o365_new_email_forwarding_rule_enabled.yml | 43 +++++++---
 .../cloud/o365_new_federated_domain_added.yml | 40 ++++++----
 ...5_new_forwarding_mailflow_rule_created.yml | 39 ++++++---
 .../cloud/o365_new_mfa_method_registered.yml  | 42 +++++++---
 .../o365_oauth_app_mailbox_access_via_ews.yml | 41 ++++++----
 ...oauth_app_mailbox_access_via_graph_api.yml | 40 ++++++----
 ...ivileged_graph_api_permission_assigned.yml | 45 +++++++----
 .../cloud/o365_privileged_role_assigned.yml   | 46 +++++++----
 ...ged_role_assigned_to_service_principal.yml | 49 ++++++++----
 detections/cloud/o365_pst_export_alert.yml    | 42 ++++++----
 .../cloud/o365_safe_links_detection.yml       | 33 +++++---
 ...ecurity_and_compliance_alert_triggered.yml | 43 +++++++---
 ...rvice_principal_new_client_credentials.yml | 36 ++++++---
 ...repoint_allowed_domains_policy_changed.yml | 44 ++++++----
 .../o365_sharepoint_malware_detection.yml     | 34 ++++----
 ...o365_tenant_wide_admin_consent_granted.yml | 39 ++++++---
 ...ntelligence_suspicious_email_delivered.yml | 43 ++++++----
 ..._intelligence_suspicious_file_detected.yml | 37 ++++++---
 ..._consent_blocked_for_risky_application.yml | 44 ++++++----
 ...r_consent_denied_for_oauth_application.yml | 36 ++++++---
 .../cloud/o365_zap_activity_detection.yml     | 35 +++++---
 ...isk_rule_for_dev_sec_ops_by_repository.yml | 34 ++++++--
 ...ly_high_aws_instances_launched_by_user.yml |  5 --
 ..._aws_instances_launched_by_user___mltk.yml |  5 --
 ..._high_aws_instances_terminated_by_user.yml |  5 --
 ...ws_instances_terminated_by_user___mltk.yml |  5 --
 .../deprecated/asl_aws_createaccesskey.yml    | 80 +++++++++----------
 .../asl_aws_excessive_security_scanning.yml   | 24 +++---
 .../asl_aws_password_policy_changes.yml       | 48 +++++------
 ...ovisioning_from_previously_unseen_city.yml | 31 +++----
 ...sioning_from_previously_unseen_country.yml | 29 +++----
 ...ning_from_previously_unseen_ip_address.yml | 31 +++----
 ...isioning_from_previously_unseen_region.yml | 27 +++----
 ...rnetes_cluster_sensitive_object_access.yml |  2 -
 ...nts_connecting_to_multiple_dns_servers.yml | 27 +++----
 ...ud_network_access_control_list_deleted.yml | 10 ---
 .../correlation_by_repository_and_risk.yml    |  2 -
 .../correlation_by_user_and_risk.yml          |  2 -
 ...ivity_related_to_pass_the_hash_attacks.yml | 26 +++---
 ...ct_api_activity_from_users_without_mfa.yml | 30 +++----
 ...pi_activities_from_unapproved_accounts.yml | 38 +++------
 ...to_phishing_sites_leveraging_evilginx2.yml | 30 +++----
 .../detect_long_dns_txt_record_response.yml   |  7 --
 .../detect_mimikatz_using_loaded_images.yml   | 17 ++--
 ...katz_via_powershell_and_eventcode_4703.yml |  7 --
 .../detect_new_api_calls_from_user_roles.yml  |  7 --
 .../detect_new_user_aws_console_login.yml     |  4 -
 .../detect_spike_in_aws_api_activity.yml      | 35 +++-----
 .../detect_spike_in_network_acl_activity.yml  |  3 -
 ...etect_spike_in_security_group_activity.yml |  3 -
 .../detect_usb_device_insertion.yml           |  6 --
 ...eb_traffic_to_dynamic_domain_providers.yml | 30 +++----
 .../deprecated/detection_of_dns_tunnels.yml   | 16 +---
 ...s_resolved_by_unauthorized_dns_servers.yml |  6 --
 detections/deprecated/dns_record_changed.yml  | 32 +++-----
 .../dump_lsass_via_procdump_rename.yml        | 27 +++----
 ...e_modified_with_previously_unseen_user.yml |  4 -
 ...ce_started_in_previously_unseen_region.yml |  3 -
 ...nce_started_with_previously_unseen_ami.yml |  5 --
 ...d_with_previously_unseen_instance_type.yml |  5 --
 ...ce_started_with_previously_unseen_user.yml |  5 --
 ...n_of_file_with_spaces_before_extension.yml |  7 --
 ...d_without_successful_netbackup_backups.yml |  4 -
 .../first_time_seen_command_line_argument.yml |  6 --
 ...counts_with_high_risk_roles_by_project.yml |  8 --
 ...sk_permissions_by_resource_and_account.yml |  8 --
 .../gcp_detect_oauth_token_abuse.yml          |  2 -
 .../gcp_kubernetes_cluster_scan_detection.yml |  2 -
 .../deprecated/identify_new_user_accounts.yml |  2 -
 ...ct_most_active_service_accounts_by_pod.yml |  2 -
 ...s_detect_rbac_authorization_by_account.yml |  4 +-
 ...netes_aws_detect_sensitive_role_access.yml |  6 +-
 ...vice_accounts_forbidden_failure_access.yml |  2 -
 ...tive_service_accounts_by_pod_namespace.yml |  2 -
 ...e_detect_rbac_authorization_by_account.yml |  2 -
 ...s_azure_detect_sensitive_object_access.yml |  2 -
 ...tes_azure_detect_sensitive_role_access.yml |  6 +-
 ...vice_accounts_forbidden_failure_access.yml |  2 -
 ..._azure_detect_suspicious_kubectl_calls.yml |  2 -
 .../kubernetes_azure_pod_scan_fingerprint.yml |  2 -
 .../kubernetes_azure_scan_fingerprint.yml     |  2 -
 ...ct_most_active_service_accounts_by_pod.yml |  2 -
 ..._detect_rbac_authorizations_by_account.yml |  2 -
 ...tes_gcp_detect_sensitive_object_access.yml |  2 -
 ...netes_gcp_detect_sensitive_role_access.yml |  6 +-
 ...vice_accounts_forbidden_failure_access.yml |  2 -
 ...es_gcp_detect_suspicious_kubectl_calls.yml |  2 -
 .../monitor_dns_for_brand_abuse.yml           |  2 -
 ...h_invalid_credentials_from_the_same_ip.yml | 27 ++-----
 ...o365_suspicious_admin_email_forwarding.yml | 14 ++--
 .../o365_suspicious_rights_delegation.yml     | 39 ++++++---
 .../o365_suspicious_user_email_forwarding.yml | 37 +++++++--
 .../deprecated/okta_account_locked_out.yml    | 25 +++---
 .../okta_account_lockout_events.yml           | 33 +++-----
 .../deprecated/okta_failed_sso_attempts.yml   | 12 +--
 ..._login_failure_with_high_unknown_users.yml | 31 ++++---
 ...insight_suspected_passwordspray_attack.yml | 31 +++----
 .../okta_two_or_more_rejected_okta_pushes.yml | 15 +---
 .../osquery_pack___coldroot_detection.yml     |  5 +-
 .../deprecated/processes_created_by_netsh.yml |  2 -
 .../prohibited_software_on_endpoint.yml       |  2 -
 ...de_files_directories_via_registry_keys.yml |  2 -
 .../remote_registry_key_modifications.yml     |  2 -
 ...led_tasks_used_in_badrabbit_ransomware.yml |  6 +-
 ...pectre_and_meltdown_vulnerable_systems.yml |  2 -
 ...uspicious_changes_to_file_associations.yml |  2 -
 .../suspicious_email___uba_anomaly.yml        |  2 -
 .../deprecated/suspicious_file_write.yml      |  2 -
 ...ious_powershell_command_line_arguments.yml |  2 -
 .../deprecated/suspicious_rundll32_rename.yml | 13 ---
 ...us_writes_to_system_volume_information.yml |  6 +-
 .../uncommon_processes_on_endpoint.yml        |  2 -
 .../unsigned_image_loaded_by_lsass.yml        |  7 +-
 .../unsuccessful_netbackup_backups.yml        |  2 -
 .../web_fraud___account_harvesting.yml        |  5 --
 .../web_fraud___anomalous_user_clickspeed.yml |  4 -
 ...aud___password_sharing_across_accounts.yml |  4 -
 ...indows_connhost_exe_started_forcefully.yml | 10 +--
 ...indows_dll_search_order_hijacking_hunt.yml | 13 +--
 .../windows_hosts_file_modification.yml       |  2 -
 ...supply_chain_attack_network_indicators.yml | 28 ++++---
 .../7zip_commandline_to_smb_share_path.yml    | 44 ++++++----
 .../access_lsass_memory_for_dump_creation.yml | 47 +++++++----
 .../account_discovery_with_net_app.yml        | 50 +++++++-----
 ..._directory_lateral_movement_identified.yml | 54 +++++++++----
 ...ectory_privilege_escalation_identified.yml | 54 +++++++++----
 .../active_setup_registry_autostart.yml       | 40 ++++++----
 ...d_defaultuser_and_password_in_registry.yml | 39 +++++----
 .../add_or_set_windows_defender_exclusion.yml | 53 +++++++-----
 .../adsisearcher_account_discovery.yml        | 38 ++++++---
 ..._file_and_printing_sharing_in_firewall.yml | 56 ++++++++-----
 ...ound_traffic_by_firewall_rule_registry.yml | 34 ++++----
 ...allow_inbound_traffic_in_firewall_rule.yml | 37 ++++++---
 .../allow_network_discovery_in_firewall.yml   | 56 ++++++++-----
 .../allow_operation_with_consent_admin.yml    | 43 +++++-----
 .../endpoint/anomalous_usage_of_7zip.yml      | 53 +++++++-----
 .../endpoint/any_powershell_downloadfile.yml  | 57 ++++++++-----
 .../any_powershell_downloadstring.yml         | 57 ++++++++-----
 .../endpoint/attacker_tools_on_endpoint.yml   | 48 ++++++++---
 ..._to_add_certificate_to_untrusted_store.yml | 52 ++++++++----
 .../attempt_to_stop_security_service.yml      | 58 +++++++++-----
 ...dential_dump_from_registry_via_reg_exe.yml | 58 +++++++++-----
 .../auto_admin_logon_registry_entry.yml       | 45 ++++++-----
 .../endpoint/batch_file_write_to_system32.yml | 60 +++++++++-----
 ...dedit_command_back_to_normal_mode_boot.yml | 47 +++++++----
 .../bcdedit_failure_recovery_modification.yml | 47 ++++++++---
 detections/endpoint/bits_job_persistence.yml  | 60 +++++++++-----
 .../endpoint/bitsadmin_download_file.yml      | 59 +++++++++-----
 ...load_with_urlcache_and_split_arguments.yml | 56 ++++++++-----
 ...oad_with_verifyctl_and_split_arguments.yml | 56 ++++++++-----
 .../certutil_exe_certificate_extraction.yml   | 56 ++++++++-----
 .../certutil_with_decode_argument.yml         | 56 ++++++++-----
 .../change_default_file_association.yml       | 40 +++++++---
 ...hange_to_safe_mode_with_network_config.yml | 44 +++++++---
 .../endpoint/chcp_command_execution.yml       | 53 ++++++++----
 .../check_elevated_cmd_using_whoami.yml       | 44 ++++++----
 .../child_processes_of_spoolsv_exe.yml        | 39 ++++++---
 ...ar_unallocated_sector_using_cipher_app.yml | 52 +++++++-----
 .../endpoint/clop_common_exec_parameter.yml   | 53 +++++++-----
 .../clop_ransomware_known_service_name.yml    | 36 ++++++---
 ...cmd_carry_out_string_command_parameter.yml | 45 +++++++----
 .../endpoint/cmd_echo_pipe___escalation.yml   | 56 ++++++++-----
 ...cmdline_tool_not_executed_in_cmd_shell.yml | 62 +++++++++-----
 .../endpoint/cmlua_or_cmstplua_uac_bypass.yml | 44 ++++++----
 .../endpoint/cobalt_strike_named_pipes.yml    | 45 +++++++----
 .../endpoint/common_ransomware_extensions.yml | 42 +++++++---
 .../endpoint/common_ransomware_notes.yml      | 35 +++++---
 ...nnectwise_screenconnect_path_traversal.yml | 45 ++++++++---
 ...eenconnect_path_traversal_windows_sacl.yml | 43 ++++++----
 .../endpoint/conti_common_exec_parameter.yml  | 57 ++++++++-----
 ..._loading_from_world_writable_directory.yml | 56 ++++++++-----
 ...ate_local_admin_accounts_using_net_exe.yml | 56 ++++++++-----
 ...or_delete_windows_shares_using_net_exe.yml | 56 ++++++++-----
 ...ate_remote_thread_in_shell_application.yml | 42 ++++++----
 .../create_remote_thread_into_lsass.yml       | 45 +++++++----
 .../creation_of_lsass_dump_with_taskmgr.yml   | 43 ++++++----
 .../endpoint/creation_of_shadow_copy.yml      | 60 +++++++++-----
 ...f_shadow_copy_with_wmic_and_powershell.yml | 53 +++++++-----
 ...ping_via_copy_command_from_shadow_copy.yml | 55 ++++++++-----
 ...ial_dumping_via_symlink_to_shadow_copy.yml | 54 ++++++++-----
 ...crowdstrike_admin_weak_password_policy.yml | 38 ++++++---
 ...wdstrike_admin_with_duplicate_password.yml | 38 ++++++---
 ...rowdstrike_high_identity_risk_severity.yml | 36 ++++++---
 ...wdstrike_medium_identity_risk_severity.yml | 38 ++++++---
 .../crowdstrike_medium_severity_alert.yml     | 44 ++++++----
 ...owdstrike_multiple_low_severity_alerts.yml | 41 ++++++----
 ...rivilege_escalation_for_non_admin_user.yml | 45 +++++++----
 .../crowdstrike_user_weak_password_policy.yml | 38 ++++++---
 ...owdstrike_user_with_duplicate_password.yml | 38 ++++++---
 .../csc_net_on_the_fly_compilation.yml        | 44 ++++++----
 .../curl_download_and_bash_execution.yml      | 38 +++++----
 .../delete_shadowcopy_with_powershell.yml     | 38 ++++++---
 detections/endpoint/deleting_of_net_users.yml | 55 ++++++++-----
 .../endpoint/deleting_shadow_copies.yml       | 57 ++++++++-----
 ...tect_azurehound_command_line_arguments.yml | 51 +++++++-----
 .../detect_azurehound_file_modifications.yml  | 44 ++++++----
 .../detect_baron_samedit_cve_2021_3156.yml    | 16 +++-
 ...t_baron_samedit_cve_2021_3156_segfault.yml | 24 ++++--
 ...aron_samedit_cve_2021_3156_via_osquery.yml | 17 ++--
 .../detect_certify_command_line_arguments.yml | 47 +++++++----
 ...y_with_powershell_script_block_logging.yml | 42 +++++++---
 .../detect_certipy_file_modifications.yml     | 56 +++++++------
 ...omputer_changed_with_anonymous_account.yml | 30 ++++---
 ...f_shadowcopy_with_script_block_logging.yml | 41 ++++++----
 ...redential_dumping_through_lsass_access.yml | 49 ++++++++----
 ...ct_critical_alerts_from_security_tools.yml | 62 +++++++++-----
 ...e_with_powershell_script_block_logging.yml | 42 ++++++----
 ...cessive_account_lockouts_from_endpoint.yml | 61 +++++++++-----
 ...detect_excessive_user_account_lockouts.yml | 37 ++++++---
 .../endpoint/detect_exchange_web_shell.yml    | 51 ++++++++----
 .../endpoint/detect_html_help_renamed.yml     | 48 ++++++-----
 .../detect_html_help_spawn_child_process.yml  | 56 ++++++++-----
 .../detect_html_help_url_in_command_line.yml  | 55 ++++++++-----
 ...l_help_using_infotech_storage_handlers.yml | 55 ++++++++-----
 ...z_with_powershell_script_block_logging.yml | 41 ++++++----
 .../detect_mshta_inline_hta_execution.yml     | 57 ++++++++-----
 detections/endpoint/detect_mshta_renamed.yml  | 47 ++++++-----
 .../detect_mshta_url_in_command_line.yml      | 55 ++++++++-----
 .../detect_new_local_admin_account.yml        | 48 +++++++----
 .../detect_outlook_exe_writing_a_zip_file.yml | 40 +++++++---
 ...word_spray_attack_behavior_from_source.yml | 51 ++++++++----
 ...password_spray_attack_behavior_on_user.yml | 51 ++++++++----
 ...nterception_by_creation_of_program_exe.yml | 54 ++++++++-----
 ...system_network_configuration_discovery.yml | 61 +++++++++-----
 ...ohibited_applications_spawning_cmd_exe.yml | 49 +++++++-----
 .../detect_psexec_with_accepteula_flag.yml    | 57 ++++++++-----
 .../endpoint/detect_rare_executables.yml      | 46 ++++++++---
 .../detect_rclone_command_line_usage.yml      | 55 +++++++++----
 .../detect_regasm_spawning_a_process.yml      | 54 +++++++++----
 .../detect_regasm_with_network_connection.yml | 45 +++++++----
 ..._regasm_with_no_command_line_arguments.yml | 56 ++++++++-----
 .../detect_regsvcs_spawning_a_process.yml     | 54 +++++++++----
 ...detect_regsvcs_with_network_connection.yml | 45 +++++++----
 ...regsvcs_with_no_command_line_arguments.yml | 55 ++++++++-----
 ...ct_regsvr32_application_control_bypass.yml | 56 ++++++++-----
 ...tect_remote_access_software_usage_file.yml | 51 +++++++++---
 ..._remote_access_software_usage_fileinfo.yml | 47 +++++++----
 ...t_remote_access_software_usage_process.yml | 57 +++++++++----
 detections/endpoint/detect_renamed_7_zip.yml  | 49 +++++++-----
 detections/endpoint/detect_renamed_psexec.yml | 48 ++++++-----
 detections/endpoint/detect_renamed_rclone.yml | 48 ++++++-----
 detections/endpoint/detect_renamed_winrar.yml | 48 ++++++-----
 .../endpoint/detect_rtlo_in_file_name.yml     | 43 +++++++---
 .../endpoint/detect_rtlo_in_process.yml       | 53 +++++++-----
 ...2_application_control_bypass___advpack.yml | 56 ++++++++-----
 ..._application_control_bypass___setupapi.yml | 56 ++++++++-----
 ..._application_control_bypass___syssetup.yml | 56 ++++++++-----
 .../detect_rundll32_inline_hta_execution.yml  | 52 +++++++-----
 ...tect_sharphound_command_line_arguments.yml | 47 +++++++----
 .../detect_sharphound_file_modifications.yml  | 41 +++++++---
 .../endpoint/detect_sharphound_usage.yml      | 52 +++++++-----
 ...ssnames_using_pretrained_model_in_dsdl.yml | 42 +++++++---
 ..._cmd_exe_to_launch_script_interpreters.yml | 47 +++++++----
 .../detect_webshell_exploit_behavior.yml      | 57 +++++++++----
 ...ect_wmi_event_subscription_persistence.yml | 37 ++++++---
 .../detection_of_tools_built_by_nirsoft.yml   | 34 +++++---
 .../disable_amsi_through_registry.yml         | 34 ++++----
 .../disable_defender_antivirus_registry.yml   | 42 +++++-----
 ...able_defender_blockatfirstseen_feature.yml | 32 ++++----
 ...disable_defender_enhanced_notification.yml | 61 +++++++++-----
 .../disable_defender_mpengine_registry.yml    | 35 ++++----
 .../disable_defender_spynet_reporting.yml     | 41 +++++-----
 ...efender_submit_samples_consent_feature.yml | 41 +++++-----
 .../endpoint/disable_etw_through_registry.yml | 31 ++++---
 .../endpoint/disable_logs_using_wevtutil.yml  | 48 +++++++----
 detections/endpoint/disable_registry_tool.yml | 36 +++++----
 detections/endpoint/disable_schedule_task.yml | 45 ++++++++---
 ...le_security_logs_using_minint_registry.yml | 39 +++++----
 .../endpoint/disable_show_hidden_files.yml    | 40 ++++++----
 .../disable_uac_remote_restriction.yml        | 40 ++++++----
 .../endpoint/disable_windows_app_hotkeys.yml  | 41 +++++-----
 .../disable_windows_behavior_monitoring.yml   | 54 +++++++------
 ...disable_windows_smartscreen_protection.yml | 40 ++++++----
 ...thentication_discovery_with_get_aduser.yml | 38 ++++++---
 ...uthentication_discovery_with_powerview.yml | 35 +++++---
 .../endpoint/disabling_cmd_application.yml    | 35 ++++----
 .../endpoint/disabling_controlpanel.yml       | 36 +++++----
 .../endpoint/disabling_defender_services.yml  | 41 +++++-----
 .../disabling_firewall_with_netsh.yml         | 52 +++++++-----
 ...isabling_folderoptions_windows_feature.yml | 36 +++++----
 .../endpoint/disabling_net_user_account.yml   | 52 +++++++-----
 .../endpoint/disabling_norun_windows_app.yml  | 37 +++++----
 .../disabling_remote_user_account_control.yml | 46 +++++++----
 .../disabling_systemrestore_in_registry.yml   | 58 ++++++++------
 .../endpoint/disabling_task_manager.yml       | 34 ++++----
 ...curity_authority_defences_via_registry.yml | 52 +++++++++---
 ...no_command_line_arguments_with_network.yml | 47 +++++++----
 .../dns_exfiltration_using_nslookup_app.yml   | 54 ++++++++-----
 .../domain_account_discovery_with_dsquery.yml | 39 +++++----
 .../domain_account_discovery_with_net_app.yml | 45 +++++++----
 .../domain_account_discovery_with_wmic.yml    | 47 +++++++----
 ...omain_controller_discovery_with_nltest.yml | 47 +++++++----
 .../domain_controller_discovery_with_wmic.yml | 41 ++++++----
 ...main_group_discovery_with_adsisearcher.yml | 35 +++++---
 .../domain_group_discovery_with_dsquery.yml   | 41 ++++++----
 .../domain_group_discovery_with_net.yml       | 41 ++++++----
 .../domain_group_discovery_with_wmic.yml      | 42 ++++++----
 .../download_files_using_telegram.yml         | 40 ++++++----
 .../endpoint/drop_icedid_license_dat.yml      | 24 ++++--
 .../endpoint/dsquery_domain_discovery.yml     | 51 ++++++++----
 .../endpoint/dump_lsass_via_comsvcs_dll.yml   | 52 +++++++-----
 .../endpoint/dump_lsass_via_procdump.yml      | 51 ++++++++----
 .../elevated_group_discovery_with_net.yml     | 52 ++++++++----
 ...levated_group_discovery_with_powerview.yml | 32 +++++---
 .../elevated_group_discovery_with_wmic.yml    | 51 ++++++++----
 .../enable_rdp_in_other_port_number.yml       | 40 ++++++----
 ...le_wdigest_uselogoncredential_registry.yml | 31 ++++---
 ...erate_users_local_group_using_telegram.yml | 43 +++++-----
 detections/endpoint/esentutl_sam_copy.yml     | 46 ++++++-----
 detections/endpoint/etw_registry_disabled.yml | 41 +++++-----
 detections/endpoint/eventvwr_uac_bypass.yml   | 61 ++++++++------
 .../endpoint/excel_spawning_powershell.yml    | 54 ++++++++-----
 .../excel_spawning_windows_script_host.yml    | 51 ++++++++----
 .../excessive_attempt_to_disable_services.yml | 48 +++++++----
 ...e_distinct_processes_from_windows_temp.yml | 47 ++++++++---
 ...ve_file_deletion_in_windefender_folder.yml | 45 ++++++-----
 ...r_of_service_control_start_as_disabled.yml | 54 +++++++++----
 ...excessive_number_of_taskhost_processes.yml | 52 +++++++++---
 .../excessive_service_stop_attempt.yml        | 54 ++++++++-----
 .../endpoint/excessive_usage_of_cacls_app.yml | 50 ++++++++----
 .../endpoint/excessive_usage_of_net_app.yml   | 52 +++++++-----
 .../excessive_usage_of_nslookup_app.yml       | 39 ++++++---
 .../excessive_usage_of_sc_service_utility.yml | 39 ++++++---
 .../endpoint/excessive_usage_of_taskkill.yml  | 47 +++++++----
 .../exchange_powershell_abuse_via_ssrf.yml    | 27 ++++---
 .../exchange_powershell_module_usage.yml      | 40 +++++++---
 ...le_written_in_administrative_smb_share.yml | 46 +++++++----
 ..._or_script_creation_in_suspicious_path.yml | 52 ++++++++----
 ...cute_javascript_with_jscript_com_clsid.yml | 49 ++++++++----
 ...ution_of_file_with_multiple_extensions.yml | 46 ++++++++---
 .../endpoint/extraction_of_registry_hives.yml | 56 ++++++++-----
 .../endpoint/file_with_samsam_extension.yml   | 44 +++++++---
 .../firewall_allowed_program_enable.yml       | 54 +++++++++----
 .../first_time_seen_child_process_of_zoom.yml | 45 +++++++----
 ...irst_time_seen_running_windows_service.yml | 33 +++++---
 detections/endpoint/fodhelper_uac_bypass.yml  | 49 ++++++++----
 detections/endpoint/fsutil_zeroing_file.yml   | 44 +++++++---
 ...ltdomainpasswordpolicy_with_powershell.yml | 40 ++++++----
 ...ordpolicy_with_powershell_script_block.yml | 30 ++++---
 .../endpoint/get_aduser_with_powershell.yml   | 40 ++++++----
 ...et_aduser_with_powershell_script_block.yml | 30 ++++---
 ...esultantpasswordpolicy_with_powershell.yml | 47 +++++++----
 ...ordpolicy_with_powershell_script_block.yml | 38 ++++++---
 .../get_domainpolicy_with_powershell.yml      | 47 +++++++----
 ...ainpolicy_with_powershell_script_block.yml | 41 ++++++----
 .../get_domaintrust_with_powershell.yml       | 55 ++++++++-----
 ...maintrust_with_powershell_script_block.yml | 41 ++++++----
 .../get_domainuser_with_powershell.yml        | 48 +++++++----
 ...omainuser_with_powershell_script_block.yml | 37 ++++++---
 .../get_foresttrust_with_powershell.yml       | 55 ++++++++-----
 ...resttrust_with_powershell_script_block.yml | 39 +++++----
 .../get_wmiobject_group_discovery.yml         | 42 ++++++----
 ...up_discovery_with_script_block_logging.yml | 26 +++---
 .../getadcomputer_with_powershell.yml         | 41 ++++++----
 ...dcomputer_with_powershell_script_block.yml | 30 ++++---
 .../endpoint/getadgroup_with_powershell.yml   | 41 ++++++----
 ...etadgroup_with_powershell_script_block.yml | 29 ++++---
 .../getcurrent_user_with_powershell.yml       | 42 ++++++----
 ...rent_user_with_powershell_script_block.yml | 33 +++++---
 .../getdomaincomputer_with_powershell.yml     | 48 +++++++----
 ...ncomputer_with_powershell_script_block.yml | 35 +++++---
 .../getdomaincontroller_with_powershell.yml   | 41 ++++++----
 ...ontroller_with_powershell_script_block.yml | 37 ++++++---
 .../getdomaingroup_with_powershell.yml        | 49 ++++++++----
 ...maingroup_with_powershell_script_block.yml | 37 ++++++---
 .../endpoint/getlocaluser_with_powershell.yml | 34 ++++++--
 ...localuser_with_powershell_script_block.yml | 30 ++++---
 .../getnettcpconnection_with_powershell.yml   | 41 ++++++----
 ...onnection_with_powershell_script_block.yml | 30 ++++---
 ...twmiobject_ds_computer_with_powershell.yml | 50 ++++++++----
 ..._computer_with_powershell_script_block.yml | 37 ++++++---
 .../getwmiobject_ds_group_with_powershell.yml | 49 ++++++++----
 ..._ds_group_with_powershell_script_block.yml | 38 ++++++---
 .../getwmiobject_ds_user_with_powershell.yml  | 48 +++++++----
 ...t_ds_user_with_powershell_script_block.yml | 39 ++++++---
 ...wmiobject_user_account_with_powershell.yml | 34 ++++++--
 ...r_account_with_powershell_script_block.yml | 28 ++++---
 ...no_command_line_arguments_with_network.yml | 56 +++++++++----
 ...dless_browser_mockbin_or_mocky_request.yml | 47 +++++++----
 .../endpoint/headless_browser_usage.yml       | 38 +++++----
 .../hide_user_account_from_sign_in_screen.yml | 45 ++++++-----
 ..._files_and_directories_with_attrib_exe.yml | 45 ++++++++---
 ...equency_copy_of_files_in_network_share.yml | 46 +++++++----
 .../high_process_termination_frequency.yml    | 36 ++++++---
 .../hunting_3cxdesktopapp_software.yml        | 45 ++++++-----
 detections/endpoint/icacls_deny_command.yml   | 50 ++++++++----
 detections/endpoint/icacls_grant_command.yml  | 47 +++++++----
 ...did_exfiltrated_archived_file_creation.yml | 29 ++++---
 ...ateral_movement_commandline_parameters.yml | 57 ++++++++-----
 ...ovement_smbexec_commandline_parameters.yml | 53 ++++++++----
 ...ovement_wmiexec_commandline_parameters.yml | 52 ++++++++----
 ...ion_on_remote_endpoint_with_powershell.yml | 39 ++++++---
 detections/endpoint/java_writing_jsp_file.yml | 61 ++++++++------
 .../jscript_execution_using_cscript_app.yml   | 48 +++++++----
 ...asting_spn_request_with_rc4_encryption.yml | 36 +++++----
 ...on_flag_disabled_in_useraccountcontrol.yml | 33 +++++---
 ...tication_flag_disabled_with_powershell.yml | 38 ++++++---
 ...ce_ticket_request_using_rc4_encryption.yml | 46 +++++++----
 ...beros_tgt_request_using_rc4_encryption.yml | 39 ++++++---
 .../endpoint/kerberos_user_enumeration.yml    | 38 ++++++---
 .../known_services_killed_by_ransomware.yml   | 41 +++++++---
 ...nt_manipulation_of_ssh_config_and_keys.yml | 42 ++++++----
 ...add_files_in_known_crontab_directories.yml | 39 ++++++---
 .../endpoint/linux_add_user_account.yml       | 40 ++++++----
 ...ux_adding_crontab_using_list_parameter.yml | 41 ++++++----
 .../linux_apt_get_privilege_escalation.yml    | 49 ++++++++----
 .../linux_apt_privilege_escalation.yml        | 49 ++++++++----
 .../linux_at_allow_config_file_creation.yml   | 40 +++++++---
 .../linux_at_application_execution.yml        | 49 ++++++++----
 .../linux_auditd_add_user_account.yml         | 40 +++++++---
 .../linux_auditd_add_user_account_type.yml    | 43 ++++++----
 .../linux_auditd_at_application_execution.yml | 49 ++++++++----
 .../linux_auditd_auditd_service_stop.yml      | 43 +++++++---
 .../linux_auditd_base64_decode_files.yml      | 45 ++++++++---
 ...linux_auditd_change_file_owner_to_root.yml | 43 +++++++---
 .../linux_auditd_clipboard_data_copy.yml      | 45 ++++++++---
 .../linux_auditd_data_destruction_command.yml | 39 ++++++---
 ...td_data_transfer_size_limits_via_split.yml | 41 +++++++---
 ...transfer_size_limits_via_split_syscall.yml | 48 +++++++----
 ..._database_file_and_directory_discovery.yml | 51 +++++++++---
 .../linux_auditd_dd_file_overwrite.yml        | 40 +++++++---
 ...ditd_disable_or_modify_system_firewall.yml | 46 +++++++----
 .../linux_auditd_doas_conf_file_creation.yml  | 41 +++++++---
 .../linux_auditd_doas_tool_execution.yml      | 47 +++++++----
 ...linux_auditd_edit_cron_table_parameter.yml | 48 +++++++----
 ...ux_auditd_file_and_directory_discovery.yml | 55 ++++++++++---
 ...file_permission_modification_via_chmod.yml | 45 ++++++++---
 ...le_permissions_modification_via_chattr.yml | 45 ++++++++---
 ...ind_credentials_from_password_managers.yml | 51 +++++++++---
 ..._find_credentials_from_password_stores.yml | 46 ++++++++---
 .../linux_auditd_find_private_keys.yml        | 45 ++++++++---
 .../linux_auditd_find_ssh_private_keys.yml    | 47 ++++++++---
 ...linux_auditd_hardware_addition_swapoff.yml | 45 ++++++++---
 ..._hidden_files_and_directories_creation.yml | 45 ++++++++---
 ...ert_kernel_module_using_insmod_utility.yml | 47 +++++++----
 ...l_kernel_module_using_modprobe_utility.yml | 47 +++++++----
 ...linux_auditd_kernel_module_enumeration.yml | 47 +++++++----
 ...ditd_kernel_module_using_rmmod_utility.yml | 48 +++++++----
 ..._auditd_nopasswd_entry_in_sudoers_file.yml | 43 +++++++---
 .../linux_auditd_osquery_service_stop.yml     | 47 +++++++----
 ...ss_or_modification_of_sshd_config_file.yml | 45 ++++++++---
 ...td_possible_access_to_credential_files.yml | 45 ++++++++---
 ...auditd_possible_access_to_sudoers_file.yml | 44 +++++++---
 ...cronjob_entry_on_existing_cronjob_file.yml | 38 ++++++---
 ...ux_auditd_preload_hijack_library_calls.yml | 44 +++++++---
 ...auditd_preload_hijack_via_preload_file.yml | 42 +++++++---
 .../linux_auditd_service_restarted.yml        | 44 +++++++---
 .../endpoint/linux_auditd_service_started.yml | 44 +++++++---
 ...inux_auditd_setuid_using_chmod_utility.yml | 44 +++++++---
 ...nux_auditd_setuid_using_setcap_utility.yml | 46 ++++++++---
 .../linux_auditd_shred_overwrite_command.yml  | 44 +++++++---
 .../endpoint/linux_auditd_stop_services.yml   | 46 +++++++----
 .../linux_auditd_sudo_or_su_execution.yml     | 43 +++++++---
 .../linux_auditd_sysmon_service_stop.yml      | 46 +++++++----
 ...system_network_configuration_discovery.yml | 51 ++++++++----
 ..._unix_shell_configuration_modification.yml | 50 +++++++++---
 ...inux_auditd_unload_module_via_modprobe.yml | 45 ++++++++---
 ...tual_disk_file_and_directory_discovery.yml | 46 ++++++++---
 .../linux_auditd_whoami_user_discovery.yml    | 48 +++++++----
 .../linux_awk_privilege_escalation.yml        | 51 ++++++++----
 .../linux_busybox_privilege_escalation.yml    | 48 +++++++----
 .../linux_c89_privilege_escalation.yml        | 48 +++++++----
 .../linux_c99_privilege_escalation.yml        | 48 +++++++----
 .../linux_change_file_owner_to_root.yml       | 47 +++++++----
 .../endpoint/linux_clipboard_data_copy.yml    | 54 ++++++++-----
 ...x_common_process_for_elevation_control.yml | 46 +++++++----
 .../linux_composer_privilege_escalation.yml   | 49 ++++++++----
 .../linux_cpulimit_privilege_escalation.yml   | 48 +++++++----
 .../linux_csvtool_privilege_escalation.yml    | 48 +++++++----
 .../endpoint/linux_curl_upload_file.yml       | 58 +++++++++-----
 .../linux_data_destruction_command.yml        | 48 +++++++----
 .../endpoint/linux_dd_file_overwrite.yml      | 47 +++++++----
 .../endpoint/linux_decode_base64_to_shell.yml | 57 ++++++++-----
 ...ng_critical_directory_using_rm_command.yml | 52 ++++++++----
 .../endpoint/linux_deletion_of_cron_jobs.yml  | 40 ++++++----
 .../linux_deletion_of_init_daemon_script.yml  | 40 ++++++----
 .../endpoint/linux_deletion_of_services.yml   | 42 ++++++----
 .../linux_deletion_of_ssl_certificate.yml     | 42 ++++++----
 .../endpoint/linux_disable_services.yml       | 51 ++++++++----
 .../linux_doas_conf_file_creation.yml         | 39 ++++++---
 .../endpoint/linux_doas_tool_execution.yml    | 47 +++++++----
 .../linux_docker_privilege_escalation.yml     | 51 ++++++++----
 .../linux_edit_cron_table_parameter.yml       | 40 ++++++----
 .../linux_emacs_privilege_escalation.yml      | 48 +++++++----
 ...ile_created_in_kernel_driver_directory.yml | 37 ++++++---
 ...x_file_creation_in_init_boot_directory.yml | 38 ++++++---
 ...nux_file_creation_in_profile_directory.yml | 39 ++++++---
 .../linux_find_privilege_escalation.yml       | 51 ++++++++----
 .../linux_gdb_privilege_escalation.yml        | 48 +++++++----
 .../linux_gem_privilege_escalation.yml        | 48 +++++++----
 .../linux_gnu_awk_privilege_escalation.yml    | 47 +++++++----
 .../linux_hardware_addition_swapoff.yml       | 47 +++++++----
 ...quency_of_file_deletion_in_boot_folder.yml | 42 ++++++----
 ...equency_of_file_deletion_in_etc_folder.yml | 42 ++++++----
 .../linux_impair_defenses_process_kill.yml    | 44 ++++++----
 .../linux_indicator_removal_clear_cache.yml   | 48 +++++++----
 ...ndicator_removal_service_file_deletion.yml | 51 ++++++++----
 .../linux_ingress_tool_transfer_hunting.yml   | 48 ++++++-----
 .../linux_ingress_tool_transfer_with_curl.yml | 54 ++++++++-----
 ...ert_kernel_module_using_insmod_utility.yml | 48 +++++++----
 ...l_kernel_module_using_modprobe_utility.yml | 48 +++++++----
 .../linux_iptables_firewall_modification.yml  | 56 +++++++++----
 .../endpoint/linux_java_spawning_shell.yml    | 55 ++++++++-----
 .../linux_kernel_module_enumeration.yml       | 54 ++++++++-----
 ...orker_process_in_writable_process_path.yml | 42 ++++++----
 .../linux_make_privilege_escalation.yml       | 47 +++++++----
 .../linux_mysql_privilege_escalation.yml      | 51 ++++++++----
 .../linux_ngrok_reverse_proxy_usage.yml       | 55 ++++++++-----
 .../linux_node_privilege_escalation.yml       | 52 ++++++++----
 .../linux_nopasswd_entry_in_sudoers_file.yml  | 48 +++++++----
 ...ted_files_or_information_base64_decode.yml | 55 ++++++++-----
 .../linux_octave_privilege_escalation.yml     | 49 ++++++++----
 .../linux_openvpn_privilege_escalation.yml    | 49 ++++++++----
 ...and_privilege_escalation_risk_behavior.yml | 53 ++++++++----
 .../linux_php_privilege_escalation.yml        | 48 +++++++----
 .../linux_pkexec_privilege_escalation.yml     | 52 +++++++-----
 ...ss_or_modification_of_sshd_config_file.yml | 48 +++++++----
 ...ux_possible_access_to_credential_files.yml | 47 +++++++----
 .../linux_possible_access_to_sudoers_file.yml | 47 +++++++----
 ...append_command_to_at_allow_config_file.yml | 48 +++++++----
 ..._append_command_to_profile_config_file.yml | 49 ++++++++----
 ...cronjob_entry_on_existing_cronjob_file.yml | 42 ++++++----
 ...sible_cronjob_modification_with_editor.yml | 41 ++++++----
 .../linux_possible_ssh_key_file_creation.yml  | 38 ++++++---
 .../linux_preload_hijack_library_calls.yml    | 48 +++++++----
 .../endpoint/linux_proxy_socks_curl.yml       | 56 ++++++++-----
 .../linux_puppet_privilege_escalation.yml     | 49 ++++++++----
 .../linux_rpm_privilege_escalation.yml        | 51 ++++++++----
 .../linux_ruby_privilege_escalation.yml       | 50 ++++++++----
 ...vice_file_created_in_systemd_directory.yml | 44 +++++++---
 .../endpoint/linux_service_restarted.yml      | 49 ++++++++----
 .../linux_service_started_or_enabled.yml      | 50 ++++++++----
 .../linux_setuid_using_chmod_utility.yml      | 49 ++++++++----
 .../linux_setuid_using_setcap_utility.yml     | 50 ++++++++----
 .../linux_shred_overwrite_command.yml         | 48 +++++++----
 .../linux_sqlite3_privilege_escalation.yml    | 48 +++++++----
 ...linux_ssh_authorized_keys_modification.yml | 55 ++++++++-----
 ...nux_ssh_remote_services_script_execute.yml | 53 +++++++-----
 ...ux_stdout_redirection_to_dev_null_file.yml | 38 ++++++---
 detections/endpoint/linux_stop_services.yml   | 51 ++++++++----
 .../endpoint/linux_sudo_or_su_execution.yml   | 41 ++++++----
 .../linux_sudoers_tmp_file_creation.yml       | 40 +++++++---
 .../linux_system_network_discovery.yml        | 50 ++++++++----
 ...x_system_reboot_via_system_request_key.yml | 46 +++++++----
 ..._unix_shell_enable_all_sysrq_functions.yml | 50 ++++++++----
 .../linux_visudo_utility_execution.yml        | 48 +++++++----
 .../living_off_the_land_detection.yml         | 50 +++++++++---
 .../endpoint/loading_of_dynwrapx_module.yml   | 47 +++++++----
 .../local_account_discovery_with_net.yml      | 31 +++++--
 .../local_account_discovery_with_wmic.yml     | 30 +++++--
 .../log4shell_cve_2021_44228_exploitation.yml | 49 +++++++++---
 .../logon_script_event_trigger_execution.yml  | 39 ++++++---
 .../endpoint/lolbas_with_network_traffic.yml  | 64 +++++++++------
 .../macos___re_opened_applications.yml        | 42 +++++++---
 detections/endpoint/macos_lolbin.yml          | 41 ++++++----
 detections/endpoint/macos_plutil.yml          | 38 +++++----
 .../endpoint/mailsniper_invoke_functions.yml  | 37 ++++++---
 .../malicious_inprocserver32_modification.yml | 56 +++++++++----
 ...cious_powershell_executed_as_a_service.yml | 45 +++++++----
 ...s_powershell_process___encoded_command.yml | 38 ++++++---
 ...hell_process___execution_policy_bypass.yml | 53 +++++++-----
 ...ll_process_with_obfuscation_techniques.yml | 55 ++++++++-----
 ...z_passtheticket_commandline_parameters.yml | 52 ++++++++----
 .../mmc_lolbas_execution_process_spawn.yml    | 62 +++++++++-----
 .../endpoint/modification_of_wallpaper.yml    | 42 ++++++----
 ...dify_acl_permission_to_files_or_folder.yml | 45 ++++++++---
 ...nitor_registry_keys_for_print_monitors.yml | 38 +++++----
 ...oveit_certificate_store_access_failure.yml | 28 ++++---
 ...key_fingerprint_authentication_attempt.yml | 30 ++++---
 ...on_service_writing_active_server_pages.yml | 46 +++++++----
 ..._scripting_process_loading_ldap_module.yml | 41 ++++++----
 ...s_scripting_process_loading_wmi_module.yml | 43 ++++++----
 ...d_suspicious_spawned_by_script_process.yml | 52 ++++++++----
 ..._spawning_rundll32_or_regsvr32_process.yml | 54 ++++++++-----
 .../mshtml_module_load_in_office_product.yml  | 42 ++++++----
 ...msi_module_loaded_by_non_system_binary.yml | 34 ++++----
 .../msmpeng_application_dll_side_loading.yml  | 40 +++++++---
 .../endpoint/net_localgroup_discovery.yml     | 42 ++++++----
 .../endpoint/net_profiler_uac_bypass.yml      | 42 +++++++---
 .../network_connection_discovery_with_arp.yml | 41 ++++++----
 .../network_connection_discovery_with_net.yml | 41 ++++++----
 ...work_connection_discovery_with_netstat.yml | 40 ++++++----
 ...work_discovery_using_route_windows_app.yml | 42 ++++++----
 ...etwork_share_discovery_via_dir_command.yml | 39 +++++----
 ...active_directory_web_services_protocol.yml | 38 ++++++---
 .../endpoint/nishang_powershelltcponeline.yml | 51 +++++++-----
 .../nltest_domain_trust_discovery.yml         | 48 +++++++----
 ...e_process_accessing_chrome_default_dir.yml | 43 ++++++----
 ...fox_process_access_firefox_profile_dir.yml | 42 ++++++----
 ...notepad_with_no_command_line_arguments.yml | 56 ++++++++-----
 detections/endpoint/ntdsutil_export_ntds.yml  | 47 +++++++----
 .../office_application_drop_executable.yml    | 43 +++++++---
 ...ice_application_spawn_regsvr32_process.yml | 53 +++++++-----
 ...ice_application_spawn_rundll32_process.yml | 50 ++++++++----
 ...office_document_creating_schedule_task.yml | 47 +++++++----
 .../office_document_executing_macro_code.yml  | 44 ++++++----
 ...ment_spawned_child_process_to_download.yml | 52 +++++++-----
 .../office_product_spawn_cmd_process.yml      | 60 +++++++++-----
 .../office_product_spawning_bitsadmin.yml     | 54 ++++++++-----
 .../office_product_spawning_certutil.yml      | 53 +++++++-----
 .../office_product_spawning_mshta.yml         | 53 +++++++-----
 ..._product_spawning_rundll32_with_no_dll.yml | 58 +++++++++-----
 ...e_product_spawning_windows_script_host.yml | 56 ++++++++-----
 .../endpoint/office_product_spawning_wmic.yml | 53 +++++++-----
 .../office_product_writing_cab_or_inf.yml     | 51 ++++++++----
 .../endpoint/office_spawning_control.yml      | 52 +++++++-----
 ...nnection_from_java_using_default_ports.yml | 57 ++++++++-----
 .../overwriting_accessibility_binaries.yml    | 42 +++++++---
 ...ercut_ng_suspicious_behavior_debug_log.yml | 35 +++++---
 .../password_policy_discovery_with_net.yml    | 41 ++++++----
 ...mission_modification_using_takeown_app.yml | 52 ++++++++----
 ...etitpotam_network_share_access_request.yml | 41 ++++++----
 ...tpotam_suspicious_kerberos_tgt_request.yml | 40 ++++++----
 .../endpoint/ping_sleep_batch_command.yml     | 54 ++++++++-----
 .../possible_browser_pass_view_parameter.yml  | 46 +++++++----
 ...ible_lateral_movement_powershell_spawn.yml | 59 +++++++++-----
 .../potential_password_in_username.yml        | 41 +++++++---
 ...entially_malicious_code_on_commandline.yml | 55 +++++++++----
 .../endpoint/powershell_4104_hunting.yml      | 55 ++++++++++---
 ...connect_to_internet_with_hidden_window.yml | 44 +++++++---
 ..._hijacking_inprocserver32_modification.yml | 41 ++++++----
 .../powershell_creating_thread_mutex.yml      | 37 ++++++---
 ...powershell_disable_security_monitoring.yml | 52 +++++++-----
 .../powershell_domain_enumeration.yml         | 39 ++++++---
 .../powershell_enable_powershell_remoting.yml | 36 ++++++---
 ...powershell_enable_smb1protocol_feature.yml | 33 +++++---
 .../powershell_execute_com_object.yml         | 33 +++++---
 ...s_process_injection_via_getprocaddress.yml | 35 +++++---
 ...script_contains_base64_encoded_content.yml | 35 +++++---
 .../powershell_get_localgroup_discovery.yml   | 42 ++++++----
 ...up_discovery_with_script_block_logging.yml | 28 ++++---
 ...powershell_invoke_cimmethod_cimsession.yml | 39 ++++++---
 .../powershell_invoke_wmiexec_usage.yml       | 35 +++++---
 .../powershell_load_module_in_meterpreter.yml | 43 ++++++----
 ...ding_dotnet_into_memory_via_reflection.yml | 45 +++++++----
 .../powershell_processing_stream_of_data.yml  | 41 ++++++----
 ...rshell_remote_services_add_trustedhost.yml | 39 ++++++---
 ...remote_thread_to_known_windows_process.yml | 46 ++++++-----
 ...hell_remove_windows_defender_directory.yml | 37 ++++++---
 ...powershell_script_block_with_url_chain.yml | 41 +++++++---
 .../powershell_start_bitstransfer.yml         | 56 ++++++++-----
 .../powershell_start_or_stop_service.yml      | 38 ++++++---
 ...wershell_using_memory_as_backing_store.yml | 38 ++++++---
 ...ershell_webrequest_using_memory_stream.yml | 39 ++++++---
 ...ll_windows_defender_exclusion_commands.yml | 35 +++++---
 ...nt_automatic_repair_mode_using_bcdedit.yml | 53 ++++++++----
 .../print_processor_registry_autostart.yml    | 35 +++++---
 .../print_spooler_adding_a_printer_driver.yml | 34 +++++---
 ...print_spooler_failed_to_load_a_plug_in.yml | 37 ++++++---
 ...eating_lnk_file_in_suspicious_location.yml | 51 ++++++++----
 ...process_deleting_its_process_file_path.yml | 50 ++++++++----
 .../endpoint/process_execution_via_wmi.yml    | 49 ++++++++----
 .../process_kill_base_on_file_path.yml        | 53 +++++++-----
 .../process_writing_dynamicwrapperx.yml       | 46 +++++++----
 .../endpoint/processes_launching_netsh.yml    | 51 ++++++++----
 .../processes_tapping_keyboard_events.yml     | 33 +++++---
 ...randomly_generated_scheduled_task_name.yml | 23 +++---
 ...andomly_generated_windows_service_name.yml | 25 +++---
 .../ransomware_notes_bulk_creation.yml        | 40 ++++++----
 .../recon_avproduct_through_pwh_or_wmi.yml    | 35 +++++---
 detections/endpoint/recon_using_wmi_class.yml | 41 +++++++---
 ...rsive_delete_of_directory_in_batch_cmd.yml | 52 +++++++-----
 ...ulating_windows_services_registry_keys.yml | 49 ++++++++----
 ...istry_keys_for_creating_shim_databases.yml | 43 +++++-----
 .../registry_keys_used_for_persistence.yml    | 62 +++++++++++---
 ...try_keys_used_for_privilege_escalation.yml | 44 +++++-----
 ...2_silent_and_install_param_dll_loading.yml | 55 ++++++++-----
 ...svr32_with_known_silent_switch_cmdline.yml | 55 ++++++++-----
 .../remcos_client_registry_install_entry.yml  | 61 ++++++++------
 ...cos_rat_file_creation_in_remcos_folder.yml | 35 +++++---
 ...mote_desktop_process_running_on_system.yml | 31 ++++---
 ..._instantiation_via_dcom_and_powershell.yml | 56 ++++++++-----
 ...n_via_dcom_and_powershell_script_block.yml | 40 +++++++---
 ...instantiation_via_winrm_and_powershell.yml | 56 ++++++++-----
 ..._via_winrm_and_powershell_script_block.yml | 43 ++++++----
 ...cess_instantiation_via_winrm_and_winrs.yml | 53 +++++++-----
 .../remote_process_instantiation_via_wmi.yml  | 56 ++++++++-----
 ...s_instantiation_via_wmi_and_powershell.yml | 55 ++++++++-----
 ...on_via_wmi_and_powershell_script_block.yml | 43 +++++++---
 ...ote_system_discovery_with_adsisearcher.yml | 35 +++++---
 .../remote_system_discovery_with_dsquery.yml  | 41 ++++++----
 .../remote_system_discovery_with_net.yml      | 42 ++++++----
 .../remote_system_discovery_with_wmic.yml     | 49 ++++++++----
 .../endpoint/remote_wmi_command_attempt.yml   | 49 ++++++++----
 .../endpoint/resize_shadowstorage_volume.yml  | 51 ++++++++----
 .../endpoint/revil_common_exec_parameter.yml  | 53 ++++++++----
 detections/endpoint/revil_registry_entry.yml  | 64 +++++++++------
 .../rubeus_command_line_parameters.yml        | 54 +++++++++----
 ...ticket_exports_through_winlogon_access.yml | 45 +++++++----
 .../runas_execution_in_commandline.yml        | 43 ++++++----
 .../endpoint/rundll32_control_rundll_hunt.yml | 47 ++++++-----
 ...ontrol_rundll_world_writable_directory.yml | 55 ++++++++-----
 ...ll32_create_remote_thread_to_a_process.yml | 40 ++++++----
 ...rundll32_createremotethread_in_browser.yml | 42 ++++++----
 detections/endpoint/rundll32_dnsquery.yml     | 40 +++++++---
 .../endpoint/rundll32_lockworkstation.yml     | 45 +++++++----
 ...undll32_process_creating_exe_dll_files.yml | 35 +++++---
 .../endpoint/rundll32_shimcache_flush.yml     | 48 ++++++-----
 ...no_command_line_arguments_with_network.yml | 59 +++++++++-----
 .../rundll_loading_dll_by_ordinal.yml         | 56 ++++++++-----
 .../endpoint/ryuk_test_files_detected.yml     | 37 ++++++---
 .../endpoint/ryuk_wake_on_lan_command.yml     | 49 ++++++++----
 .../sam_database_file_access_attempt.yml      | 34 +++++---
 .../endpoint/samsam_test_file_write.yml       | 36 ++++++---
 .../sc_exe_manipulating_windows_services.yml  | 50 ++++++++----
 ..._by_app_connect_and_create_adsi_object.yml | 41 ++++++----
 ...edule_task_with_http_command_arguments.yml | 40 ++++++----
 ...ule_task_with_rundll32_command_trigger.yml | 40 ++++++----
 ...k_creation_on_remote_endpoint_using_at.yml | 52 +++++++-----
 ...eduled_task_deleted_or_created_via_cmd.yml | 52 ++++++++----
 ...led_task_initiation_on_remote_endpoint.yml | 51 +++++++-----
 .../endpoint/schtasks_run_task_on_demand.yml  | 52 ++++++++----
 ...htasks_scheduling_job_on_remote_system.yml | 50 ++++++++----
 .../schtasks_used_for_forcing_a_reboot.yml    | 50 ++++++++----
 .../screensaver_event_trigger_execution.yml   | 39 ++++++---
 .../endpoint/script_execution_via_wmi.yml     | 44 +++++++---
 detections/endpoint/sdclt_uac_bypass.yml      | 63 +++++++++------
 .../sdelete_application_execution.yml         | 50 +++++++-----
 ...host_with_no_command_line_with_network.yml | 52 ++++++++----
 .../secretdumps_offline_ntds_dumping_tool.yml | 51 ++++++++----
 ...incipalnames_discovery_with_powershell.yml | 37 ++++++---
 ...ceprincipalnames_discovery_with_setspn.yml | 55 ++++++++-----
 detections/endpoint/services_escalate_exe.yml | 51 ++++++++----
 ...ervices_lolbas_execution_process_spawn.yml | 62 +++++++++-----
 ...ution_policy_to_unrestricted_or_bypass.yml | 67 ++++++++++------
 .../endpoint/shim_database_file_creation.yml  | 39 ++++++---
 ...nstallation_with_suspicious_parameters.yml | 44 +++++++---
 .../endpoint/short_lived_scheduled_task.yml   | 39 +++++----
 .../endpoint/short_lived_windows_accounts.yml | 45 ++++++++---
 .../endpoint/silentcleanup_uac_bypass.yml     | 62 ++++++++------
 .../single_letter_process_on_endpoint.yml     | 47 ++++++++---
 detections/endpoint/slui_runas_elevated.yml   | 51 ++++++++----
 .../endpoint/slui_spawning_a_process.yml      | 51 ++++++++----
 detections/endpoint/spike_in_file_writes.yml  | 30 +++++--
 .../endpoint/spoolsv_spawning_rundll32.yml    | 56 ++++++++-----
 .../spoolsv_suspicious_loaded_modules.yml     | 37 ++++++---
 .../spoolsv_suspicious_process_access.yml     | 40 +++++++---
 detections/endpoint/spoolsv_writing_a_dll.yml | 48 +++++++----
 .../spoolsv_writing_a_dll___sysmon.yml        | 40 ++++++----
 .../endpoint/sqlite_module_in_temp_folder.yml | 36 ++++++---
 ...ation_certificates_behavior_identified.yml | 45 ++++++++---
 ...urst_correlation_dll_and_network_event.yml | 25 ++++--
 ...uspicious_computer_account_name_change.yml | 41 ++++++----
 .../endpoint/suspicious_copy_on_system32.yml  | 53 +++++++-----
 .../suspicious_curl_network_connection.yml    | 35 +++++---
 ...ious_dllhost_no_command_line_arguments.yml | 54 ++++++++-----
 .../suspicious_driver_loaded_path.yml         | 40 ++++++----
 .../suspicious_event_log_service_behavior.yml | 25 +++---
 ...ous_gpupdate_no_command_line_arguments.yml | 54 ++++++++-----
 .../suspicious_icedid_rundll32_cmdline.yml    | 51 +++++++-----
 ...cious_image_creation_in_appdata_folder.yml | 45 +++++++----
 ...icious_kerberos_service_ticket_request.yml | 39 ++++++---
 .../suspicious_linux_discovery_commands.yml   | 47 ++++++++---
 ...ous_microsoft_workflow_compiler_rename.yml | 48 ++++++-----
 ...ious_microsoft_workflow_compiler_usage.yml | 51 +++++++-----
 .../endpoint/suspicious_msbuild_path.yml      | 53 +++++++-----
 .../endpoint/suspicious_msbuild_rename.yml    | 45 ++++++-----
 .../endpoint/suspicious_msbuild_spawn.yml     | 52 +++++++-----
 .../suspicious_mshta_child_process.yml        | 51 ++++++++----
 .../endpoint/suspicious_mshta_spawn.yml       | 52 +++++++-----
 .../endpoint/suspicious_plistbuddy_usage.yml  | 39 ++++++---
 ...uspicious_plistbuddy_usage_via_osquery.yml | 23 ++++--
 ...ess_dns_query_known_abuse_web_services.yml | 41 ++++++----
 ...s_process_executed_from_container_file.yml | 42 +++++++---
 .../endpoint/suspicious_process_file_path.yml | 57 +++++++++----
 ...picious_process_with_discord_dns_query.yml | 36 ++++++---
 .../endpoint/suspicious_reg_exe_process.yml   | 57 +++++++++----
 ...ious_regsvr32_register_suspicious_path.yml | 57 ++++++++-----
 .../suspicious_rundll32_dllregisterserver.yml | 55 ++++++++-----
 ...ous_rundll32_no_command_line_arguments.yml | 54 ++++++++-----
 .../suspicious_rundll32_plugininit.yml        | 51 +++++++-----
 .../endpoint/suspicious_rundll32_startw.yml   | 52 +++++++-----
 ...s_scheduled_task_from_public_directory.yml | 52 ++++++++----
 ...protocolhost_no_command_line_arguments.yml | 55 ++++++++-----
 ...spicious_sqlite3_lsquarantine_behavior.yml | 35 +++++---
 ...picious_ticket_granting_ticket_request.yml | 34 +++++---
 .../suspicious_wav_file_in_appdata_folder.yml | 45 +++++++----
 .../endpoint/suspicious_wevtutil_usage.yml    | 48 ++++++++---
 ...spicious_writes_to_windows_recycle_bin.yml | 48 +++++++----
 ...svchost_lolbas_execution_process_spawn.yml | 61 +++++++++-----
 ...nfo_gathering_using_dxdiag_application.yml | 45 ++++++-----
 ...system_information_discovery_detection.yml | 44 +++++++---
 ...rocesses_run_from_unexpected_locations.yml | 51 ++++++++----
 .../system_user_discovery_with_query.yml      | 41 ++++++----
 .../system_user_discovery_with_whoami.yml     | 41 ++++++----
 .../time_provider_persistence_registry.yml    | 31 ++++---
 detections/endpoint/trickbot_named_pipe.yml   | 36 +++++----
 .../uac_bypass_mmc_load_unsigned_dll.yml      | 46 +++++++----
 .../uac_bypass_with_colorui_com_object.yml    | 41 ++++++----
 .../endpoint/uninstall_app_using_msiexec.yml  | 46 +++++++----
 ...wn_process_using_the_kerberos_protocol.yml | 58 +++++++++-----
 .../endpoint/unload_sysmon_filter_driver.yml  | 44 +++++++---
 .../unloading_amsi_via_reflection.yml         | 38 ++++++---
 ..._of_computer_service_tickets_requested.yml | 33 +++++---
 ..._of_kerberos_service_tickets_requested.yml | 46 ++++++-----
 ..._remote_endpoint_authentication_events.yml | 33 +++++---
 .../endpoint/unusually_long_command_line.yml  | 34 +++++---
 .../unusually_long_command_line___mltk.yml    | 41 +++++++---
 ...ser_discovery_with_env_vars_powershell.yml | 42 ++++++----
 ..._with_env_vars_powershell_script_block.yml | 33 +++++---
 detections/endpoint/usn_journal_deletion.yml  | 44 +++++++---
 .../vbscript_execution_using_wscript_app.yml  | 50 +++++++-----
 .../endpoint/verclsid_clsid_execution.yml     | 50 +++++++-----
 detections/endpoint/w3wp_spawning_shell.yml   | 53 +++++++-----
 .../wbadmin_delete_system_backups.yml         | 42 +++++++---
 .../wbemprox_com_object_execution.yml         | 44 ++++++----
 ...ss_connecting_to_ip_check_web_services.yml | 42 ++++++----
 .../wermgr_process_create_executable_file.yml | 36 ++++++---
 ...cess_spawned_cmd_or_powershell_process.yml | 50 +++++++-----
 .../wget_download_and_bash_execution.yml      | 37 +++++----
 .../endpoint/windows_abused_web_services.yml  | 39 +++++----
 ...ss_token_manipulation_sedebugprivilege.yml | 47 ++++++-----
 ...lation_winlogon_duplicate_token_handle.yml | 41 +++++-----
 ...ogon_duplicate_handle_in_uncommon_path.yml | 49 +++++++-----
 ...iscovery_for_none_disable_user_account.yml | 34 +++++---
 ...account_discovery_for_sam_account_name.yml | 38 ++++++---
 ...scovery_with_netuser_preauthnotrequire.yml | 33 +++++---
 ...ows_ad_abnormal_object_access_activity.yml | 44 +++++++---
 .../windows_ad_adminsdholder_acl_modified.yml | 66 +++++++++++----
 ...s_ad_cross_domain_sid_history_addition.yml | 42 ++++++----
 ...omain_controller_audit_policy_disabled.yml | 41 +++++++---
 ...windows_ad_domain_controller_promotion.yml | 43 +++++++---
 ...ows_ad_domain_replication_acl_addition.yml | 72 +++++++++++++----
 .../windows_ad_dsrm_account_changes.yml       | 46 ++++++++---
 .../windows_ad_dsrm_password_reset.yml        | 39 +++++----
 ...rivileged_account_sid_history_addition.yml | 46 +++++++----
 ...s_ad_privileged_object_access_activity.yml | 47 ++++++++---
 ...tion_request_initiated_by_user_account.yml | 61 +++++++++-----
 ...t_initiated_from_unsanctioned_location.yml | 67 ++++++++++------
 ...ws_ad_same_domain_sid_history_addition.yml | 39 +++++----
 ...eprincipalname_added_to_domain_account.yml | 23 +++---
 ...ed_domain_account_serviceprincipalname.yml | 40 +++++++---
 ..._lived_domain_controller_spn_attribute.yml | 47 +++++++----
 .../windows_ad_short_lived_server_object.yml  | 39 ++++++---
 ...dows_ad_sid_history_attribute_modified.yml | 43 ++++++----
 detections/endpoint/windows_adfind_exe.yml    | 54 +++++++++----
 .../windows_admin_permission_discovery.yml    | 47 +++++++----
 ...tive_shares_accessed_on_multiple_hosts.yml | 43 ++++++----
 ...n_default_group_policy_object_modified.yml | 39 ++++++---
 ...dows_admon_group_policy_object_created.yml | 37 ++++++---
 ..._alternate_datastream___base64_content.yml | 41 +++++++---
 ...ernate_datastream___executable_content.yml | 40 ++++++----
 ...ternate_datastream___process_execution.yml | 45 +++++++----
 .../windows_apache_benchmark_binary.yml       | 55 ++++++++-----
 ...ws_app_layer_protocol_qakbot_namedpipe.yml | 41 ++++++----
 ...r_protocol_wermgr_connect_to_namedpipe.yml | 41 ++++++----
 ...yer_protocol_rms_radmin_tool_namedpipe.yml | 41 ++++++----
 .../windows_applocker_block_events.yml        | 38 ++++++---
 ...cker_execution_from_uncommon_locations.yml | 36 ++++++---
 ...ege_escalation_via_unauthorized_bypass.yml | 43 +++++++---
 ...cker_rare_application_launch_detection.yml | 36 ++++++---
 ..._archive_collected_data_via_powershell.yml | 34 +++++---
 ...windows_archive_collected_data_via_rar.yml | 54 ++++++++-----
 ...archived_collected_data_in_temp_folder.yml | 40 +++++-----
 .../endpoint/windows_autoit3_execution.yml    | 51 ++++++++----
 ...ion_lsass_driver_registry_modification.yml | 48 +++++++----
 ...roxy_execution_mavinject_dll_injection.yml | 55 ++++++++-----
 ..._autostart_execution_in_startup_folder.yml | 41 ++++++----
 .../endpoint/windows_bootloader_inventory.yml | 23 ++++--
 .../windows_bypass_uac_via_pkgmgr_tool.yml    | 57 ++++++++-----
 .../endpoint/windows_cab_file_on_disk.yml     | 43 +++++++---
 ...ws_cached_domain_credentials_reg_query.yml | 56 ++++++++-----
 ...fault_file_association_for_no_file_ext.yml | 56 ++++++++-----
 ...ndows_clipboard_data_via_get_clipboard.yml | 37 ++++++---
 ..._hijacking_inprocserver32_modification.yml | 41 ++++++++--
 ...ing_interpreter_hunting_path_traversal.yml | 52 +++++++-----
 ...ipting_interpreter_path_traversal_exec.yml | 53 +++++++-----
 ...s_command_shell_dcrat_forkbomb_payload.yml | 57 ++++++++-----
 ...dows_command_shell_fetch_env_variables.yml | 57 ++++++++-----
 ..._common_abused_cmd_shell_risk_behavior.yml | 57 +++++++++----
 ...er_account_created_by_computer_account.yml | 45 +++++++----
 ...ter_account_requesting_kerberos_ticket.yml | 43 ++++++----
 .../windows_computer_account_with_spn.yml     | 46 +++++++----
 ...windows_conhost_with_headless_argument.yml | 48 +++++++----
 .../endpoint/windows_create_local_account.yml | 38 ++++++---
 ...ial_access_from_browser_password_store.yml | 52 ++++++++----
 ...ential_dumping_lsass_memory_createdump.yml | 56 ++++++++-----
 ...sword_stores_chrome_copied_in_temp_dir.yml | 42 +++++-----
 ...assword_stores_chrome_extension_access.yml | 48 ++++++-----
 ...ssword_stores_chrome_localstate_access.yml | 49 +++++++-----
 ...ssword_stores_chrome_login_data_access.yml | 50 +++++++-----
 ...dentials_from_password_stores_creation.yml | 53 +++++++-----
 ...dentials_from_password_stores_deletion.yml | 52 +++++++-----
 ...credentials_from_password_stores_query.yml | 55 ++++++++-----
 ...from_web_browsers_saved_in_temp_folder.yml | 41 +++++-----
 ...dows_credentials_in_registry_reg_query.yml | 55 ++++++++-----
 ...ndows_curl_download_to_suspicious_path.yml | 56 ++++++++-----
 ...dows_curl_upload_to_remote_destination.yml | 54 ++++++++-----
 ...truction_recursive_exec_files_deletion.yml | 45 +++++++----
 .../windows_debugger_tool_execution.yml       | 41 ++++++----
 ...cement_modify_transcodedwallpaper_file.yml | 51 ++++++++----
 ...s_default_group_policy_object_modified.yml | 45 +++++++----
 ...group_policy_object_modified_with_gpme.yml | 56 ++++++++-----
 .../windows_defender_asr_audit_events.yml     | 43 +++++++---
 .../windows_defender_asr_block_events.yml     | 44 +++++++---
 ...ows_defender_asr_registry_modification.yml | 41 +++++++---
 .../windows_defender_asr_rule_disabled.yml    | 45 ++++++++---
 .../windows_defender_asr_rules_stacking.yml   | 39 ++++++---
 ...dows_defender_exclusion_registry_entry.yml | 40 ++++++----
 ...ndows_delete_or_modify_system_firewall.yml | 45 +++++++----
 ...ry_by_a_non_critical_process_file_path.yml | 62 ++++++++------
 ...sable_change_password_through_registry.yml | 32 +++++---
 ...k_workstation_feature_through_registry.yml | 36 +++++----
 ...disable_logoff_button_through_registry.yml | 39 +++++----
 .../windows_disable_memory_crash_dump.yml     | 39 ++++++---
 .../windows_disable_notification_center.yml   | 38 +++++----
 ...s_disable_or_modify_tools_via_taskkill.yml | 50 ++++++++----
 ...indows_disable_or_stop_browser_process.yml | 48 +++++------
 ...sable_shutdown_button_through_registry.yml | 35 ++++----
 ...ows_event_logging_disable_http_logging.yml | 56 ++++++++-----
 ...group_policy_features_through_registry.yml | 45 +++++++----
 .../windows_disableantispyware_registry.yml   | 42 +++++++---
 .../endpoint/windows_diskcryptor_usage.yml    | 49 +++++++-----
 .../windows_diskshadow_proxy_execution.yml    | 49 ++++++++----
 ...ows_dism_install_powershell_web_access.yml | 50 ++++++++----
 .../endpoint/windows_dism_remove_defender.yml | 56 ++++++++-----
 ...earch_order_hijacking_hunt_with_sysmon.yml | 30 ++++---
 ...l_search_order_hijacking_with_iscsicpl.yml | 55 ++++++++-----
 .../windows_dll_side_loading_in_calc.yml      | 46 +++++++----
 ...dll_side_loading_process_child_of_calc.yml | 49 +++++++-----
 .../windows_dns_gather_network_info.yml       | 51 +++++++-----
 .../windows_dnsadmins_new_member_added.yml    | 39 +++++----
 ..._account_discovery_via_get_netcomputer.yml | 38 ++++++---
 ...s_domain_admin_impersonation_indicator.yml | 55 ++++++++++---
 ...ows_dotnet_binary_in_non_standard_path.yml | 59 +++++++++-----
 .../endpoint/windows_driver_inventory.yml     | 25 +++---
 .../windows_driver_load_non_standard_path.yml | 36 +++++----
 .../windows_drivers_loaded_by_signature.yml   | 34 ++++----
 .../windows_enable_powershell_web_access.yml  | 35 +++++---
 ...enable_win32_scheduledjob_via_registry.yml | 51 ++++++++----
 ...x_admins_group_creation_security_event.yml | 41 ++++++----
 ...dows_esx_admins_group_creation_via_net.yml | 47 +++++++----
 ...x_admins_group_creation_via_powershell.yml | 40 +++++++---
 .../windows_event_for_service_disabled.yml    | 30 ++++---
 .../endpoint/windows_event_log_cleared.yml    | 32 +++++---
 ...image_file_execution_options_injection.yml | 32 +++++---
 ...dows_excessive_disabled_services_event.yml | 40 ++++++----
 .../windows_executable_in_loaded_modules.yml  | 41 +++++-----
 ...s_execute_arbitrary_commands_with_msdt.yml | 57 ++++++++-----
 ...ltration_over_c2_via_invoke_restmethod.yml | 38 ++++++---
 ...on_over_c2_via_powershell_uploadstring.yml | 37 ++++++---
 .../endpoint/windows_export_certificate.yml   | 37 ++++++---
 ...ws_file_share_discovery_with_powerview.yml | 41 ++++++----
 ...er_protocol_in_non_common_process_path.yml | 49 +++++++-----
 ...e_without_extension_in_critical_folder.yml | 46 +++++++----
 ..._access_rights_modification_via_icacls.yml | 55 ++++++++-----
 ..._organizational_units_with_getdomainou.yml | 40 +++++++---
 ...ting_acl_with_findinterestingdomainacl.yml | 39 ++++++---
 .../windows_findstr_gpp_discovery.yml         | 53 +++++++-----
 ..._forest_discovery_with_getforestdomain.yml | 39 ++++++---
 ..._gather_victim_host_information_camera.yml | 37 ++++++---
 ...indows_gather_victim_identity_sam_info.yml | 36 +++++----
 ...ork_info_through_ip_check_web_services.yml | 37 +++++----
 ...ter_unconstrained_delegation_discovery.yml | 38 ++++++---
 ..._local_admin_with_findlocaladminaccess.yml | 40 +++++++---
 .../windows_group_policy_object_created.yml   | 43 ++++++----
 .../windows_hidden_schedule_task_settings.yml | 37 +++++----
 ...notification_features_through_registry.yml | 30 ++++---
 .../windows_high_file_deletion_frequency.yml  | 47 +++++++----
 ...k_execution_flow_version_dll_side_load.yml | 38 +++++----
 ...hunting_system_account_targeting_lsass.yml | 37 +++++----
 ...dentify_powershell_web_access_iis_pool.yml | 52 ++++++++----
 .../windows_identify_protocol_handlers.yml    | 48 ++++++-----
 .../windows_iis_components_add_new_module.yml | 56 ++++++++-----
 ...nents_get_webglobalmodule_module_query.yml | 27 ++++---
 ...s_iis_components_module_failed_to_load.yml | 35 +++++---
 ...indows_iis_components_new_module_added.yml | 35 +++++---
 ...impair_defense_add_xml_applocker_rules.yml | 41 +++++++---
 ...ge_win_defender_health_check_intervals.yml | 43 ++++++----
 ...hange_win_defender_quick_scan_interval.yml | 42 ++++++----
 ...ense_change_win_defender_throttle_rate.yml | 43 ++++++----
 ...ense_change_win_defender_tracing_level.yml | 43 ++++++----
 ..._defense_configure_app_install_control.yml | 31 ++++---
 ...ense_define_win_defender_threat_action.yml | 43 ++++++----
 ...fense_delete_win_defender_context_menu.yml | 36 ++++++---
 ...e_delete_win_defender_profile_registry.yml | 43 ++++++----
 ..._deny_security_software_with_applocker.yml | 56 ++++++++-----
 ...fense_disable_controlled_folder_access.yml | 44 ++++++----
 ..._disable_defender_firewall_and_network.yml | 46 +++++++----
 ..._disable_defender_protocol_recognition.yml | 43 ++++++----
 ..._impair_defense_disable_pua_protection.yml | 43 ++++++----
 ...se_disable_realtime_signature_delivery.yml | 43 ++++++----
 ..._impair_defense_disable_web_evaluation.yml | 30 ++++---
 ...defense_disable_win_defender_app_guard.yml | 43 ++++++----
 ...sable_win_defender_compute_file_hashes.yml | 43 ++++++----
 ...fense_disable_win_defender_gen_reports.yml | 43 ++++++----
 ...isable_win_defender_network_protection.yml | 44 ++++++----
 ..._disable_win_defender_report_infection.yml | 46 +++++++----
 ...se_disable_win_defender_scan_on_update.yml | 42 ++++++----
 ...able_win_defender_signature_retirement.yml | 46 +++++++----
 ...e_overide_win_defender_phishing_filter.yml | 44 ++++++----
 ...ir_defense_override_smartscreen_prompt.yml | 26 +++---
 ...in_defender_smart_screen_level_to_warn.yml | 43 ++++++----
 ...nses_disable_av_autostart_via_registry.yml | 42 +++++++---
 .../windows_impair_defenses_disable_hvci.yml  | 42 ++++++----
 ...nses_disable_win_defender_auto_logging.yml | 43 ++++++----
 .../windows_indicator_removal_via_rmdir.yml   | 52 +++++++-----
 ...ndirect_command_execution_via_forfiles.yml | 53 +++++++-----
 ..._indirect_command_execution_via_pcalua.yml | 52 +++++++-----
 ...mmand_execution_via_series_of_forfiles.yml | 50 +++++++-----
 .../windows_information_discovery_fsutil.yml  | 52 +++++++-----
 ...s_ingress_tool_transfer_using_explorer.yml | 57 ++++++++-----
 ...indows_inprocserver32_new_outlook_form.yml | 44 ++++++----
 ..._input_capture_using_credential_ui_dll.yml | 34 ++++----
 .../windows_installutil_credential_theft.yml  | 47 ++++++-----
 ...ndows_installutil_in_non_standard_path.yml | 57 ++++++++-----
 ..._installutil_remote_network_connection.yml | 63 +++++++++------
 .../windows_installutil_uninstall_option.yml  | 57 ++++++++-----
 ...tallutil_uninstall_option_with_network.yml | 63 +++++++++------
 ...indows_installutil_url_in_command_line.yml | 56 ++++++++-----
 .../windows_iso_lnk_file_creation.yml         | 37 ++++++---
 .../endpoint/windows_java_spawning_shells.yml | 44 +++++-----
 ...indows_kerberos_local_successful_logon.yml | 43 ++++++----
 .../windows_known_abused_dll_created.yml      | 69 +++++++++++-----
 ...s_known_abused_dll_loaded_suspiciously.yml | 40 +++++++---
 ...s_known_graphicalproton_loaded_modules.yml | 43 ++++++----
 .../windows_krbrelayup_service_creation.yml   | 35 +++++---
 ..._of_computer_service_tickets_requested.yml | 41 +++++++---
 .../windows_lateral_tool_transfer_remcom.yml  | 55 ++++++++-----
 ...ndows_ldifde_directory_object_behavior.yml | 52 +++++++-----
 ...dows_linked_policies_in_adsi_discovery.yml | 35 +++++---
 ...ocal_administrator_credential_stuffing.yml | 39 ++++++---
 ...indows_lolbas_executed_as_renamed_file.yml | 52 ++++++++----
 ..._lolbas_executed_outside_expected_path.yml | 44 ++++++----
 .../windows_lsa_secrets_nolmhash_registry.yml | 29 +++----
 ...il_protocol_in_non_common_process_path.yml | 50 +++++++-----
 .../windows_mark_of_the_web_bypass.yml        | 35 +++++---
 ...masquerading_explorer_as_child_process.yml | 53 +++++++-----
 .../windows_masquerading_msdtc_process.yml    | 50 +++++++-----
 .../windows_mimikatz_binary_execution.yml     | 55 ++++++++-----
 ...mimikatz_crypto_export_file_extensions.yml | 43 +++++++---
 ...y_registry_authenticationleveloverride.yml | 42 ++++++----
 ...ows_modify_registry_auto_minor_updates.yml | 36 +++++----
 ...dows_modify_registry_auto_update_notif.yml | 43 ++++++----
 ...ws_modify_registry_configure_bitlocker.yml | 48 +++++++----
 ...s_modify_registry_default_icon_setting.yml | 44 ++++++----
 ..._modify_registry_delete_firewall_rules.yml | 39 +++++----
 .../windows_modify_registry_disable_rdp.yml   | 44 ++++++----
 ...dify_registry_disable_restricted_admin.yml | 29 +++----
 ...y_registry_disable_toast_notifications.yml | 43 ++++++----
 ...y_disable_win_defender_raw_write_notif.yml | 46 +++++++----
 ...stry_disable_windefender_notifications.yml | 44 ++++++----
 ..._disable_windows_security_center_notif.yml | 46 +++++++----
 ...registry_disableremotedesktopantialias.yml | 45 +++++++----
 ...odify_registry_disablesecuritysettings.yml | 45 +++++++----
 ...modify_registry_disabling_wer_settings.yml | 41 ++++++----
 ...s_modify_registry_disallow_windows_app.yml | 42 ++++++----
 ..._registry_do_not_connect_to_win_update.yml | 44 ++++++----
 .../windows_modify_registry_dontshowui.yml    | 42 ++++++----
 ...odify_registry_enablelinkedconnections.yml | 26 +++---
 ...ndows_modify_registry_longpathsenabled.yml | 29 +++----
 ...modify_registry_maxconnectionperserver.yml | 44 ++++++----
 ...egistry_no_auto_reboot_with_logon_user.yml | 43 ++++++----
 ...windows_modify_registry_no_auto_update.yml | 42 ++++++----
 ...ws_modify_registry_nochangingwallpaper.yml | 25 +++---
 ...fy_registry_on_smart_card_group_policy.yml | 46 ++++++-----
 .../windows_modify_registry_proxyenable.yml   | 42 ++++++----
 .../windows_modify_registry_proxyserver.yml   | 41 ++++++----
 ...y_registry_qakbot_binary_data_registry.yml | 64 ++++++++++-----
 .../windows_modify_registry_reg_restore.yml   | 47 ++++++-----
 ...ify_registry_regedit_silent_reg_import.yml | 56 ++++++++-----
 .../windows_modify_registry_risk_behavior.yml | 48 ++++++++---
 ...y_registry_suppress_win_defender_notif.yml | 45 +++++++----
 ...dows_modify_registry_tamper_protection.yml | 44 ++++++----
 ...egistry_to_add_or_modify_firewall_rule.yml | 43 +++++-----
 ...ify_registry_updateserviceurlalternate.yml | 43 ++++++----
 .../windows_modify_registry_usewuserver.yml   | 36 +++++----
 ...indows_modify_registry_utilize_progids.yml | 38 ++++++---
 ...ws_modify_registry_valleyrat_c2_config.yml | 40 +++++++---
 ...odify_registry_valleyrat_pwn_reg_entry.yml | 38 ++++++---
 ..._modify_registry_with_md5_reg_key_name.yml | 44 ++++++----
 .../windows_modify_registry_wuserver.yml      | 36 +++++----
 ...windows_modify_registry_wustatusserver.yml | 36 +++++----
 ...w_compress_color_and_info_tip_registry.yml | 35 ++++----
 ...tem_firewall_with_notable_process_path.yml | 54 +++++++++----
 ..._mof_event_triggered_execution_via_wmi.yml | 56 ++++++++-----
 .../windows_moveit_transfer_writing_aspx.yml  | 44 ++++++----
 ...change_management_mailbox_cmdlet_usage.yml | 39 ++++++---
 .../windows_mshta_execution_in_registry.yml   | 40 +++++++---
 ...s_mshta_writing_to_world_writable_path.yml | 51 +++++++++---
 .../windows_msiexec_dllregisterserver.yml     | 55 ++++++++-----
 ..._msiexec_hidewindow_rundll32_execution.yml | 57 ++++++++-----
 .../windows_msiexec_remote_download.yml       | 54 ++++++++-----
 ...indows_msiexec_spawn_discovery_command.yml | 57 ++++++++-----
 .../endpoint/windows_msiexec_spawn_windbg.yml | 53 +++++++-----
 ...s_msiexec_unregister_dllregisterserver.yml | 55 ++++++++-----
 ...ndows_msiexec_with_network_connections.yml | 54 ++++++++-----
 ...dows_multi_hop_proxy_tor_website_query.yml | 42 ++++++----
 ...ows_multiple_account_passwords_changed.yml | 46 ++++++-----
 .../windows_multiple_accounts_deleted.yml     | 42 ++++++----
 .../windows_multiple_accounts_disabled.yml    | 43 ++++++----
 ...rs_failed_to_authenticate_wth_kerberos.yml | 38 ++++++---
 ...rs_fail_to_authenticate_using_kerberos.yml | 38 ++++++---
 ...sers_failed_to_authenticate_using_ntlm.yml | 39 ++++++---
 ...tiple_ntlm_null_domain_authentications.yml | 47 ++++++++---
 ...o_authenticate_wth_explicitcredentials.yml | 41 +++++++---
 ...d_to_authenticate_from_host_using_ntlm.yml | 38 ++++++---
 ...rs_failed_to_authenticate_from_process.yml | 40 ++++++----
 ..._failed_to_authenticate_using_kerberos.yml | 38 ++++++---
 ...otely_failed_to_authenticate_from_host.yml | 41 +++++++---
 ...ows_network_share_interaction_with_net.yml | 45 +++++++----
 .../windows_new_inprocserver32_added.yml      | 36 +++++----
 .../windows_ngrok_reverse_proxy_usage.yml     | 54 ++++++++-----
 .../endpoint/windows_nirsoft_advancedrun.yml  | 56 ++++++++-----
 .../endpoint/windows_nirsoft_utilities.yml    | 47 ++++++-----
 ...ws_njrat_fileless_storage_via_registry.yml | 41 ++++++----
 ...non_discord_app_access_discord_leveldb.yml | 41 ++++++----
 ...ows_non_system_account_targeting_lsass.yml | 45 +++++++----
 .../endpoint/windows_odbcconf_hunting.yml     | 47 ++++++-----
 .../endpoint/windows_odbcconf_load_dll.yml    | 55 ++++++++-----
 .../windows_odbcconf_load_response_file.yml   | 55 ++++++++-----
 .../windows_office_product_spawning_msdt.yml  | 53 +++++++-----
 ..._outlook_webview_registry_modification.yml | 48 ++++++++---
 .../windows_papercut_ng_spawn_shell.yml       | 55 ++++++++-----
 ...dows_parent_pid_spoofing_with_explorer.yml | 48 ++++++-----
 .../windows_password_managers_discovery.yml   | 57 ++++++++-----
 ..._phishing_outlook_drop_dll_in_form_dir.yml | 43 ++++++----
 ...ws_phishing_pdf_file_executes_url_link.yml | 54 ++++++++-----
 ...dows_phishing_recent_iso_exec_registry.yml | 40 ++++++----
 .../windows_possible_credential_dumping.yml   | 48 +++++++----
 ...indows_post_exploitation_risk_behavior.yml | 51 ++++++++----
 ...ll_add_module_to_global_assembly_cache.yml | 39 ++++++---
 ...dows_powershell_cryptography_namespace.yml | 40 +++++++---
 ...indows_powershell_disable_http_logging.yml | 40 +++++++---
 .../windows_powershell_export_certificate.yml | 39 ++++++---
 ...ndows_powershell_export_pfxcertificate.yml | 38 ++++++---
 ...rshell_get_ciminstance_remote_computer.yml | 39 ++++++---
 ...l_iis_components_webglobalmodule_usage.yml | 39 ++++++---
 ...ows_powershell_import_applocker_policy.yml | 40 +++++++---
 .../windows_powershell_remotesigned_file.yml  | 48 +++++++----
 .../windows_powershell_scheduletask.yml       | 45 ++++++++---
 ...dows_powershell_wmi_win32_scheduledjob.yml | 42 +++++++---
 .../windows_powersploit_gpp_discovery.yml     | 38 ++++++---
 ...iew_ad_access_control_list_enumeration.yml | 37 ++++++---
 ...rview_constrained_delegation_discovery.yml | 38 ++++++---
 ...erview_kerberos_service_ticket_request.yml | 39 ++++++---
 .../windows_powerview_spn_discovery.yml       | 37 ++++++---
 ...iew_unconstrained_delegation_discovery.yml | 38 ++++++---
 .../windows_private_keys_discovery.yml        | 58 +++++++++-----
 ...scalation_suspicious_process_elevation.yml | 70 +++++++++++-----
 ...n_system_process_without_system_parent.yml | 40 ++++++----
 ...tion_user_process_spawn_system_process.yml | 54 +++++++++----
 .../windows_privileged_group_modification.yml | 57 +++++++++----
 .../windows_process_commandline_discovery.yml | 49 +++++++-----
 ...injection_in_non_service_searchindexer.yml | 50 +++++++-----
 ...windows_process_injection_into_notepad.yml | 44 ++++++----
 ...s_injection_of_wermgr_to_known_browser.yml | 46 ++++++-----
 ...indows_process_injection_remote_thread.yml | 48 ++++++-----
 ...process_injection_wermgr_child_process.yml | 50 +++++++-----
 ...cess_injection_with_public_source_path.yml | 42 +++++-----
 ...ows_process_with_namedpipe_commandline.yml | 54 ++++++++-----
 ...ss_writing_file_to_world_writable_path.yml | 51 +++++++++---
 ...ocesses_killed_by_industroyer2_malware.yml | 46 ++++++-----
 .../windows_protocol_tunneling_with_plink.yml | 60 +++++++++-----
 .../endpoint/windows_proxy_via_netsh.yml      | 52 ++++++++----
 .../endpoint/windows_proxy_via_registry.yml   | 40 ++++++----
 ...uery_registry_browser_list_application.yml | 42 ++++++----
 .../windows_query_registry_reg_save.yml       | 46 ++++++-----
 ..._query_registry_uninstall_program_list.yml | 39 +++++----
 ...indows_raccine_scheduled_task_deletion.yml | 51 +++++++-----
 ...rapid_authentication_on_multiple_hosts.yml | 41 ++++++----
 .../windows_rasautou_dll_execution.yml        | 55 ++++++++-----
 ...ws_raw_access_to_disk_volume_partition.yml | 44 ++++++----
 ...raw_access_to_master_boot_record_drive.yml | 42 ++++++----
 .../windows_rdp_connection_successful.yml     | 31 ++++---
 ...dows_registry_bootexecute_modification.yml | 26 +++---
 .../windows_registry_certificate_added.yml    | 35 ++++----
 .../windows_registry_delete_task_sd.yml       | 40 +++++-----
 ...modification_for_safe_mode_persistence.yml | 32 +++++---
 .../windows_registry_payload_injection.yml    | 61 +++++++++-----
 ...ows_registry_sip_provider_modification.yml | 42 +++++++---
 .../windows_regsvr32_renamed_binary.yml       | 48 ++++++-----
 ...remote_access_software_brc4_loaded_dll.yml | 48 +++++++----
 .../windows_remote_access_software_hunt.yml   | 45 +++++++----
 ...ws_remote_access_software_rms_registry.yml | 41 ++++++----
 ...ows_remote_assistance_spawning_process.yml | 55 ++++++++-----
 .../windows_remote_create_service.yml         | 55 ++++++++-----
 ...remote_service_rdpwinst_tool_execution.yml | 53 +++++++-----
 ..._remote_services_allow_rdp_in_firewall.yml | 52 ++++++++----
 ...emote_services_allow_remote_assistance.yml | 42 ++++++----
 .../windows_remote_services_rdp_enable.yml    | 42 ++++++----
 ...ws_replication_through_removable_media.yml | 48 +++++++----
 ..._root_domain_linked_policies_discovery.yml | 37 ++++++---
 ...s_rundll32_apply_user_settings_changes.yml | 47 +++++++----
 .../windows_rundll32_webdav_request.yml       | 54 ++++++++-----
 ...undll32_webdav_with_network_connection.yml | 56 ++++++++-----
 ...windows_scheduled_task_created_via_xml.yml | 53 +++++++-----
 ...ndows_scheduled_task_dll_module_loaded.yml | 43 ++++++----
 ...s_scheduled_task_service_spawned_shell.yml | 53 ++++++++----
 ...scheduled_task_with_highest_privileges.yml | 56 ++++++++-----
 ...tasks_for_compmgmtlauncher_or_eventvwr.yml | 36 ++++++---
 .../windows_schtasks_create_run_as_system.yml | 56 ++++++++-----
 .../windows_screen_capture_in_temp_folder.yml | 37 +++++----
 .../windows_screen_capture_via_powershell.yml | 37 ++++++---
 ...ndows_security_account_manager_stopped.yml | 48 ++++++++---
 ...ws_security_support_provider_reg_query.yml | 54 ++++++++-----
 ...tware_component_gacutil_install_to_gac.yml | 55 ++++++++-----
 ...dows_service_create_kernel_mode_driver.yml | 53 +++++++-----
 .../windows_service_create_remcomsvc.yml      | 37 ++++++---
 .../windows_service_create_sliverc2.yml       | 35 +++++---
 .../windows_service_create_with_tscon.yml     | 64 ++++++++++-----
 ...e_created_with_suspicious_service_path.yml | 43 ++++++----
 ...ows_service_created_within_public_path.yml | 39 ++++++---
 ...ws_service_creation_on_remote_endpoint.yml | 53 +++++++-----
 ..._service_creation_using_registry_entry.yml | 35 ++++----
 .../windows_service_deletion_in_registry.yml  | 50 +++++++-----
 ..._service_initiation_on_remote_endpoint.yml | 51 +++++++-----
 .../windows_service_stop_by_deletion.yml      | 53 ++++++++----
 ...rvice_stop_via_net__and_sc_application.yml | 52 +++++++-----
 .../windows_service_stop_win_updates.yml      | 44 ++++++----
 .../windows_sip_provider_inventory.yml        | 29 ++++---
 ...winverifytrust_failed_trust_validation.yml | 35 +++++---
 ...snake_malware_file_modification_crmlog.yml | 42 +++++++---
 ...s_snake_malware_kernel_driver_comadmin.yml | 41 +++++++---
 ...istry_modification_wav_openwithprogids.yml | 45 +++++++----
 .../windows_snake_malware_service_create.yml  | 40 ++++++----
 .../windows_soaphound_binary_execution.yml    | 57 ++++++++-----
 ...hment_connect_to_none_ms_office_domain.yml | 36 ++++++---
 ...hishing_attachment_onenote_spawn_mshta.yml | 52 +++++++-----
 ...ial_privileged_logon_on_multiple_hosts.yml | 41 +++++++---
 .../windows_sql_spawning_certutil.yml         | 44 ++++++----
 ...ndows_sqlwriter_sqldumper_dll_sideload.yml | 45 ++++++++---
 ...thentication_certificates___esc1_abuse.yml | 52 +++++++++---
 ...ion_certificates___esc1_authentication.yml | 60 ++++++++++----
 ...cation_certificates_certificate_issued.yml | 37 ++++++---
 ...ation_certificates_certificate_request.yml | 37 ++++++---
 ...ntication_certificates_certutil_backup.yml | 56 ++++++++-----
 ..._authentication_certificates_cryptoapi.yml | 35 +++++---
 ..._authentication_certificates_cs_backup.yml | 38 ++++++---
 ...cation_certificates_export_certificate.yml | 55 ++++++++-----
 ...ion_certificates_export_pfxcertificate.yml | 55 ++++++++-----
 ..._steal_or_forge_kerberos_tickets_klist.yml | 45 ++++++-----
 ...ct_process_with_authentication_traffic.yml | 49 ++++++++----
 ...execution_compiled_html_file_decompile.yml | 51 +++++++-----
 ...s_system_discovery_using_ldap_nslookup.yml | 53 +++++++-----
 ...windows_system_discovery_using_qwinsta.yml | 46 ++++++-----
 .../endpoint/windows_system_file_on_disk.yml  | 35 ++++----
 .../windows_system_logoff_commandline.yml     | 54 ++++++++-----
 ...m_network_config_discovery_display_dns.yml | 52 +++++++-----
 ...em_network_connections_discovery_netsh.yml | 52 +++++++-----
 .../windows_system_reboot_commandline.yml     | 54 ++++++++-----
 ...oxy_execution_syncappvpublishingserver.yml | 57 ++++++++-----
 .../windows_system_shutdown_commandline.yml   | 53 +++++++-----
 ...dows_system_time_discovery_w32tm_delay.yml | 54 ++++++++-----
 ...indows_system_user_discovery_via_quser.yml | 48 ++++++-----
 ...indows_system_user_privilege_discovery.yml | 47 ++++++-----
 .../windows_terminating_lsass_process.yml     | 42 ++++++----
 .../endpoint/windows_time_based_evasion.yml   | 46 +++++++----
 ...ows_time_based_evasion_via_choice_exec.yml | 49 ++++++++----
 ...ws_uac_bypass_suspicious_child_process.yml | 49 ++++++++----
 ..._bypass_suspicious_escalation_behavior.yml | 61 ++++++++++----
 ...outlook_credentials_access_in_registry.yml | 42 ++++++----
 .../windows_unsigned_dll_side_loading.yml     | 45 ++++++-----
 ..._dll_side_loading_in_same_process_path.yml | 46 ++++++-----
 .../windows_unsigned_ms_dll_side_loading.yml  | 49 +++++++++---
 ...abled_users_failed_auth_using_kerberos.yml | 40 +++++++---
 ...alid_users_fail_to_auth_using_kerberos.yml | 40 +++++++---
 ...nvalid_users_failed_to_auth_using_ntlm.yml | 42 +++++++---
 ...s_fail_to_auth_wth_explicitcredentials.yml | 42 +++++++---
 ...of_users_failed_to_auth_using_kerberos.yml | 40 +++++++---
 ...rs_failed_to_authenticate_from_process.yml | 43 ++++++----
 ...sers_failed_to_authenticate_using_ntlm.yml | 42 +++++++---
 ...sers_remotely_failed_to_auth_from_host.yml | 42 +++++++---
 ..._authentication_destinations_by_source.yml | 46 ++++++++---
 ...lm_authentication_destinations_by_user.yml | 46 ++++++++---
 ...lm_authentication_users_by_destination.yml | 48 ++++++++---
 ...al_ntlm_authentication_users_by_source.yml | 46 ++++++++---
 ..._execution_malicious_url_shortcut_file.yml | 41 ++++++----
 ...id_account_with_never_expires_password.yml | 56 ++++++++-----
 .../windows_vulnerable_3cx_software.yml       | 42 ++++++----
 .../windows_vulnerable_driver_installed.yml   | 38 ++++++---
 .../windows_vulnerable_driver_loaded.yml      | 33 +++++---
 .../windows_windbg_spawning_autoit3.yml       | 53 ++++++++----
 ...inlogon_with_public_network_connection.yml | 48 +++++++----
 .../windows_wmi_impersonate_token.yml         | 48 ++++++-----
 .../windows_wmi_process_and_service_list.yml  | 55 ++++++++-----
 .../windows_wmi_process_call_create.yml       | 45 ++++++-----
 ..._scheduled_task_created_to_spawn_shell.yml | 41 +++++++---
 ...eduled_task_created_within_public_path.yml | 38 ++++++---
 ...ws_task_scheduler_event_action_started.yml | 19 ++---
 .../endpoint/winhlp32_spawning_a_process.yml  | 55 ++++++++-----
 .../winrar_spawning_shell_application.yml     | 55 +++++++++----
 .../endpoint/winrm_spawning_a_process.yml     | 37 ++++++---
 detections/endpoint/winword_spawning_cmd.yml  | 55 ++++++++-----
 .../endpoint/winword_spawning_powershell.yml  | 55 ++++++++-----
 .../winword_spawning_windows_script_host.yml  | 47 +++++++----
 .../wmi_permanent_event_subscription.yml      | 28 ++++---
 ..._permanent_event_subscription___sysmon.yml | 40 ++++++----
 .../wmi_recon_running_process_or_services.yml | 40 +++++++---
 .../wmi_temporary_event_subscription.yml      | 31 ++++---
 detections/endpoint/wmic_group_discovery.yml  | 42 ++++++----
 ...wmic_noninteractive_app_uninstallation.yml | 49 +++++++-----
 .../endpoint/wmic_xsl_execution_via_url.yml   | 55 ++++++++-----
 ...miprsve_lolbas_execution_process_spawn.yml | 62 +++++++++-----
 ...pt_or_cscript_suspicious_child_process.yml | 51 +++++++-----
 ...rovhost_lolbas_execution_process_spawn.yml | 62 +++++++++-----
 detections/endpoint/wsreset_uac_bypass.yml    | 63 +++++++++------
 detections/endpoint/xmrig_driver_loaded.yml   | 36 ++++++---
 .../xsl_script_execution_with_wmic.yml        | 48 +++++++----
 detections/network/detect_arp_poisoning.yml   | 36 ++++++---
 ...domains_using_pretrained_model_in_dsdl.yml | 61 +++++++-------
 ...tration_using_pretrained_model_in_dsdl.yml | 48 ++++++++---
 ...connecting_to_dynamic_domain_providers.yml | 61 +++++++++-----
 ...ct_ipv6_network_infrastructure_threats.yml | 37 +++++----
 .../detect_large_outbound_icmp_packets.yml    | 55 +++++++------
 .../network/detect_outbound_ldap_traffic.yml  | 36 ++++++---
 .../network/detect_outbound_smb_traffic.yml   | 42 +++++-----
 .../detect_port_security_violation.yml        | 39 +++++----
 ...etect_remote_access_software_usage_dns.yml | 46 ++++++++---
 ...t_remote_access_software_usage_traffic.yml | 50 ++++++++----
 .../network/detect_rogue_dhcp_server.yml      | 31 ++++---
 .../detect_snicat_sni_exfiltration.yml        | 24 ++++--
 ...ct_software_download_to_network_device.yml | 36 ++++++---
 ...records_using_pretrained_model_in_dsdl.yml | 65 ++++++++-------
 .../network/detect_traffic_mirroring.yml      | 31 ++++---
 ...ect_unauthorized_assets_by_mac_address.yml | 32 +++++---
 ...t_windows_dns_sigred_via_splunk_stream.yml | 21 +++--
 .../detect_windows_dns_sigred_via_zeek.yml    | 27 ++++---
 .../network/detect_zerologon_via_zeek.yml     | 23 ++++--
 .../dns_query_length_outliers___mltk.yml      | 45 ++++++++---
 ...ry_length_with_high_standard_deviation.yml | 36 ++++++---
 detections/network/excessive_dns_failures.yml | 31 ++++---
 ...ntrol_rest_vulnerability_cve_2022_1388.yml | 42 ++++++----
 ...e_of_network_traffic_from_email_server.yml | 40 +++++++---
 .../network/internal_horizontal_port_scan.yml | 44 +++++++---
 ...ernal_horizontal_port_scan_nmap_top_20.yml | 62 +++++++-------
 .../network/internal_vertical_port_scan.yml   | 42 +++++++---
 .../network/internal_vulnerability_scan.yml   | 36 ++++++---
 .../large_volume_of_dns_any_queries.yml       | 25 +++---
 .../ngrok_reverse_proxy_on_network.yml        | 34 +++++---
 .../prohibited_network_traffic_allowed.yml    | 38 ++++++---
 .../network/protocol_or_port_mismatch.yml     | 29 ++++---
 ...ls_passing_authentication_in_cleartext.yml | 33 +++++---
 .../remote_desktop_network_bruteforce.yml     | 27 ++++---
 .../remote_desktop_network_traffic.yml        | 44 +++++++---
 detections/network/smb_traffic_spike.yml      | 29 ++++---
 .../network/smb_traffic_spike___mltk.yml      | 51 ++++++++----
 .../ssl_certificates_with_punycode.yml        | 35 +++++---
 detections/network/tor_traffic.yml            | 41 +++++++---
 ...windows_ad_replication_service_traffic.yml | 24 ++++--
 ...gue_domain_controller_network_activity.yml | 21 +++--
 .../zeek_x509_certificate_with_punycode.yml   | 30 ++++---
 ...vanti_connect_secure_bookmark_endpoint.yml | 40 +++++++---
 ...adobe_coldfusion_access_control_bypass.yml | 47 +++++++----
 ...on_unauthenticated_arbitrary_file_read.yml | 46 +++++++----
 .../web/cisco_ios_xe_implant_access.yml       | 40 +++++++---
 ...d_gateway_unauthorized_data_disclosure.yml | 47 +++++++----
 .../citrix_adc_exploitation_cve_2023_3519.yml | 31 ++++---
 ..._sharefile_exploitation_cve_2023_24489.yml | 37 ++++++---
 ...e_cve_2023_22515_trigger_vulnerability.yml | 43 ++++++----
 ...center_and_server_privilege_escalation.yml | 43 ++++++----
 ..._rce_via_ognl_injection_cve_2023_22527.yml | 40 +++++++---
 ...d_remote_code_execution_cve_2022_26134.yml | 47 +++++++----
 ...se_screenconnect_authentication_bypass.yml | 49 ++++++++----
 ..._scanning_for_vulnerable_jboss_servers.yml | 27 ++++---
 .../web/detect_f5_tmui_rce_cve_2020_5902.yml  | 19 ++++-
 ...ious_requests_to_exploit_jboss_servers.yml | 27 ++++---
 ...etect_remote_access_software_usage_url.yml | 50 +++++++++---
 ...ng_application_via_apache_commons_text.yml | 51 ++++++++----
 ...acing_fortinet_fortinac_cve_2022_39952.yml | 42 ++++++----
 .../web/f5_tmui_authentication_bypass.yml     | 44 ++++++----
 .../web/fortinet_appliance_auth_bypass.yml    | 46 +++++++----
 .../web/high_volume_of_bytes_out_to_url.yml   | 45 +++++++----
 detections/web/hunting_for_log4shell.yml      | 38 +++++++--
 ...nect_secure_command_injection_attempts.yml | 39 ++++++---
 ..._connect_secure_ssrf_in_saml_component.yml | 41 +++++++---
 ...tem_information_access_via_auth_bypass.yml | 39 ++++++---
 ...pm_sql_injection_remote_code_execution.yml | 56 +++++++++----
 ...uthenticated_api_access_cve_2023_35078.yml | 42 +++++++---
 ...uthenticated_api_access_cve_2023_35082.yml | 44 +++++++---
 .../ivanti_sentry_authentication_bypass.yml   | 44 ++++++----
 ...class_file_download_by_java_user_agent.yml | 37 +++++----
 ...ins_arbitrary_file_read_cve_2024_23897.yml | 38 ++++++---
 ...y_authentication_bypass_cve_2024_27198.yml | 47 +++++++----
 ...ication_bypass_suricata_cve_2024_27198.yml | 44 ++++++----
 ...ed_auth_bypass_suricata_cve_2024_27199.yml | 49 ++++++++----
 .../web/jetbrains_teamcity_rce_attempt.yml    | 42 ++++++----
 ...emote_code_execution_exploit_detection.yml | 49 ++++++++----
 ...g4shell_jndi_payload_injection_attempt.yml | 44 +++++-----
 ...oad_injection_with_outbound_connection.yml | 50 +++++++-----
 ...arepoint_server_elevation_of_privilege.yml | 42 ++++++----
 .../monitor_web_traffic_for_brand_abuse.yml   | 22 +++--
 ...ltiple_archive_files_http_post_traffic.yml | 44 ++++++----
 ...se_screenconnect_authentication_bypass.yml | 50 +++++++-----
 .../papercut_ng_remote_web_access_attempt.yml | 40 ++++++----
 .../web/plain_http_post_exfiltrated_data.yml  | 40 ++++++----
 ...yshell_proxynotshell_behavior_detected.yml | 49 +++++++++---
 .../web/spring4shell_payload_url_request.yml  | 38 +++++----
 .../web/sql_injection_with_long_urls.yml      | 42 +++++++---
 detections/web/supernova_webshell.yml         | 31 ++++---
 .../unusually_long_content_type_length.yml    | 34 ++++----
 ...vmware_aria_operations_exploit_attempt.yml | 42 ++++++----
 ...re_server_side_template_injection_hunt.yml | 36 ++++++---
 ...emarker_server_side_template_injection.yml | 42 ++++++----
 detections/web/web_jsp_request_via_url.yml    | 41 ++++++----
 .../web/web_remote_shellservlet_access.yml    | 44 ++++++----
 ...spring4shell_http_request_class_module.yml | 44 ++++++----
 ...b_spring_cloud_function_functionrouter.yml | 41 ++++++----
 ...ndows_exchange_autodiscover_ssrf_abuse.yml | 42 ++++++----
 ...windows_iis_server_pswa_console_access.yml | 33 +++++---
 .../wordpress_bricks_builder_plugin_rce.yml   | 46 +++++++----
 .../web/ws_ftp_remote_code_execution.yml      | 44 ++++++----
 ...caler_adware_activities_threat_blocked.yml | 41 ++++++----
 ...caler_behavior_analysis_threat_blocked.yml | 43 ++++++----
 ..._cryptominer_downloaded_threat_blocked.yml | 44 ++++++----
 ...zscaler_employment_search_web_activity.yml | 44 ++++++----
 .../web/zscaler_exploit_threat_blocked.yml    | 41 ++++++----
 ...zscaler_legal_liability_threat_blocked.yml | 40 ++++++----
 ...scaler_malware_activity_threat_blocked.yml | 41 ++++++----
 ...caler_phishing_activity_threat_blocked.yml | 41 ++++++----
 ...caler_potentially_abused_file_download.yml | 40 ++++++----
 ...ivacy_risk_destinations_threat_blocked.yml | 41 ++++++----
 ...caler_scam_destinations_threat_blocked.yml | 40 ++++++----
 .../zscaler_virus_download_threat_blocked.yml | 41 ++++++----
 investigations/all_backup_logs_for_host.yml   |  3 -
 ...azon_eks_kubernetes_activity_by_src_ip.yml |  8 --
 ...nvestigate_security_hub_alerts_by_dest.yml | 11 ---
 ...stigate_user_activities_by_accesskeyid.yml |  9 ---
 ...aws_investigate_user_activities_by_arn.yml | 11 ---
 .../aws_network_acl_details_from_id.yml       |  6 --
 ...twork_interface_details_via_resourceid.yml |  9 ---
 .../aws_s3_bucket_details_via_bucketname.yml  |  9 ---
 .../gcp_kubernetes_activity_by_src_ip.yml     | 10 ---
 .../get_all_aws_activity_from_city.yml        |  9 ---
 .../get_all_aws_activity_from_country.yml     |  9 ---
 .../get_all_aws_activity_from_ip_address.yml  |  9 ---
 .../get_all_aws_activity_from_region.yml      |  9 ---
 .../get_backup_logs_for_endpoint.yml          |  4 -
 .../get_certificate_logs_for_a_domain.yml     |  7 --
 .../get_dns_server_history_for_a_host.yml     |  5 --
 investigations/get_dns_traffic_ratio.yml      | 11 +--
 ...get_ec2_instance_details_by_instanceid.yml | 13 ---
 investigations/get_ec2_launch_details.yml     |  9 ---
 investigations/get_email_info.yml             |  3 -
 .../get_emails_from_specific_sender.yml       |  3 -
 ...e_and_last_occurrence_of_a_mac_address.yml |  7 --
 .../get_history_of_email_sources.yml          |  5 --
 ...ogon_rights_modifications_for_endpoint.yml |  5 --
 ...et_logon_rights_modifications_for_user.yml |  5 --
 investigations/get_notable_history.yml        |  2 -
 ...d_emails_to_hidden_cobra_threat_actors.yml |  6 --
 investigations/get_parent_process_info.yml    |  6 --
 investigations/get_process_file_activity.yml  |  7 --
 investigations/get_process_info.yml           |  6 --
 ..._process_information_for_port_activity.yml |  9 ---
 ...rocess_responsible_for_the_dns_traffic.yml | 10 ---
 .../get_sysmon_wmi_activity_for_host.yml      | 11 ---
 ...web_session_information_via_session_id.yml |  6 --
 ...stigate_aws_activities_via_region_name.yml |  6 --
 ...gate_aws_user_activities_by_user_field.yml | 11 ---
 ...ailed_logins_for_multiple_destinations.yml |  6 --
 ...nvestigate_network_traffic_from_src_ip.yml |  3 -
 .../investigate_okta_activity_by_app.yml      | 11 ---
 ...nvestigate_okta_activity_by_ip_address.yml | 11 ---
 .../investigate_pass_the_hash_attempts.yml    |  7 --
 .../investigate_pass_the_ticket_attempts.yml  |  5 --
 .../investigate_previous_unseen_user.yml      |  6 --
 ...cessful_remote_desktop_authentications.yml | 13 +--
 ...gate_suspicious_strings_in_http_header.yml |  6 --
 .../investigate_user_activities_in_okta.yml   | 10 ---
 .../investigate_web_posts_from_src.yml        | 10 +--
 1830 files changed, 48293 insertions(+), 26051 deletions(-)

diff --git a/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml b/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml
index e22bb34be0..3a8db22f36 100644
--- a/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml
+++ b/baselines/baseline_of_blocked_outbound_traffic_from_aws.yml
@@ -33,9 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - action
-  - src_ip
-  - dest_ip
   security_domain: network
diff --git a/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml b/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml
index 52893b3622..91be9465f9 100644
--- a/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml
+++ b/baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml
@@ -38,14 +38,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.user
-  - All_Changes.status
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_cloud_instances_destroyed.yml b/baselines/baseline_of_cloud_instances_destroyed.yml
index 462b3e82ad..b8df805a56 100644
--- a/baselines/baseline_of_cloud_instances_destroyed.yml
+++ b/baselines/baseline_of_cloud_instances_destroyed.yml
@@ -18,7 +18,7 @@ search: '| tstats count as instances_destroyed from datamodel=Change where All_C
   <= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction
   instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1
   dist=expon show_density=true'
-how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you will
+how_to_implement: "You must have Enterprise Security 6.0 or later, if not you will
   need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
   installed, along with any required dependencies. Depending on the number of users
   in your environment, you may also need to adjust the value for max_inputs in the
@@ -26,9 +26,8 @@ how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you wi
   in a reasonable timeframe. By default, the search builds the model using the past
   30 days of data. You can modify the search window to build the model over a longer
   period of time, which may give you better results. You may also want to periodically
-  re-run this search to rebuild the model with the latest data.
-
-  More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
+  re-run this search to rebuild the model with the latest data.\nMore information
+  on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`."
 known_false_positives: none
 references: []
 tags:
@@ -41,15 +40,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.object_category
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_cloud_instances_launched.yml b/baselines/baseline_of_cloud_instances_launched.yml
index b21df77c5b..3555b055c2 100644
--- a/baselines/baseline_of_cloud_instances_launched.yml
+++ b/baselines/baseline_of_cloud_instances_launched.yml
@@ -18,7 +18,7 @@ search: '| tstats count as instances_launched from datamodel=Change where (All_C
   <= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction
   instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1
   dist=expon show_density=true'
-how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you will
+how_to_implement: "You must have Enterprise Security 6.0 or later, if not you will
   need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
   installed, along with any required dependencies. Depending on the number of users
   in your environment, you may also need to adjust the value for max_inputs in the
@@ -26,9 +26,8 @@ how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you wi
   in a reasonable timeframe. By default, the search builds the model using the past
   90 days of data. You can modify the search window to build the model over a longer
   period of time, which may give you better results. You may also want to periodically
-  re-run this search to rebuild the model with the latest data.
-
-  More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
+  re-run this search to rebuild the model with the latest data.\nMore information
+  on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`."
 known_false_positives: none
 references: []
 tags:
@@ -41,15 +40,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.object_category
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml b/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml
index c81d81f730..e1bef841f4 100644
--- a/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml
+++ b/baselines/baseline_of_cloud_security_group_api_calls_per_user.yml
@@ -37,15 +37,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.user
-  - All_Changes.status
-  - All_Changes.object_category
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_command_line_length___mltk.yml b/baselines/baseline_of_command_line_length___mltk.yml
index 4d26446951..1fbadefc68 100644
--- a/baselines/baseline_of_command_line_length___mltk.yml
+++ b/baselines/baseline_of_command_line_length___mltk.yml
@@ -23,7 +23,8 @@ how_to_implement: You must be ingesting endpoint data and populating the Endpoin
   the past 30 days of data. You can modify the search window to build the model over
   a longer period of time, which may give you better results. You may also want to
   periodically re-run this search to rebuild the model with the latest data. More
-  information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
+  information on the algorithm used in the search can be found at 
+  `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
 known_false_positives: none
 references: []
 tags:
@@ -40,12 +41,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_name
-  - Processes.process
   security_domain: endpoint
 deployment:
   scheduling:
diff --git a/baselines/baseline_of_dns_query_length___mltk.yml b/baselines/baseline_of_dns_query_length___mltk.yml
index f19503616e..7b326473e9 100644
--- a/baselines/baseline_of_dns_query_length___mltk.yml
+++ b/baselines/baseline_of_dns_query_length___mltk.yml
@@ -20,7 +20,8 @@ how_to_implement: To successfully implement this search, you will need to ensure
   days of data. You can modify the search window to build the model over a longer
   period of time, which may give you better results. You may also want to periodically
   re-run this search to rebuild the model with the latest data. More information on
-  the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
+  the algorithm used in the search can be found at 
+  `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
 known_false_positives: none
 references: []
 tags:
@@ -34,10 +35,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.query
-  - DNS.record_type
   security_domain: network
 deployment:
   scheduling:
diff --git a/baselines/baseline_of_kubernetes_container_network_io.yml b/baselines/baseline_of_kubernetes_container_network_io.yml
index 3a28cd7c22..74505f05a9 100644
--- a/baselines/baseline_of_kubernetes_container_network_io.yml
+++ b/baselines/baseline_of_kubernetes_container_network_io.yml
@@ -4,28 +4,37 @@ version: 4
 date: '2024-09-24'
 author: Matthew Moore, Splunk
 type: Baseline
-description: This baseline rule calculates the average and standard deviation of inbound and outbound network IO for each Kubernetes container. 
-  It uses metrics from the Kubernetes API and the Splunk Infrastructure Monitoring Add-on. The rule generates a lookup table with the average and 
-  standard deviation of the network IO for each container. This baseline can be used to detect anomalies in network communication behavior, 
-  which may indicate security threats such as data exfiltration, command and control communication, or compromised container behavior.
-search: '| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s 
-  | eval service = replace(''k8s.pod.name'', "-\w{5}$|-[abcdef0-9]{8,10}-\w{5}$", "")
-  | eval key = ''k8s.cluster.name'' + ":" + ''service'' 
-  | stats avg(eval(if(direction="transmit", io,null()))) as avg_outbound_network_io avg(eval(if(direction="receive", io,null()))) as avg_inbound_network_io 
-        stdev(eval(if(direction="transmit", io,null()))) as stdev_outbound_network_io stdev(eval(if(direction="receive", io,null()))) as stdev_inbound_network_io
-        count latest(_time) as last_seen by key
-  | outputlookup k8s_container_network_io_baseline'
-how_to_implement: 'To implement this detection, follow these steps:
-  1. Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-  2. Enable the hostmetrics/process receiver in the OTEL configuration.
-  3. Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-  4. Install the Splunk Infrastructure Monitoring (SIM) add-on (ref: https://splunkbase.splunk.com/app/5247)
-  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-  6. Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-  7. In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-  8. Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-  9. Set the Metric Resolution to 10000.
-  10. Leave all other settings at their default values.'
+description: This baseline rule calculates the average and standard deviation of inbound
+  and outbound network IO for each Kubernetes container. It uses metrics from the
+  Kubernetes API and the Splunk Infrastructure Monitoring Add-on. The rule generates
+  a lookup table with the average and standard deviation of the network IO for each
+  container. This baseline can be used to detect anomalies in network communication
+  behavior, which may indicate security threats such as data exfiltration, command
+  and control communication, or compromised container behavior.
+search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name
+  k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name',
+  \"-\\w{5}$|-[abcdef0-9]{8,10}-\\w{5}$\", \"\") | eval key = 'k8s.cluster.name' +
+  \":\" + 'service' | stats avg(eval(if(direction=\"transmit\", io,null()))) as avg_outbound_network_io
+  avg(eval(if(direction=\"receive\", io,null()))) as avg_inbound_network_io stdev(eval(if(direction=\"\
+  transmit\", io,null()))) as stdev_outbound_network_io stdev(eval(if(direction=\"\
+  receive\", io,null()))) as stdev_inbound_network_io count latest(_time) as last_seen
+  by key | outputlookup k8s_container_network_io_baseline"
+how_to_implement: "To implement this detection, follow these steps: 1. Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster. 2. Enable the hostmetrics/process
+  receiver in the OTEL configuration. 3. Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled. 4. Install
+  the Splunk Infrastructure Monitoring (SIM) add-on (ref: https://splunkbase.splunk.com/app/5247)
+  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token. 6. Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\". 7. In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID. 8. Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K') 9. Set the Metric Resolution to 10000.
+  10. Leave all other settings at their default values."
 known_false_positives: none
 references: []
 tags:
@@ -37,15 +46,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.pod.network.io
-  - k8s.cluster.name 
-  - k8s.node.name
-  - k8s.pod.name  
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -30d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_kubernetes_container_network_io_ratio.yml b/baselines/baseline_of_kubernetes_container_network_io_ratio.yml
index 7beee64eae..135f36eb3e 100644
--- a/baselines/baseline_of_kubernetes_container_network_io_ratio.yml
+++ b/baselines/baseline_of_kubernetes_container_network_io_ratio.yml
@@ -4,31 +4,37 @@ version: 2
 date: '2024-09-24'
 author: Matthew Moore, Splunk
 type: Baseline
-description: This baseline rule calculates the average ratio of inbound to outbound network IO for each Kubernetes container. 
-  It uses metrics from the Kubernetes API and the Splunk Infrastructure Monitoring Add-on. 
-  The rule generates a lookup table with the average and standard deviation of the network IO ratio for each container. 
-  This baseline can be used to detect anomalies in network communication behavior, which may indicate security threats such as data exfiltration, 
-  command and control communication, or compromised container behavior.
-search: '| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s 
-  | eval service = replace(''k8s.pod.name'', "-\w{5}$|-[abcdef0-9]{8,10}-\w{5}$", "") 
-  | eval key = ''k8s.cluster.name'' + ":" + ''service'' 
-  | stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by key _time 
-  | eval inbound:outbound = inbound_network_io/outbound_network_io 
-  | eval outbound:inbound = outbound_network_io/inbound_network_io 
-  | stats avg(*:*) as avg_*:* stdev(*:*) as stdev_*:*
-    count latest(_time) as last_seen by key 
-  | outputlookup k8s_container_network_io_ratio_baseline'
-how_to_implement: 'To implement this detection, follow these steps:
-  1. Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-  2. Enable the hostmetrics/process receiver in the OTEL configuration.
-  3. Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-  4. Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-  6. Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-  7. In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-  8. Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-  9. Set the Metric Resolution to 10000.
-  10. Leave all other settings at their default values.'
+description: This baseline rule calculates the average ratio of inbound to outbound
+  network IO for each Kubernetes container. It uses metrics from the Kubernetes API
+  and the Splunk Infrastructure Monitoring Add-on. The rule generates a lookup table
+  with the average and standard deviation of the network IO ratio for each container.
+  This baseline can be used to detect anomalies in network communication behavior,
+  which may indicate security threats such as data exfiltration, command and control
+  communication, or compromised container behavior.
+search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name
+  k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name',
+  \"-\\w{5}$|-[abcdef0-9]{8,10}-\\w{5}$\", \"\") | eval key = 'k8s.cluster.name' +
+  \":\" + 'service' | stats avg(eval(if(direction=\"transmit\", io,null()))) as outbound_network_io
+  avg(eval(if(direction=\"receive\", io,null()))) as inbound_network_io by key _time
+  | eval inbound:outbound = inbound_network_io/outbound_network_io | eval outbound:inbound
+  = outbound_network_io/inbound_network_io | stats avg(*:*) as avg_*:* stdev(*:*)
+  as stdev_*:* count latest(_time) as last_seen by key | outputlookup k8s_container_network_io_ratio_baseline"
+how_to_implement: "To implement this detection, follow these steps: 1. Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster. 2. Enable the hostmetrics/process
+  receiver in the OTEL configuration. 3. Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled. 4. Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
+  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token. 6. Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\". 7. In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID. 8. Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K') 9. Set the Metric Resolution to 10000.
+  10. Leave all other settings at their default values."
 known_false_positives: none
 references: []
 tags:
@@ -40,15 +46,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.pod.network.io
-  - k8s.cluster.name 
-  - k8s.node.name
-  - k8s.pod.name  
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -30d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_kubernetes_process_resource.yml b/baselines/baseline_of_kubernetes_process_resource.yml
index 1e00dd7cf7..5fff3368d3 100644
--- a/baselines/baseline_of_kubernetes_process_resource.yml
+++ b/baselines/baseline_of_kubernetes_process_resource.yml
@@ -4,25 +4,33 @@ version: 1
 date: '2023-12-18'
 author: Matthew Moore, Splunk
 type: Baseline
-description: This baseline rule calculates the average and standard deviation of various process resources in a Kubernetes environment. 
-  It uses metrics from the Kubernetes API and the Splunk Infrastructure Monitoring Add-on. The rule generates a lookup table with the average and 
-  standard deviation of the resource utilization for each process. This baseline can be used to detect anomalies in process resource utilization, 
-  which may indicate security threats such as resource exhaustion attacks, cryptojacking, or compromised process behavior.
-search: '| mstats avg(process.*) as avg_process.* stdev(*) as stdev_* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name 
-  | eval key = ''k8s.cluster.name'' + ":" + ''host.name'' + ":" + ''process.executable.name''
-  | fillnull
-  | outputlookup k8s_process_resource_baseline'
-how_to_implement: 'To implement this detection, follow these steps:
-  1. Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-  2. Enable the hostmetrics/process receiver in the OTEL configuration.
-  3. Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-  4. Install the Splunk Infrastructure Monitoring (SIM) add-on.
-  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-  6. Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-  7. In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-  8. Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-  9. Set the Metric Resolution to 10000.
-  10. Leave all other settings at their default values.'
+description: This baseline rule calculates the average and standard deviation of various
+  process resources in a Kubernetes environment. It uses metrics from the Kubernetes
+  API and the Splunk Infrastructure Monitoring Add-on. The rule generates a lookup
+  table with the average and standard deviation of the resource utilization for each
+  process. This baseline can be used to detect anomalies in process resource utilization,
+  which may indicate security threats such as resource exhaustion attacks, cryptojacking,
+  or compromised process behavior.
+search: "| mstats avg(process.*) as avg_process.* stdev(*) as stdev_* where `kubernetes_metrics`
+  by host.name k8s.cluster.name k8s.node.name process.executable.name | eval key =
+  'k8s.cluster.name' + \":\" + 'host.name' + \":\" + 'process.executable.name' | fillnull
+  | outputlookup k8s_process_resource_baseline"
+how_to_implement: "To implement this detection, follow these steps: 1. Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster. 2. Enable the hostmetrics/process
+  receiver in the OTEL configuration. 3. Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled. 4. Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. 5. Configure the SIM add-on with
+  your Observability Cloud Organization ID and Access Token. 6. Set up the SIM modular
+  input to ingest Process Metrics. Name this input \"sim_process_metrics_to_metrics_index\"\
+  . 7. In the SIM configuration, set the Organization ID to your Observability Cloud
+  Organization ID. 8. Set the Signal Flow Program to the following: data('process.threads').publish(label='A');
+  data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C');
+  data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E');
+  data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G');
+  data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I');
+  data('process.handles').publish(label='J'); data('process.threads').publish(label='K')
+  9. Set the Metric Resolution to 10000. 10. Leave all other settings at their default
+  values."
 known_false_positives: none
 references: []
 tags:
@@ -34,16 +42,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.*
-  - host.name 
-  - k8s.cluster.name 
-  - k8s.node.name 
-  - process.executable.name
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -30d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_kubernetes_process_resource_ratio.yml b/baselines/baseline_of_kubernetes_process_resource_ratio.yml
index 2ebf2cbf8d..b767c26d4c 100644
--- a/baselines/baseline_of_kubernetes_process_resource_ratio.yml
+++ b/baselines/baseline_of_kubernetes_process_resource_ratio.yml
@@ -4,33 +4,41 @@ version: 2
 date: '2024-09-24'
 author: Matthew Moore, Splunk
 type: Baseline
-description: This baseline rule calculates the average and standard deviation of the ratio of various process resources in a Kubernetes environment. 
-  It uses metrics from the Kubernetes API and the Splunk Infrastructure Monitoring Add-on. The rule generates a lookup table with the average and 
-  standard deviation of the resource ratios for each process. This baseline can be used to detect anomalies in process resource utilization, 
-  which may indicate security threats such as resource exhaustion attacks, cryptojacking, or compromised process behavior.
-search: '| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s 
-  | eval cpu:mem = ''process.cpu.utilization''/''process.memory.utilization'' 
-  | eval cpu:disk = ''process.cpu.utilization''/''process.disk.operations'' 
-  | eval mem:disk = ''process.memory.utilization''/''process.memory.utilization'' 
-  | eval cpu:threads = ''process.cpu.utilization''/''process.threads'' 
-  | eval disk:threads = ''process.disk.operations''/''process.threads'' 
-  | eval key = ''k8s.cluster.name'' + ":" + ''host.name'' + ":" + ''process.executable.name''
-  | fillnull 
-  | stats avg(cpu:mem) as avg_cpu:mem stdev(cpu:mem) as stdev_cpu:mem avg(cpu:disk) as avg_cpu:disk stdev(cpu:disk) as stdev_cpu:disk
-    avg(mem:disk) as avg_mem:disk stdev(mem:disk) as stdev_mem:disk avg(cpu:threads) as avg_cpu:threads stdev(cpu:threads) as stdev_cpu:threads
-    avg(disk:threads) as avg_disk:threads stdev(disk:threads) as stdev_disk:threads count latest(_time) as last_seen by key 
-  | outputlookup k8s_process_resource_ratio_baseline'
-how_to_implement: 'To implement this detection, follow these steps:
-  1. Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-  2. Enable the hostmetrics/process receiver in the OTEL configuration.
-  3. Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-  4. Install the Splunk Infrastructure Monitoring (SIM) add-on.(ref: https://splunkbase.splunk.com/app/5247) 
-  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-  6. Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-  7. In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-  8. Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-  9. Set the Metric Resolution to 10000.
-  10. Leave all other settings at their default values.'
+description: This baseline rule calculates the average and standard deviation of the
+  ratio of various process resources in a Kubernetes environment. It uses metrics
+  from the Kubernetes API and the Splunk Infrastructure Monitoring Add-on. The rule
+  generates a lookup table with the average and standard deviation of the resource
+  ratios for each process. This baseline can be used to detect anomalies in process
+  resource utilization, which may indicate security threats such as resource exhaustion
+  attacks, cryptojacking, or compromised process behavior.
+search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name
+  k8s.cluster.name k8s.node.name process.executable.name span=10s | eval cpu:mem =
+  'process.cpu.utilization'/'process.memory.utilization' | eval cpu:disk = 'process.cpu.utilization'/'process.disk.operations'
+  | eval mem:disk = 'process.memory.utilization'/'process.memory.utilization' | eval
+  cpu:threads = 'process.cpu.utilization'/'process.threads' | eval disk:threads =
+  'process.disk.operations'/'process.threads' | eval key = 'k8s.cluster.name' + \"\
+  :\" + 'host.name' + \":\" + 'process.executable.name' | fillnull | stats avg(cpu:mem)
+  as avg_cpu:mem stdev(cpu:mem) as stdev_cpu:mem avg(cpu:disk) as avg_cpu:disk stdev(cpu:disk)
+  as stdev_cpu:disk avg(mem:disk) as avg_mem:disk stdev(mem:disk) as stdev_mem:disk
+  avg(cpu:threads) as avg_cpu:threads stdev(cpu:threads) as stdev_cpu:threads avg(disk:threads)
+  as avg_disk:threads stdev(disk:threads) as stdev_disk:threads count latest(_time)
+  as last_seen by key | outputlookup k8s_process_resource_ratio_baseline"
+how_to_implement: "To implement this detection, follow these steps: 1. Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster. 2. Enable the hostmetrics/process
+  receiver in the OTEL configuration. 3. Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled. 4. Install
+  the Splunk Infrastructure Monitoring (SIM) add-on.(ref: https://splunkbase.splunk.com/app/5247)
+  5. Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token. 6. Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\". 7. In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID. 8. Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K') 9. Set the Metric Resolution to 10000.
+  10. Leave all other settings at their default values."
 known_false_positives: none
 references: []
 tags:
@@ -48,16 +56,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.*
-  - host.name 
-  - k8s.cluster.name 
-  - k8s.node.name 
-  - process.executable.name
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -30d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/baseline_of_network_acl_activity_by_arn.yml b/baselines/baseline_of_network_acl_activity_by_arn.yml
index 82ac4df5d7..b6d39532c0 100644
--- a/baselines/baseline_of_network_acl_activity_by_arn.yml
+++ b/baselines/baseline_of_network_acl_activity_by_arn.yml
@@ -28,7 +28,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.arn
   security_domain: network
diff --git a/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml b/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml
index ac43b37653..4ae9abb798 100644
--- a/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml
+++ b/baselines/baseline_of_s3_bucket_deletion_activity_by_arn.yml
@@ -27,7 +27,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.arn
   security_domain: network
diff --git a/baselines/baseline_of_security_group_activity_by_arn.yml b/baselines/baseline_of_security_group_activity_by_arn.yml
index 33b85c94dd..cd9a7837f5 100644
--- a/baselines/baseline_of_security_group_activity_by_arn.yml
+++ b/baselines/baseline_of_security_group_activity_by_arn.yml
@@ -28,7 +28,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.arn
   security_domain: network
diff --git a/baselines/baseline_of_smb_traffic___mltk.yml b/baselines/baseline_of_smb_traffic___mltk.yml
index cd42260410..88765d150a 100644
--- a/baselines/baseline_of_smb_traffic___mltk.yml
+++ b/baselines/baseline_of_smb_traffic___mltk.yml
@@ -27,7 +27,8 @@ how_to_implement: You must be ingesting network traffic and populating the Netwo
   You can modify the search window to build the model over a longer period of time,
   which may give you better results. You may also want to periodically re-run this
   search to rebuild the model with the latest data. More information on the algorithm
-  used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
+  used in the search can be found at 
+  `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
 known_false_positives: none
 references: []
 tags:
@@ -44,9 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_port
-  - All_Traffic.app
-  - All_Traffic.src
   security_domain: network
diff --git a/baselines/count_of_assets_by_category.yml b/baselines/count_of_assets_by_category.yml
index 3ade69b595..edd45150fe 100644
--- a/baselines/count_of_assets_by_category.yml
+++ b/baselines/count_of_assets_by_category.yml
@@ -24,8 +24,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Identity_Management.All_Assets
-  - category
   security_domain: endpoint
diff --git a/baselines/count_of_unique_ips_connecting_to_ports.yml b/baselines/count_of_unique_ips_connecting_to_ports.yml
index 4c14cfba44..cee44026b8 100644
--- a/baselines/count_of_unique_ips_connecting_to_ports.yml
+++ b/baselines/count_of_unique_ips_connecting_to_ports.yml
@@ -24,8 +24,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_port
-  - All_Traffic.src
   security_domain: network
diff --git a/baselines/create_a_list_of_approved_aws_service_accounts.yml b/baselines/create_a_list_of_approved_aws_service_accounts.yml
index edf408c808..2070b5f700 100644
--- a/baselines/create_a_list_of_approved_aws_service_accounts.yml
+++ b/baselines/create_a_list_of_approved_aws_service_accounts.yml
@@ -26,8 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - errorCode
-  - userName
   security_domain: network
diff --git a/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml b/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml
index 524f07c10c..2364121ba3 100644
--- a/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml
+++ b/baselines/deprecated/add_prohibited_processes_to_enterprise_security.yml
@@ -25,6 +25,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  security_domain: endpoint
\ No newline at end of file
+  security_domain: endpoint
diff --git a/baselines/deprecated/baseline_of_api_calls_per_user_arn.yml b/baselines/deprecated/baseline_of_api_calls_per_user_arn.yml
index a4ee55ea35..2b4bb26ab1 100644
--- a/baselines/deprecated/baseline_of_api_calls_per_user_arn.yml
+++ b/baselines/deprecated/baseline_of_api_calls_per_user_arn.yml
@@ -27,8 +27,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType
-  - userIdentity.arn
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml b/baselines/deprecated/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml
index 0e03fd3ef8..96be5c56e5 100644
--- a/baselines/deprecated/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml
+++ b/baselines/deprecated/baseline_of_excessive_aws_instances_launched_by_user___mltk.yml
@@ -9,23 +9,20 @@ description: This search is used to build a Machine Learning Toolkit (MLTK) mode
   the last 90 days of data to build the model. The model created by this search is
   then used in the corresponding detection search, which identifies subsequent outliers
   in the number of RunInstances performed by a user in a small time window.
-search: '`cloudtrail` eventName=RunInstances errorCode=success
-  | bucket span=10m _time | stats count as instances_launched by _time src_user |
-  fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1'
-how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
+search: '`cloudtrail` eventName=RunInstances errorCode=success | bucket span=10m _time
+  | stats count as instances_launched by _time src_user | fit DensityFunction instances_launched
+  threshold=0.0005 into ec2_excessive_runinstances_v1'
+how_to_implement: "You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs.
-
-  In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed,
-  along with any required dependencies. Depending on the number of users in your environment,
-  you may also need to adjust the value for max_inputs in the MLTK settings for the
-  DensityFunction algorithm, then ensure that the search completes in a reasonable
-  timeframe. By default, the search builds the model using the past 30 days of data.
-  You can modify the search window to build the model over a longer period of time,
-  which may give you better results. You may also want to periodically re-run this
-  search to rebuild the model with the latest data.
-
-  More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
+  inputs.\nIn addition, you must have the Machine Learning Toolkit (MLTK) version
+  >= 4.2 installed, along with any required dependencies. Depending on the number
+  of users in your environment, you may also need to adjust the value for max_inputs
+  in the MLTK settings for the DensityFunction algorithm, then ensure that the search
+  completes in a reasonable timeframe. By default, the search builds the model using
+  the past 30 days of data. You can modify the search window to build the model over
+  a longer period of time, which may give you better results. You may also want to
+  periodically re-run this search to rebuild the model with the latest data.\nMore
+  information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`."
 known_false_positives: none
 references: []
 tags:
@@ -38,9 +35,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - src_user
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml b/baselines/deprecated/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml
index e2d389bd2b..080da29767 100644
--- a/baselines/deprecated/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml
+++ b/baselines/deprecated/baseline_of_excessive_aws_instances_terminated_by_user___mltk.yml
@@ -10,23 +10,20 @@ description: This search is used to build a Machine Learning Toolkit (MLTK) mode
   is then used in the corresponding detection search, which identifies subsequent
   outliers in the number of TerminateInstances performed by a user in a small time
   window.
-search: '`cloudtrail` eventName=TerminateInstances errorCode=success
-  | bucket span=10m _time | stats count as instances_terminated by _time src_user
-  | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1'
-how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
+search: '`cloudtrail` eventName=TerminateInstances errorCode=success | bucket span=10m
+  _time | stats count as instances_terminated by _time src_user | fit DensityFunction
+  instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1'
+how_to_implement: "You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
-  inputs.
-
-  In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed,
-  along with any required dependencies. Depending on the number of users in your environment,
-  you may also need to adjust the value for max_inputs in the MLTK settings for the
-  DensityFunction algorithm, then ensure that the search completes in a reasonable
-  timeframe. By default, the search builds the model using the past 30 days of data.
-  You can modify the search window to build the model over a longer period of time,
-  which may give you better results. You may also want to periodically re-run this
-  search to rebuild the model with the latest data.
-
-  More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
+  inputs.\nIn addition, you must have the Machine Learning Toolkit (MLTK) version
+  >= 4.2 installed, along with any required dependencies. Depending on the number
+  of users in your environment, you may also need to adjust the value for max_inputs
+  in the MLTK settings for the DensityFunction algorithm, then ensure that the search
+  completes in a reasonable timeframe. By default, the search builds the model using
+  the past 30 days of data. You can modify the search window to build the model over
+  a longer period of time, which may give you better results. You may also want to
+  periodically re-run this search to rebuild the model with the latest data.\nMore
+  information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`."
 known_false_positives: none
 references: []
 tags:
@@ -38,9 +35,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - src_user
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/previously_seen_api_call_per_user_roles_in_cloudtrail.yml b/baselines/deprecated/previously_seen_api_call_per_user_roles_in_cloudtrail.yml
index 4bc5dbe156..e347df4a88 100644
--- a/baselines/deprecated/previously_seen_api_call_per_user_roles_in_cloudtrail.yml
+++ b/baselines/deprecated/previously_seen_api_call_per_user_roles_in_cloudtrail.yml
@@ -27,11 +27,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType
-  - errorCode
-  - userIdentity.type
-  - userName
-  - eventName
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/previously_seen_aws_provisioning_activity_sources.yml b/baselines/deprecated/previously_seen_aws_provisioning_activity_sources.yml
index c878d1d43d..c468db240d 100644
--- a/baselines/deprecated/previously_seen_aws_provisioning_activity_sources.yml
+++ b/baselines/deprecated/previously_seen_aws_provisioning_activity_sources.yml
@@ -9,8 +9,8 @@ description: This search builds a table of the first and last times seen for eve
   activity. This is broadly defined as any event that runs or creates something.
 search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
   | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress,
-  City, Region, Country | outputlookup previously_seen_provisioning_activity_src
-  | stats count'
+  City, Region, Country | outputlookup previously_seen_provisioning_activity_src |
+  stats count'
 how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail
   inputs.
@@ -28,8 +28,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - sourceIPAddress
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/previously_seen_ec2_amis.yml b/baselines/deprecated/previously_seen_ec2_amis.yml
index 3d92fde46d..ac7b2876f3 100644
--- a/baselines/deprecated/previously_seen_ec2_amis.yml
+++ b/baselines/deprecated/previously_seen_ec2_amis.yml
@@ -23,9 +23,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - requestParameters.instancesSet.items{}.imageId
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/previously_seen_ec2_instance_types.yml b/baselines/deprecated/previously_seen_ec2_instance_types.yml
index 0fdf66f2dd..561eb2f0a7 100644
--- a/baselines/deprecated/previously_seen_ec2_instance_types.yml
+++ b/baselines/deprecated/previously_seen_ec2_instance_types.yml
@@ -23,9 +23,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - requestParameters.instanceType
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/previously_seen_ec2_launches_by_user.yml b/baselines/deprecated/previously_seen_ec2_launches_by_user.yml
index f366b3287a..82598bf151 100644
--- a/baselines/deprecated/previously_seen_ec2_launches_by_user.yml
+++ b/baselines/deprecated/previously_seen_ec2_launches_by_user.yml
@@ -24,9 +24,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - requestParameters.instanceType
   security_domain: network
diff --git a/baselines/deprecated/previously_seen_users_in_cloudtrail.yml b/baselines/deprecated/previously_seen_users_in_cloudtrail.yml
index d9cc0396a0..51d88ff3c7 100644
--- a/baselines/deprecated/previously_seen_users_in_cloudtrail.yml
+++ b/baselines/deprecated/previously_seen_users_in_cloudtrail.yml
@@ -31,9 +31,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.arn
-  - src
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/deprecated/update_previously_seen_users_in_cloudtrail.yml b/baselines/deprecated/update_previously_seen_users_in_cloudtrail.yml
index 2a76d6ad78..a250a53230 100644
--- a/baselines/deprecated/update_previously_seen_users_in_cloudtrail.yml
+++ b/baselines/deprecated/update_previously_seen_users_in_cloudtrail.yml
@@ -33,9 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.arn
-  - src
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/discover_dns_records.yml b/baselines/discover_dns_records.yml
index 2379660842..9aeea88185 100644
--- a/baselines/discover_dns_records.yml
+++ b/baselines/discover_dns_records.yml
@@ -31,9 +31,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.record_type
-  - DNS.answer
-  - DNS.query
   security_domain: network
diff --git a/baselines/dnstwist_domain_names.yml b/baselines/dnstwist_domain_names.yml
index e32d99c01c..2fae219f27 100644
--- a/baselines/dnstwist_domain_names.yml
+++ b/baselines/dnstwist_domain_names.yml
@@ -26,6 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/baselines/identify_systems_creating_remote_desktop_traffic.yml b/baselines/identify_systems_creating_remote_desktop_traffic.yml
index 2e873f01ce..616b6beb32 100644
--- a/baselines/identify_systems_creating_remote_desktop_traffic.yml
+++ b/baselines/identify_systems_creating_remote_desktop_traffic.yml
@@ -25,8 +25,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_port
-  - All_Traffic.src
   security_domain: network
diff --git a/baselines/identify_systems_receiving_remote_desktop_traffic.yml b/baselines/identify_systems_receiving_remote_desktop_traffic.yml
index cbf0810c87..e9f2f3148e 100644
--- a/baselines/identify_systems_receiving_remote_desktop_traffic.yml
+++ b/baselines/identify_systems_receiving_remote_desktop_traffic.yml
@@ -26,8 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_port
-  - All_Traffic.dest
   security_domain: network
diff --git a/baselines/identify_systems_using_remote_desktop.yml b/baselines/identify_systems_using_remote_desktop.yml
index 4e67950fad..8e315c2ae3 100644
--- a/baselines/identify_systems_using_remote_desktop.yml
+++ b/baselines/identify_systems_using_remote_desktop.yml
@@ -25,8 +25,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
diff --git a/baselines/monitor_successful_backups.yml b/baselines/monitor_successful_backups.yml
index ef7f1265a2..c738c4698b 100644
--- a/baselines/monitor_successful_backups.yml
+++ b/baselines/monitor_successful_backups.yml
@@ -23,6 +23,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/baselines/monitor_unsuccessful_backups.yml b/baselines/monitor_unsuccessful_backups.yml
index 3de5fddd24..8834fe6fa1 100644
--- a/baselines/monitor_unsuccessful_backups.yml
+++ b/baselines/monitor_unsuccessful_backups.yml
@@ -22,6 +22,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/baselines/previously_seen_aws_cross_account_activity.yml b/baselines/previously_seen_aws_cross_account_activity.yml
index 4c0eec779b..47e3777161 100644
--- a/baselines/previously_seen_aws_cross_account_activity.yml
+++ b/baselines/previously_seen_aws_cross_account_activity.yml
@@ -27,9 +27,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.accountId
-  - resources{}.accountId
   security_domain: network
diff --git a/baselines/previously_seen_aws_cross_account_activity___initial.yml b/baselines/previously_seen_aws_cross_account_activity___initial.yml
index 04bff64d88..532f7d9cb3 100644
--- a/baselines/previously_seen_aws_cross_account_activity___initial.yml
+++ b/baselines/previously_seen_aws_cross_account_activity___initial.yml
@@ -30,17 +30,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.vendor_account
-  - Authentication.user
-  - Authentication.src
-  - Authentication.user_role
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_aws_cross_account_activity___update.yml b/baselines/previously_seen_aws_cross_account_activity___update.yml
index 16870a777a..526e8761f8 100644
--- a/baselines/previously_seen_aws_cross_account_activity___update.yml
+++ b/baselines/previously_seen_aws_cross_account_activity___update.yml
@@ -30,11 +30,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.vendor_account
-  - Authentication.user
-  - Authentication.src
-  - Authentication.user_role
   security_domain: network
diff --git a/baselines/previously_seen_aws_regions.yml b/baselines/previously_seen_aws_regions.yml
index ea892b6e8a..5f086d0138 100644
--- a/baselines/previously_seen_aws_regions.yml
+++ b/baselines/previously_seen_aws_regions.yml
@@ -25,7 +25,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - awsRegion
   security_domain: network
diff --git a/baselines/previously_seen_cloud_api_calls_per_user_role___initial.yml b/baselines/previously_seen_cloud_api_calls_per_user_role___initial.yml
index 571bd9d207..66917eb309 100644
--- a/baselines/previously_seen_cloud_api_calls_per_user_role___initial.yml
+++ b/baselines/previously_seen_cloud_api_calls_per_user_role___initial.yml
@@ -26,16 +26,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.user_type
-  - All_Changes.status
-  - All_Changes.user
-  - All_Changes.command
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_cloud_api_calls_per_user_role___update.yml b/baselines/previously_seen_cloud_api_calls_per_user_role___update.yml
index 1150ca7733..e49adc7a96 100644
--- a/baselines/previously_seen_cloud_api_calls_per_user_role___update.yml
+++ b/baselines/previously_seen_cloud_api_calls_per_user_role___update.yml
@@ -28,10 +28,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.user_type
-  - All_Changes.status
-  - All_Changes.user
-  - All_Changes.command
   security_domain: network
diff --git a/baselines/previously_seen_cloud_compute_creations_by_user___initial.yml b/baselines/previously_seen_cloud_compute_creations_by_user___initial.yml
index 3d8a606883..865f24389d 100644
--- a/baselines/previously_seen_cloud_compute_creations_by_user___initial.yml
+++ b/baselines/previously_seen_cloud_compute_creations_by_user___initial.yml
@@ -23,15 +23,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.object_category
-  - All_Changes.user
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_cloud_compute_creations_by_user___update.yml b/baselines/previously_seen_cloud_compute_creations_by_user___update.yml
index 81fb3e32ef..ca1fe683b6 100644
--- a/baselines/previously_seen_cloud_compute_creations_by_user___update.yml
+++ b/baselines/previously_seen_cloud_compute_creations_by_user___update.yml
@@ -26,9 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.object_category
-  - All_Changes.user
   security_domain: network
diff --git a/baselines/previously_seen_cloud_compute_images___initial.yml b/baselines/previously_seen_cloud_compute_images___initial.yml
index af282b3fd9..eb3b830b1a 100644
--- a/baselines/previously_seen_cloud_compute_images___initial.yml
+++ b/baselines/previously_seen_cloud_compute_images___initial.yml
@@ -25,14 +25,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.Instance_Changes.image_id
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_cloud_compute_images___update.yml b/baselines/previously_seen_cloud_compute_images___update.yml
index 3976294135..63773389da 100644
--- a/baselines/previously_seen_cloud_compute_images___update.yml
+++ b/baselines/previously_seen_cloud_compute_images___update.yml
@@ -26,8 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.Instance_Changes.image_id
   security_domain: network
diff --git a/baselines/previously_seen_cloud_compute_instance_types___initial.yml b/baselines/previously_seen_cloud_compute_instance_types___initial.yml
index 8e1ae4cc3b..60e2a0386a 100644
--- a/baselines/previously_seen_cloud_compute_instance_types___initial.yml
+++ b/baselines/previously_seen_cloud_compute_instance_types___initial.yml
@@ -24,10 +24,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.Instance_Changes.instance_type
   security_domain: network
 deployment:
   scheduling:
diff --git a/baselines/previously_seen_cloud_compute_instance_types___update.yml b/baselines/previously_seen_cloud_compute_instance_types___update.yml
index 9450467291..cf50524d02 100644
--- a/baselines/previously_seen_cloud_compute_instance_types___update.yml
+++ b/baselines/previously_seen_cloud_compute_instance_types___update.yml
@@ -26,8 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.Instance_Changes.instance_type
   security_domain: network
diff --git a/baselines/previously_seen_cloud_instance_modifications_by_user___initial.yml b/baselines/previously_seen_cloud_instance_modifications_by_user___initial.yml
index 7b70f83ea9..dc993e3fb5 100644
--- a/baselines/previously_seen_cloud_instance_modifications_by_user___initial.yml
+++ b/baselines/previously_seen_cloud_instance_modifications_by_user___initial.yml
@@ -24,16 +24,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.change_type
-  - All_Changes.status
-  - All_Changes.user
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_cloud_instance_modifications_by_user___update.yml b/baselines/previously_seen_cloud_instance_modifications_by_user___update.yml
index a8fc841e03..b89c7d40bf 100644
--- a/baselines/previously_seen_cloud_instance_modifications_by_user___update.yml
+++ b/baselines/previously_seen_cloud_instance_modifications_by_user___update.yml
@@ -28,10 +28,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.change_type
-  - All_Changes.status
-  - All_Changes.user
   security_domain: network
diff --git a/baselines/previously_seen_cloud_provisioning_activity_sources___initial.yml b/baselines/previously_seen_cloud_provisioning_activity_sources___initial.yml
index 872af180a7..e5a85d2ea8 100644
--- a/baselines/previously_seen_cloud_provisioning_activity_sources___initial.yml
+++ b/baselines/previously_seen_cloud_provisioning_activity_sources___initial.yml
@@ -31,15 +31,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.src
-  - All_Changes.status
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_cloud_provisioning_activity_sources___update.yml b/baselines/previously_seen_cloud_provisioning_activity_sources___update.yml
index 6f11c9ed4a..b7b4696662 100644
--- a/baselines/previously_seen_cloud_provisioning_activity_sources___update.yml
+++ b/baselines/previously_seen_cloud_provisioning_activity_sources___update.yml
@@ -36,9 +36,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.src
-  - All_Changes.status
   security_domain: network
diff --git a/baselines/previously_seen_cloud_regions___initial.yml b/baselines/previously_seen_cloud_regions___initial.yml
index f924b83c3d..2af26937ff 100644
--- a/baselines/previously_seen_cloud_regions___initial.yml
+++ b/baselines/previously_seen_cloud_regions___initial.yml
@@ -26,14 +26,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.vendor_region
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_cloud_regions___update.yml b/baselines/previously_seen_cloud_regions___update.yml
index 006cb36969..6bd55881bb 100644
--- a/baselines/previously_seen_cloud_regions___update.yml
+++ b/baselines/previously_seen_cloud_regions___update.yml
@@ -29,8 +29,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.vendor_region
   security_domain: network
diff --git a/baselines/previously_seen_command_line_arguments.yml b/baselines/previously_seen_command_line_arguments.yml
index d83ed5bca9..223e790d2b 100644
--- a/baselines/previously_seen_command_line_arguments.yml
+++ b/baselines/previously_seen_command_line_arguments.yml
@@ -34,8 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
   security_domain: endpoint
diff --git a/baselines/previously_seen_ec2_modifications_by_user.yml b/baselines/previously_seen_ec2_modifications_by_user.yml
index 3213c9c27b..8c1d9a7dd8 100644
--- a/baselines/previously_seen_ec2_modifications_by_user.yml
+++ b/baselines/previously_seen_ec2_modifications_by_user.yml
@@ -23,8 +23,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.arn
-  - errorCode
   security_domain: network
diff --git a/baselines/previously_seen_running_windows_services___initial.yml b/baselines/previously_seen_running_windows_services___initial.yml
index 06240d790b..3bd5dd8724 100644
--- a/baselines/previously_seen_running_windows_services___initial.yml
+++ b/baselines/previously_seen_running_windows_services___initial.yml
@@ -25,14 +25,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
   security_domain: endpoint
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_running_windows_services___update.yml b/baselines/previously_seen_running_windows_services___update.yml
index d2ad24749f..cb43f77348 100644
--- a/baselines/previously_seen_running_windows_services___update.yml
+++ b/baselines/previously_seen_running_windows_services___update.yml
@@ -30,14 +30,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
   security_domain: endpoint
 deployment:
   scheduling:
     cron_schedule: 55 * * * *
     earliest_time: -70m@m
     latest_time: -10m@m
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml b/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml
index c441eb298d..35b04b57b8 100644
--- a/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml
+++ b/baselines/previously_seen_s3_bucket_access_by_remote_ip.yml
@@ -26,9 +26,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_status
-  - bucket_name
-  - remote_ip
   security_domain: network
diff --git a/baselines/previously_seen_users_in_cloudtrail___initial.yml b/baselines/previously_seen_users_in_cloudtrail___initial.yml
index d9591f0fa1..c4993b029c 100644
--- a/baselines/previously_seen_users_in_cloudtrail___initial.yml
+++ b/baselines/previously_seen_users_in_cloudtrail___initial.yml
@@ -32,15 +32,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.user
-  - Authentication.src
   security_domain: network
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_users_in_cloudtrail___update.yml b/baselines/previously_seen_users_in_cloudtrail___update.yml
index 68769ffce9..b2e2ffe0f8 100644
--- a/baselines/previously_seen_users_in_cloudtrail___update.yml
+++ b/baselines/previously_seen_users_in_cloudtrail___update.yml
@@ -32,9 +32,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.user
-  - Authentication.src
   security_domain: network
diff --git a/baselines/previously_seen_zoom_child_processes___initial.yml b/baselines/previously_seen_zoom_child_processes___initial.yml
index 832fbab845..a5ea66b367 100644
--- a/baselines/previously_seen_zoom_child_processes___initial.yml
+++ b/baselines/previously_seen_zoom_child_processes___initial.yml
@@ -26,15 +26,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
 deployment:
   scheduling:
     cron_schedule: 0 2 * * 0
     earliest_time: -90d@d
     latest_time: -1d@d
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/previously_seen_zoom_child_processes___update.yml b/baselines/previously_seen_zoom_child_processes___update.yml
index cd02609097..69a8b515ae 100644
--- a/baselines/previously_seen_zoom_child_processes___update.yml
+++ b/baselines/previously_seen_zoom_child_processes___update.yml
@@ -31,15 +31,10 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
 deployment:
   scheduling:
     cron_schedule: 55 * * * *
     earliest_time: -70m@m
     latest_time: -10m@m
-    schedule_window: auto
\ No newline at end of file
+    schedule_window: auto
diff --git a/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml b/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml
index 36c4ec6837..5326bc7c88 100644
--- a/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml
+++ b/baselines/systems_ready_for_spectre_meltdown_windows_patch.yml
@@ -28,12 +28,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_category
-  - All_Changes.object_path
-  - All_Changes.dest
-  - All_Changes.command
-  - All_Changes.user
-  - All_Changes.object
   security_domain: endpoint
diff --git a/baselines/windows_updates_install_failures.yml b/baselines/windows_updates_install_failures.yml
index a4c2642b4b..9eab1ba798 100644
--- a/baselines/windows_updates_install_failures.yml
+++ b/baselines/windows_updates_install_failures.yml
@@ -22,8 +22,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Updates.vendor_product
-  - Updates.status
   security_domain: endpoint
diff --git a/baselines/windows_updates_install_successes.yml b/baselines/windows_updates_install_successes.yml
index 2e63bdae6c..9f460357c8 100644
--- a/baselines/windows_updates_install_successes.yml
+++ b/baselines/windows_updates_install_successes.yml
@@ -22,8 +22,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Updates.vendor_product
-  - Updates.status
   security_domain: endpoint
diff --git a/detections/application/crushftp_server_side_template_injection.yml b/detections/application/crushftp_server_side_template_injection.yml
index b5fd623ec8..7a3aa9ba8c 100644
--- a/detections/application/crushftp_server_side_template_injection.yml
+++ b/detections/application/crushftp_server_side_template_injection.yml
@@ -7,10 +7,30 @@ data_source:
 - CrushFTP
 type: TTP
 status: production
-description: This analytic is designed to identify attempts to exploit a server-side template injection vulnerability in CrushFTP, designated as CVE-2024-4040. This severe vulnerability enables unauthenticated remote attackers to access and read files beyond the VFS Sandbox, circumvent authentication protocols, and execute arbitrary commands on the affected server. The issue impacts all versions of CrushFTP up to 10.7.1 and 11.1.0 on all supported platforms. It is highly recommended to apply patches immediately to prevent unauthorized access to the system and avoid potential data compromises. The search specifically looks for patterns in the raw log data that match the exploitation attempts, including READ or WRITE actions, and extracts relevant information such as the protocol, session ID, user, IP address, HTTP method, and the URI queried. It then evaluates these logs to confirm traces of exploitation based on the presence of specific keywords and the originating IP address, counting and sorting these events for further analysis.
-search: '`crushftp` | rex field=_raw "\[(?<protocol>HTTPS|HTTP):(?<session_id>[^\:]+):(?<user>[^\:]+):(?<src_ip>\d+\.\d+\.\d+\.\d+)\] (?<action>READ|WROTE): \*(?<http_method>[A-Z]+) (?<uri_query>[^\s]+) HTTP/[^\*]+\*" | eval message=if(match(_raw, "INCLUDE") and isnotnull(src_ip), "traces of exploitation by " . src_ip, "false") | search message!=false | rename host as dest | stats count by _time, dest, source, message, src_ip, http_method, uri_query, user, action | sort -_time| `crushftp_server_side_template_injection_filter`'
-how_to_implement: CrushFTP Session logs, from Windows or Linux, must be ingested to Splunk. Currently, there is no TA for CrushFTP, so the data must be extracted from the raw logs.
-known_false_positives: False positives should be limited, however tune or filter as needed.
+description: This analytic is designed to identify attempts to exploit a server-side
+  template injection vulnerability in CrushFTP, designated as CVE-2024-4040. This
+  severe vulnerability enables unauthenticated remote attackers to access and read
+  files beyond the VFS Sandbox, circumvent authentication protocols, and execute arbitrary
+  commands on the affected server. The issue impacts all versions of CrushFTP up to
+  10.7.1 and 11.1.0 on all supported platforms. It is highly recommended to apply
+  patches immediately to prevent unauthorized access to the system and avoid potential
+  data compromises. The search specifically looks for patterns in the raw log data
+  that match the exploitation attempts, including READ or WRITE actions, and extracts
+  relevant information such as the protocol, session ID, user, IP address, HTTP method,
+  and the URI queried. It then evaluates these logs to confirm traces of exploitation
+  based on the presence of specific keywords and the originating IP address, counting
+  and sorting these events for further analysis.
+search: '`crushftp` | rex field=_raw "\[(?<protocol>HTTPS|HTTP):(?<session_id>[^\:]+):(?<user>[^\:]+):(?<src_ip>\d+\.\d+\.\d+\.\d+)\]
+  (?<action>READ|WROTE): \*(?<http_method>[A-Z]+) (?<uri_query>[^\s]+) HTTP/[^\*]+\*"
+  | eval message=if(match(_raw, "INCLUDE") and isnotnull(src_ip), "traces of exploitation
+  by " . src_ip, "false") | search message!=false | rename host as dest | stats count
+  by _time, dest, source, message, src_ip, http_method, uri_query, user, action |
+  sort -_time| `crushftp_server_side_template_injection_filter`'
+how_to_implement: CrushFTP Session logs, from Windows or Linux, must be ingested to
+  Splunk. Currently, there is no TA for CrushFTP, so the data must be extracted from
+  the raw logs.
+known_false_positives: False positives should be limited, however tune or filter as
+  needed.
 references:
 - https://github.com/airbus-cert/CVE-2024-4040
 - https://www.bleepingcomputer.com/news/security/crushftp-warns-users-to-patch-exploited-zero-day-immediately/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +54,8 @@ tags:
   asset_type: Web Application
   confidence: 80
   impact: 80
-  message: Potential exploitation of CrushFTP Server Side Template Injection Vulnerability on $dest$ by $src_ip$.
+  message: Potential exploitation of CrushFTP Server Side Template Injection Vulnerability
+    on $dest$ by $src_ip$.
   mitre_attack_id:
   - T1190
   observable:
@@ -45,22 +71,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - source
-  - src_ip
-  - http_method
-  - uri_query
-  - user
-  - action
-  - message
   security_domain: network
   cve:
   - CVE-2024-4040
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/crushftp/crushftp.log
     sourcetype: crushftp:sessionlogs
     source: crushftp
diff --git a/detections/application/detect_distributed_password_spray_attempts.yml b/detections/application/detect_distributed_password_spray_attempts.yml
index a1f79e1866..07de543dc5 100644
--- a/detections/application/detect_distributed_password_spray_attempts.yml
+++ b/detections/application/detect_distributed_password_spray_attempts.yml
@@ -7,42 +7,53 @@ status: production
 type: Hunting
 data_source:
 - Azure Active Directory Sign-in activity
-description: This analytic employs the 3-sigma approach to identify distributed password spray attacks. A 
-  distributed password spray attack is a type of brute force attack where the attacker attempts a few 
-  common passwords against many different accounts, connecting from multiple IP addresses to avoid detection. 
-  By utilizing the Authentication Data Model, this detection is effective for all CIM-mapped authentication 
-  events, providing comprehensive coverage and enhancing security against these attacks.
+description: This analytic employs the 3-sigma approach to identify distributed password
+  spray attacks. A distributed password spray attack is a type of brute force attack
+  where the attacker attempts a few common passwords against many different accounts,
+  connecting from multiple IP addresses to avoid detection. By utilizing the Authentication
+  Data Model, this detection is effective for all CIM-mapped authentication events,
+  providing comprehensive coverage and enhancing security against these attacks.
 search: >-
-  | tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts dc(Authentication.src) as unique_src values(Authentication.app) as app values(Authentication.src) as src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" NOT Authentication.src IN ("-","unknown") Authentication.user_agent="*" by Authentication.signature_id, Authentication.user_agent, sourcetype, _time  span=10m
+  | tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts
+  dc(Authentication.src) as unique_src values(Authentication.app) as app values(Authentication.src)
+  as src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication
+  where Authentication.action="failure" NOT Authentication.src IN ("-","unknown")
+  Authentication.user_agent="*" by Authentication.signature_id, Authentication.user_agent,
+  sourcetype, _time  span=10m
   | `drop_dm_object_name("Authentication")`
   ```fill out time buckets for 0-count events during entire search length```
   | appendpipe [| timechart limit=0 span=10m count | table _time]
   | fillnull value=0 unique_accounts, unique_src
   ``` Create aggregation field & apply to all null events```
   | eval counter=sourcetype+"__"+signature_id
-  | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter) 
-  | stats values(total_failures) as total_failures values(signature_id) as signature_id values(src) as src values(sourcetype) as sourcetype values(app) as app count by counter unique_accounts unique_src user_agent _time
+  | eventstats values(counter) as fnscounter | eval counter=coalesce(counter,fnscounter)  |
+  stats values(total_failures) as total_failures values(signature_id) as signature_id
+  values(src) as src values(sourcetype) as sourcetype values(app) as app count by
+  counter unique_accounts unique_src user_agent _time
     ``` remove 0 count rows where counter has data```
   | sort - _time unique_accounts
   | dedup _time counter
   ``` 3-sigma detection logic ```
-  | eventstats avg(unique_accounts) as comp_avg_user , stdev(unique_accounts) as comp_std_user avg(unique_src) as comp_avg_src , stdev(unique_src) as comp_std_src by counter user_agent
+  | eventstats avg(unique_accounts) as comp_avg_user , stdev(unique_accounts) as comp_std_user
+  avg(unique_src) as comp_avg_src , stdev(unique_src) as comp_std_src by counter user_agent
   | eval upperBoundUser=(comp_avg_user+comp_std_user*3), upperBoundsrc=(comp_avg_src+comp_std_src*3)
-  | eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser) and (unique_src > 30 and unique_src >= upperBoundsrc), 1, 0)
+  | eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser)
+  and (unique_src > 30 and unique_src >= upperBoundsrc), 1, 0)
   | replace "::ffff:*" with * in src
   | where isOutlier=1
   | foreach * 
       [ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)] 
-  | mvexpand src 
-  | iplocation src 
-  | table _time, unique_src, unique_accounts, total_failures, sourcetype, signature_id, user_agent, src, Country
+  | mvexpand src  | iplocation src  | table _time, unique_src, unique_accounts, total_failures,
+  sourcetype, signature_id, user_agent, src, Country
   | eval date_wday=strftime(_time,"%a"), date_hour=strftime(_time,"%H")
   | `detect_distributed_password_spray_attempts_filter`
-how_to_implement: Ensure that all relevant authentication data is mapped to the Common Information Model (CIM)
-  and that the src field is populated with the source device information. Additionally, ensure that 
-  fill_nullvalue is set within the security_content_summariesonly macro to include authentication events from 
-  log sources that do not feature the signature_id field in the results.
-known_false_positives: It is common to see a spike of legitimate failed authentication events on monday mornings.
+how_to_implement: Ensure that all relevant authentication data is mapped to the Common
+  Information Model (CIM) and that the src field is populated with the source device
+  information. Additionally, ensure that fill_nullvalue is set within the security_content_summariesonly
+  macro to include authentication events from log sources that do not feature the
+  signature_id field in the results.
+known_false_positives: It is common to see a spike of legitimate failed authentication
+  events on monday mornings.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 tags:
@@ -71,14 +82,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Authentication.user
-  - Authentication.src
   security_domain: access
-  manual_test: The dataset & hardcoded timerange doesn't meet the criteria for this detetion.
+  manual_test: The dataset & hardcoded timerange doesn't meet the criteria for this
+    detetion.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log
     source: azure:monitor:aad
     sourcetype: azure:monitor:aad
diff --git a/detections/application/detect_new_login_attempts_to_routers.yml b/detections/application/detect_new_login_attempts_to_routers.yml
index ce0f276757..b6dde1bd85 100644
--- a/detections/application/detect_new_login_attempts_to_routers.yml
+++ b/detections/application/detect_new_login_attempts_to_routers.yml
@@ -5,10 +5,23 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies new login attempts to routers. It leverages authentication logs from the ES Assets and Identity Framework, focusing on assets categorized as routers. The detection flags connections that have not been observed in the past 30 days. This activity is significant because unauthorized access to routers can lead to network disruptions or data interception. If confirmed malicious, attackers could gain control over network traffic, potentially leading to data breaches or further network compromise.
+description: The following analytic identifies new login attempts to routers. It leverages
+  authentication logs from the ES Assets and Identity Framework, focusing on assets
+  categorized as routers. The detection flags connections that have not been observed
+  in the past 30 days. This activity is significant because unauthorized access to
+  routers can lead to network disruptions or data interception. If confirmed malicious,
+  attackers could gain control over network traffic, potentially leading to data breaches
+  or further network compromise.
 data_source: []
-search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)` | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter`'
-how_to_implement: To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.
+search: '| tstats `security_content_summariesonly` count earliest(_time) as earliest
+  latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router
+  by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(),
+  "-30d@d"), 1, 0) | where isOutlier=1| `security_content_ctime(earliest)`| `security_content_ctime(latest)`
+  | `drop_dm_object_name("Authentication")` | `detect_new_login_attempts_to_routers_filter`'
+how_to_implement: To successfully implement this search, you must ensure the network
+  router devices are categorized as "router" in the Assets and identity table. You
+  must also populate the Authentication data model with logs related to users authenticating
+  to routing infrastructure.
 known_false_positives: Legitimate router connections may appear as new connections
 references: []
 tags:
@@ -31,9 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.dest_category
-  - Authentication.dest
-  - Authentication.user
   security_domain: network
diff --git a/detections/application/detect_password_spray_attempts.yml b/detections/application/detect_password_spray_attempts.yml
index b923f20f15..8b40f6acdd 100644
--- a/detections/application/detect_password_spray_attempts.yml
+++ b/detections/application/detect_password_spray_attempts.yml
@@ -7,42 +7,48 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 4625
-description: This analytic employs the 3-sigma approach to detect an unusual volume of failed authentication attempts
-  from a single source. A password spray attack is a type of brute force attack where an attacker tries a few
-  common passwords across many different accounts to avoid detection and account lockouts. By utilizing the
-  Authentication Data Model, this detection is effective for all CIM-mapped authentication events, providing
-  comprehensive coverage and enhancing security against these attacks.
+description: This analytic employs the 3-sigma approach to detect an unusual volume
+  of failed authentication attempts from a single source. A password spray attack
+  is a type of brute force attack where an attacker tries a few common passwords across
+  many different accounts to avoid detection and account lockouts. By utilizing the
+  Authentication Data Model, this detection is effective for all CIM-mapped authentication
+  events, providing comprehensive coverage and enhancing security against these attacks.
 search: >-
-  | tstats `security_content_summariesonly` values(Authentication.user) AS unique_user_names dc(Authentication.user) AS unique_accounts values(Authentication.app) as app count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" NOT Authentication.src IN ("-","unknown") by Authentication.src, Authentication.action, Authentication.signature_id, sourcetype, _time span=5m 
-  | `drop_dm_object_name("Authentication")`
+  | tstats `security_content_summariesonly` values(Authentication.user) AS unique_user_names
+  dc(Authentication.user) AS unique_accounts values(Authentication.app) as app count(Authentication.user)
+  as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure"
+  NOT Authentication.src IN ("-","unknown") by Authentication.src, Authentication.action,
+  Authentication.signature_id, sourcetype, _time span=5m  | `drop_dm_object_name("Authentication")`
       ```fill out time buckets for 0-count events during entire search length```
   | appendpipe [| timechart limit=0 span=5m count | table _time]
   | fillnull value=0 unique_accounts
     ``` Create aggregation field & apply to all null events```
-  | eval counter=src+"__"+sourcetype+"__"+signature_id 
-  | eventstats values(counter) as fnscounter 
-  | eval counter=coalesce(counter,fnscounter) 
+  | eval counter=src+"__"+sourcetype+"__"+signature_id  | eventstats values(counter)
+  as fnscounter  | eval counter=coalesce(counter,fnscounter) 
     ``` stats version of mvexpand ```
-  | stats values(app) as app values(unique_user_names) as unique_user_names values(total_failures) as total_failures values(src) as src values(signature_id) as signature_id values(sourcetype) as sourcetype count by counter unique_accounts _time
+  | stats values(app) as app values(unique_user_names) as unique_user_names values(total_failures)
+  as total_failures values(src) as src values(signature_id) as signature_id values(sourcetype)
+  as sourcetype count by counter unique_accounts _time
       ``` remove duplicate time buckets for each unique source```
   | sort - _time unique_accounts
   | dedup _time counter
       ```Find the outliers```
-  | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by counter
+  | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std
+  by counter
   | eval upperBound=(comp_avg+comp_std*3)
   | eval isOutlier=if(unique_accounts > 30 and unique_accounts >= upperBound, 1, 0)
-  | replace "::ffff:*" with * in src 
-  | where isOutlier=1 
-  | foreach * 
+  | replace "::ffff:*" with * in src  | where isOutlier=1  | foreach * 
       [ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)] 
-  | table _time, src, action, app, unique_accounts, unique_user_names, total_failures, sourcetype, signature_id, counter
+  | table _time, src, action, app, unique_accounts, unique_user_names, total_failures,
+  sourcetype, signature_id, counter
   | `detect_password_spray_attempts_filter`
 how_to_implement: >-
-  Ensure in-scope authentication data is CIM mapped and the src field is populated with the source device. 
-  Also ensure fill_nullvalue is set within the macro security_content_summariesonly. 
+  Ensure in-scope authentication data is CIM mapped and the src field is populated
+  with the source device.  Also ensure fill_nullvalue is set within the macro security_content_summariesonly.
+  
 
-  This search opporates best on a 5 minute schedule, looking back over the past 70 minutes. 
-  Configure 70 minute throttling on the two fields _time and counter. 
+  This search opporates best on a 5 minute schedule, looking back over the past 70
+  minutes.  Configure 70 minute throttling on the two fields _time and counter. 
 known_false_positives: Unknown
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -52,7 +58,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$sourcetype$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$sourcetype$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$sourcetype$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -64,7 +75,8 @@ tags:
   - 90bc2e54-6c84-47a5-9439-0a2a92b4b175
   confidence: 70
   impact: 70
-  message: Potential Password Spraying attack from $src$ targeting $unique_accounts$ unique accounts.
+  message: Potential Password Spraying attack from $src$ targeting $unique_accounts$
+    unique accounts.
   mitre_attack_id:
   - T1110.003
   - T1110
@@ -81,14 +93,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Authentication.action
-  - Authentication.user
-  - Authentication.src
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/email_attachments_with_lots_of_spaces.yml b/detections/application/email_attachments_with_lots_of_spaces.yml
index 2a478349b4..8315b78175 100644
--- a/detections/application/email_attachments_with_lots_of_spaces.yml
+++ b/detections/application/email_attachments_with_lots_of_spaces.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects email attachments with an unusually high number of spaces in their file names, which is a common tactic used by attackers to obfuscate file extensions. It leverages the Email data model to identify attachments where the ratio of spaces to the total file name length exceeds 10%. This behavior is significant as it may indicate an attempt to bypass security filters and deliver malicious payloads. If confirmed malicious, this activity could lead to the execution of harmful code or unauthorized access to sensitive information within the recipient's environment.
+description: The following analytic detects email attachments with an unusually high
+  number of spaces in their file names, which is a common tactic used by attackers
+  to obfuscate file extensions. It leverages the Email data model to identify attachments
+  where the ratio of spaces to the total file name length exceeds 10%. This behavior
+  is significant as it may indicate an attempt to bypass security filters and deliver
+  malicious payloads. If confirmed malicious, this activity could lead to the execution
+  of harmful code or unauthorized access to sensitive information within the recipient's
+  environment.
 data_source: []
-search: '| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 |  rex field=recipient_address "(?<recipient_user>.*)@" | `email_attachments_with_lots_of_spaces_filter`'
-how_to_implement: 'You need to ingest data from emails. Specifically, the sender''s address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment.
-
-  **Splunk Phantom Playbook Integration**
-
-  If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user''s inbox.'
+search: '| tstats `security_content_summariesonly` count values(All_Email.recipient)
+  as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email
+  where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")`
+  | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio
+  >= 0.1 |  rex field=recipient_address "(?<recipient_user>.*)@" | `email_attachments_with_lots_of_spaces_filter`'
+how_to_implement: "You need to ingest data from emails. Specifically, the sender's
+  address and the file names of any attachments must be mapped to the Email data model.
+  The threshold ratio is set to 10%, but this value can be configured to suit each
+  environment.\n**Splunk Phantom Playbook Integration**\nIf Splunk Phantom is also
+  configured in your environment, a playbook called \"Suspicious Email Attachment
+  Investigate and Delete\" can be configured to run when any results are found by
+  this detection search. To use this integration, install the Phantom App for Splunk
+  `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the \"\
+  Phantom Instance\" field in the Adaptive Response Actions when configuring this
+  detection search. The notable event will be sent to Phantom and the playbook will
+  gather further information about the file attachment and its network behaviors.
+  If Phantom finds malicious behavior and an analyst approves of the results, the
+  email will be deleted from the user's inbox."
 known_false_positives: None at this time
 references: []
 tags:
@@ -34,11 +54,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Email.recipient
-  - All_Email.file_name
-  - All_Email.src_user
-  - All_Email.file_name
-  - All_Email.message_id
   security_domain: network
diff --git a/detections/application/email_files_written_outside_of_the_outlook_directory.yml b/detections/application/email_files_written_outside_of_the_outlook_directory.yml
index a598f0b09e..294cc0e4ac 100644
--- a/detections/application/email_files_written_outside_of_the_outlook_directory.yml
+++ b/detections/application/email_files_written_outside_of_the_outlook_directory.yml
@@ -5,12 +5,32 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects email files (.pst or .ost) being created outside the standard Outlook directories. It leverages the Endpoint.Filesystem data model to identify file creation events and filters for email files not located in "C:\Users\*\My Documents\Outlook Files\*" or "C:\Users\*\AppData\Local\Microsoft\Outlook*". This activity is significant as it may indicate data exfiltration or unauthorized access to email data. If confirmed malicious, an attacker could potentially access sensitive email content, leading to data breaches or further exploitation within the network.
+description: The following analytic detects email files (.pst or .ost) being created
+  outside the standard Outlook directories. It leverages the Endpoint.Filesystem data
+  model to identify file creation events and filters for email files not located in
+  "C:\Users\*\My Documents\Outlook Files\*" or "C:\Users\*\AppData\Local\Microsoft\Outlook*".
+  This activity is significant as it may indicate data exfiltration or unauthorized
+  access to email data. If confirmed malicious, an attacker could potentially access
+  sensitive email content, leading to data breaches or further exploitation within
+  the network.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*"  Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `email_files_written_outside_of_the_outlook_directory_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
-known_false_positives: Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.
+search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path)
+  as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem
+  where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path
+  != "C:\\Users\\*\\My Documents\\Outlook Files\\*"  Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*"
+  by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest
+  | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `email_files_written_outside_of_the_outlook_directory_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records the file-system activity from your hosts to populate the Endpoint.Filesystem
+  data model node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or by other endpoint data sources, such as Sysmon.
+  The data used for this search is typically generated via logs that report file-system
+  reads and writes.
+known_false_positives: Administrators and users sometimes prefer backing up their
+  email data by moving the email files into a different folder. These attempts will
+  be detected by the search.
 references: []
 tags:
   analytic_story:
@@ -31,11 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.file_name
-  - Filesystem.action
-  - Filesystem.process_id
-  - Filesystem.dest
   security_domain: endpoint
diff --git a/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml b/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml
index 66fa59958e..8cc744c343 100644
--- a/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml
+++ b/detections/application/email_servers_sending_high_volume_traffic_to_hosts.yml
@@ -5,11 +5,38 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies a significant increase in data transfers from your email server to client hosts. It leverages the Network_Traffic data model to monitor outbound traffic from email servers, using statistical analysis to detect anomalies based on average and standard deviation metrics. This activity is significant as it may indicate a malicious actor exfiltrating data via your email server. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising sensitive information and impacting organizational security.
+description: The following analytic identifies a significant increase in data transfers
+  from your email server to client hosts. It leverages the Network_Traffic data model
+  to monitor outbound traffic from email servers, using statistical analysis to detect
+  anomalies based on average and standard deviation metrics. This activity is significant
+  as it may indicate a malicious actor exfiltrating data via your email server. If
+  confirmed malicious, this could lead to unauthorized data access and potential data
+  breaches, compromising sensitive information and impacting organizational security.
 data_source: []
-search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter`'
-how_to_implement: This search requires you to be ingesting your network traffic and populating the Network_Traffic data model.  Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.
-known_false_positives: The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.
+search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out
+  from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip
+  _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out)
+  as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples
+  avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out
+  stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out
+  by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples
+  >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold *
+  stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold
+  * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average
+  = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average
+  = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out,
+  2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average,
+  num_standard_deviations_away_from_client_average | `email_servers_sending_high_volume_traffic_to_hosts_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic and
+  populating the Network_Traffic data model.  Your email servers must be categorized
+  as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold
+  and minimum_data_samples values based on the network traffic in your environment.
+  The "deviation_threshold" field is a multiplying factor to control how much variation
+  you're willing to tolerate. The "minimum_data_samples" field is the minimum number
+  of connections of data samples required for the statistic to be valid.
+known_false_positives: The false-positive rate will vary based on how you set the
+  deviation_threshold and data_samples values. Our recommendation is to adjust these
+  values based on your network traffic to and from your email servers.
 references: []
 tags:
   analytic_story:
@@ -31,9 +58,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.bytes_out
-  - All_Traffic.src_category
-  - All_Traffic.dest_ip
   security_domain: network
diff --git a/detections/application/ivanti_vtm_new_account_creation.yml b/detections/application/ivanti_vtm_new_account_creation.yml
index fa5ff0d906..5c72811d47 100644
--- a/detections/application/ivanti_vtm_new_account_creation.yml
+++ b/detections/application/ivanti_vtm_new_account_creation.yml
@@ -7,10 +7,24 @@ data_source:
 - Ivanti VTM Audit
 type: TTP
 status: production
-description: This analytic detects potential exploitation of the Ivanti Virtual Traffic Manager (vTM) authentication bypass vulnerability (CVE-2024-7593) to create new administrator accounts. The vulnerability allows unauthenticated remote attackers to bypass authentication on the admin panel and create new admin users. This detection looks for suspicious new account creation events in the Ivanti vTM audit logs that lack expected authentication details, which may indicate exploitation attempts.
-search: '`ivanti_vtm_audit` OPERATION="adduser" MODGROUP="admin" IP="!!ABSENT!!" | stats count min(_time) as firstTime max(_time) as lastTime by IP, MODUSER, OPERATION, MODGROUP, AUTH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_vtm_new_account_creation_filter`'
-how_to_implement: To implement this detection, ensure that Ivanti vTM audit logs are being ingested into Splunk. Configure the Ivanti vTM to send its audit logs to Splunk via syslog or by monitoring the log files directly. The sourcetype should be set to "ivanti_vtm_audit" or a similar custom sourcetype for these logs.
-known_false_positives: Legitimate new account creation by authorized administrators will generate similar log entries. However, those should include proper authentication details. Verify any detected events against expected administrative activities and authorized user lists.
+description: This analytic detects potential exploitation of the Ivanti Virtual Traffic
+  Manager (vTM) authentication bypass vulnerability (CVE-2024-7593) to create new
+  administrator accounts. The vulnerability allows unauthenticated remote attackers
+  to bypass authentication on the admin panel and create new admin users. This detection
+  looks for suspicious new account creation events in the Ivanti vTM audit logs that
+  lack expected authentication details, which may indicate exploitation attempts.
+search: '`ivanti_vtm_audit` OPERATION="adduser" MODGROUP="admin" IP="!!ABSENT!!" |
+  stats count min(_time) as firstTime max(_time) as lastTime by IP, MODUSER, OPERATION,
+  MODGROUP, AUTH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `ivanti_vtm_new_account_creation_filter`'
+how_to_implement: To implement this detection, ensure that Ivanti vTM audit logs are
+  being ingested into Splunk. Configure the Ivanti vTM to send its audit logs to Splunk
+  via syslog or by monitoring the log files directly. The sourcetype should be set
+  to "ivanti_vtm_audit" or a similar custom sourcetype for these logs.
+known_false_positives: Legitimate new account creation by authorized administrators
+  will generate similar log entries. However, those should include proper authentication
+  details. Verify any detected events against expected administrative activities and
+  authorized user lists.
 references:
 - https://www.ivanti.com/security/security-advisories/ivanti-virtual-traffic-manager-vtm-cve-2024-7593
 - https://nvd.nist.gov/vuln/detail/CVE-2024-7593
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$MODUSER$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MODUSER$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MODUSER$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Web Application
   confidence: 80
   impact: 90
-  message: A new administrator account, $MODUSER$, was created on Ivanti vTM device without proper authentication, which may indicate exploitation of CVE-2024-7593.
+  message: A new administrator account, $MODUSER$, was created on Ivanti vTM device
+    without proper authentication, which may indicate exploitation of CVE-2024-7593.
   mitre_attack_id:
   - T1190
   observable:
@@ -41,19 +61,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - IP
-  - MODUSER
-  - OPERATION
-  - MODGROUP
-  - AUTH
   security_domain: access
   cve:
   - CVE-2024-7593
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_vtm_audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_vtm_audit.log
     sourcetype: ivanti_vtm_audit
     source: ivanti_vtm
diff --git a/detections/application/monitor_email_for_brand_abuse.yml b/detections/application/monitor_email_for_brand_abuse.yml
index 14cbb4c2ff..c3cee8f3b2 100644
--- a/detections/application/monitor_email_for_brand_abuse.yml
+++ b/detections/application/monitor_email_for_brand_abuse.yml
@@ -5,10 +5,25 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies emails claiming to be sent from a domain similar to one you are monitoring for potential abuse. It leverages email header data, specifically the sender's address, and cross-references it with a lookup table of known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. This activity is significant as it can indicate phishing attempts or brand impersonation, which are common tactics used in social engineering attacks. If confirmed malicious, this could lead to unauthorized access, data theft, or reputational damage.
+description: The following analytic identifies emails claiming to be sent from a domain
+  similar to one you are monitoring for potential abuse. It leverages email header
+  data, specifically the sender's address, and cross-references it with a lookup table
+  of known domain permutations generated by the "ESCU - DNSTwist Domain Names" search.
+  This activity is significant as it can indicate phishing attempts or brand impersonation,
+  which are common tactics used in social engineering attacks. If confirmed malicious,
+  this could lead to unauthorized access, data theft, or reputational damage.
 data_source: []
-search: '| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter`'
-how_to_implement: You need to ingest email header data. Specifically the sender's address (src_user) must be populated.  You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
+search: '| tstats `security_content_summariesonly` values(All_Email.recipient) as
+  recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email
+  by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval
+  temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true
+  brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true
+  | table message_id, src_user, email_domain, recipients, firstTime, lastTime | `monitor_email_for_brand_abuse_filter`'
+how_to_implement: You need to ingest email header data. Specifically the sender's
+  address (src_user) must be populated.  You also need to have run the search "ESCU
+  - DNSTwist Domain Names", which creates the permutations of the domain that will
+  be checked for.
 known_false_positives: None at this time
 references: []
 tags:
@@ -28,9 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Email.recipient
-  - All_Email.src_user
-  - All_Email.message_id
   security_domain: network
diff --git a/detections/application/no_windows_updates_in_a_time_frame.yml b/detections/application/no_windows_updates_in_a_time_frame.yml
index c69bf19291..3e17bc6455 100644
--- a/detections/application/no_windows_updates_in_a_time_frame.yml
+++ b/detections/application/no_windows_updates_in_a_time_frame.yml
@@ -5,10 +5,28 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. It leverages the 'Update' data model in Splunk, specifically looking for the latest 'Installed' status events from Microsoft Windows. This activity is significant for a SOC because endpoints that are not regularly patched are vulnerable to known exploits and security vulnerabilities. If confirmed malicious, this could indicate a compromised endpoint that is intentionally being kept unpatched, potentially allowing attackers to exploit unpatched vulnerabilities and gain unauthorized access or control.
+description: The following analytic identifies Windows endpoints that have not generated
+  an event indicating a successful Windows update in the last 60 days. It leverages
+  the 'Update' data model in Splunk, specifically looking for the latest 'Installed'
+  status events from Microsoft Windows. This activity is significant for a SOC because
+  endpoints that are not regularly patched are vulnerable to known exploits and security
+  vulnerabilities. If confirmed malicious, this could indicate a compromised endpoint
+  that is intentionally being kept unpatched, potentially allowing attackers to exploit
+  unpatched vulnerabilities and gain unauthorized access or control.
 data_source: []
-search: '| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0)  | `security_content_ctime(lastTime)`  | search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter`'
-how_to_implement: To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems.
+search: '| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates
+  where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest
+  Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status
+  as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(lastTime
+  <= relative_time(now(), "-60d@d"), 1, 0)  | `security_content_ctime(lastTime)`  |
+  search isOutlier=1 | rename lastTime as "Last Update Time", | table Host, "Update
+  Status", Product, "Last Update Time" | `no_windows_updates_in_a_time_frame_filter`'
+how_to_implement: To successfully implement this search, it requires that the 'Update'
+  data model is being populated. This can be accomplished by ingesting Windows events
+  or the Windows Update log via a universal forwarder on the Windows endpoints you
+  wish to monitor. The Windows add-on should be also be installed and configured to
+  properly parse Windows events in Splunk. There may be other data sources which can
+  populate this data model, including vulnerability management systems.
 known_false_positives: None identified
 references: []
 tags:
@@ -27,9 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Updates.status
-  - Updates.vendor_product
-  - Updates.dest
   security_domain: endpoint
diff --git a/detections/application/okta_authentication_failed_during_mfa_challenge.yml b/detections/application/okta_authentication_failed_during_mfa_challenge.yml
index ea00f8ed4f..1efc275fd6 100644
--- a/detections/application/okta_authentication_failed_during_mfa_challenge.yml
+++ b/detections/application/okta_authentication_failed_during_mfa_challenge.yml
@@ -7,10 +7,27 @@ data_source:
 - Okta
 type: TTP
 status: production
-description: The following analytic identifies failed authentication attempts during the Multi-Factor Authentication (MFA) challenge in an Okta tenant. It uses the Authentication datamodel to detect specific failed events where the authentication signature is `user.authentication.auth_via_mfa`. This activity is significant as it may indicate an adversary attempting to authenticate with compromised credentials on an account with MFA enabled. If confirmed malicious, this could suggest an ongoing attempt to bypass MFA protections, potentially leading to unauthorized access and further compromise of the affected account.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  values(Authentication.app) as app values(Authentication.reason) as reason values(Authentication.signature) as signature  values(Authentication.method) as method  from datamodel=Authentication where  Authentication.signature=user.authentication.auth_via_mfa Authentication.action = failure by _time Authentication.src Authentication.user Authentication.dest Authentication.action | `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| iplocation src | `okta_authentication_failed_during_mfa_challenge_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: A user may have accidentally entered the wrong credentials during the MFA challenge. If the user is new to MFA, they may have trouble authenticating. Ensure that the user is aware of the MFA process and has the correct credentials.
+description: The following analytic identifies failed authentication attempts during
+  the Multi-Factor Authentication (MFA) challenge in an Okta tenant. It uses the Authentication
+  datamodel to detect specific failed events where the authentication signature is
+  `user.authentication.auth_via_mfa`. This activity is significant as it may indicate
+  an adversary attempting to authenticate with compromised credentials on an account
+  with MFA enabled. If confirmed malicious, this could suggest an ongoing attempt
+  to bypass MFA protections, potentially leading to unauthorized access and further
+  compromise of the affected account.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime  values(Authentication.app) as app values(Authentication.reason) as
+  reason values(Authentication.signature) as signature  values(Authentication.method)
+  as method  from datamodel=Authentication where  Authentication.signature=user.authentication.auth_via_mfa
+  Authentication.action = failure by _time Authentication.src Authentication.user
+  Authentication.dest Authentication.action | `drop_dm_object_name("Authentication")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| iplocation
+  src | `okta_authentication_failed_during_mfa_challenge_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: A user may have accidentally entered the wrong credentials
+  during the MFA challenge. If the user is new to MFA, they may have trouble authenticating.
+  Ensure that the user is aware of the MFA process and has the correct credentials.
 references:
 - https://sec.okta.com/everythingisyes
 - https://splunkbase.splunk.com/app/6553
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,20 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.app
-  - Authentication.action
-  - Authentication.user
-  - Authentication.reason
-  - Authentication.dest
-  - Authentication.signature
-  - Authentication.method
-  - Authentication.src
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mfa_login_failed/okta_mfa_login_failed.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mfa_login_failed/okta_mfa_login_failed.log
     source: okta_log
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_idp_lifecycle_modifications.yml b/detections/application/okta_idp_lifecycle_modifications.yml
index 28f64b9f22..556487b68f 100644
--- a/detections/application/okta_idp_lifecycle_modifications.yml
+++ b/detections/application/okta_idp_lifecycle_modifications.yml
@@ -7,10 +7,26 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic identifies modifications to Okta Identity Provider (IDP) lifecycle events, including creation, activation, deactivation, and deletion of IDP configurations. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud. Monitoring these events is crucial for maintaining the integrity and security of authentication mechanisms. Unauthorized or anomalous changes could indicate potential security breaches or misconfigurations. If confirmed malicious, attackers could manipulate authentication processes, potentially gaining unauthorized access or disrupting identity management systems.
-search: '`okta` eventType IN ("system.idp.lifecycle.activate","system.idp.lifecycle.create","system.idp.lifecycle.delete","system.idp.lifecycle.deactivate") |  stats count  min(_time) as firstTime max(_time) as lastTime values(target{}.id) as target_id values(target{}.type) as target_modified by src dest src_user_id user user_agent command description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_idp_lifecycle_modifications_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: It's possible for legitimate administrative actions or automated processes to trigger this detection, especially if there are bulk modifications to Okta IDP lifecycle events. Review the context of the modification, such as the user making the change and the specific lifecycle event modified, to determine if it aligns with expected behavior.
+description: The following analytic identifies modifications to Okta Identity Provider
+  (IDP) lifecycle events, including creation, activation, deactivation, and deletion
+  of IDP configurations. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta
+  Identity Cloud. Monitoring these events is crucial for maintaining the integrity
+  and security of authentication mechanisms. Unauthorized or anomalous changes could
+  indicate potential security breaches or misconfigurations. If confirmed malicious,
+  attackers could manipulate authentication processes, potentially gaining unauthorized
+  access or disrupting identity management systems.
+search: '`okta` eventType IN ("system.idp.lifecycle.activate","system.idp.lifecycle.create","system.idp.lifecycle.delete","system.idp.lifecycle.deactivate")
+  |  stats count  min(_time) as firstTime max(_time) as lastTime values(target{}.id)
+  as target_id values(target{}.type) as target_modified by src dest src_user_id user
+  user_agent command description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `okta_idp_lifecycle_modifications_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: It's possible for legitimate administrative actions or automated
+  processes to trigger this detection, especially if there are bulk modifications
+  to Okta IDP lifecycle events. Review the context of the modification, such as the
+  user making the change and the specific lifecycle event modified, to determine if
+  it aligns with expected behavior.
 references:
 - https://www.obsidiansecurity.com/blog/behind-the-breach-cross-tenant-impersonation-in-okta/
 - https://splunkbase.splunk.com/app/6553
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 90
   impact: 90
-  message: A user [$user$] is attempting IDP lifecycle modification - [$description$] from IP Address - [$src$]"
+  message: A user [$user$] is attempting IDP lifecycle modification - [$description$]
+    from IP Address - [$src$]"
   mitre_attack_id:
   - T1087.004
   observable:
@@ -45,21 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - eventType
-  - target{}.id
-  - target{}.type
-  - src
-  - dest
-  - src_user_id
-  - user
-  - user_agent
-  - command
-  - description
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/okta_idp/okta.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/okta_idp/okta.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_mfa_exhaustion_hunt.yml b/detections/application/okta_mfa_exhaustion_hunt.yml
index 618f9e90c1..9453791e78 100644
--- a/detections/application/okta_mfa_exhaustion_hunt.yml
+++ b/detections/application/okta_mfa_exhaustion_hunt.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Marissa Bower, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects patterns of successful and failed Okta MFA push attempts to identify potential MFA exhaustion attacks. It leverages Okta event logs, specifically focusing on push verification events, and uses statistical evaluations to determine suspicious activity. This activity is significant as it may indicate an attacker attempting to bypass MFA by overwhelming the user with push notifications. If confirmed malicious, this could lead to unauthorized access, compromising the security of the affected accounts and potentially the entire environment.
+description: The following analytic detects patterns of successful and failed Okta
+  MFA push attempts to identify potential MFA exhaustion attacks. It leverages Okta
+  event logs, specifically focusing on push verification events, and uses statistical
+  evaluations to determine suspicious activity. This activity is significant as it
+  may indicate an attacker attempting to bypass MFA by overwhelming the user with
+  push notifications. If confirmed malicious, this could lead to unauthorized access,
+  compromising the security of the affected accounts and potentially the entire environment.
 data_source:
 - Okta
-search: '`okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) | stats count(eval(legacyEventType="core.user.factor.attempt_success"))  as successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures count(eval(eventType="system.push.send_factor_verify_push")) as pushes by user,_time | stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as successes sum(failures) as failures sum(pushes) as pushes by user | eval seconds=lasttime-firsttime | eval lasttime=strftime(lasttime, "%c") | search (pushes>1) | eval totalattempts=successes+failures | eval finding="Normal authentication pattern" | eval finding=if(failures==pushes AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding) | eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) | eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple pushes sent, eventual successful authentication!",finding) | `okta_mfa_exhaustion_hunt_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete.
+search: '`okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success)
+  AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail)
+  AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) | stats count(eval(legacyEventType="core.user.factor.attempt_success"))  as
+  successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures
+  count(eval(eventType="system.push.send_factor_verify_push")) as pushes by user,_time
+  | stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as
+  successes sum(failures) as failures sum(pushes) as pushes by user | eval seconds=lasttime-firsttime
+  | eval lasttime=strftime(lasttime, "%c") | search (pushes>1) | eval totalattempts=successes+failures
+  | eval finding="Normal authentication pattern" | eval finding=if(failures==pushes
+  AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding)
+  | eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) |
+  eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple
+  pushes sent, eventual successful authentication!",finding) | `okta_mfa_exhaustion_hunt_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: False positives may be present. Tune Okta and tune the analytic
+  to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning
+  is complete.
 references:
 - https://developer.okta.com/docs/reference/api/event-types/?q=user.acount.lock
 - https://sec.okta.com/everythingisyes
@@ -34,16 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - src_ip
-  - eventType
-  - status
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_pushes/okta_multiple_failed_mfa_pushes.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_pushes/okta_multiple_failed_mfa_pushes.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml b/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml
index b5bbc16855..0e55ab3237 100644
--- a/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml
+++ b/detections/application/okta_mismatch_between_source_and_response_for_verify_push_request.yml
@@ -7,10 +7,38 @@ type: TTP
 status: experimental
 data_source:
 - Okta
-description: The following analytic identifies discrepancies between the source and response events for Okta Verify Push requests, indicating potential suspicious behavior. It leverages Okta System Log events, specifically `system.push.send_factor_verify_push` and `user.authentication.auth_via_mfa` with the factor "OKTA_VERIFY_PUSH." The detection groups events by SessionID, calculates the ratio of successful sign-ins to push requests, and checks for session roaming and new device/IP usage. This activity is significant as it may indicate push spam or unauthorized access attempts. If confirmed malicious, attackers could bypass MFA, leading to unauthorized access to sensitive systems.
-search: '`okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH") | eval groupby="authenticationContext.externalSessionId" | eval group_push_time=_time | bin span=2s group_push_time | fillnull value=NULL | stats min(_time) as _time by authenticationContext.externalSessionId eventType debugContext.debugData.factor outcome.result actor.alternateId client.device client.ipAddress client.userAgent.rawUserAgent debugContext.debugData.behaviors group_push_time groupby | iplocation client.ipAddress | fields - lat, lon, group_push_time | stats min(_time) as _time dc(client.ipAddress) as dc_ip sum(eval(if(eventType="system.push.send_factor_verify_push" AND "outcome.result"="SUCCESS",1,0))) as total_pushes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="SUCCESS",1,0))) as total_successes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="FAILURE",1,0))) as total_rejected sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors" LIKE "%New Device=POSITIVE%",1,0))) as suspect_device_from_source sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors" LIKE "%New IP=POSITIVE%",0,0))) as suspect_ip_from_source values(eval(if(eventType="system.push.send_factor_verify_push","client.ipAddress",""))) as src values(eval(if(eventType="user.authentication.auth_via_mfa","client.ipAddress",""))) as dest values(*) as * by groupby | eval ratio = round(total_successes/total_pushes,2) | search ((ratio < 0.5 AND total_pushes > 1) OR (total_rejected > 0)) AND dc_ip > 1 AND suspect_device_from_source > 0 AND suspect_ip_from_source > 0 | `okta_mismatch_between_source_and_response_for_verify_push_request_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: False positives may be present based on organization size and configuration of Okta. Monitor, tune and filter as needed.
+description: The following analytic identifies discrepancies between the source and
+  response events for Okta Verify Push requests, indicating potential suspicious behavior.
+  It leverages Okta System Log events, specifically `system.push.send_factor_verify_push`
+  and `user.authentication.auth_via_mfa` with the factor "OKTA_VERIFY_PUSH." The detection
+  groups events by SessionID, calculates the ratio of successful sign-ins to push
+  requests, and checks for session roaming and new device/IP usage. This activity
+  is significant as it may indicate push spam or unauthorized access attempts. If
+  confirmed malicious, attackers could bypass MFA, leading to unauthorized access
+  to sensitive systems.
+search: '`okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN
+  (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH")
+  | eval groupby="authenticationContext.externalSessionId" | eval group_push_time=_time
+  | bin span=2s group_push_time | fillnull value=NULL | stats min(_time) as _time
+  by authenticationContext.externalSessionId eventType debugContext.debugData.factor
+  outcome.result actor.alternateId client.device client.ipAddress client.userAgent.rawUserAgent
+  debugContext.debugData.behaviors group_push_time groupby | iplocation client.ipAddress
+  | fields - lat, lon, group_push_time | stats min(_time) as _time dc(client.ipAddress)
+  as dc_ip sum(eval(if(eventType="system.push.send_factor_verify_push" AND "outcome.result"="SUCCESS",1,0)))
+  as total_pushes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="SUCCESS",1,0)))
+  as total_successes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND
+  "outcome.result"="FAILURE",1,0))) as total_rejected sum(eval(if(eventType="system.push.send_factor_verify_push"
+  AND "debugContext.debugData.behaviors" LIKE "%New Device=POSITIVE%",1,0))) as suspect_device_from_source
+  sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors"
+  LIKE "%New IP=POSITIVE%",0,0))) as suspect_ip_from_source values(eval(if(eventType="system.push.send_factor_verify_push","client.ipAddress","")))
+  as src values(eval(if(eventType="user.authentication.auth_via_mfa","client.ipAddress","")))
+  as dest values(*) as * by groupby | eval ratio = round(total_successes/total_pushes,2)
+  | search ((ratio < 0.5 AND total_pushes > 1) OR (total_rejected > 0)) AND dc_ip
+  > 1 AND suspect_device_from_source > 0 AND suspect_ip_from_source > 0 | `okta_mismatch_between_source_and_response_for_verify_push_request_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: False positives may be present based on organization size and
+  configuration of Okta. Monitor, tune and filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1621
 - https://splunkbase.splunk.com/app/6553
@@ -21,7 +49,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 80
   impact: 80
-  message: A mismatch between source and response for verifying a push request has occurred for $actor.alternateId$
+  message: A mismatch between source and response for verifying a push request has
+    occurred for $actor.alternateId$
   mitre_attack_id:
   - T1621
   observable:
@@ -33,16 +62,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - authenticationContext.externalSessionId
-  - eventType
-  - debugContext.debugData.factor
-  - outcome.result
-  - actor.alternateId
-  - client.device
-  - client.ipAddress
-  - client.userAgent.rawUserAgent
-  - debugContext.debugData.behaviors
-  - group_push_time
   security_domain: access
diff --git a/detections/application/okta_multi_factor_authentication_disabled.yml b/detections/application/okta_multi_factor_authentication_disabled.yml
index 71d4326260..e231eec691 100644
--- a/detections/application/okta_multi_factor_authentication_disabled.yml
+++ b/detections/application/okta_multi_factor_authentication_disabled.yml
@@ -7,10 +7,24 @@ data_source:
 - Okta
 type: TTP
 status: production
-description: The following analytic identifies an attempt to disable multi-factor authentication (MFA) for an Okta user. It leverages OktaIM2 logs to detect when the 'user.mfa.factor.deactivate' command is executed. This activity is significant because disabling MFA can allow an adversary to maintain persistence within the environment using a compromised valid account. If confirmed malicious, this action could enable attackers to bypass additional security layers, potentially leading to unauthorized access to sensitive information and prolonged undetected presence in the network.
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime from datamodel=Change where sourcetype="OktaIM2:log" All_Changes.object_category=User AND All_Changes.action=modified All_Changes.command=user.mfa.factor.deactivate by All_Changes.user All_Changes.result All_Changes.command sourcetype All_Changes.src | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_multi_factor_authentication_disabled_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: Legitimate use case may require for users to disable MFA. Filter lightly and monitor for any unusual activity.
+description: The following analytic identifies an attempt to disable multi-factor
+  authentication (MFA) for an Okta user. It leverages OktaIM2 logs to detect when
+  the 'user.mfa.factor.deactivate' command is executed. This activity is significant
+  because disabling MFA can allow an adversary to maintain persistence within the
+  environment using a compromised valid account. If confirmed malicious, this action
+  could enable attackers to bypass additional security layers, potentially leading
+  to unauthorized access to sensitive information and prolonged undetected presence
+  in the network.
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time)
+  as firstTime from datamodel=Change where sourcetype="OktaIM2:log" All_Changes.object_category=User
+  AND All_Changes.action=modified All_Changes.command=user.mfa.factor.deactivate by
+  All_Changes.user All_Changes.result All_Changes.command sourcetype All_Changes.src
+  | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `okta_multi_factor_authentication_disabled_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: Legitimate use case may require for users to disable MFA. Filter
+  lightly and monitor for any unusual activity.
 references:
 - https://attack.mitre.org/techniques/T1556/
 - https://splunkbase.splunk.com/app/6553
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 60
   impact: 50
-  message: MFA was disabled for User [$user$] initiated by [$src$]. Investigate further to determine if this was authorized.
+  message: MFA was disabled for User [$user$] initiated by [$src$]. Investigate further
+    to determine if this was authorized.
   mitre_attack_id:
   - T1556
   - T1556.006
@@ -46,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_category
-  - All_Changes.action
-  - All_Changes.command
-  - All_Changes.user
-  - All_Changes.result
-  - All_Changes.src
-  - sourcetype
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/okta_mfa_method_disabled/okta_mfa_method_disabled.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/okta_mfa_method_disabled/okta_mfa_method_disabled.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_multiple_accounts_locked_out.yml b/detections/application/okta_multiple_accounts_locked_out.yml
index f368fb1aca..1275be665a 100644
--- a/detections/application/okta_multiple_accounts_locked_out.yml
+++ b/detections/application/okta_multiple_accounts_locked_out.yml
@@ -7,10 +7,23 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic detects multiple Okta accounts being locked out within a short period. It uses the user.account.lock event from Okta logs, aggregated over a 5-minute window, to identify this behavior. This activity is significant as it may indicate a brute force or password spraying attack, where an adversary attempts to guess passwords, leading to account lockouts. If confirmed malicious, this could result in potential account takeovers or unauthorized access to sensitive Okta accounts, posing a significant security risk.
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime values(All_Changes.user) as user from datamodel=Change where All_Changes.change_type=AAA All_Changes.object_category=User AND All_Changes.action=lockout AND All_Changes.command=user.account.lock by _time span=5m All_Changes.result All_Changes.command sourcetype All_Changes.src | where count > 5 | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_multiple_accounts_locked_out_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: Multiple account lockouts may be also triggered by an application malfunction. Filter as needed, and monitor for any unusual activity.
+description: The following analytic detects multiple Okta accounts being locked out
+  within a short period. It uses the user.account.lock event from Okta logs, aggregated
+  over a 5-minute window, to identify this behavior. This activity is significant
+  as it may indicate a brute force or password spraying attack, where an adversary
+  attempts to guess passwords, leading to account lockouts. If confirmed malicious,
+  this could result in potential account takeovers or unauthorized access to sensitive
+  Okta accounts, posing a significant security risk.
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time)
+  as firstTime values(All_Changes.user) as user from datamodel=Change where All_Changes.change_type=AAA
+  All_Changes.object_category=User AND All_Changes.action=lockout AND All_Changes.command=user.account.lock
+  by _time span=5m All_Changes.result All_Changes.command sourcetype All_Changes.src
+  | where count > 5 | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `okta_multiple_accounts_locked_out_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: Multiple account lockouts may be also triggered by an application
+  malfunction. Filter as needed, and monitor for any unusual activity.
 references:
 - https://attack.mitre.org/techniques/T1110/
 - https://splunkbase.splunk.com/app/6553
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 70
   impact: 70
-  message: Multiple accounts locked out in Okta from [$src$]. Investigate further to determine if this was authorized.
+  message: Multiple accounts locked out in Okta from [$src$]. Investigate further
+    to determine if this was authorized.
   mitre_attack_id:
   - T1110
   observable:
@@ -45,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.change_type
-  - All_Changes.object_category
-  - All_Changes.action
-  - All_Changes.command
-  - All_Changes.result
-  - All_Changes.src
-  - sourcetype
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_multiple_failed_mfa_requests_for_user.yml b/detections/application/okta_multiple_failed_mfa_requests_for_user.yml
index 79c76bf941..c46de59771 100644
--- a/detections/application/okta_multiple_failed_mfa_requests_for_user.yml
+++ b/detections/application/okta_multiple_failed_mfa_requests_for_user.yml
@@ -7,10 +7,23 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within an Okta tenant. It triggers when more than 10 MFA attempts fail within 5 minutes, using Okta event logs to detect this pattern. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests, a technique used by threat actors like Lapsus and APT29. If confirmed malicious, this could lead to unauthorized access, potentially compromising sensitive information and systems.
-search: '`okta` eventType=user.authentication.auth_via_mfa outcome.result=FAILURE debugContext.debugData.factor!=PASSWORD_AS_FACTOR | bucket _time span=5m | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) values(src_ip) as src_ip values(debugContext.debugData.factor) by _time src_user | where count >= 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_multiple_failed_mfa_requests_for_user_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed and monitor for any unusual activity.
+description: The following analytic identifies multiple failed multi-factor authentication
+  (MFA) requests for a single user within an Okta tenant. It triggers when more than
+  10 MFA attempts fail within 5 minutes, using Okta event logs to detect this pattern.
+  This activity is significant as it may indicate an adversary attempting to bypass
+  MFA by bombarding the user with repeated authentication requests, a technique used
+  by threat actors like Lapsus and APT29. If confirmed malicious, this could lead
+  to unauthorized access, potentially compromising sensitive information and systems.
+search: '`okta` eventType=user.authentication.auth_via_mfa outcome.result=FAILURE
+  debugContext.debugData.factor!=PASSWORD_AS_FACTOR | bucket _time span=5m | stats
+  count min(_time) as firstTime max(_time) as lastTime values(displayMessage) values(src_ip)
+  as src_ip values(debugContext.debugData.factor) by _time src_user | where count
+  >= 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `okta_multiple_failed_mfa_requests_for_user_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: Multiple Failed MFA requests may also be a sign of authentication
+  or application issues. Filter as needed and monitor for any unusual activity.
 references:
 - https://attack.mitre.org/techniques/T1621/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType
-  - outcome.result
-  - debugContext.debugData.factor
-  - displayMessage
-  - src_user
-  - src_ip
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_requests/okta_multiple_failed_mfa_requests.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_multiple_failed_mfa_requests/okta_multiple_failed_mfa_requests.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_multiple_failed_requests_to_access_applications.yml b/detections/application/okta_multiple_failed_requests_to_access_applications.yml
index 120a986500..4da9fbd1da 100644
--- a/detections/application/okta_multiple_failed_requests_to_access_applications.yml
+++ b/detections/application/okta_multiple_failed_requests_to_access_applications.yml
@@ -7,10 +7,34 @@ type: Hunting
 status: experimental
 data_source:
 - Okta
-description: The following analytic detects multiple failed attempts to access applications in Okta, potentially indicating the reuse of a stolen web session cookie. It leverages Okta logs to evaluate policy and SSO events, aggregating data by user, session, and IP. The detection triggers when more than half of the app sign-on attempts are unsuccessful across multiple applications. This activity is significant as it may indicate an attempt to bypass authentication mechanisms. If confirmed malicious, it could lead to unauthorized access to sensitive applications and data, posing a significant security risk.
-search: '`okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE) OR (eventType=user.authentication.sso outcome.result=SUCCESS) | eval targets=mvzip(''target{}.type'', ''target{}.displayName'', ": ") | eval targets=mvfilter(targets LIKE "AppInstance%") | stats count min(_time) as _time values(outcome.result) as outcome.result dc(eval(if(eventType="policy.evaluate_sign_on",targets,NULL))) as total_challenges sum(eval(if(eventType="user.authentication.sso",1,0))) as total_successes by authenticationContext.externalSessionId targets actor.alternateId client.ipAddress | search total_challenges > 0 | stats min(_time) as _time values(*) as * sum(total_challenges) as total_challenges sum(total_successes) as total_successes values(eval(if("outcome.result"="SUCCESS",targets,NULL))) as success_apps values(eval(if(":outcome.result"!="SUCCESS",targets,NULL))) as no_success_apps by authenticationContext.externalSessionId actor.alternateId client.ipAddress | fillnull | eval ratio=round(total_successes/total_challenges,2), severity="HIGH", mitre_technique_id="T1538", description="actor.alternateId". " from " . "client.ipAddress" . " seen opening " . total_challenges . " chiclets/apps with " . total_successes . " challenges successfully passed" | fields - count, targets | search ratio < 0.5 total_challenges > 2 | `okta_multiple_failed_requests_to_access_applications_filter`'
-how_to_implement: This analytic is specific to Okta and requires Okta:im2 logs to be ingested.
-known_false_positives: False positives may be present based on organization size and configuration of Okta.
+description: The following analytic detects multiple failed attempts to access applications
+  in Okta, potentially indicating the reuse of a stolen web session cookie. It leverages
+  Okta logs to evaluate policy and SSO events, aggregating data by user, session,
+  and IP. The detection triggers when more than half of the app sign-on attempts are
+  unsuccessful across multiple applications. This activity is significant as it may
+  indicate an attempt to bypass authentication mechanisms. If confirmed malicious,
+  it could lead to unauthorized access to sensitive applications and data, posing
+  a significant security risk.
+search: "`okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE)
+  OR (eventType=user.authentication.sso outcome.result=SUCCESS) | eval targets=mvzip('target{}.type',
+  'target{}.displayName', \": \") | eval targets=mvfilter(targets LIKE \"AppInstance%\"\
+  ) | stats count min(_time) as _time values(outcome.result) as outcome.result dc(eval(if(eventType=\"\
+  policy.evaluate_sign_on\",targets,NULL))) as total_challenges sum(eval(if(eventType=\"\
+  user.authentication.sso\",1,0))) as total_successes by authenticationContext.externalSessionId
+  targets actor.alternateId client.ipAddress | search total_challenges > 0 | stats
+  min(_time) as _time values(*) as * sum(total_challenges) as total_challenges sum(total_successes)
+  as total_successes values(eval(if(\"outcome.result\"=\"SUCCESS\",targets,NULL)))
+  as success_apps values(eval(if(\":outcome.result\"!=\"SUCCESS\",targets,NULL)))
+  as no_success_apps by authenticationContext.externalSessionId actor.alternateId
+  client.ipAddress | fillnull | eval ratio=round(total_successes/total_challenges,2),
+  severity=\"HIGH\", mitre_technique_id=\"T1538\", description=\"actor.alternateId\"\
+  . \" from \" . \"client.ipAddress\" . \" seen opening \" . total_challenges . \"\
+  \ chiclets/apps with \" . total_successes . \" challenges successfully passed\"
+  | fields - count, targets | search ratio < 0.5 total_challenges > 2 | `okta_multiple_failed_requests_to_access_applications_filter`"
+how_to_implement: This analytic is specific to Okta and requires Okta:im2 logs to
+  be ingested.
+known_false_positives: False positives may be present based on organization size and
+  configuration of Okta.
 references:
 - https://attack.mitre.org/techniques/T1538
 - https://attack.mitre.org/techniques/T1550/004
@@ -33,11 +57,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - authenticationContext.externalSessionId
-  - targets
-  - actor.alternateId
-  - client.ipAddress
-  - eventType
   security_domain: access
diff --git a/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml b/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml
index 9f39281cb5..c961108322 100644
--- a/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml
+++ b/detections/application/okta_multiple_users_failing_to_authenticate_from_ip.yml
@@ -7,10 +7,25 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic identifies instances where more than 10 unique user accounts have failed to authenticate from a single IP address within a 5-minute window in an Okta tenant. This detection uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud. Such activity is significant as it may indicate brute-force attacks or password spraying attempts. If confirmed malicious, this behavior suggests an external entity is attempting to compromise multiple user accounts, potentially leading to unauthorized access to organizational resources and data breaches.
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime dc(Authentication.user) as unique_accounts values(Authentication.signature) as signature values(Authentication.user) as user values(Authentication.app) as app values(Authentication.authentication_method) as authentication_method from datamodel=Authentication where Authentication.action="failure" AND Authentication.signature=user.session.start by _time span=5m Authentication.src sourcetype | where unique_accounts > 9 | `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_multiple_users_failing_to_authenticate_from_ip_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: A source Ip failing to authenticate with multiple users in a short period of time is not common legitimate behavior.
+description: The following analytic identifies instances where more than 10 unique
+  user accounts have failed to authenticate from a single IP address within a 5-minute
+  window in an Okta tenant. This detection uses OktaIm2 logs ingested via the Splunk
+  Add-on for Okta Identity Cloud. Such activity is significant as it may indicate
+  brute-force attacks or password spraying attempts. If confirmed malicious, this
+  behavior suggests an external entity is attempting to compromise multiple user accounts,
+  potentially leading to unauthorized access to organizational resources and data
+  breaches.
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time)
+  as firstTime dc(Authentication.user) as unique_accounts values(Authentication.signature)
+  as signature values(Authentication.user) as user values(Authentication.app) as app
+  values(Authentication.authentication_method) as authentication_method from datamodel=Authentication
+  where Authentication.action="failure" AND Authentication.signature=user.session.start
+  by _time span=5m Authentication.src sourcetype | where unique_accounts > 9 | `drop_dm_object_name("Authentication")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_multiple_users_failing_to_authenticate_from_ip_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: A source Ip failing to authenticate with multiple users in
+  a short period of time is not common legitimate behavior.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://splunkbase.splunk.com/app/6553
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 90
   impact: 60
-  message: Multiple users failing to authenticate from a single source IP Address - [$src$]. Investigate further to determine if this was authorized.
+  message: Multiple users failing to authenticate from a single source IP Address
+    - [$src$]. Investigate further to determine if this was authorized.
   mitre_attack_id:
   - T1110.003
   observable:
@@ -45,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.user
-  - Authentication.signature
-  - Authentication.user
-  - Authentication.app
-  - Authentication.authentication_method
-  - Authentication.action
-  - Authentication.src
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/okta_multiple_users_from_ip/okta_multiple_users_from_ip.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/okta_multiple_users_from_ip/okta_multiple_users_from_ip.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_new_api_token_created.yml b/detections/application/okta_new_api_token_created.yml
index e0e01fd181..4ba39284dc 100644
--- a/detections/application/okta_new_api_token_created.yml
+++ b/detections/application/okta_new_api_token_created.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a new API token within an Okta tenant. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to identify events where the `system.api_token.create` command is executed. This activity is significant because creating a new API token can indicate potential account takeover attempts or unauthorized access, allowing an adversary to maintain persistence. If confirmed malicious, this could enable attackers to execute API calls, access sensitive data, and perform administrative actions within the Okta environment.
+description: The following analytic detects the creation of a new API token within
+  an Okta tenant. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity
+  Cloud to identify events where the `system.api_token.create` command is executed.
+  This activity is significant because creating a new API token can indicate potential
+  account takeover attempts or unauthorized access, allowing an adversary to maintain
+  persistence. If confirmed malicious, this could enable attackers to execute API
+  calls, access sensitive data, and perform administrative actions within the Okta
+  environment.
 data_source:
 - Okta
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime from datamodel=Change where All_Changes.action=created AND All_Changes.command=system.api_token.create by _time span=5m All_Changes.user All_Changes.result All_Changes.command sourcetype All_Changes.src All_Changes.action All_Changes.object_category | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_new_api_token_created_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed.
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time)
+  as firstTime from datamodel=Change where All_Changes.action=created AND All_Changes.command=system.api_token.create
+  by _time span=5m All_Changes.user All_Changes.result All_Changes.command sourcetype
+  All_Changes.src All_Changes.action All_Changes.object_category | `drop_dm_object_name("All_Changes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_new_api_token_created_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: False positives may be present. Tune Okta and tune the analytic
+  to ensure proper fidelity. Modify risk score as needed.
 references:
 - https://developer.okta.com/docs/reference/api/event-types/?q=security.threat.detected
 - https://splunkbase.splunk.com/app/6553
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 80
   impact: 80
-  message: A new API token was created in Okta by [$user$]. Investigate further to determine if this was authorized.
+  message: A new API token was created in Okta by [$user$]. Investigate further to
+    determine if this was authorized.
   mitre_attack_id:
   - T1078
   - T1078.001
@@ -42,23 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - signature
-  - eventType
-  - displayMessage
-  - client.device
-  - city
-  - state
-  - country
-  - user_agent
-  - outcome.reason
-  - outcome.result
-  - severity
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/okta_new_api_token_created/okta_new_api_token_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/okta_new_api_token_created/okta_new_api_token_created.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_new_device_enrolled_on_account.yml b/detections/application/okta_new_device_enrolled_on_account.yml
index 2110d17da3..3418b90590 100644
--- a/detections/application/okta_new_device_enrolled_on_account.yml
+++ b/detections/application/okta_new_device_enrolled_on_account.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user setting up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
+description: The following analytic identifies when a new device is enrolled on an
+  Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity
+  Cloud to detect the creation of new device enrollments. This activity is significant
+  as it may indicate a legitimate user setting up a new device or an adversary adding
+  a device to maintain unauthorized access. If confirmed malicious, this could lead
+  to potential account takeover, unauthorized access, and persistent control over
+  the compromised Okta account. Monitoring this behavior is crucial for detecting
+  and mitigating unauthorized access attempts.
 data_source:
 - Okta
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime from datamodel=Change where All_Changes.action=created All_Changes.command=device.enrollment.create by _time span=5m All_Changes.user All_Changes.result All_Changes.command sourcetype All_Changes.src All_Changes.action All_Changes.object_category | `drop_dm_object_name("All_Changes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_new_device_enrolled_on_account_filter`'
-how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: It is possible that the user has legitimately added a new device to their account. Please verify this activity.
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time)
+  as firstTime from datamodel=Change where All_Changes.action=created All_Changes.command=device.enrollment.create
+  by _time span=5m All_Changes.user All_Changes.result All_Changes.command sourcetype
+  All_Changes.src All_Changes.action All_Changes.object_category | `drop_dm_object_name("All_Changes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_new_device_enrolled_on_account_filter`'
+how_to_implement: The analytic leverages Okta OktaIm2 logs to be ingested using the
+  Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: It is possible that the user has legitimately added a new device
+  to their account. Please verify this activity.
 references:
 - https://attack.mitre.org/techniques/T1098/005/
 - https://developer.okta.com/docs/reference/api/event-types/?q=device.enrollment.create
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 60
   impact: 40
-  message: A new device was enrolled on an Okta account for user [$user$]. Investigate further to determine if this was authorized.
+  message: A new device was enrolled on an Okta account for user [$user$]. Investigate
+    further to determine if this was authorized.
   mitre_attack_id:
   - T1098
   - T1098.005
@@ -42,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - displayMessage
-  - user
-  - eventType
-  - client.userAgent.rawUserAgent
-  - client.userAgent.browser
-  - client.geographicalContext.city
-  - client.geographicalContext.country
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/okta_new_device_enrolled/okta_new_device_enrolled.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/okta_new_device_enrolled/okta_new_device_enrolled.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml b/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml
index caf537fe72..93b7006329 100644
--- a/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml
+++ b/detections/application/okta_phishing_detection_with_fastpass_origin_check.yml
@@ -7,10 +7,23 @@ type: TTP
 status: experimental
 data_source:
 - Okta
-description: The following analytic identifies failed user authentication attempts in Okta due to FastPass declining a phishing attempt. It leverages Okta logs, specifically looking for events where multi-factor authentication (MFA) fails with the reason "FastPass declined phishing attempt." This activity is significant as it indicates that attackers are targeting users with real-time phishing proxies, attempting to capture credentials. If confirmed malicious, this could lead to unauthorized access to user accounts, potentially compromising sensitive information and furthering lateral movement within the organization.
-search: '`okta` eventType="user.authentication.auth_via_mfa" AND result="FAILURE" AND outcome.reason="FastPass declined phishing attempt" | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_phishing_detection_with_fastpass_origin_check_filter`'
-how_to_implement: This search is specific to Okta and requires Okta logs to be ingested in your Splunk deployment.
-known_false_positives: Fidelity of this is high as Okta is specifying malicious infrastructure. Filter and modify as needed.
+description: The following analytic identifies failed user authentication attempts
+  in Okta due to FastPass declining a phishing attempt. It leverages Okta logs, specifically
+  looking for events where multi-factor authentication (MFA) fails with the reason
+  "FastPass declined phishing attempt." This activity is significant as it indicates
+  that attackers are targeting users with real-time phishing proxies, attempting to
+  capture credentials. If confirmed malicious, this could lead to unauthorized access
+  to user accounts, potentially compromising sensitive information and furthering
+  lateral movement within the organization.
+search: '`okta` eventType="user.authentication.auth_via_mfa" AND result="FAILURE"
+  AND outcome.reason="FastPass declined phishing attempt" | stats count min(_time)
+  as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent
+  client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `okta_phishing_detection_with_fastpass_origin_check_filter`'
+how_to_implement: This search is specific to Okta and requires Okta logs to be ingested
+  in your Splunk deployment.
+known_false_positives: Fidelity of this is high as Okta is specifying malicious infrastructure.
+  Filter and modify as needed.
 references:
 - https://sec.okta.com/fastpassphishingdetection
 tags:
@@ -33,11 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType
-  - client.userAgent.rawUserAgent
-  - client.userAgent.browser
-  - outcome.reason
-  - displayMessage
   security_domain: access
diff --git a/detections/application/okta_risk_threshold_exceeded.yml b/detections/application/okta_risk_threshold_exceeded.yml
index 35cbdf6760..dc7d3f8ab0 100644
--- a/detections/application/okta_risk_threshold_exceeded.yml
+++ b/detections/application/okta_risk_threshold_exceeded.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Bhavin Patel, Splunk
 status: production
 type: Correlation
-description: The following correlation identifies when a user exceeds a risk threshold based on multiple suspicious Okta activities. It leverages the Risk Framework from Enterprise Security, aggregating risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. This detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs) within a 24-hour period. If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
+description: The following correlation identifies when a user exceeds a risk threshold
+  based on multiple suspicious Okta activities. It leverages the Risk Framework from
+  Enterprise Security, aggregating risk events from "Suspicious Okta Activity," "Okta
+  Account Takeover," and "Okta MFA Exhaustion" analytic stories. This detection is
+  significant as it highlights potentially compromised user accounts exhibiting multiple
+  tactics, techniques, and procedures (TTPs) within a 24-hour period. If confirmed
+  malicious, this activity could indicate a serious security breach, allowing attackers
+  to gain unauthorized access, escalate privileges, or persist within the environment.
 data_source:
 - Okta
-search: '| tstats `security_content_summariesonly` values(All_Risk.analyticstories) as analyticstories  sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count,values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk  where All_Risk.risk_object_type = user All_Risk.analyticstories IN ("Okta Account Takeover", "Suspicious Okta Activity","Okta MFA Exhaustion") by All_Risk.risk_object,All_Risk.risk_object_type | `drop_dm_object_name("All_Risk")` |  search mitre_technique_id_count > 5 | `okta_risk_threshold_exceeded_filter`'
-how_to_implement: This search leverages the Risk Framework from Enterprise Security. Ensure that "Suspicious Okta Activity", "Okta Account Takeover", and "Okta MFA Exhaustion" analytic stories are enabled. TTPs may be set to Notables for point detections; anomalies should not be notables but rather risk generators. The correlation relies on risk before generating a notable. Modify the value as needed.
-known_false_positives: False positives will be limited to the number of events generated by the analytics tied to the stories. Analytics will need to be tested and tuned, and the risk score reduced as needed based on the organization.
+search: '| tstats `security_content_summariesonly` values(All_Risk.analyticstories)
+  as analyticstories  sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count,values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk  where All_Risk.risk_object_type
+  = user All_Risk.analyticstories IN ("Okta Account Takeover", "Suspicious Okta Activity","Okta
+  MFA Exhaustion") by All_Risk.risk_object,All_Risk.risk_object_type | `drop_dm_object_name("All_Risk")`
+  |  search mitre_technique_id_count > 5 | `okta_risk_threshold_exceeded_filter`'
+how_to_implement: This search leverages the Risk Framework from Enterprise Security.
+  Ensure that "Suspicious Okta Activity", "Okta Account Takeover", and "Okta MFA Exhaustion"
+  analytic stories are enabled. TTPs may be set to Notables for point detections;
+  anomalies should not be notables but rather risk generators. The correlation relies
+  on risk before generating a notable. Modify the value as needed.
+known_false_positives: False positives will be limited to the number of events generated
+  by the analytics tied to the stories. Analytics will need to be tested and tuned,
+  and the risk score reduced as needed based on the organization.
 references:
 - https://developer.okta.com/docs/reference/api/event-types
 - https://sec.okta.com/everythingisyes
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +59,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 80
   impact: 70
-  message: Okta Risk threshold exceeded for user [$risk_object$]. Investigate further to determine if this was authorized.
+  message: Okta Risk threshold exceeded for user [$risk_object$]. Investigate further
+    to determine if this was authorized.
   mitre_attack_id:
   - T1078
   - T1110
@@ -44,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Risk.risk_object
-  - All_Risk.risk_object_type
-  - All_Risk.analyticstories
-  - All_Risk.calculated_risk_score
-  - All_Risk.annotations.mitre_attack.mitre_tactic_id
-  - All_Risk.annotations.mitre_attack.mitre_technique_id
-  - All_Risk.tag
-  - _time
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/okta_account_takeover_risk_events/okta_risk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/okta_account_takeover_risk_events/okta_risk.log
     source: risk_data
     sourcetype: stash
diff --git a/detections/application/okta_successful_single_factor_authentication.yml b/detections/application/okta_successful_single_factor_authentication.yml
index bb068a87ae..f85319d013 100644
--- a/detections/application/okta_successful_single_factor_authentication.yml
+++ b/detections/application/okta_successful_single_factor_authentication.yml
@@ -7,10 +7,24 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic identifies successful single-factor authentication events against the Okta Dashboard for accounts without Multi-Factor Authentication (MFA) enabled. It detects this activity by analyzing Okta logs for successful authentication events where "Okta Verify" is not used. This behavior is significant as it may indicate a misconfiguration, policy violation, or potential account takeover. If confirmed malicious, an attacker could gain unauthorized access to the account, potentially leading to data breaches or further exploitation within the environment.
-search: '`okta`  action=success src_user_type = User eventType = user.authentication.verify OR eventType = user.authentication.auth_via_mfa| stats dc(eventType) values(eventType) as eventType values(target{}.displayName) as targets values(debugContext.debugData.url) min(_time) as firstTime max(_time) as lastTime values(authentication_method) by src_ip user action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search targets !="Okta Verify" | `okta_successful_single_factor_authentication_filter`'
-how_to_implement: This detection utilizes logs from Okta environments and requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: Although not recommended, certain users may be exempt from multi-factor authentication. Adjust the filter as necessary.
+description: The following analytic identifies successful single-factor authentication
+  events against the Okta Dashboard for accounts without Multi-Factor Authentication
+  (MFA) enabled. It detects this activity by analyzing Okta logs for successful authentication
+  events where "Okta Verify" is not used. This behavior is significant as it may indicate
+  a misconfiguration, policy violation, or potential account takeover. If confirmed
+  malicious, an attacker could gain unauthorized access to the account, potentially
+  leading to data breaches or further exploitation within the environment.
+search: '`okta`  action=success src_user_type = User eventType = user.authentication.verify
+  OR eventType = user.authentication.auth_via_mfa| stats dc(eventType) values(eventType)
+  as eventType values(target{}.displayName) as targets values(debugContext.debugData.url)
+  min(_time) as firstTime max(_time) as lastTime values(authentication_method) by
+  src_ip user action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | search targets !="Okta Verify" | `okta_successful_single_factor_authentication_filter`'
+how_to_implement: This detection utilizes logs from Okta environments and requires
+  the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud
+  (https://splunkbase.splunk.com/app/6553).
+known_false_positives: Although not recommended, certain users may be exempt from
+  multi-factor authentication. Adjust the filter as necessary.
 references:
 - https://sec.okta.com/everythingisyes
 - https://attack.mitre.org/techniques/T1078/004/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 60
   impact: 80
-  message: A user [$user$] has successfully logged in to Okta Dashboard with single factor authentication from IP Address - [$src_ip$].
+  message: A user [$user$] has successfully logged in to Okta Dashboard with single
+    factor authentication from IP Address - [$src_ip$].
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -45,21 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - tag
-  - action
-  - src_user_type
-  - eventType
-  - target{}.displayName
-  - debugContext.debugData.url
-  - authentication_method
-  - src_ip
-  - user
-  - _time
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_single_factor_auth/okta_single_factor_auth.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_single_factor_auth/okta_single_factor_auth.log
     source: okta_log
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_suspicious_activity_reported.yml b/detections/application/okta_suspicious_activity_reported.yml
index 4153d4c718..1ae1cf868a 100644
--- a/detections/application/okta_suspicious_activity_reported.yml
+++ b/detections/application/okta_suspicious_activity_reported.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies when an associate reports a login attempt as suspicious via an email from Okta. It leverages Okta Identity Management logs, specifically the `user.account.report_suspicious_activity_by_enduser` event type. This activity is significant as it indicates potential unauthorized access attempts, warranting immediate investigation to prevent possible security breaches. If confirmed malicious, the attacker could gain unauthorized access to sensitive systems and data, leading to data theft, privilege escalation, or further compromise of the environment.
+description: The following analytic identifies when an associate reports a login attempt
+  as suspicious via an email from Okta. It leverages Okta Identity Management logs,
+  specifically the `user.account.report_suspicious_activity_by_enduser` event type.
+  This activity is significant as it indicates potential unauthorized access attempts,
+  warranting immediate investigation to prevent possible security breaches. If confirmed
+  malicious, the attacker could gain unauthorized access to sensitive systems and
+  data, leading to data theft, privilege escalation, or further compromise of the
+  environment.
 data_source:
 - Okta
-search: '`okta` eventType=user.account.report_suspicious_activity_by_enduser | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser client.geographicalContext.city  client.geographicalContext.country | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_suspicious_activity_reported_filter`'
-how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). Additionally, it necessitates the activation of suspicious activity reporting and training for associates to report such activities.
-known_false_positives: False positives should be minimal, given the high fidelity of this detection. marker.
+search: '`okta` eventType=user.account.report_suspicious_activity_by_enduser | stats
+  count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user
+  eventType client.userAgent.rawUserAgent client.userAgent.browser client.geographicalContext.city  client.geographicalContext.country
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_suspicious_activity_reported_filter`'
+how_to_implement: This detection utilizes logs from Okta Identity Management (IM)
+  environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on
+  for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553). Additionally,
+  it necessitates the activation of suspicious activity reporting and training for
+  associates to report such activities.
+known_false_positives: False positives should be minimal, given the high fidelity
+  of this detection. marker.
 references:
 - https://help.okta.com/en-us/Content/Topics/Security/suspicious-activity-reporting.htm
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +48,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 50
   impact: 50
-  message: A user [$user$] reported suspicious activity in Okta. Investigate further to determine if this was authorized.
+  message: A user [$user$] reported suspicious activity in Okta. Investigate further
+    to determine if this was authorized.
   mitre_attack_id:
   - T1078
   - T1078.001
@@ -41,19 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - displayMessage
-  - user
-  - eventType
-  - client.userAgent.rawUserAgent
-  - client.userAgent.browser
-  - client.geographicalContext.city
-  - client.geographicalContext.country
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/okta_suspicious_activity_reported_by_user/okta_suspicious_activity_reported_by_user.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/okta_suspicious_activity_reported_by_user/okta_suspicious_activity_reported_by_user.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_suspicious_use_of_a_session_cookie.yml b/detections/application/okta_suspicious_use_of_a_session_cookie.yml
index 17b39f8a09..b822cf34db 100644
--- a/detections/application/okta_suspicious_use_of_a_session_cookie.yml
+++ b/detections/application/okta_suspicious_use_of_a_session_cookie.yml
@@ -7,10 +7,27 @@ type: Anomaly
 status: production
 data_source:
 - Okta
-description: The following analytic identifies suspicious use of a session cookie by detecting multiple client values (IP, User Agent, etc.) changing for the same Device Token associated with a specific user. It leverages policy evaluation events from successful authentication logs in Okta. This activity is significant as it may indicate an adversary attempting to reuse a stolen web session cookie, potentially bypassing authentication mechanisms. If confirmed malicious, this could allow unauthorized access to user accounts, leading to data breaches or further exploitation within the environment.
-search: '`okta` eventType IN (policy.evaluate_sign_on) outcome.result IN (ALLOW, SUCCESS) | stats earliest(_time) as _time, values(client.ipAddress) as src_ip, values(client.userAgent.rawUserAgent) as user_agent, values(client.userAgent.os) as userAgentOS_list, values(client.geographicalContext.city) as city, values(client.userAgent.browser) as userAgentBrowser_list, values(device.os_platform) as okta_device_os, dc(client.userAgent.browser) as dc_userAgentBrowser, dc(client.userAgent.os) as dc_userAgentOS, dc(client.ipAddress) as dc_src_ip, values(outcome.reason) as reason by debugContext.debugData.dtHash, user | where dc_src_ip>1 AND (dc_userAgentOS>1 OR dc_userAgentBrowser>1) | `okta_suspicious_use_of_a_session_cookie_filter`'
-how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: False positives may occur, depending on the organization's size and the configuration of Okta.
+description: The following analytic identifies suspicious use of a session cookie
+  by detecting multiple client values (IP, User Agent, etc.) changing for the same
+  Device Token associated with a specific user. It leverages policy evaluation events
+  from successful authentication logs in Okta. This activity is significant as it
+  may indicate an adversary attempting to reuse a stolen web session cookie, potentially
+  bypassing authentication mechanisms. If confirmed malicious, this could allow unauthorized
+  access to user accounts, leading to data breaches or further exploitation within
+  the environment.
+search: '`okta` eventType IN (policy.evaluate_sign_on) outcome.result IN (ALLOW, SUCCESS)
+  | stats earliest(_time) as _time, values(client.ipAddress) as src_ip, values(client.userAgent.rawUserAgent)
+  as user_agent, values(client.userAgent.os) as userAgentOS_list, values(client.geographicalContext.city)
+  as city, values(client.userAgent.browser) as userAgentBrowser_list, values(device.os_platform)
+  as okta_device_os, dc(client.userAgent.browser) as dc_userAgentBrowser, dc(client.userAgent.os)
+  as dc_userAgentOS, dc(client.ipAddress) as dc_src_ip, values(outcome.reason) as
+  reason by debugContext.debugData.dtHash, user | where dc_src_ip>1 AND (dc_userAgentOS>1
+  OR dc_userAgentBrowser>1) | `okta_suspicious_use_of_a_session_cookie_filter`'
+how_to_implement: This detection utilizes logs from Okta Identity Management (IM)
+  environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on
+  for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: False positives may occur, depending on the organization's
+  size and the configuration of Okta.
 references:
 - https://attack.mitre.org/techniques/T1539/
 drilldown_searches:
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +51,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 70
   impact: 80
-  message: A user [$user$] is attempting to use a session cookie from multiple IP addresses or devices. Investigate further to determine if this was authorized.
+  message: A user [$user$] is attempting to use a session cookie from multiple IP
+    addresses or devices. Investigate further to determine if this was authorized.
   mitre_attack_id:
   - T1539
   observable:
@@ -41,20 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - client.ipAddress
-  - client.userAgent.rawUserAgent
-  - client.userAgent.os
-  - client.geographicalContext.city
-  - client.userAgent.browser
-  - device.os_platform
-  - debugContext.debugData.dtHash
-  - actor.alternateId
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1539/okta_web_session_multiple_ip/okta_web_session_multiple_ip.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1539/okta_web_session_multiple_ip/okta_web_session_multiple_ip.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_threatinsight_threat_detected.yml b/detections/application/okta_threatinsight_threat_detected.yml
index 0ca328d3c8..248987367b 100644
--- a/detections/application/okta_threatinsight_threat_detected.yml
+++ b/detections/application/okta_threatinsight_threat_detected.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies threats detected by Okta ThreatInsight, such as password spraying, login failures, and high counts of unknown user login attempts. It leverages Okta Identity Management logs, specifically focusing on security.threat.detected events. This activity is significant for a SOC as it highlights potential unauthorized access attempts and credential-based attacks. If confirmed malicious, these activities could lead to unauthorized access, data breaches, and further exploitation of compromised accounts, posing a significant risk to the organization's security posture.
+description: The following analytic identifies threats detected by Okta ThreatInsight,
+  such as password spraying, login failures, and high counts of unknown user login
+  attempts. It leverages Okta Identity Management logs, specifically focusing on security.threat.detected
+  events. This activity is significant for a SOC as it highlights potential unauthorized
+  access attempts and credential-based attacks. If confirmed malicious, these activities
+  could lead to unauthorized access, data breaches, and further exploitation of compromised
+  accounts, posing a significant risk to the organization's security posture.
 data_source:
 - Okta
-search: '`okta` eventType = security.threat.detected | rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city | stats count min(_time) as firstTime max(_time) as lastTime by app src_ip signature eventType displayMessage client.device city state country user_agent outcome.reason outcome.result severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_threatinsight_threat_detected_filter`'
-how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: False positives may occur. It is recommended to fine-tune Okta settings and the analytic to ensure high fidelity. Adjust the risk score as necessary.
+search: '`okta` eventType = security.threat.detected | rename client.geographicalContext.country
+  as country, client.geographicalContext.state as state, client.geographicalContext.city
+  as city | stats count min(_time) as firstTime max(_time) as lastTime by app src_ip
+  signature eventType displayMessage client.device city state country user_agent outcome.reason
+  outcome.result severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `okta_threatinsight_threat_detected_filter`'
+how_to_implement: This detection utilizes logs from Okta Identity Management (IM)
+  environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on
+  for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: False positives may occur. It is recommended to fine-tune Okta
+  settings and the analytic to ensure high fidelity. Adjust the risk score as necessary.
 references:
 - https://developer.okta.com/docs/reference/api/event-types/?q=security.threat.detected
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$app$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$app$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$app$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Infrastructure
   confidence: 50
   impact: 50
-  message: The following $src_ip$ has been identified as a threat by Okta ThreatInsight. Investigate further to determine if this was authorized.
+  message: The following $src_ip$ has been identified as a threat by Okta ThreatInsight.
+    Investigate further to determine if this was authorized.
   mitre_attack_id:
   - T1078
   - T1078.004
@@ -45,23 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - signature
-  - eventType
-  - displayMessage
-  - client.device
-  - city
-  - state
-  - country
-  - user_agent
-  - outcome.reason
-  - outcome.result
-  - severity
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_threatinsight_threat_detected/okta_threatinsight_threat_detected.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/okta_threatinsight_threat_detected/okta_threatinsight_threat_detected.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_unauthorized_access_to_application.yml b/detections/application/okta_unauthorized_access_to_application.yml
index dc505246b4..ab9806c2e1 100644
--- a/detections/application/okta_unauthorized_access_to_application.yml
+++ b/detections/application/okta_unauthorized_access_to_application.yml
@@ -7,10 +7,25 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic identifies attempts by users to access Okta applications that have not been assigned to them. It leverages Okta Identity Management logs, specifically focusing on failed access attempts to unassigned applications. This activity is significant for a SOC as it may indicate potential unauthorized access attempts, which could lead to exposure of sensitive information or disruption of services. If confirmed malicious, such activity could result in data breaches, non-compliance with data protection laws, and overall compromise of the IT environment.
-search: '| tstats values(Authentication.app) as app values(Authentication.action) as action values(Authentication.user) as user values(Authentication.reason) as reason from datamodel=Authentication where Authentication.signature=app.generic.unauth_app_access_attempt Authentication.action="failure" by _time Authentication.src Authentication.user | `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | iplocation src | `okta_unauthorized_access_to_application_filter`'
-how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments and requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: There is a possibility that a user may accidentally click on the wrong application, which could trigger this event. It is advisable to verify the location from which this activity originates.
+description: The following analytic identifies attempts by users to access Okta applications
+  that have not been assigned to them. It leverages Okta Identity Management logs,
+  specifically focusing on failed access attempts to unassigned applications. This
+  activity is significant for a SOC as it may indicate potential unauthorized access
+  attempts, which could lead to exposure of sensitive information or disruption of
+  services. If confirmed malicious, such activity could result in data breaches, non-compliance
+  with data protection laws, and overall compromise of the IT environment.
+search: '| tstats values(Authentication.app) as app values(Authentication.action)
+  as action values(Authentication.user) as user values(Authentication.reason) as reason
+  from datamodel=Authentication where Authentication.signature=app.generic.unauth_app_access_attempt
+  Authentication.action="failure" by _time Authentication.src Authentication.user
+  | `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | iplocation src | `okta_unauthorized_access_to_application_filter`'
+how_to_implement: This detection utilizes logs from Okta Identity Management (IM)
+  environments and requires the ingestion of OktaIm2 logs through the Splunk Add-on
+  for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: There is a possibility that a user may accidentally click on
+  the wrong application, which could trigger this event. It is advisable to verify
+  the location from which this activity originates.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +48,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 90
   impact: 90
-  message: A user [$user$] is attempting to access an unauthorized application from IP Address - [$src$]
+  message: A user [$user$] is attempting to access an unauthorized application from
+    IP Address - [$src$]
   mitre_attack_id:
   - T1087.004
   observable:
@@ -44,20 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.app
-  - Authentication.action
-  - Authentication.user
-  - Authentication.reason
-  - Authentication.dest
-  - Authentication.signature
-  - Authentication.method
-  - Authentication.src
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/okta_unauth_access/okta_unauth_access.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.004/okta_unauth_access/okta_unauth_access.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/okta_user_logins_from_multiple_cities.yml b/detections/application/okta_user_logins_from_multiple_cities.yml
index 4d7257a7f7..d704dcc2b2 100644
--- a/detections/application/okta_user_logins_from_multiple_cities.yml
+++ b/detections/application/okta_user_logins_from_multiple_cities.yml
@@ -7,10 +7,29 @@ data_source:
 - Okta
 type: Anomaly
 status: production
-description: The following analytic identifies instances where the same Okta user logs in from different cities within a 24-hour period. This detection leverages Okta Identity Management logs, analyzing login events and their geographic locations. Such behavior is significant as it may indicate a compromised account, with an attacker attempting unauthorized access from multiple locations. If confirmed malicious, this activity could lead to account takeovers and data breaches, allowing attackers to access sensitive information and potentially escalate their privileges within the environment.
-search: '| tstats  `security_content_summariesonly` values(Authentication.app) as app values(Authentication.action) as action values(Authentication.user) as user values(Authentication.reason) as reason values(Authentication.dest) as dest values(Authentication.signature) as signature  values(Authentication.method) as method  from datamodel=Authentication where  Authentication.signature=user.session.start by _time Authentication.src | `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | iplocation src | stats count min(_time) as firstTime max(_time) as lastTime dc(src) as distinct_src dc(City) as distinct_city values(src) as src values(City) as City values(Country) as Country values(action) as action by user | where distinct_city > 1 | `okta_user_logins_from_multiple_cities_filter`'
-how_to_implement: This detection utilizes logs from Okta Identity Management (IM) environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
-known_false_positives: It is uncommon for a user to log in from multiple cities simultaneously, which may indicate a false positive.
+description: The following analytic identifies instances where the same Okta user
+  logs in from different cities within a 24-hour period. This detection leverages
+  Okta Identity Management logs, analyzing login events and their geographic locations.
+  Such behavior is significant as it may indicate a compromised account, with an attacker
+  attempting unauthorized access from multiple locations. If confirmed malicious,
+  this activity could lead to account takeovers and data breaches, allowing attackers
+  to access sensitive information and potentially escalate their privileges within
+  the environment.
+search: '| tstats  `security_content_summariesonly` values(Authentication.app) as
+  app values(Authentication.action) as action values(Authentication.user) as user
+  values(Authentication.reason) as reason values(Authentication.dest) as dest values(Authentication.signature)
+  as signature  values(Authentication.method) as method  from datamodel=Authentication
+  where  Authentication.signature=user.session.start by _time Authentication.src |
+  `drop_dm_object_name("Authentication")` | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | iplocation src | stats count min(_time) as
+  firstTime max(_time) as lastTime dc(src) as distinct_src dc(City) as distinct_city
+  values(src) as src values(City) as City values(Country) as Country values(action)
+  as action by user | where distinct_city > 1 | `okta_user_logins_from_multiple_cities_filter`'
+how_to_implement: This detection utilizes logs from Okta Identity Management (IM)
+  environments. It requires the ingestion of OktaIm2 logs through the Splunk Add-on
+  for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).
+known_false_positives: It is uncommon for a user to log in from multiple cities simultaneously,
+  which may indicate a false positive.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +52,8 @@ tags:
   asset_type: Okta Tenant
   confidence: 90
   impact: 90
-  message: A user [$user$] has logged in from multiple cities [$City$] from IP Address - [$src$]. Investigate further to determine if this was authorized.
+  message: A user [$user$] has logged in from multiple cities [$City$] from IP Address
+    - [$src$]. Investigate further to determine if this was authorized.
   mitre_attack_id:
   - T1586.003
   observable:
@@ -44,20 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.app
-  - Authentication.action
-  - Authentication.user
-  - Authentication.reason
-  - Authentication.dest
-  - Authentication.signature
-  - Authentication.method
-  - Authentication.src
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/okta_multiple_city/okta_multiple_city_im2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/okta_multiple_city/okta_multiple_city_im2.log
     source: Okta
     sourcetype: OktaIM2:log
diff --git a/detections/application/pingid_mismatch_auth_source_and_verification_response.yml b/detections/application/pingid_mismatch_auth_source_and_verification_response.yml
index 28dfb82f25..afdf680990 100644
--- a/detections/application/pingid_mismatch_auth_source_and_verification_response.yml
+++ b/detections/application/pingid_mismatch_auth_source_and_verification_response.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies discrepancies between the IP address of an authentication event and the IP address of the verification response event, focusing on differences in the originating countries. It leverages JSON logs from PingID, comparing the 'auth_Country' and 'verify_Country' fields. This activity is significant as it may indicate suspicious sign-in behavior, such as account compromise or unauthorized access attempts. If confirmed malicious, this could allow attackers to bypass authentication mechanisms, potentially leading to unauthorized access to sensitive systems and data.
+description: The following analytic identifies discrepancies between the IP address
+  of an authentication event and the IP address of the verification response event,
+  focusing on differences in the originating countries. It leverages JSON logs from
+  PingID, comparing the 'auth_Country' and 'verify_Country' fields. This activity
+  is significant as it may indicate suspicious sign-in behavior, such as account compromise
+  or unauthorized access attempts. If confirmed malicious, this could allow attackers
+  to bypass authentication mechanisms, potentially leading to unauthorized access
+  to sensitive systems and data.
 data_source:
 - PingID
-search: '`pingid` ("result.status" IN ("SUCCESS*","FAIL*","UNSUCCESSFUL*") NOT "result.message" IN ("*pair*","*create*","*delete*")) | eval user = upper(''actors{}.name''), session_id = ''resources{}.websession'', dest = ''resources{}.ipaddress'', reason = ''result.message'', object = ''resources{}.devicemodel'', status = ''result.status'' | join user session_id [ search `pingid` ("result.status" IN ("POLICY") AND "resources{}.ipaddress"=*) AND "result.message" IN("*Action: Authenticate*","*Action: Approve*","*Action: Allowed*") | rex field=result.message "IP Address: (?:N\/A)?(?<policy_ipaddress>.+)?\n" | rex field=result.message "Action: (?:N\/A)?(?<signature>.+)?\n" | rex field=result.message "Requested Application Name: (?:N\/A)?(?<Requested_Application_Name>.+)?\n" | rex field=result.message "Requested Application ID: (?:N\/A)?(?<Requested_Application_ID>.+)?\n" | eval user = upper(''actors{}.name''), session_id = ''resources{}.websession'', src = coalesce(''resources{}.ipaddress'',policy_ipaddress), app = coalesce(Requested_Application_ID,Requested_Application_Name) | fields app, user, session_id, src, signature ] | iplocation prefix=auth_ dest | iplocation prefix=verify_ src | stats count min(_time) as firstTime max(_time) as lastTime values(app) as app values(session_id) as session_id by user, dest, auth_Country, src, verify_Country, object, signature, status, reason | where auth_Country != verify_Country | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `pingid_mismatch_auth_source_and_verification_response_filter`'
-how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription.
-known_false_positives: False positives may be generated by users working out the geographic region where the organizations services or technology is hosted.
+search: "`pingid` (\"result.status\" IN (\"SUCCESS*\",\"FAIL*\",\"UNSUCCESSFUL*\"
+  ) NOT \"result.message\" IN (\"*pair*\",\"*create*\",\"*delete*\")) | eval user
+  = upper('actors{}.name'), session_id = 'resources{}.websession', dest = 'resources{}.ipaddress',
+  reason = 'result.message', object = 'resources{}.devicemodel', status = 'result.status'
+  | join user session_id [ search `pingid` (\"result.status\" IN (\"POLICY\") AND
+  \"resources{}.ipaddress\"=*) AND \"result.message\" IN(\"*Action: Authenticate*\"\
+  ,\"*Action: Approve*\",\"*Action: Allowed*\") | rex field=result.message \"IP Address:
+  (?:N\\/A)?(?<policy_ipaddress>.+)?\\n\" | rex field=result.message \"Action: (?:N\\\
+  /A)?(?<signature>.+)?\\n\" | rex field=result.message \"Requested Application Name:
+  (?:N\\/A)?(?<Requested_Application_Name>.+)?\\n\" | rex field=result.message \"
+  Requested Application ID: (?:N\\/A)?(?<Requested_Application_ID>.+)?\\n\" | eval
+  user = upper('actors{}.name'), session_id = 'resources{}.websession', src = coalesce('resources{}.ipaddress',policy_ipaddress),
+  app = coalesce(Requested_Application_ID,Requested_Application_Name) | fields app,
+  user, session_id, src, signature ] | iplocation prefix=auth_ dest | iplocation prefix=verify_
+  src | stats count min(_time) as firstTime max(_time) as lastTime values(app) as
+  app values(session_id) as session_id by user, dest, auth_Country, src, verify_Country,
+  object, signature, status, reason | where auth_Country != verify_Country | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `pingid_mismatch_auth_source_and_verification_response_filter`"
+how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne)
+  enterprise environment, either via Webhook or Push Subscription.
+known_false_positives: False positives may be generated by users working out the geographic
+  region where the organizations services or technology is hosted.
 references:
 - https://twitter.com/jhencinski/status/1618660062352007174
 - https://attack.mitre.org/techniques/T1098/005/
@@ -22,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as
+    lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message"
+    values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations"
+    values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object
+    | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +62,8 @@ tags:
   asset_type: Identity
   confidence: 50
   impact: 50
-  message: An authentication by [$user$] was detected from [$dest$ - $auth_Country$] and the verification was received from [$src$ - $verify_Country$].
+  message: An authentication by [$user$] was detected from [$dest$ - $auth_Country$]
+    and the verification was received from [$src$ - $verify_Country$].
   mitre_attack_id:
   - T1621
   - T1556.006
@@ -53,18 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - resources{}.ipaddress
-  - actors{}.name
-  - result.message
-  - resources{}.devicemodel
-  - result.status
-  - resources{}.websession
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
     source: PINGID
     sourcetype: _json
diff --git a/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml b/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml
index 0a47e81a7c..5970f6a23c 100644
--- a/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml
+++ b/detections/application/pingid_multiple_failed_mfa_requests_for_user.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within a PingID environment. It triggers when 10 or more MFA prompts fail within 10 minutes, using JSON logs from PingID. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this could lead to unauthorized access, as the user might eventually accept the fraudulent request, compromising the security of the account and potentially the entire network.
+description: The following analytic identifies multiple failed multi-factor authentication
+  (MFA) requests for a single user within a PingID environment. It triggers when 10
+  or more MFA prompts fail within 10 minutes, using JSON logs from PingID. This activity
+  is significant as it may indicate an adversary attempting to bypass MFA by bombarding
+  the user with repeated authentication requests. If confirmed malicious, this could
+  lead to unauthorized access, as the user might eventually accept the fraudulent
+  request, compromising the security of the account and potentially the entire network.
 data_source:
 - PingID
-search: '`pingid` "result.status" IN ("FAILURE,authFail","UNSUCCESSFUL_ATTEMPT") | eval time = _time, src = coalesce(''resources{}.ipaddress'',''resources{}.devicemodel''), user = upper(''actors{}.name''), object = ''resources{}.devicemodel'', reason = ''result.message''| bucket span=10m _time | stats dc(_raw) AS mfa_prompts min(time) as firstTime, max(time) as lastTime values(src) as src by user, reason, _time | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | where mfa_prompts >= 10 | `pingid_multiple_failed_mfa_requests_for_user_filter`'
-how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription.
-known_false_positives: False positives may be generated by normal provisioning workflows for user device registration.
+search: "`pingid` \"result.status\" IN (\"FAILURE,authFail\",\"UNSUCCESSFUL_ATTEMPT\"\
+  ) | eval time = _time, src = coalesce('resources{}.ipaddress','resources{}.devicemodel'),
+  user = upper('actors{}.name'), object = 'resources{}.devicemodel', reason = 'result.message'|
+  bucket span=10m _time | stats dc(_raw) AS mfa_prompts min(time) as firstTime, max(time)
+  as lastTime values(src) as src by user, reason, _time | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | where mfa_prompts >= 10 | `pingid_multiple_failed_mfa_requests_for_user_filter`"
+how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne)
+  enterprise environment, either via Webhook or Push Subscription.
+known_false_positives: False positives may be generated by normal provisioning workflows
+  for user device registration.
 references:
 - https://therecord.media/russian-hackers-bypass-2fa-by-annoying-victims-with-repeated-push-notifications/
 - https://attack.mitre.org/techniques/T1621/
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +50,8 @@ tags:
   asset_type: Identity
   confidence: 50
   impact: 100
-  message: Multiple Failed MFA requests $mfa_prompts$ for user $user$ between $firstTime$ and $lastTime$.
+  message: Multiple Failed MFA requests $mfa_prompts$ for user $user$ between $firstTime$
+    and $lastTime$.
   mitre_attack_id:
   - T1621
   - T1078
@@ -46,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - resources{}.ipaddress
-  - actors{}.name
-  - result.message
-  - resources{}.devicemodel
-  - result.status
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
     source: PINGID
     sourcetype: _json
diff --git a/detections/application/pingid_new_mfa_method_after_credential_reset.yml b/detections/application/pingid_new_mfa_method_after_credential_reset.yml
index d717840f25..9776b6b172 100644
--- a/detections/application/pingid_new_mfa_method_after_credential_reset.yml
+++ b/detections/application/pingid_new_mfa_method_after_credential_reset.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the provisioning of a new MFA device shortly after a password reset. It detects this activity by correlating Windows Event Log events for password changes (EventID 4723, 4724) with PingID logs indicating device pairing. This behavior is significant as it may indicate a social engineering attack where a threat actor impersonates a valid user to reset credentials and add a new MFA device. If confirmed malicious, this activity could allow an attacker to gain persistent access to the compromised account, bypassing traditional security measures.
+description: The following analytic identifies the provisioning of a new MFA device
+  shortly after a password reset. It detects this activity by correlating Windows
+  Event Log events for password changes (EventID 4723, 4724) with PingID logs indicating
+  device pairing. This behavior is significant as it may indicate a social engineering
+  attack where a threat actor impersonates a valid user to reset credentials and add
+  a new MFA device. If confirmed malicious, this activity could allow an attacker
+  to gain persistent access to the compromised account, bypassing traditional security
+  measures.
 data_source:
 - PingID
-search: '`pingid` "result.message" = "*Device Paired*" | rex field=result.message "Device (Unp)?(P)?aired (?<device_extract>.+)" | eval src = coalesce(''resources{}.ipaddress'',''resources{}.devicemodel''), user = upper(''actors{}.name''), reason = ''result.message'' | eval object=CASE(ISNOTNULL(''resources{}.devicemodel''),''resources{}.devicemodel'',true(),device_extract) | eval action=CASE(match(''result.message'',"Device Paired*"),"created",match(''result.message'', "Device Unpaired*"),"deleted") | stats count min(_time) as firstTime, max(_time) as lastTime, values(reason) as reason by src,user,action,object | join type=outer user [| search `wineventlog_security` EventID IN(4723,4724) | eval PW_Change_Time = _time, user = upper(user) | fields user,src_user,EventID,PW_Change_Time] | eval timeDiffRaw = round(lastTime - PW_Change_Time) | eval timeDiff = replace(tostring(abs(timeDiffRaw) ,"duration"),"(\d*)\+*(\d+):(\d+):(\d+)","\2 hours \3 minutes") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `security_content_ctime(PW_Change_Time)` | where timeDiffRaw > 0 AND timeDiffRaw < 3600 | `pingid_new_mfa_method_after_credential_reset_filter`'
-how_to_implement: Target environment must ingest Windows Event Log and PingID(PingOne) data sources. Specifically from logs from Active Directory Domain Controllers and JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription.
-known_false_positives: False positives may be generated by normal provisioning workflows that generate a password reset followed by a device registration.
+search: "`pingid` \"result.message\" = \"*Device Paired*\" | rex field=result.message
+  \"Device (Unp)?(P)?aired (?<device_extract>.+)\" | eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'),
+  user = upper('actors{}.name'), reason = 'result.message' | eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract)
+  | eval action=CASE(match('result.message',\"Device Paired*\"),\"created\",match('result.message',
+  \"Device Unpaired*\"),\"deleted\") | stats count min(_time) as firstTime, max(_time)
+  as lastTime, values(reason) as reason by src,user,action,object | join type=outer
+  user [| search `wineventlog_security` EventID IN(4723,4724) | eval PW_Change_Time
+  = _time, user = upper(user) | fields user,src_user,EventID,PW_Change_Time] | eval
+  timeDiffRaw = round(lastTime - PW_Change_Time) | eval timeDiff = replace(tostring(abs(timeDiffRaw)
+  ,\"duration\"),\"(\\d*)\\+*(\\d+):(\\d+):(\\d+)\",\"\\2 hours \\3 minutes\") | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(PW_Change_Time)`
+  | where timeDiffRaw > 0 AND timeDiffRaw < 3600 | `pingid_new_mfa_method_after_credential_reset_filter`"
+how_to_implement: Target environment must ingest Windows Event Log and PingID(PingOne)
+  data sources. Specifically from logs from Active Directory Domain Controllers and
+  JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or
+  Push Subscription.
+known_false_positives: False positives may be generated by normal provisioning workflows
+  that generate a password reset followed by a device registration.
 references:
 - https://techcommunity.microsoft.com/t5/microsoft-entra-azure-ad-blog/defend-your-users-from-mfa-fatigue-attacks/ba-p/2365677
 - https://www.bleepingcomputer.com/news/security/mfa-fatigue-hackers-new-favorite-tactic-in-high-profile-breaches/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Identity
   confidence: 50
   impact: 100
-  message: An MFA configuration change was detected for [$user$] within [$timeDiff$] of a password reset. The device [$object$] was $action$.
+  message: An MFA configuration change was detected for [$user$] within [$timeDiff$]
+    of a password reset. The device [$object$] was $action$.
   mitre_attack_id:
   - T1621
   - T1556.006
@@ -50,23 +78,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - src_user
-  - src
-  - EventID
-  - resources{}.ipaddress
-  - actors{}.name
-  - result.message
-  - resources{}.devicemodel
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/windows_pw_reset.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/windows_pw_reset.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
     source: PINGID
     sourcetype: _json
diff --git a/detections/application/pingid_new_mfa_method_registered_for_user.yml b/detections/application/pingid_new_mfa_method_registered_for_user.yml
index bad11e6e5c..ba4e98d8eb 100644
--- a/detections/application/pingid_new_mfa_method_registered_for_user.yml
+++ b/detections/application/pingid_new_mfa_method_registered_for_user.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a PingID (PingOne) account. It leverages JSON logs from PingID, specifically looking for successful device pairing events. This activity is significant as adversaries who gain unauthorized access to a user account may register a new MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security measures, maintain long-term access, and potentially escalate their privileges within the compromised environment.
+description: The following analytic detects the registration of a new Multi-Factor
+  Authentication (MFA) method for a PingID (PingOne) account. It leverages JSON logs
+  from PingID, specifically looking for successful device pairing events. This activity
+  is significant as adversaries who gain unauthorized access to a user account may
+  register a new MFA method to maintain persistence. If confirmed malicious, this
+  could allow attackers to bypass existing security measures, maintain long-term access,
+  and potentially escalate their privileges within the compromised environment.
 data_source:
 - PingID
-search: '`pingid` "result.message"="Device Paired*" result.status="SUCCESS"   | rex field=result.message "Device (Unp)?(P)?aired (?<device_extract>.+)" | eval src = coalesce(''resources{}.ipaddress'',''resources{}.devicemodel''), user = upper(''actors{}.name''), reason = ''result.message'' | eval object=CASE(ISNOTNULL(''resources{}.devicemodel''),''resources{}.devicemodel'',true(),device_extract) | eval action=CASE(match(''result.message'',"Device Paired*"),"created",match(''result.message'', "Device Unpaired*"),"deleted") | stats count min(_time) as firstTime, max(_time) as lastTime by src,user,object,action,reason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `pingid_new_mfa_method_registered_for_user_filter`'
-how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription.
-known_false_positives: False positives may be generated by normal provisioning workflows for user device registration.
+search: "`pingid` \"result.message\"=\"Device Paired*\" result.status=\"SUCCESS\"\
+  \   | rex field=result.message \"Device (Unp)?(P)?aired (?<device_extract>.+)\"
+  | eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), user =
+  upper('actors{}.name'), reason = 'result.message' | eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract)
+  | eval action=CASE(match('result.message',\"Device Paired*\"),\"created\",match('result.message',
+  \"Device Unpaired*\"),\"deleted\") | stats count min(_time) as firstTime, max(_time)
+  as lastTime by src,user,object,action,reason | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `pingid_new_mfa_method_registered_for_user_filter`"
+how_to_implement: Target environment must ingest JSON logging from a PingID(PingOne)
+  enterprise environment, either via Webhook or Push Subscription.
+known_false_positives: False positives may be generated by normal provisioning workflows
+  for user device registration.
 references:
 - https://twitter.com/jhencinski/status/1618660062352007174
 - https://attack.mitre.org/techniques/T1098/005/
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as
+    lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message"
+    values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations"
+    values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object
+    | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +51,8 @@ tags:
   asset_type: Identity
   confidence: 50
   impact: 20
-  message: An MFA configuration change was detected for [$user$], the device [$object$] was $action$.
+  message: An MFA configuration change was detected for [$user$], the device [$object$]
+    was $action$.
   mitre_attack_id:
   - T1621
   - T1556.006
@@ -53,17 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - resources{}.ipaddress
-  - actors{}.name
-  - result.message
-  - resources{}.devicemodel
-  - result.status
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/pingid/pingid.log
     source: PINGID
     sourcetype: _json
diff --git a/detections/application/suspicious_email_attachment_extensions.yml b/detections/application/suspicious_email_attachment_extensions.yml
index 827b9b97a0..9aa760a719 100644
--- a/detections/application/suspicious_email_attachment_extensions.yml
+++ b/detections/application/suspicious_email_attachment_extensions.yml
@@ -5,14 +5,31 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects emails containing attachments with suspicious file extensions. It leverages the Email data model in Splunk, using the tstats command to identify emails where the attachment filename is not empty. This detection is significant for SOC analysts as it highlights potential phishing or malware delivery attempts, which are common vectors for data breaches and malware infections. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, system compromise, or data exfiltration. Immediate review and analysis of the identified emails and attachments are crucial to mitigate these risks.
+description: The following analytic detects emails containing attachments with suspicious
+  file extensions. It leverages the Email data model in Splunk, using the tstats command
+  to identify emails where the attachment filename is not empty. This detection is
+  significant for SOC analysts as it highlights potential phishing or malware delivery
+  attempts, which are common vectors for data breaches and malware infections. If
+  confirmed malicious, this activity could lead to unauthorized access to sensitive
+  information, system compromise, or data exfiltration. Immediate review and analysis
+  of the identified emails and attachments are crucial to mitigate these risks.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` | `suspicious_email_attachment_extensions_filter`'
-how_to_implement: 'You need to ingest data from emails. Specifically, the sender''s address and the file names of any attachments must be mapped to the Email data model.
-
-  **Splunk Phantom Playbook Integration**
-
-  If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user''s inbox.'''
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user,
+  All_Email.file_name All_Email.message_id | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments`
+  | `suspicious_email_attachment_extensions_filter`'
+how_to_implement: "You need to ingest data from emails. Specifically, the sender's
+  address and the file names of any attachments must be mapped to the Email data model.\n
+  **Splunk Phantom Playbook Integration**\nIf Splunk Phantom is also configured in
+  your environment, a Playbook called \"Suspicious Email Attachment Investigate and
+  Delete\" can be configured to run when any results are found by this detection search.
+  To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,
+  and add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response
+  Actions when configuring this detection search. The notable event will be sent to
+  Phantom and the playbook will gather further information about the file attachment
+  and its network behaviors. If Phantom finds malicious behavior and an analyst approves
+  of the results, the email will be deleted from the user's inbox.'"
 known_false_positives: None identified
 references: []
 tags:
@@ -37,9 +54,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Email.file_name
-  - All_Email.src_user
-  - All_Email.message_id
   security_domain: network
diff --git a/detections/application/suspicious_java_classes.yml b/detections/application/suspicious_java_classes.yml
index 5ae748f1e7..add6d281fb 100644
--- a/detections/application/suspicious_java_classes.yml
+++ b/detections/application/suspicious_java_classes.yml
@@ -5,10 +5,24 @@ date: '2024-10-17'
 author: Jose Hernandez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies suspicious Java classes often used for remote command execution exploits in Java frameworks like Apache Struts. It detects this activity by analyzing HTTP POST requests with specific content patterns using Splunk's `stream_http` data source. This behavior is significant because it may indicate an attempt to exploit vulnerabilities in web applications, potentially leading to unauthorized remote code execution. If confirmed malicious, this activity could allow attackers to execute arbitrary commands on the server, leading to data breaches, system compromise, and further network infiltration.
+description: The following analytic identifies suspicious Java classes often used
+  for remote command execution exploits in Java frameworks like Apache Struts. It
+  detects this activity by analyzing HTTP POST requests with specific content patterns
+  using Splunk's `stream_http` data source. This behavior is significant because it
+  may indicate an attempt to exploit vulnerabilities in web applications, potentially
+  leading to unauthorized remote code execution. If confirmed malicious, this activity
+  could allow attackers to execute arbitrary commands on the server, leading to data
+  breaches, system compromise, and further network infiltration.
 data_source: []
-search: '`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_java_classes_filter`'
-how_to_implement: In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro.
+search: '`stream_http` http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)"
+  | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time)
+  as lastTime, values(url) as uri, values(status) as status, values(http_user_agent)
+  as http_user_agent by src, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_java_classes_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from your web-traffic appliances that serve or sit in the path of your Struts application
+  servers. This can be accomplished by indexing data from a web proxy, or by using
+  network traffic-analysis tools, such as Splunk Stream or Bro.
 known_false_positives: There are no known false positives.
 references: []
 tags:
@@ -31,14 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_method
-  - http_content_length
-  - src_ip
-  - url
-  - status
-  - http_user_agent
-  - src
-  - dest
   security_domain: threat
diff --git a/detections/application/web_servers_executing_suspicious_processes.yml b/detections/application/web_servers_executing_suspicious_processes.yml
index 710c6419a7..46722b9eb8 100644
--- a/detections/application/web_servers_executing_suspicious_processes.yml
+++ b/detections/application/web_servers_executing_suspicious_processes.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the execution of suspicious processes on systems identified as web servers. It leverages the Splunk data model "Endpoint.Processes" to search for specific process names such as "whoami", "ping", "iptables", "wget", "service", and "curl". This activity is significant because these processes are often used by attackers for reconnaissance, persistence, or data exfiltration. If confirmed malicious, this could lead to data theft, deployment of additional malware, or even ransomware attacks. Immediate investigation is required to determine the legitimacy of the activity and mitigate potential threats.
+description: The following analytic detects the execution of suspicious processes
+  on systems identified as web servers. It leverages the Splunk data model "Endpoint.Processes"
+  to search for specific process names such as "whoami", "ping", "iptables", "wget",
+  "service", and "curl". This activity is significant because these processes are
+  often used by attackers for reconnaissance, persistence, or data exfiltration. If
+  confirmed malicious, this could lead to data theft, deployment of additional malware,
+  or even ransomware attacks. Immediate investigation is required to determine the
+  legitimacy of the activity and mitigate potential threats.
 data_source:
 - Sysmon EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server"
+  AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*"
+  OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*")
+  by Processes.process Processes.process_name, Processes.dest Processes.user| `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some of these processes may be used legitimately on web servers
+  during maintenance or other administrative tasks.
 references: []
 tags:
   analytic_story:
@@ -34,11 +55,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest_category
-  - Processes.process
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
diff --git a/detections/application/windows_ad_add_self_to_group.yml b/detections/application/windows_ad_add_self_to_group.yml
index ac684b8704..dd802293a5 100644
--- a/detections/application/windows_ad_add_self_to_group.yml
+++ b/detections/application/windows_ad_add_self_to_group.yml
@@ -7,8 +7,16 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 4728
-description: This analytic detects instances where a user adds themselves to an Active Directory (AD) group. This activity is a common indicator of privilege escalation, where a user attempts to gain unauthorized access to higher privileges or sensitive resources. By monitoring AD logs, this detection identifies such suspicious behavior, which could be part of a larger attack strategy aimed at compromising critical systems and data.
-search: '`wineventlog_security` EventCode IN (4728) | where user=src_user | stats min(_time) as _time dc(user) as usercount, values(user) as user values(user_category) as user_category values(src_user_category) as src_user_category values(dvc) as dvc by signature, Group_Name, src_user | `windows_ad_add_self_to_group_filter`'
+description: This analytic detects instances where a user adds themselves to an Active
+  Directory (AD) group. This activity is a common indicator of privilege escalation,
+  where a user attempts to gain unauthorized access to higher privileges or sensitive
+  resources. By monitoring AD logs, this detection identifies such suspicious behavior,
+  which could be part of a larger attack strategy aimed at compromising critical systems
+  and data.
+search: '`wineventlog_security` EventCode IN (4728) | where user=src_user | stats
+  min(_time) as _time dc(user) as usercount, values(user) as user values(user_category)
+  as user_category values(src_user_category) as src_user_category values(dvc) as dvc
+  by signature, Group_Name, src_user | `windows_ad_add_self_to_group_filter`'
 how_to_implement: This analytic requires eventCode 4728 to be ingested.
 known_false_positives: Unknown
 references: []
@@ -18,7 +26,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,16 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - user
-  - src_user
-  - signature
-  - Group_Name
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_dangerous_deny_acl_modification.yml b/detections/application/windows_ad_dangerous_deny_acl_modification.yml
index bed335d752..6079b03c14 100644
--- a/detections/application/windows_ad_dangerous_deny_acl_modification.yml
+++ b/detections/application/windows_ad_dangerous_deny_acl_modification.yml
@@ -7,9 +7,40 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: This detection identifies an Active Directory access-control list (ACL) modification event, which applies permissions that deny the ability to enumerate permissions of the object.
-search: '`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$"  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search aceType IN ("Access denied",D) AND aceAccessRights IN ("Full control","Read permissions",RC) | `windows_ad_dangerous_deny_acl_modification_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: This detection identifies an Active Directory access-control list (ACL)
+  modification event, which applies permissions that deny the ability to enumerate
+  permissions of the object.
+search: "`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType==\"\
+  %%14675\",AttributeValue,null))) as old_value values(eval(if(OperationType==\"%%14674\"\
+  ,AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass
+  ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000
+  \"\\((?P<old_values>.*?)\\)\"  | rex field=new_value max_match=10000 \"\\((?P<new_ace>.*?)\\\
+  )\"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace
+  \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$\"\
+  \  | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup
+  flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution
+  lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName
+  as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` |
+  lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name
+  as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,\"\
+  This object only\"), aceAccessRights=if(aceAccessRights=\"CCDCLCSWRPWPDTLOCRSDRCWDWO\"\
+  ,\"Full control\",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType
+  values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid)
+  as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user
+  OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | search
+  aceType IN (\"Access denied\",D) AND aceAccessRights IN (\"Full control\",\"Read
+  permissions\",RC) | `windows_ad_dangerous_deny_acl_modification_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: None.
 references:
 - https://happycamper84.medium.com/sneaky-persistence-via-hidden-objects-in-ad-1c91fc37bf54
@@ -21,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has added ACL rights to deny $user$ $aceControlAccessRights$ $aceAccessRights$ to $ObjectDN$
+  message: $src_user$ has added ACL rights to deny $user$ $aceControlAccessRights$
+    $aceAccessRights$ to $ObjectDN$
   mitre_attack_id:
   - T1484
   - T1222
@@ -48,21 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_object_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_object_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_dangerous_group_acl_modification.yml b/detections/application/windows_ad_dangerous_group_acl_modification.yml
index 98b8c7976b..b76580eccd 100644
--- a/detections/application/windows_ad_dangerous_group_acl_modification.yml
+++ b/detections/application/windows_ad_dangerous_group_acl_modification.yml
@@ -7,9 +7,48 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: 'This detection monitors the addition of the following ACLs to an Active Directory group object: "Full control", "All extended rights", "All validated writes",  "Create all child objects", "Delete all child objects", "Delete subtree", "Delete", "Modify permissions", "Modify owner", and "Write all properties".  Such modifications can indicate potential privilege escalation or malicious activity. Immediate investigation is recommended upon alert.'
-search: '`wineventlog_security` EventCode=5136 ObjectClass=group | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$"  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceInheritance=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=if((ControlAccessRights="Write member" OR aceObjectGuid="bf9679c0-0de6-11d0-a285-00aa003049e2") AND (aceAccessRights="All validated writes" OR AccessRights="SW"),"Add/remove self as member",coalesce(ControlAccessRights,aceObjectGuid)), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceInheritance) as aceInheritance values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search NOT aceType IN ("*denied*","D","OD","XD") AND aceAccessRights IN ("Full control","All extended rights","All validated writes","Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_group_acl_modification_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: 'This detection monitors the addition of the following ACLs to an Active
+  Directory group object: "Full control", "All extended rights", "All validated writes",  "Create
+  all child objects", "Delete all child objects", "Delete subtree", "Delete", "Modify
+  permissions", "Modify owner", and "Write all properties".  Such modifications can
+  indicate potential privilege escalation or malicious activity. Immediate investigation
+  is recommended upon alert.'
+search: "`wineventlog_security` EventCode=5136 ObjectClass=group | stats min(_time)
+  as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null))) as old_value
+  values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as new_value values(OperationType)
+  as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId\
+  \  | rex field=old_value max_match=10000 \"\\((?P<old_values>.*?)\\)\"  | rex field=new_value
+  max_match=10000 \"\\((?P<new_ace>.*?)\\)\"  | mvexpand new_ace  | where NOT new_ace
+  IN (old_values)  | rex field=new_ace \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$\"\
+  \  | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup
+  flag_string as aceFlags OUTPUT flag_value as ace_flag_value | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights ``` Optional SID
+  resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT
+  downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT
+  cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT
+  builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType),
+  aceInheritance=coalesce(ace_flag_value,\"This object only\"), aceAccessRights=if(aceAccessRights=\"\
+  CCDCLCSWRPWPDTLOCRSDRCWDWO\",\"Full control\",coalesce(access_rights_value,AccessRights)),
+  aceControlAccessRights=if((ControlAccessRights=\"Write member\" OR aceObjectGuid=\"\
+  bf9679c0-0de6-11d0-a285-00aa003049e2\") AND (aceAccessRights=\"All validated writes\"\
+  \ OR AccessRights=\"SW\"),\"Add/remove self as member\",coalesce(ControlAccessRights,aceObjectGuid)),
+  user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType
+  values(aceInheritance) as aceInheritance values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid)
+  as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user
+  OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | search
+  NOT aceType IN (\"*denied*\",\"D\",\"OD\",\"XD\") AND aceAccessRights IN (\"Full
+  control\",\"All extended rights\",\"All validated writes\",\"Create all child objects\"\
+  ,\"Delete all child objects\",\"Delete subtree\",\"Delete\",\"Modify permissions\"\
+  ,\"Modify owner\",\"Write all properties\",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_group_acl_modification_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings
@@ -22,7 +61,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +75,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$ $aceAccessRights$ to group $ObjectDN$
+  message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$
+    $aceAccessRights$ to group $ObjectDN$
   mitre_attack_id:
   - T1484
   - T1222
@@ -49,21 +94,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/group_dacl_mod_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/group_dacl_mod_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_dangerous_user_acl_modification.yml b/detections/application/windows_ad_dangerous_user_acl_modification.yml
index e6b58f0709..09c285beb6 100644
--- a/detections/application/windows_ad_dangerous_user_acl_modification.yml
+++ b/detections/application/windows_ad_dangerous_user_acl_modification.yml
@@ -7,9 +7,45 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: 'This detection monitors the addition of the following ACLs to an Active Directory user object: "Full control","All extended rights","All validated writes", "Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties".  Such modifications can indicate potential privilege escalation or malicious activity. Immediate investigation is recommended upon alert.'
-search: '`wineventlog_security` EventCode=5136 ObjectClass=user  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$"  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search NOT aceType IN (*denied*,D,OD,XD) AND aceAccessRights IN ("Full control","All extended rights","All validated writes","Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_user_acl_modification_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: 'This detection monitors the addition of the following ACLs to an Active
+  Directory user object: "Full control","All extended rights","All validated writes",
+  "Create all child objects","Delete all child objects","Delete subtree","Delete","Modify
+  permissions","Modify owner","Write all properties".  Such modifications can indicate
+  potential privilege escalation or malicious activity. Immediate investigation is
+  recommended upon alert.'
+search: "`wineventlog_security` EventCode=5136 ObjectClass=user  | stats min(_time)
+  as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null))) as old_value
+  values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as new_value values(OperationType)
+  as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId\
+  \  | rex field=old_value max_match=10000 \"\\((?P<old_values>.*?)\\)\"  | rex field=new_value
+  max_match=10000 \"\\((?P<new_ace>.*?)\\)\"  | mvexpand new_ace  | where NOT new_ace
+  IN (old_values)  | rex field=new_ace \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$\"\
+  \  | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup
+  flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution
+  lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName
+  as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` |
+  lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name
+  as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,\"\
+  This object only\"), aceAccessRights=if(aceAccessRights=\"CCDCLCSWRPWPDTLOCRSDRCWDWO\"\
+  ,\"Full control\",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType
+  values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid)
+  as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user
+  OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | search
+  NOT aceType IN (*denied*,D,OD,XD) AND aceAccessRights IN (\"Full control\",\"All
+  extended rights\",\"All validated writes\",\"Create all child objects\",\"Delete
+  all child objects\",\"Delete subtree\",\"Delete\",\"Modify permissions\",\"Modify
+  owner\",\"Write all properties\",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_dangerous_user_acl_modification_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings
@@ -22,7 +58,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +72,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$ $aceAccessRights$ to user $ObjectDN$
+  message: $src_user$ has added ACL rights to grant $user$ $aceControlAccessRights$
+    $aceAccessRights$ to user $ObjectDN$
   mitre_attack_id:
   - T1484
   - T1222
@@ -49,21 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/user_dacl_mod_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/user_dacl_mod_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_dcshadow_privileges_acl_addition.yml b/detections/application/windows_ad_dcshadow_privileges_acl_addition.yml
index f61462eb6d..5dee147552 100644
--- a/detections/application/windows_ad_dcshadow_privileges_acl_addition.yml
+++ b/detections/application/windows_ad_dcshadow_privileges_acl_addition.yml
@@ -7,9 +7,41 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: This detection identifies an Active Directory access-control list (ACL) modification event, which applies the minimum required extended rights to perform the DCShadow attack.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$" | search aceObjectGuid IN ("9923a32a-3607-11d2-b9be-0000f87a36b2","1131f6ab-9c07-11d1-f79f-00c04fc2dcd2","1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user user | search (aceControlAccessRights="Add/Remove Replica In Domain" AND aceControlAccessRights="Manage Replication Topology" AND aceControlAccessRights="Replication Synchronization") OR (aceControlAccessRights="9923a32a-3607-11d2-b9be-0000f87a36b2" AND aceControlAccessRights="1131f6ab-9c07-11d1-f79f-00c04fc2dcd2" AND aceControlAccessRights="1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") | `windows_ad_dcshadow_privileges_acl_addition_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: This detection identifies an Active Directory access-control list (ACL)
+  modification event, which applies the minimum required extended rights to perform
+  the DCShadow attack.
+search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time)
+  as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value
+  values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType)
+  as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  |
+  rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value
+  max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace | where NOT new_ace IN
+  (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$"
+  | search aceObjectGuid IN ("9923a32a-3607-11d2-b9be-0000f87a36b2","1131f6ab-9c07-11d1-f79f-00c04fc2dcd2","1131f6ac-9c07-11d1-f79f-00c04fc2dcd2")
+  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100
+  field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid
+  OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string
+  as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string
+  as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags
+  OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup
+  identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  |
+  lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup
+  builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval
+  aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This
+  object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full
+  control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType)
+  as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights)
+  as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace)
+  as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user
+  user | search (aceControlAccessRights="Add/Remove Replica In Domain" AND aceControlAccessRights="Manage
+  Replication Topology" AND aceControlAccessRights="Replication Synchronization")
+  OR (aceControlAccessRights="9923a32a-3607-11d2-b9be-0000f87a36b2" AND aceControlAccessRights="1131f6ab-9c07-11d1-f79f-00c04fc2dcd2"
+  AND aceControlAccessRights="1131f6ac-9c07-11d1-f79f-00c04fc2dcd2") | `windows_ad_dcshadow_privileges_acl_addition_filter`'
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://www.labofapenetrationtester.com/2018/04/dcshadow.html
@@ -22,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +68,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: ACL modification Event Initiated by $src_user$ applying $user$ the minimum required extended rights to perform a DCShadow attack.
+  message: ACL modification Event Initiated by $src_user$ applying $user$ the minimum
+    required extended rights to perform a DCShadow attack.
   mitre_attack_id:
   - T1484
   - T1207
@@ -49,19 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/DCShadowPermissions/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/DCShadowPermissions/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_domain_root_acl_deletion.yml b/detections/application/windows_ad_domain_root_acl_deletion.yml
index e4e6459a69..283b6d64dc 100644
--- a/detections/application/windows_ad_domain_root_acl_deletion.yml
+++ b/detections/application/windows_ad_domain_root_acl_deletion.yml
@@ -7,9 +7,38 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: ACL deletion performed on the domain root object, significant AD change with high impact. Following MS guidance all changes at this level should be reviewed. Drill into the logonID within EventCode 4624 for information on the source device during triage.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_values>.*?)\)"  | mvexpand old_values  | where NOT old_values IN (new_values)  | rex field=old_values "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$"  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(old_values) as old_values by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | `windows_ad_domain_root_acl_deletion_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: ACL deletion performed on the domain root object, significant AD change
+  with high impact. Following MS guidance all changes at this level should be reviewed.
+  Drill into the logonID within EventCode 4624 for information on the source device
+  during triage.
+search: "`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time)
+  as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null))) as old_value
+  values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as new_value values(OperationType)
+  as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId\
+  \  | rex field=old_value max_match=10000 \"\\((?P<old_values>.*?)\\)\"  | rex field=new_value
+  max_match=10000 \"\\((?P<new_values>.*?)\\)\"  | mvexpand old_values  | where NOT
+  old_values IN (new_values)  | rex field=old_values \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$\"\
+  \  | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string
+  as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups
+  | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName
+  as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` |
+  lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name
+  as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,\"\
+  This object only\"), aceAccessRights=if(aceAccessRights=\"CCDCLCSWRPWPDTLOCRSDRCWDWO\"\
+  ,\"Full control\",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType
+  values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(old_values) as old_values by _time
+  ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | `windows_ad_domain_root_acl_deletion_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings
@@ -22,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +65,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has removed $user$ $aceAccessRights$ ACL rights to domain root $ObjectDN$
+  message: $src_user$ has removed $user$ $aceAccessRights$ ACL rights to domain root
+    $ObjectDN$
   mitre_attack_id:
   - T1484
   - T1222
@@ -49,21 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_deletion_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_deletion_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_domain_root_acl_modification.yml b/detections/application/windows_ad_domain_root_acl_modification.yml
index 2edfa9b0a0..cd9b9049ee 100644
--- a/detections/application/windows_ad_domain_root_acl_modification.yml
+++ b/detections/application/windows_ad_domain_root_acl_modification.yml
@@ -7,9 +7,38 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: ACL modification performed on the domain root object, significant AD change with high impact. Following MS guidance all changes at this level should be reviewed. Drill into the logonID within EventCode 4624 for information on the source device during triage.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$" | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",''access_rights_value''), aceType=ace_type_value, aceFlags=coalesce(ace_flag_value,"This object only"), aceControlAccessRights=ControlAccessRights, user=coalesce(user, group, builtin_group, aceSid)  | stats values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | `windows_ad_domain_root_acl_modification_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: ACL modification performed on the domain root object, significant AD
+  change with high impact. Following MS guidance all changes at this level should
+  be reviewed. Drill into the logonID within EventCode 4624 for information on the
+  source device during triage.
+search: "`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time)
+  as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null))) as old_value
+  values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as new_value values(OperationType)
+  as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId\
+  \  | rex field=old_value max_match=10000 \"\\((?P<old_values>.*?)\\)\"  | rex field=new_value
+  max_match=10000 \"\\((?P<new_ace>.*?)\\)\"  | mvexpand new_ace | where NOT new_ace
+  IN (old_values)  | rex field=new_ace \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$\"\
+  \ | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string
+  as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups
+  | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName
+  as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` |
+  lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name
+  as builtin_group | eval aceAccessRights=if(aceAccessRights=\"CCDCLCSWRPWPDTLOCRSDRCWDWO\"\
+  ,\"Full control\",'access_rights_value'), aceType=ace_type_value, aceFlags=coalesce(ace_flag_value,\"\
+  This object only\"), aceControlAccessRights=ControlAccessRights, user=coalesce(user,
+  group, builtin_group, aceSid)  | stats values(aceType) as aceType values(aceFlags)
+  as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace by _time ObjectClass
+  ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | `windows_ad_domain_root_acl_modification_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings
@@ -22,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +65,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has granted $user$ $aceAccessRights$ ACL rights to domain root $ObjectDN$
+  message: $src_user$ has granted $user$ $aceAccessRights$ ACL rights to domain root
+    $ObjectDN$
   mitre_attack_id:
   - T1484
   - T1222
@@ -49,21 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_mod_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/domain_root_acl_mod_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_gpo_deleted.yml b/detections/application/windows_ad_gpo_deleted.yml
index f3ff910408..8dda703769 100644
--- a/detections/application/windows_ad_gpo_deleted.yml
+++ b/detections/application/windows_ad_gpo_deleted.yml
@@ -7,9 +7,24 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: This detection identifies when an Active Directory Group Policy is deleted using the Group Policy Management Console.
-search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=gpLink | eval  ObjectDN=upper(ObjectDN) | stats min(_time) as eventTime values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType values(src_user) as src_user by OpCorrelationID ObjectDN SubjectLogonId | rex field=old_value max_match=10000 "(?i)LDAP://(?P<old_dn>cn.*?);(?P<old_flag>\d)\]" | rex field=new_value max_match=10000 "(?i)LDAP://(?P<new_dn>cn.*?);(?P<new_flag>\d)\]" | mvexpand old_dn | where NOT old_dn IN (new_dn) | eval ObjectDN=upper(old_dn) | join ObjectDN type=outer [| search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update | eval ObjectDN=upper(distinguishedName) | stats latest(displayName) as displayName by ObjectDN ] | stats min(eventTime) as _time values(OpCorrelationID) as OpCorrelationID values(displayName) as policyName values(src_user) as src_user by ObjectDN SubjectLogonId | `windows_ad_gpo_deleted_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136, admon data is also used to display the display name of the GPO. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security and admon macros are configured with the correct indexes.
+description: This detection identifies when an Active Directory Group Policy is deleted
+  using the Group Policy Management Console.
+search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=gpLink | eval  ObjectDN=upper(ObjectDN)
+  | stats min(_time) as eventTime values(eval(if(OperationType=="%%14675",AttributeValue,null)))
+  as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value
+  values(OperationType) as OperationType values(src_user) as src_user by OpCorrelationID
+  ObjectDN SubjectLogonId | rex field=old_value max_match=10000 "(?i)LDAP://(?P<old_dn>cn.*?);(?P<old_flag>\d)\]"
+  | rex field=new_value max_match=10000 "(?i)LDAP://(?P<new_dn>cn.*?);(?P<new_flag>\d)\]"
+  | mvexpand old_dn | where NOT old_dn IN (new_dn) | eval ObjectDN=upper(old_dn) |
+  join ObjectDN type=outer [| search `admon` objectCategory="CN=Group-Policy-Container*"
+  admonEventType=Update | eval ObjectDN=upper(distinguishedName) | stats latest(displayName)
+  as displayName by ObjectDN ] | stats min(eventTime) as _time values(OpCorrelationID)
+  as OpCorrelationID values(displayName) as policyName values(src_user) as src_user
+  by ObjectDN SubjectLogonId | `windows_ad_gpo_deleted_filter`'
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136, admon data is also used to display the display name of the GPO. See
+  lantern article in references for further on how to onboard AD audit data. Ensure
+  the wineventlog_security and admon macros are configured with the correct indexes.
 known_false_positives: Unknown
 references:
 - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,24 +61,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-admon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_deleted/windows-admon.log
     source: ActiveDirectory
     sourcetype: ActiveDirectory
diff --git a/detections/application/windows_ad_gpo_disabled.yml b/detections/application/windows_ad_gpo_disabled.yml
index 4e8b0e74fb..846d0cc173 100644
--- a/detections/application/windows_ad_gpo_disabled.yml
+++ b/detections/application/windows_ad_gpo_disabled.yml
@@ -7,9 +7,22 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: This detection identifies when an Active Directory Group Policy is disabled using the Group Policy Management Console.
-search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=flags OperationType="%%14674" AttributeValue!=0 | eval AttributeValueExp=case(AttributeValue==0,"Enabled",AttributeValue==1,"User configuration settings disabled",AttributeValue==2,"Computer configuration settings disabled",AttributeValue==3,"Disabled"), ObjectDN=upper(ObjectDN) | join ObjectDN type=inner [| search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update | eval ObjectDN=upper(distinguishedName) | stats latest(displayName) as displayName by ObjectDN ] | stats min(_time) as _time values(AttributeValue) as AttributeValue values(AttributeValueExp) as AttributeValueExp values(OpCorrelationID) as OpCorrelationID values(displayName) as policyName values(src_user) as src_user by ObjectDN SubjectLogonId | `windows_ad_gpo_disabled_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136, admon data is also used to display the display name of the GPO. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security and admon macros are configured with the correct indexes.
+description: This detection identifies when an Active Directory Group Policy is disabled
+  using the Group Policy Management Console.
+search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=flags OperationType="%%14674"
+  AttributeValue!=0 | eval AttributeValueExp=case(AttributeValue==0,"Enabled",AttributeValue==1,"User
+  configuration settings disabled",AttributeValue==2,"Computer configuration settings
+  disabled",AttributeValue==3,"Disabled"), ObjectDN=upper(ObjectDN) | join ObjectDN
+  type=inner [| search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update
+  | eval ObjectDN=upper(distinguishedName) | stats latest(displayName) as displayName
+  by ObjectDN ] | stats min(_time) as _time values(AttributeValue) as AttributeValue
+  values(AttributeValueExp) as AttributeValueExp values(OpCorrelationID) as OpCorrelationID
+  values(displayName) as policyName values(src_user) as src_user by ObjectDN SubjectLogonId
+  | `windows_ad_gpo_disabled_filter`'
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136, admon data is also used to display the display name of the GPO. See
+  lantern article in references for further on how to onboard AD audit data. Ensure
+  the wineventlog_security and admon macros are configured with the correct indexes.
 known_false_positives: Unknown
 references:
 - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,24 +59,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-admon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_disabled/windows-admon.log
     source: ActiveDirectory
     sourcetype: ActiveDirectory
diff --git a/detections/application/windows_ad_gpo_new_cse_addition.yml b/detections/application/windows_ad_gpo_new_cse_addition.yml
index ee14e47902..63898818a5 100644
--- a/detections/application/windows_ad_gpo_new_cse_addition.yml
+++ b/detections/application/windows_ad_gpo_new_cse_addition.yml
@@ -7,10 +7,31 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: This detection identifies when a a new client side extension is added to an Active Directory Group Policy using the Group Policy Management Console.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=gPCMachineExtensionNames | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId | rex field=old_value max_match=10000 "(?P<old_values>\{.*?\})" | rex field=new_value max_match=10000 "(?P<new_values>\{.*?\})" | rex field=ObjectDN max_match=10000 "CN=(?P<policy_guid>\{.*?\})" | mvexpand new_values | where NOT new_values IN (old_values,"{00000000-0000-0000-0000-000000000000}",policy_guid) AND match(new_values, "^\{[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\}") | lookup msad_guid_lookup guid as new_values OUTPUTNEW displayName as policyType | eval newPolicy=if(policyType like "%",policyType,new_values) | join ObjectDN  [| search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update | stats latest(displayName) as displayName by distinguishedName | eval ObjectDN=upper(distinguishedName)] | stats values(OpCorrelationID) as OpCorrelationID values(src_user) as src_user values(SubjectLogonId) as SubjectLogonId values(newPolicy) as newPolicy values(displayName) as policyName by ObjectDN | `windows_ad_gpo_new_cse_addition_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136, admon data is also used to display the display name of the GPO. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security and admon macros are configured with the correct indexes.
-known_false_positives: General usage of group policy will trigger this detection, also please not GPOs modified using tools such as SharpGPOAbuse will not generate the AD audit events which enable this detection.
+description: This detection identifies when a a new client side extension is added
+  to an Active Directory Group Policy using the Group Policy Management Console.
+search: '`wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=gPCMachineExtensionNames
+  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null)))
+  as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value
+  values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user
+  SubjectLogonId | rex field=old_value max_match=10000 "(?P<old_values>\{.*?\})" |
+  rex field=new_value max_match=10000 "(?P<new_values>\{.*?\})" | rex field=ObjectDN
+  max_match=10000 "CN=(?P<policy_guid>\{.*?\})" | mvexpand new_values | where NOT
+  new_values IN (old_values,"{00000000-0000-0000-0000-000000000000}",policy_guid)
+  AND match(new_values, "^\{[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\-[A-Z|\d]+\}")
+  | lookup msad_guid_lookup guid as new_values OUTPUTNEW displayName as policyType
+  | eval newPolicy=if(policyType like "%",policyType,new_values) | join ObjectDN  [|
+  search `admon` objectCategory="CN=Group-Policy-Container*" admonEventType=Update
+  | stats latest(displayName) as displayName by distinguishedName | eval ObjectDN=upper(distinguishedName)]
+  | stats values(OpCorrelationID) as OpCorrelationID values(src_user) as src_user
+  values(SubjectLogonId) as SubjectLogonId values(newPolicy) as newPolicy values(displayName)
+  as policyName by ObjectDN | `windows_ad_gpo_new_cse_addition_filter`'
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136, admon data is also used to display the display name of the GPO. See
+  lantern article in references for further on how to onboard AD audit data. Ensure
+  the wineventlog_security and admon macros are configured with the correct indexes.
+known_false_positives: General usage of group policy will trigger this detection,
+  also please not GPOs modified using tools such as SharpGPOAbuse will not generate
+  the AD audit events which enable this detection.
 references:
 - https://wald0.com/?p=179
 - https://learn.microsoft.com/en-gb/archive/blogs/mempson/group-policy-client-side-extension-list
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has added new GPO Client Side Extensions $newPolicy$ to the policy $policyName$
+  message: $src_user$ has added new GPO Client Side Extensions $newPolicy$ to the
+    policy $policyName$
   mitre_attack_id:
   - T1484
   - T1484.001
@@ -46,24 +73,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-admon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_new_cse/windows-admon.log
     source: ActiveDirectory
     sourcetype: ActiveDirectory
diff --git a/detections/application/windows_ad_hidden_ou_creation.yml b/detections/application/windows_ad_hidden_ou_creation.yml
index fdbe51cf0d..97189fe045 100644
--- a/detections/application/windows_ad_hidden_ou_creation.yml
+++ b/detections/application/windows_ad_hidden_ou_creation.yml
@@ -7,9 +7,40 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: This analytic is looking for when an ACL is applied to an OU which denies listing the objects residing in the OU. This activity combined with modifying the owner of the OU will hide AD objects even from domain administrators.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=organizationalUnit | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$"  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search aceType IN ("Access denied",D) AND aceAccessRights IN ("List contents","List objects",LC,LO) | `windows_ad_hidden_ou_creation_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: This analytic is looking for when an ACL is applied to an OU which denies
+  listing the objects residing in the OU. This activity combined with modifying the
+  owner of the OU will hide AD objects even from domain administrators.
+search: "`wineventlog_security` EventCode=5136 ObjectClass=organizationalUnit | stats
+  min(_time) as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null)))
+  as old_value values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as
+  new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID
+  src_user SubjectLogonId  | rex field=old_value max_match=10000 \"\\((?P<old_values>.*?)\\\
+  )\"  | rex field=new_value max_match=10000 \"\\((?P<new_ace>.*?)\\)\"  | mvexpand
+  new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$\"\
+  \  | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights  | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value as aceType | lookup ace_flag_lookup
+  flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution
+  lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName
+  as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` |
+  lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name
+  as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,\"\
+  This object only\"), aceAccessRights=if(aceAccessRights=\"CCDCLCSWRPWPDTLOCRSDRCWDWO\"\
+  ,\"Full control\",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats values(aceType) as aceType
+  values(aceFlags) as aceFlags values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid)
+  as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user
+  OpCorrelationID | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | search
+  aceType IN (\"Access denied\",D) AND aceAccessRights IN (\"List contents\",\"List
+  objects\",LC,LO) | `windows_ad_hidden_ou_creation_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: None.
 references:
 - https://happycamper84.medium.com/sneaky-persistence-via-hidden-objects-in-ad-1c91fc37bf54
@@ -20,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,21 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_ou_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/hidden_ou_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_object_owner_updated.yml b/detections/application/windows_ad_object_owner_updated.yml
index c206f1606a..7e86099da4 100644
--- a/detections/application/windows_ad_object_owner_updated.yml
+++ b/detections/application/windows_ad_object_owner_updated.yml
@@ -7,9 +7,30 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: AD Object Owner Updated. The owner provides Full control level privileges over the target AD Object. This event has significant impact alone and is also a precursor activity for hiding an AD object.
-search: '`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId DSName | rex field=old_value "O:(?P<old_owner>.*?)G:" | rex field=new_value "O:(?P<new_owner>.*?)G:" | where old_owner!=new_owner ``` optional SID resolution lookups | lookup identity_lookup_expanded objectSid as new_owner OUTPUT downLevelDomainName as new_owner_user | lookup admon_groups_def objectSid as new_owner OUTPUT cn as new_owner_group | lookup identity_lookup_expanded objectSid as old_owner OUTPUT downLevelDomainName as old_owner_user | lookup admon_groups_def objectSid as old_owner OUTPUT cn as old_owner_group ``` | lookup builtin_groups_lookup builtin_group_string  as new_owner_group OUTPUT builtin_group_name as new_owner_group_builtin_group | lookup builtin_groups_lookup builtin_group_string  as old_owner OUTPUT builtin_group_name as old_owner_group_builtin_group | eval user=coalesce(new_owner_user, new_owner_group, new_owner_group_builtin_group, new_owner), previousOwner=coalesce(old_owner_user, old_owner_group, old_owner_group_builtin_group, old_owner) | stats values(previousOwner) as previousOwner values(user) as user values(SubjectLogonId) as SubjectLogonId by _time ObjectClass ObjectDN src_user OpCorrelationID DSName | `windows_ad_object_owner_updated_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+description: AD Object Owner Updated. The owner provides Full control level privileges
+  over the target AD Object. This event has significant impact alone and is also a
+  precursor activity for hiding an AD object.
+search: '`wineventlog_security` EventCode=5136 | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null)))
+  as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value
+  values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user
+  SubjectLogonId DSName | rex field=old_value "O:(?P<old_owner>.*?)G:" | rex field=new_value
+  "O:(?P<new_owner>.*?)G:" | where old_owner!=new_owner ``` optional SID resolution
+  lookups | lookup identity_lookup_expanded objectSid as new_owner OUTPUT downLevelDomainName
+  as new_owner_user | lookup admon_groups_def objectSid as new_owner OUTPUT cn as
+  new_owner_group | lookup identity_lookup_expanded objectSid as old_owner OUTPUT
+  downLevelDomainName as old_owner_user | lookup admon_groups_def objectSid as old_owner
+  OUTPUT cn as old_owner_group ``` | lookup builtin_groups_lookup builtin_group_string  as
+  new_owner_group OUTPUT builtin_group_name as new_owner_group_builtin_group | lookup
+  builtin_groups_lookup builtin_group_string  as old_owner OUTPUT builtin_group_name
+  as old_owner_group_builtin_group | eval user=coalesce(new_owner_user, new_owner_group,
+  new_owner_group_builtin_group, new_owner), previousOwner=coalesce(old_owner_user,
+  old_owner_group, old_owner_group_builtin_group, old_owner) | stats values(previousOwner)
+  as previousOwner values(user) as user values(SubjectLogonId) as SubjectLogonId by
+  _time ObjectClass ObjectDN src_user OpCorrelationID DSName | `windows_ad_object_owner_updated_filter`'
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://learn.microsoft.com/en-us/windows/win32/secauthz/ace-strings
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,21 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/owner_updated_windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/owner_updated_windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_privileged_group_modification.yml b/detections/application/windows_ad_privileged_group_modification.yml
index 838be6e3d5..1608636b2b 100644
--- a/detections/application/windows_ad_privileged_group_modification.yml
+++ b/detections/application/windows_ad_privileged_group_modification.yml
@@ -8,8 +8,15 @@ type: TTP
 data_source:
 - Windows Event Log Security 4728
 description: Detect users added to privileged AD Groups.
-search: '`wineventlog_security` EventCode IN (4728) | stats min(_time) as _time dc(user) as usercount, values(user) as user values(user_category) as user_category values(src_user_category) as src_user_category values(dvc) as dvc by signature, Group_Name,src_user | lookup admon_groups_def  cn as Group_Name OUTPUT category | where category="privileged" | `windows_ad_privileged_group_modification_filter`'
-how_to_implement: This analytic requires eventCode 4728 to be ingested along with the admon_groups_def lookup being configured to include a list of AD groups along with a category to identify privileged groups. See splunkbase app listed in the references for further details.
+search: '`wineventlog_security` EventCode IN (4728) | stats min(_time) as _time dc(user)
+  as usercount, values(user) as user values(user_category) as user_category values(src_user_category)
+  as src_user_category values(dvc) as dvc by signature, Group_Name,src_user | lookup
+  admon_groups_def  cn as Group_Name OUTPUT category | where category="privileged"
+  | `windows_ad_privileged_group_modification_filter`'
+how_to_implement: This analytic requires eventCode 4728 to be ingested along with
+  the admon_groups_def lookup being configured to include a list of AD groups along
+  with a category to identify privileged groups. See splunkbase app listed in the
+  references for further details.
 known_false_positives: None
 references:
 - https://splunkbase.splunk.com/app/6853
@@ -32,18 +39,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - user
-  - src_user
-  - signature
-  - Group_Name
-  - dest
   security_domain: identity
-  manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested.
+  manual_test: This search uses a lookup provided by Enterprise Security and needs
+    to be manually tested.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_self_dacl_assignment.yml b/detections/application/windows_ad_self_dacl_assignment.yml
index fb9a674634..7239856e02 100644
--- a/detections/application/windows_ad_self_dacl_assignment.yml
+++ b/detections/application/windows_ad_self_dacl_assignment.yml
@@ -8,8 +8,39 @@ type: TTP
 data_source:
 - Windows Security 5136
 description: Detect when a user creates a new DACL in AD for their own AD object.
-search: '`wineventlog_security` EventCode=5136  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$"  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value as aceType  | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value  | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights ``` Optional SID resolution lookups  | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group  ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT builtin_group_name as builtin_group  | eval aceType=coalesce(ace_type_value,aceType), aceInheritance=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=if((ControlAccessRights="Write member" OR aceObjectGuid="bf9679c0-0de6-11d0-a285-00aa003049e2") AND (aceAccessRights="All validated writes" OR AccessRights="SW"),"Add/remove self as member",coalesce(ControlAccessRights,aceObjectGuid)), user=coalesce(user, group, builtin_group, aceSid)  | stats values(aceType) as aceType values(aceInheritance) as aceInheritance values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid) as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user OpCorrelationID  | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | rex field=user "\\\(?P<nt_user>.*?)$" | where lower(src_user)=lower(nt_user) | `windows_ad_self_dacl_assignment_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
+search: "`wineventlog_security` EventCode=5136  | stats min(_time) as _time values(eval(if(OperationType==\"\
+  %%14675\",AttributeValue,null))) as old_value values(eval(if(OperationType==\"%%14674\"\
+  ,AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass
+  ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000
+  \"\\((?P<old_values>.*?)\\)\"  | rex field=new_value max_match=10000 \"\\((?P<new_ace>.*?)\\\
+  )\"  | mvexpand new_ace  | where NOT new_ace IN (old_values)  | rex field=new_ace
+  \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$\"\
+  \  | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value as aceType  | lookup ace_flag_lookup
+  flag_string as aceFlags OUTPUT flag_value as ace_flag_value  | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights ``` Optional SID
+  resolution lookups  | lookup identity_lookup_expanded objectSid as aceSid OUTPUT
+  downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT
+  cn as group  ``` | lookup builtin_groups_lookup builtin_group_string as aceSid OUTPUT
+  builtin_group_name as builtin_group  | eval aceType=coalesce(ace_type_value,aceType),
+  aceInheritance=coalesce(ace_flag_value,\"This object only\"), aceAccessRights=if(aceAccessRights=\"\
+  CCDCLCSWRPWPDTLOCRSDRCWDWO\",\"Full control\",coalesce(access_rights_value,AccessRights)),
+  aceControlAccessRights=if((ControlAccessRights=\"Write member\" OR aceObjectGuid=\"\
+  bf9679c0-0de6-11d0-a285-00aa003049e2\") AND (aceAccessRights=\"All validated writes\"\
+  \ OR AccessRights=\"SW\"),\"Add/remove self as member\",coalesce(ControlAccessRights,aceObjectGuid)),
+  user=coalesce(user, group, builtin_group, aceSid)  | stats values(aceType) as aceType
+  values(aceInheritance) as aceInheritance values(aceControlAccessRights) as aceControlAccessRights
+  values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(aceInheritedTypeGuid)
+  as aceInheritedTypeGuid by _time ObjectClass ObjectDN src_user SubjectLogonId user
+  OpCorrelationID  | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1
+  AND aceControlAccessRights=\"\",\"All rights\",'aceControlAccessRights') | rex field=user
+  \"\\\\\\(?P<nt_user>.*?)$\" | where lower(src_user)=lower(nt_user) | `windows_ad_self_dacl_assignment_filter`"
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes
+  and include lookups for SID resolution if evt_resolve_ad_obj is set to 0.
 known_false_positives: Unknown
 references:
 - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory
@@ -19,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: $src_user$ has created a DACL on $ObjectDN$ to grant themselves $aceControlAccessRights$ across $aceAccessRights$
+  message: $src_user$ has created a DACL on $ObjectDN$ to grant themselves $aceControlAccessRights$
+    across $aceAccessRights$
   mitre_attack_id:
   - T1484
   - T1098
@@ -41,21 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_suspicious_attribute_modification.yml b/detections/application/windows_ad_suspicious_attribute_modification.yml
index f8bc11e235..3b2aa34534 100644
--- a/detections/application/windows_ad_suspicious_attribute_modification.yml
+++ b/detections/application/windows_ad_suspicious_attribute_modification.yml
@@ -7,10 +7,28 @@ status: production
 type: TTP
 data_source:
 - Windows Security 5136
-description: 'This detection monitors changes to the following Active Directory attributes: "msDS-AllowedToDelegateTo", "msDS-AllowedToActOnBehalfOfOtherIdentity", "msDS-KeyCredentialLink", "scriptPath", and "msTSInitialProgram".  Modifications to these attributes can indicate potential malicious activity or privilege escalation attempts. Immediate investigation is recommended upon alert.'
-search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName IN ("msDS-AllowedToDelegateTo","msDS-AllowedToActOnBehalfOfOtherIdentity","scriptPath","msTSInitialProgram") OperationType=%%14674  ```Changes to the attribute "msDS-KeyCredentialLink" are also worth moniroting, however tuning will need to be applied``` | table _time ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId DSName AttributeValue AttributeLDAPDisplayName  | rename SubjectLogonId as TargetLogonId, src_user as initiator, _time as eventTime  | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"]  | stats min(eventTime) as _time values(initiator) as src_user, values(DSName) as targetDomain, values(ObjectDN) as ObjectDN, values(ObjectClass) as ObjectClass, values(src_category) as src_category, values(src_ip) as src_ip values(LogonType) as LogonType values(AttributeValue) as AttributeValue values(AttributeLDAPDisplayName) as AttributeLDAPDisplayName by TargetLogonId  | rex field=ObjectDN "^CN=(?P<cn>.*?),[A-Z]{2}\="  | eval dest=if(ObjectClass="computer",cn,null), user=if(ObjectClass="user",cn,null) | fields - cn | `windows_ad_suspicious_attribute_modification_filter`'
-how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically event 5136. See lantern article in references for further on how to onboard AD audit data. Ensure the wineventlog_security macro is configured with the correct indexes.
-known_false_positives: If key credentials are regularly assigned to users, these events will need to be tuned out.
+description: 'This detection monitors changes to the following Active Directory attributes:
+  "msDS-AllowedToDelegateTo", "msDS-AllowedToActOnBehalfOfOtherIdentity", "msDS-KeyCredentialLink",
+  "scriptPath", and "msTSInitialProgram".  Modifications to these attributes can indicate
+  potential malicious activity or privilege escalation attempts. Immediate investigation
+  is recommended upon alert.'
+search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName IN ("msDS-AllowedToDelegateTo","msDS-AllowedToActOnBehalfOfOtherIdentity","scriptPath","msTSInitialProgram")
+  OperationType=%%14674  ```Changes to the attribute "msDS-KeyCredentialLink" are
+  also worth moniroting, however tuning will need to be applied``` | table _time ObjectClass
+  ObjectDN OpCorrelationID src_user SubjectLogonId DSName AttributeValue AttributeLDAPDisplayName  |
+  rename SubjectLogonId as TargetLogonId, src_user as initiator, _time as eventTime  |
+  appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"]  |
+  stats min(eventTime) as _time values(initiator) as src_user, values(DSName) as targetDomain,
+  values(ObjectDN) as ObjectDN, values(ObjectClass) as ObjectClass, values(src_category)
+  as src_category, values(src_ip) as src_ip values(LogonType) as LogonType values(AttributeValue)
+  as AttributeValue values(AttributeLDAPDisplayName) as AttributeLDAPDisplayName by
+  TargetLogonId  | rex field=ObjectDN "^CN=(?P<cn>.*?),[A-Z]{2}\="  | eval dest=if(ObjectClass="computer",cn,null),
+  user=if(ObjectClass="user",cn,null) | fields - cn | `windows_ad_suspicious_attribute_modification_filter`'
+how_to_implement: Ensure you are ingesting Active Directory audit logs - specifically
+  event 5136. See lantern article in references for further on how to onboard AD audit
+  data. Ensure the wineventlog_security macro is configured with the correct indexes.
+known_false_positives: If key credentials are regularly assigned to users, these events
+  will need to be tuned out.
 references:
 - https://trustedsec.com/blog/a-hitchhackers-guide-to-dacl-based-detections-part-1-a
 - https://lantern.splunk.com/Security/Product_Tips/Enterprise_Security/Enabling_an_audit_trail_from_Active_Directory
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: $src_user$ has added $AttributeLDAPDisplayName$ ACL rights to $ObjectClass$ $ObjectDN$
+  message: $src_user$ has added $AttributeLDAPDisplayName$ ACL rights to $ObjectClass$
+    $ObjectDN$
   mitre_attack_id:
   - T1550
   - T1222
@@ -47,21 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/suspicious_acl_modification-windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/dacl_abuse/suspicious_acl_modification-windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_ad_suspicious_gpo_modification.yml b/detections/application/windows_ad_suspicious_gpo_modification.yml
index 91cf07598d..9c6747ac7a 100644
--- a/detections/application/windows_ad_suspicious_gpo_modification.yml
+++ b/detections/application/windows_ad_suspicious_gpo_modification.yml
@@ -8,10 +8,45 @@ type: TTP
 data_source:
 - Windows Security 5136
 - Windows Security 5145
-description: This analytic looks for a the creation of potentially harmful GPO which could lead to persistence or code execution on remote hosts. Note, this analyic is looking for the absence of the corresponding 5136 events which is evidence of the GPOs being manually edited (using a tool like PowerView) or potentially missing logs.
-search: "`wineventlog_security` EventCode=5145 ShareName=\"\\\\\\\\*\\\\SYSVOL\" RelativeTargetName IN (*\\\\ScheduledTasks.xml, *\\\\Groups.xml, *\\\\Registry.xml, *\\\\Services.xml, *\\\\Scripts\\\\*) NOT RelativeTargetName=*\\\\Scripts\\\\scripts.ini AccessMask=0x2  | rex field=AccessList max_match=0 \"(?P<AccessList>%%\\d+)\"  | table _time AccessMask src_ip src_user RelativeTargetName Logon_ID dvc  | rex field=RelativeTargetName \"Policies\\\\\\(?P<gpo_guid>{.*?})\\\\\\(?P<scope>\\w+?)\\\\\\(\\w+)\\\\\\(?P<folder>\\w+)\\\\\\(?P<file>\\w+\\.\\w+)$\" | eval src=if(match(src_ip, \"(?i)^fe80:\"),dvc,src_ip), folder=case(RelativeTargetName like \"%\\\\Scripts\\\\%\",\"Scripts\",folder=\"Groups\",\"Local users and groups\",1=1,folder) | appendpipe \n  [| map search=\"search `wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=gPCMachineExtensionNames $gpo_guid$\" \n  | stats min(_time) as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null))) as old_value values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId \n  | rex field=old_value max_match=10000 \"(?P<old_values>\\{.*?\\})\" \n  | rex field=new_value max_match=10000 \"(?P<new_values>\\{.*?\\})\" \n  | rex field=ObjectDN max_match=10000 \"CN=(?P<policy_guid>\\{.*?\\})\" \n  | mvexpand new_values \n  | where NOT new_values IN (old_values,\"{00000000-0000-0000-0000-000000000000}\",policy_guid) AND match(new_values, \"^\\{[A-Z|\\d]+\\-[A-Z|\\d]+\\-[A-Z|\\d]+\\-[A-Z|\\d]+\\-[A-Z|\\d]+\\}\") \n  | lookup msad_guid_lookup guid as new_values OUTPUTNEW displayName as policyType \n  | eval newPolicy=if(policyType like \"%\",policyType,new_values) \n  | stats values(OpCorrelationID) as OpCorrelationID values(newPolicy) as newPolicy by ObjectDN \n  | rex field=ObjectDN max_match=10000 \"CN=(?P<gpo_guid>\\{.*?\\})\" \n  | fields - ObjectDN] \n| stats values(AccessMask) as AccessMask values(src) as src values(src_user) as src_user values(RelativeTargetName) as RelativeTargetName values(Logon_ID) as Logon_ID values(newPolicy) as newPolicy values(OpCorrelationID) as OpCorrelationID values(folder) as folder values(file) as file by gpo_guid  | mvexpand folder  | where NOT folder IN (newPolicy) | `windows_ad_suspicious_gpo_modification_filter`"
-how_to_implement: Ingest EventCodes 5145 and 5136 from domain controllers. Additional SACLs required to capture EventCode 5136, see references for further information on how to configure this. The Group Policy - Audit Detailed File Share will need to be enabled on the DCs to generate event code 5145, this event is very noisy on DCs, consider tuning out sysvol events which do not match access mask 0x2.
-known_false_positives: When a GPO is manually edited and 5136 events are not logging to Splunk.
+description: This analytic looks for a the creation of potentially harmful GPO which
+  could lead to persistence or code execution on remote hosts. Note, this analyic
+  is looking for the absence of the corresponding 5136 events which is evidence of
+  the GPOs being manually edited (using a tool like PowerView) or potentially missing
+  logs.
+search: "`wineventlog_security` EventCode=5145 ShareName=\"\\\\\\\\*\\\\SYSVOL\" RelativeTargetName
+  IN (*\\\\ScheduledTasks.xml, *\\\\Groups.xml, *\\\\Registry.xml, *\\\\Services.xml,
+  *\\\\Scripts\\\\*) NOT RelativeTargetName=*\\\\Scripts\\\\scripts.ini AccessMask=0x2\
+  \  | rex field=AccessList max_match=0 \"(?P<AccessList>%%\\d+)\"  | table _time
+  AccessMask src_ip src_user RelativeTargetName Logon_ID dvc  | rex field=RelativeTargetName
+  \"Policies\\\\\\(?P<gpo_guid>{.*?})\\\\\\(?P<scope>\\w+?)\\\\\\(\\w+)\\\\\\(?P<folder>\\\
+  w+)\\\\\\(?P<file>\\w+\\.\\w+)$\" | eval src=if(match(src_ip, \"(?i)^fe80:\"),dvc,src_ip),
+  folder=case(RelativeTargetName like \"%\\\\Scripts\\\\%\",\"Scripts\",folder=\"\
+  Groups\",\"Local users and groups\",1=1,folder) | appendpipe \n  [| map search=\"\
+  search `wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=gPCMachineExtensionNames
+  $gpo_guid$\" \n  | stats min(_time) as _time values(eval(if(OperationType==\"%%14675\"\
+  ,AttributeValue,null))) as old_value values(eval(if(OperationType==\"%%14674\",AttributeValue,null)))
+  as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID
+  src_user SubjectLogonId \n  | rex field=old_value max_match=10000 \"(?P<old_values>\\\
+  {.*?\\})\" \n  | rex field=new_value max_match=10000 \"(?P<new_values>\\{.*?\\})\"\
+  \ \n  | rex field=ObjectDN max_match=10000 \"CN=(?P<policy_guid>\\{.*?\\})\" \n\
+  \  | mvexpand new_values \n  | where NOT new_values IN (old_values,\"{00000000-0000-0000-0000-000000000000}\"\
+  ,policy_guid) AND match(new_values, \"^\\{[A-Z|\\d]+\\-[A-Z|\\d]+\\-[A-Z|\\d]+\\\
+  -[A-Z|\\d]+\\-[A-Z|\\d]+\\}\") \n  | lookup msad_guid_lookup guid as new_values
+  OUTPUTNEW displayName as policyType \n  | eval newPolicy=if(policyType like \"%\"\
+  ,policyType,new_values) \n  | stats values(OpCorrelationID) as OpCorrelationID values(newPolicy)
+  as newPolicy by ObjectDN \n  | rex field=ObjectDN max_match=10000 \"CN=(?P<gpo_guid>\\\
+  {.*?\\})\" \n  | fields - ObjectDN] \n| stats values(AccessMask) as AccessMask values(src)
+  as src values(src_user) as src_user values(RelativeTargetName) as RelativeTargetName
+  values(Logon_ID) as Logon_ID values(newPolicy) as newPolicy values(OpCorrelationID)
+  as OpCorrelationID values(folder) as folder values(file) as file by gpo_guid  |
+  mvexpand folder  | where NOT folder IN (newPolicy) | `windows_ad_suspicious_gpo_modification_filter`"
+how_to_implement: Ingest EventCodes 5145 and 5136 from domain controllers. Additional
+  SACLs required to capture EventCode 5136, see references for further information
+  on how to configure this. The Group Policy - Audit Detailed File Share will need
+  to be enabled on the DCs to generate event code 5145, this event is very noisy on
+  DCs, consider tuning out sysvol events which do not match access mask 0x2.
+known_false_positives: When a GPO is manually edited and 5136 events are not logging
+  to Splunk.
 references:
 - https://github.com/PowerShellMafia/PowerSploit/blob/26a0757612e5654b4f792b012ab8f10f95d391c9/Recon/PowerView.ps1#L5907-L6122
 - https://github.com/X-C3LL/GPOwned
@@ -25,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: $src_user$ has added new GPO Client Side Extensions $folder$ to the policy $gpo_guid$
+  message: $src_user$ has added new GPO Client Side Extensions $folder$ to the policy
+    $gpo_guid$
   mitre_attack_id:
   - T1484
   - T1484.001
@@ -44,21 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - ObjectDN
-  - OpCorrelationID
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
-  - SubjectLogonId
-  - DSName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/gpo_new_cse/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/gpo_new_cse/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_increase_in_group_or_object_modification_activity.yml b/detections/application/windows_increase_in_group_or_object_modification_activity.yml
index 562fc1eeea..4297a30686 100644
--- a/detections/application/windows_increase_in_group_or_object_modification_activity.yml
+++ b/detections/application/windows_increase_in_group_or_object_modification_activity.yml
@@ -7,8 +7,18 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 4663
-description: This analytic detects an increase in modifications to AD groups or objects. Frequent changes to AD groups or objects can indicate potential security risks, such as unauthorized access attempts, impairing defences or establishing persistence. By monitoring AD logs for unusual modification patterns, this detection helps identify suspicious behavior that could compromise the integrity and security of the AD environment.
-search: '`wineventlog_security` EventCode IN (4670,4727,4731,4734,4735,4764) | bucket span=5m _time  | stats values(object) as object, dc(object) as objectCount, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category by _time, src_user, signature, status | eventstats avg(objectCount) as comp_avg, stdev(objectCount) as comp_std by src_user, signature | eval upperBound=(comp_avg+comp_std) | eval isOutlier=if(objectCount > 10 and (objectCount >= upperBound), 1, 0) | search isOutlier=1  | `windows_increase_in_group_or_object_modification_activity_filter`'
+description: This analytic detects an increase in modifications to AD groups or objects.
+  Frequent changes to AD groups or objects can indicate potential security risks,
+  such as unauthorized access attempts, impairing defences or establishing persistence.
+  By monitoring AD logs for unusual modification patterns, this detection helps identify
+  suspicious behavior that could compromise the integrity and security of the AD environment.
+search: '`wineventlog_security` EventCode IN (4670,4727,4731,4734,4735,4764) | bucket
+  span=5m _time  | stats values(object) as object, dc(object) as objectCount, values(src_user_category)
+  as src_user_category, values(dest) as dest, values(dest_category) as dest_category
+  by _time, src_user, signature, status | eventstats avg(objectCount) as comp_avg,
+  stdev(objectCount) as comp_std by src_user, signature | eval upperBound=(comp_avg+comp_std)
+  | eval isOutlier=if(objectCount > 10 and (objectCount >= upperBound), 1, 0) | search
+  isOutlier=1  | `windows_increase_in_group_or_object_modification_activity_filter`'
 how_to_implement: Run this detection looking over a 7 day timeframe for best results.
 known_false_positives: Unknown
 references: []
@@ -18,7 +28,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,14 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - src_user
-  - signature
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/application/windows_increase_in_user_modification_activity.yml b/detections/application/windows_increase_in_user_modification_activity.yml
index 932d3491cd..01f2ea14cf 100644
--- a/detections/application/windows_increase_in_user_modification_activity.yml
+++ b/detections/application/windows_increase_in_user_modification_activity.yml
@@ -7,8 +7,22 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 4720
-description: This analytic detects an increase in modifications to AD user objects. A large volume of changes to user objects can indicate potential security risks, such as unauthorized access attempts, impairing defences or establishing persistence. By monitoring AD logs for unusual modification patterns, this detection helps identify suspicious behavior that could compromise the integrity and security of the AD environment.
-search: '`wineventlog_security` EventCode IN (4720,4722,4723,4724,4725,4726,4728,4732,4733,4738,4743,4780) | bucket span=5m _time  | stats values(TargetDomainName) as TargetDomainName, values(user) as user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category by _time, src_user, signature, status | eventstats avg(userCount) as comp_avg , stdev(userCount) as comp_std by src_user, signature | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(userCount > 10 and userCount >= upperBound, 1, 0)  | search isOutlier=1 | stats values(TargetDomainName) as TargetDomainName, values(user) as user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category values(signature) as signature  by _time, src_user, status | `windows_increase_in_user_modification_activity_filter`'
+description: This analytic detects an increase in modifications to AD user objects.
+  A large volume of changes to user objects can indicate potential security risks,
+  such as unauthorized access attempts, impairing defences or establishing persistence.
+  By monitoring AD logs for unusual modification patterns, this detection helps identify
+  suspicious behavior that could compromise the integrity and security of the AD environment.
+search: '`wineventlog_security` EventCode IN (4720,4722,4723,4724,4725,4726,4728,4732,4733,4738,4743,4780)
+  | bucket span=5m _time  | stats values(TargetDomainName) as TargetDomainName, values(user)
+  as user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category)
+  as src_user_category, values(dest) as dest, values(dest_category) as dest_category
+  by _time, src_user, signature, status | eventstats avg(userCount) as comp_avg ,
+  stdev(userCount) as comp_std by src_user, signature | eval upperBound=(comp_avg+comp_std*3)
+  | eval isOutlier=if(userCount > 10 and userCount >= upperBound, 1, 0)  | search
+  isOutlier=1 | stats values(TargetDomainName) as TargetDomainName, values(user) as
+  user, dc(user) as userCount, values(user_category) as user_category, values(src_user_category)
+  as src_user_category, values(dest) as dest, values(dest_category) as dest_category
+  values(signature) as signature  by _time, src_user, status | `windows_increase_in_user_modification_activity_filter`'
 how_to_implement: Run this detection looking over a 7 day timeframe for best results.
 known_false_positives: Genuine activity
 references: []
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,14 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - src_user
-  - signature
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/account_manipulation/xml-windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml b/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml
index 3a445e8c0a..5103e32853 100644
--- a/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml
+++ b/detections/cloud/abnormally_high_number_of_cloud_infrastructure_api_calls.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects a spike in the number of API calls made to your cloud infrastructure by a user. It leverages cloud infrastructure logs and compares the current API call volume against a baseline probability density function to identify anomalies. This activity is significant because an unusual increase in API calls can indicate potential misuse or compromise of cloud resources. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of cloud services, posing a significant risk to the organization's cloud environment.
+description: The following analytic detects a spike in the number of API calls made
+  to your cloud infrastructure by a user. It leverages cloud infrastructure logs and
+  compares the current API call volume against a baseline probability density function
+  to identify anomalies. This activity is significant because an unusual increase
+  in API calls can indicate potential misuse or compromise of cloud resources. If
+  confirmed malicious, this could lead to unauthorized access, data exfiltration,
+  or disruption of cloud services, posing a significant risk to the organization's
+  cloud environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold = api_calls - expected_upper_threshold | table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function.
+search: '| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change
+  where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time
+  span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time,
+  "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w")
+  | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay
+  isWeekend [ summary cloud_excessive_api_calls_v1] | where cardinality >=16 | apply
+  cloud_excessive_api_calls_v1 threshold=0.005 | rename "IsOutlier(api_calls)" as
+  isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges,
+  -1), ":"), 0) | where api_calls > expected_upper_threshold | eval distance_from_threshold
+  = api_calls - expected_upper_threshold | table _time, user, command, api_calls,
+  expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to
+  create the probability density function.
 known_false_positives: None.
 references: []
 tags:
@@ -19,7 +38,8 @@ tags:
   asset_type: AWS Instance
   confidence: 50
   impact: 30
-  message: user $user$ has made $api_calls$ api calls, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$.
+  message: user $user$ has made $api_calls$ api calls, violating the dynamic threshold
+    of $expected_upper_threshold$ with the following command $command$.
   mitre_attack_id:
   - T1078.004
   - T1078
@@ -32,15 +52,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.command
-  - All_Changes.user
-  - All_Changes.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml b/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml
index eec3281c3d..b9b9abce6d 100644
--- a/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml
+++ b/detections/cloud/abnormally_high_number_of_cloud_instances_destroyed.yml
@@ -5,12 +5,35 @@ date: '2024-10-22'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies an abnormally high number of cloud instances being destroyed within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to detect outliers. This activity is significant for a SOC because a sudden spike in destroyed instances could indicate malicious activity, such as an insider threat or a compromised account attempting to disrupt services. If confirmed malicious, this could lead to significant operational disruptions, data loss, and potential financial impact due to the destruction of critical cloud resources.
+description: The following analytic identifies an abnormally high number of cloud
+  instances being destroyed within a 4-hour period. It leverages cloud infrastructure
+  logs and applies a probability density model to detect outliers. This activity is
+  significant for a SOC because a sudden spike in destroyed instances could indicate
+  malicious activity, such as an insider threat or a compromised account attempting
+  to disrupt services. If confirmed malicious, this could lead to significant operational
+  disruptions, data loss, and potential financial impact due to the destruction of
+  critical cloud resources.
 data_source:
 - AWS CloudTrail
-search: '| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005 | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_destroyed - expected_upper_threshold | table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function.
-known_false_positives: Many service accounts configured within a cloud infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
+search: '| tstats count as instances_destroyed values(All_Changes.object_id) as object_id
+  from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success
+  AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
+  | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval
+  DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
+  <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1]
+  | where cardinality >=16 | apply cloud_excessive_instances_destroyed_v1 threshold=0.005
+  | rename "IsOutlier(instances_destroyed)" as isOutlier | where isOutlier=1 | eval
+  expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) |
+  eval distance_from_threshold = instances_destroyed - expected_upper_threshold |
+  table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold,
+  object_id | `abnormally_high_number_of_cloud_instances_destroyed_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability
+  density function.
+known_false_positives: Many service accounts configured within a cloud infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
 references: []
 tags:
   analytic_story:
@@ -35,11 +58,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.object_category
-  - All_Changes.user
   security_domain: threat
diff --git a/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml b/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml
index 917123c3f8..da30b4355e 100644
--- a/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml
+++ b/detections/cloud/abnormally_high_number_of_cloud_instances_launched.yml
@@ -5,12 +5,34 @@ date: '2024-10-22'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects an abnormally high number of cloud instances launched within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to identify outliers based on historical data. This activity is significant for a SOC because a sudden spike in instance creation could indicate unauthorized access or misuse of cloud resources. If confirmed malicious, this behavior could lead to resource exhaustion, increased costs, or provide attackers with additional compute resources to further their objectives.
+description: The following analytic detects an abnormally high number of cloud instances
+  launched within a 4-hour period. It leverages cloud infrastructure logs and applies
+  a probability density model to identify outliers based on historical data. This
+  activity is significant for a SOC because a sudden spike in instance creation could
+  indicate unauthorized access or misuse of cloud resources. If confirmed malicious,
+  this behavior could lead to resource exhaustion, increased costs, or provide attackers
+  with additional compute resources to further their objectives.
 data_source:
 - AWS CloudTrail
-search: '| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005 | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | eval distance_from_threshold = instances_launched - expected_upper_threshold | table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id | `abnormally_high_number_of_cloud_instances_launched_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function.
-known_false_positives: Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.
+search: '| tstats count as instances_launched values(All_Changes.object_id) as object_id
+  from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success
+  AND All_Changes.object_category=instance by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")`
+  | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval
+  DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek
+  <= 5, 0, 1) | join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1]
+  | where cardinality >=16 | apply cloud_excessive_instances_created_v1 threshold=0.005
+  | rename "IsOutlier(instances_launched)" as isOutlier | where isOutlier=1 | eval
+  expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) |
+  eval distance_from_threshold = instances_launched - expected_upper_threshold | table
+  _time, user, instances_launched, expected_upper_threshold, distance_from_threshold,
+  object_id | `abnormally_high_number_of_cloud_instances_launched_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Instances Launched` to create the probability
+  density function.
+known_false_positives: Many service accounts configured within an AWS infrastructure
+  are known to exhibit this behavior. Please adjust the threshold values and filter
+  out service accounts from the output. Always verify if this search alerted on a
+  human user.
 references: []
 tags:
   analytic_story:
@@ -32,11 +54,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.object_category
-  - All_Changes.user
   security_domain: threat
diff --git a/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml b/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml
index 94750ce5c4..c1d0ccd3f8 100644
--- a/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml
+++ b/detections/cloud/abnormally_high_number_of_cloud_security_group_api_calls.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects a spike in the number of API calls made to cloud security groups by a user. It leverages data from the Change data model, focusing on successful firewall-related changes. This activity is significant because an abnormal increase in security group API calls can indicate potential malicious activity, such as unauthorized access or configuration changes. If confirmed malicious, this could allow an attacker to manipulate security group settings, potentially exposing sensitive resources or disrupting network security controls.
+description: The following analytic detects a spike in the number of API calls made
+  to cloud security groups by a user. It leverages data from the Change data model,
+  focusing on successful firewall-related changes. This activity is significant because
+  an abnormal increase in security group API calls can indicate potential malicious
+  activity, such as unauthorized access or configuration changes. If confirmed malicious,
+  this could allow an attacker to manipulate security group settings, potentially
+  exposing sensitive resources or disrupting network security controls.
 data_source:
 - AWS CloudTrail
-search: '| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time, "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls - expected_upper_threshold | table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model.
+search: '| tstats count as security_group_api_calls values(All_Changes.command) as
+  command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success
+  by All_Changes.user _time span=1h | `drop_dm_object_name("All_Changes")` | eval
+  HourOfDay=strftime(_time, "%H") | eval HourOfDay=floor(HourOfDay/4)*4 | eval DayOfWeek=strftime(_time,
+  "%w") | eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) | join user HourOfDay
+  isWeekend [ summary cloud_excessive_security_group_api_calls_v1] | where cardinality
+  >=16 | apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 | rename
+  "IsOutlier(security_group_api_calls)" as isOutlier | where isOutlier=1 | eval expected_upper_threshold
+  = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) | where security_group_api_calls
+  > expected_upper_threshold | eval distance_from_threshold = security_group_api_calls
+  - expected_upper_threshold | table _time, user, command, security_group_api_calls,
+  expected_upper_threshold, distance_from_threshold | `abnormally_high_number_of_cloud_security_group_api_calls_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs. You also must
+  run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to
+  create the probability density function model.
 known_false_positives: None.
 references: []
 tags:
@@ -18,7 +37,9 @@ tags:
   asset_type: AWS Instance
   confidence: 50
   impact: 30
-  message: user $user$ has made $api_calls$ api calls related to security groups, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$.
+  message: user $user$ has made $api_calls$ api calls related to security groups,
+    violating the dynamic threshold of $expected_upper_threshold$ with the following
+    command $command$.
   mitre_attack_id:
   - T1078.004
   - T1078
@@ -31,16 +52,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.command
-  - All_Changes.object_category
-  - All_Changes.status
-  - All_Changes.user
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml b/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml
index 2fc8636413..57eda0813c 100644
--- a/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml
+++ b/detections/cloud/amazon_eks_kubernetes_cluster_scan_detection.yml
@@ -5,11 +5,25 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects unauthenticated requests to an Amazon EKS Kubernetes cluster, specifically identifying actions by the "system:anonymous" user. It leverages AWS CloudWatch Logs data, focusing on user agents and authentication details. This activity is significant as it may indicate unauthorized scanning or probing of the Kubernetes cluster, which could be a precursor to an attack. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of services within the Kubernetes environment.
+description: The following analytic detects unauthenticated requests to an Amazon
+  EKS Kubernetes cluster, specifically identifying actions by the "system:anonymous"
+  user. It leverages AWS CloudWatch Logs data, focusing on user agents and authentication
+  details. This activity is significant as it may indicate unauthorized scanning or
+  probing of the Kubernetes cluster, which could be a precursor to an attack. If confirmed
+  malicious, this could lead to unauthorized access, data exfiltration, or disruption
+  of services within the Kubernetes environment.
 data_source: []
-search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`amazon_eks_kubernetes_cluster_scan_detection_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs.
-known_false_positives: Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context.
+search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS
+  Security Scanner" | rename sourceIPs{} as src_ip | stats count min(_time) as firstTime
+  max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name
+  values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI)
+  by src_ip user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  |`amazon_eks_kubernetes_cluster_scan_detection_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch
+  EKS Logs inputs.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  UA and source IPs will provide context.
 references: []
 tags:
   analytic_story:
@@ -29,16 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user.username
-  - userAgent
-  - sourceIPs{}
-  - responseStatus.reason
-  - source
-  - responseStatus.code
-  - verb
-  - requestURI
-  - src_ip
-  - user.groups{}
   security_domain: threat
diff --git a/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml b/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml
index 652cc09432..d6d0634398 100644
--- a/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml
+++ b/detections/cloud/amazon_eks_kubernetes_pod_scan_detection.yml
@@ -5,11 +5,27 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects unauthenticated requests made against the Kubernetes Pods API, indicating potential unauthorized access attempts. It leverages the `aws_cloudwatchlogs_eks` data source, filtering for events where `user.username` is "system:anonymous", `verb` is "list", and `objectRef.resource` is "pods", with `requestURI` set to "/api/v1/pods". This activity is significant as it may signal attempts to access sensitive resources or execute unauthorized commands within the Kubernetes environment. If confirmed malicious, such access could lead to data compromise, unauthorized command execution, or lateral movement within the cluster.
+description: The following analytic detects unauthenticated requests made against
+  the Kubernetes Pods API, indicating potential unauthorized access attempts. It leverages
+  the `aws_cloudwatchlogs_eks` data source, filtering for events where `user.username`
+  is "system:anonymous", `verb` is "list", and `objectRef.resource` is "pods", with
+  `requestURI` set to "/api/v1/pods". This activity is significant as it may signal
+  attempts to access sensitive resources or execute unauthorized commands within the
+  Kubernetes environment. If confirmed malicious, such access could lead to data compromise,
+  unauthorized command execution, or lateral movement within the cluster.
 data_source: []
-search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives.
-known_false_positives: Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context.
+search: '`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods
+  requestURI="/api/v1/pods" | rename source as cluster_name sourceIPs{} as src_ip
+  | stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason)
+  values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by
+  src_ip cluster_name user.username user.groups{} | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `amazon_eks_kubernetes_pod_scan_detection_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch
+  EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection`
+  macro to filter out the false positives.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  UA and source IPs and direct request to API provide context.
 references: []
 tags:
   analytic_story:
@@ -29,17 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user.username
-  - verb
-  - objectRef.resource
-  - requestURI
-  - source
-  - sourceIPs{}
-  - responseStatus.reason
-  - responseStatus.code
-  - userAgent
-  - src_ip
-  - user.groups{}
   security_domain: threat
diff --git a/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml b/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml
index b4d230408c..921eb3d9b7 100644
--- a/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml
+++ b/detections/cloud/asl_aws_concurrent_sessions_from_different_ips.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies an AWS IAM account with concurrent sessions originating from more than one unique IP address within a 5-minute span. This detection leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` API call, to identify multiple IP addresses associated with the same user session. This behavior is significant as it may indicate a session hijacking attack, where an adversary uses stolen session cookies to access AWS resources from a different location. If confirmed malicious, this activity could allow unauthorized access to sensitive corporate resources, leading to potential data breaches or further exploitation.
+description: The following analytic identifies an AWS IAM account with concurrent
+  sessions originating from more than one unique IP address within a 5-minute span.
+  This detection leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates`
+  API call, to identify multiple IP addresses associated with the same user session.
+  This behavior is significant as it may indicate a session hijacking attack, where
+  an adversary uses stolen session cookies to access AWS resources from a different
+  location. If confirmed malicious, this activity could allow unauthorized access
+  to sensitive corporate resources, leading to potential data breaches or further
+  exploitation.
 data_source: []
-search: '`amazon_security_lake` api.operation=DescribeEventAggregates src_endpoint.domain!="AWS Internal" | bin span=5m _time | stats values(src_endpoint.ip) as src_ip dc(src_endpoint.ip) as distinct_ip_count by _time identity.user.credential_uid identity.user.name | where distinct_ip_count > 1 | rename identity.user.name as user | `asl_aws_concurrent_sessions_from_different_ips_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
+search: '`amazon_security_lake` api.operation=DescribeEventAggregates src_endpoint.domain!="AWS
+  Internal" | bin span=5m _time | stats values(src_endpoint.ip) as src_ip dc(src_endpoint.ip)
+  as distinct_ip_count by _time identity.user.credential_uid identity.user.name |
+  where distinct_ip_count > 1 | rename identity.user.name as user | `asl_aws_concurrent_sessions_from_different_ips_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: A user with concurrent sessions from different Ips may also
+  represent the legitimate use of more than one device. Filter as needed and/or customize
+  the threshold to fit your environment.
 references:
 - https://attack.mitre.org/techniques/T1185/
 - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +53,8 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 70
-  message: User $user$ has concurrent sessions from more than one unique IP address in the span of 5 minutes.
+  message: User $user$ has concurrent sessions from more than one unique IP address
+    in the span of 5 minutes.
   mitre_attack_id:
   - T1185
   observable:
@@ -46,20 +70,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - src_endpoint.domain
-  - cloud.region
   security_domain: threat
   manual_test: Can't be tested automatically because of time span.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml b/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml
index c55e2b38e0..2b284626f3 100644
--- a/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml
+++ b/detections/cloud/asl_aws_defense_evasion_delete_cloudtrail.yml
@@ -5,11 +5,28 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects AWS `DeleteTrail` events within CloudTrail logs. It leverages Amazon Security Lake logs parsed in the Open Cybersecurity Schema Framework (OCSF) format to identify when a CloudTrail is deleted. This activity is significant because adversaries may delete CloudTrail logs to evade detection and operate with stealth. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and investigate other potential compromises within the AWS environment.
+description: The following analytic detects AWS `DeleteTrail` events within CloudTrail
+  logs. It leverages Amazon Security Lake logs parsed in the Open Cybersecurity Schema
+  Framework (OCSF) format to identify when a CloudTrail is deleted. This activity
+  is significant because adversaries may delete CloudTrail logs to evade detection
+  and operate with stealth. If confirmed malicious, this action could allow attackers
+  to cover their tracks, making it difficult to trace their activities and investigate
+  other potential compromises within the AWS environment.
 data_source: []
-search: '`amazon_security_lake` api.operation=DeleteTrail | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `asl_aws_defense_evasion_delete_cloudtrail_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudTrail logging. Please investigate this activity.
+search: '`amazon_security_lake` api.operation=DeleteTrail | fillnull | stats count
+  min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid
+  as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent
+  as user_agent, actor.user.account_uid as aws_account_id   | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `asl_aws_defense_evasion_delete_cloudtrail_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has stopped cloudTrail logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -18,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml b/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml
index f5bf4fa52e..c76f36e0a3 100644
--- a/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml
+++ b/detections/cloud/asl_aws_defense_evasion_delete_cloudwatch_log_group.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of CloudWatch log groups in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This method leverages Amazon Security Lake logs parsed in the OCSF format. The activity is significant because attackers may delete log groups to evade detection and disrupt logging capabilities, hindering incident response efforts. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and potentially leading to undetected data breaches or further malicious actions within the compromised AWS environment.
+description: The following analytic detects the deletion of CloudWatch log groups
+  in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This method
+  leverages Amazon Security Lake logs parsed in the OCSF format. The activity is significant
+  because attackers may delete log groups to evade detection and disrupt logging capabilities,
+  hindering incident response efforts. If confirmed malicious, this action could allow
+  attackers to cover their tracks, making it difficult to trace their activities and
+  potentially leading to undetected data breaches or further malicious actions within
+  the compromised AWS environment.
 data_source: []
-search: '`amazon_security_lake` api.operation=DeleteLogGroup | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `asl_aws_defense_evasion_delete_cloudwatch_log_group_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has deleted CloudWatch logging. Please investigate this activity.
+search: '`amazon_security_lake` api.operation=DeleteLogGroup | fillnull | stats count
+  min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid
+  as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent
+  as user_agent, actor.user.account_uid as aws_account_id   | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `asl_aws_defense_evasion_delete_cloudwatch_log_group_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has deleted CloudWatch logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -18,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/asl_ocsf_cloudtrail.json
     source: aws_asl
     sourcetype: aws:cloudtrail:lake
diff --git a/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml b/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml
index cb20aa104a..c44acf1e50 100644
--- a/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml
+++ b/detections/cloud/asl_aws_defense_evasion_impair_security_services.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: Patrick Bareiss, Bhavin Patel, Gowthamaraj Rajendran, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the deletion of critical AWS Security Services configurations, such as CloudWatch alarms, GuardDuty detectors, and Web Application Firewall rules. It leverages Amazon Security Lake logs to identify specific API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant because adversaries often use these actions to disable security monitoring and evade detection. If confirmed malicious, this could allow attackers to operate undetected, leading to potential data breaches, unauthorized access, and prolonged persistence within the AWS environment.
+description: The following analytic detects the deletion of critical AWS Security
+  Services configurations, such as CloudWatch alarms, GuardDuty detectors, and Web
+  Application Firewall rules. It leverages Amazon Security Lake logs to identify specific
+  API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant
+  because adversaries often use these actions to disable security monitoring and evade
+  detection. If confirmed malicious, this could allow attackers to operate undetected,
+  leading to potential data breaches, unauthorized access, and prolonged persistence
+  within the AWS environment.
 data_source: []
-search: '`amazon_security_lake` api.operation IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms") | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_defense_evasion_impair_security_services_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names.
+search: '`amazon_security_lake` api.operation IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms")
+  | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation
+  actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_defense_evasion_impair_security_services_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: While this search has no known false positives, it is possible
+  that it is a legitimate admin activity. Please consider filtering out these noisy
+  events using userAgent, user_arn field names.
 references:
 - https://docs.aws.amazon.com/cli/latest/reference/guardduty/index.html
 - https://docs.aws.amazon.com/cli/latest/reference/waf/index.html
@@ -20,7 +39,8 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 70
-  message: User $user$ has made potentially risky api calls $api.operation$ that could impair AWS security services for account id $aws_account_id$
+  message: User $user$ has made potentially risky api calls $api.operation$ that could
+    impair AWS security services for account id $aws_account_id$
   mitre_attack_id:
   - T1562.008
   - T1562
@@ -37,18 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml b/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml
index df4e6f27fd..99cd40861d 100644
--- a/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml
+++ b/detections/cloud/asl_aws_defense_evasion_stop_logging_cloudtrail.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects `StopLogging` events within AWS CloudTrail logs, a critical action that adversaries may use to evade detection. By halting the logging of their malicious activities, attackers aim to operate undetected within a compromised AWS environment. This detection is achieved by monitoring for specific CloudTrail log entries that indicate the cessation of logging activities. Identifying such behavior is crucial for a Security Operations Center (SOC), as it signals an attempt to undermine the integrity of logging mechanisms, potentially allowing malicious activities to proceed without observation. The impact of this evasion tactic is significant, as it can severely hamper incident response and forensic investigations by obscuring the attacker's actions.
+description: The following analytic detects `StopLogging` events within AWS CloudTrail
+  logs, a critical action that adversaries may use to evade detection. By halting
+  the logging of their malicious activities, attackers aim to operate undetected within
+  a compromised AWS environment. This detection is achieved by monitoring for specific
+  CloudTrail log entries that indicate the cessation of logging activities. Identifying
+  such behavior is crucial for a Security Operations Center (SOC), as it signals an
+  attempt to undermine the integrity of logging mechanisms, potentially allowing malicious
+  activities to proceed without observation. The impact of this evasion tactic is
+  significant, as it can severely hamper incident response and forensic investigations
+  by obscuring the attacker's actions.
 data_source: []
-search: '`amazon_security_lake` api.operation=StopLogging | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `asl_aws_defense_evasion_stop_logging_cloudtrail_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudtrail logging. Please investigate this activity.
+search: '`amazon_security_lake` api.operation=StopLogging | fillnull | stats count
+  min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid
+  as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent
+  as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `asl_aws_defense_evasion_stop_logging_cloudtrail_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has stopped cloudtrail logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -18,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +52,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 100
-  message: User $user$ has stopped Cloudtrail logging for account id $aws_account_id$ from IP $src_ip$
+  message: User $user$ has stopped Cloudtrail logging for account id $aws_account_id$
+    from IP $src_ip$
   mitre_attack_id:
   - T1562.008
   - T1562
@@ -44,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail_2.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/asl_ocsf_cloudtrail_2.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml b/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml
index 9f9ffab9a2..c421a1c549 100644
--- a/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml
+++ b/detections/cloud/asl_aws_defense_evasion_update_cloudtrail.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects `UpdateTrail` events within AWS CloudTrail logs, aiming to identify attempts by attackers to evade detection by altering logging configurations. By updating CloudTrail settings with incorrect parameters, such as changing multi-regional logging to a single region, attackers can impair the logging of their activities across other regions. This behavior is crucial for Security Operations Centers (SOCs) to identify, as it indicates an adversary's intent to operate undetected within a compromised AWS environment. The impact of such evasion tactics is significant, potentially allowing malicious activities to proceed without being logged, thereby hindering incident response and forensic investigations.
+description: The following analytic detects `UpdateTrail` events within AWS CloudTrail
+  logs, aiming to identify attempts by attackers to evade detection by altering logging
+  configurations. By updating CloudTrail settings with incorrect parameters, such
+  as changing multi-regional logging to a single region, attackers can impair the
+  logging of their activities across other regions. This behavior is crucial for Security
+  Operations Centers (SOCs) to identify, as it indicates an adversary's intent to
+  operate undetected within a compromised AWS environment. The impact of such evasion
+  tactics is significant, potentially allowing malicious activities to proceed without
+  being logged, thereby hindering incident response and forensic investigations.
 data_source: []
-search: '`amazon_security_lake` api.operation=UpdateTrail | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `asl_aws_defense_evasion_update_cloudtrail_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has updated cloudtrail logging. Please investigate this activity.
+search: '`amazon_security_lake` api.operation=UpdateTrail | fillnull | stats count
+  min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid
+  as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent
+  as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `asl_aws_defense_evasion_update_cloudtrail_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has updated cloudtrail logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -18,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +51,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 100
-  message: User $user$ has updated a cloudtrail logging for account id $aws_account_id$ from IP $src_ip$
+  message: User $user$ has updated a cloudtrail logging for account id $aws_account_id$
+    from IP $src_ip$
   mitre_attack_id:
   - T1562
   - T1562.008
@@ -44,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml b/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml
index 6dd232e0d6..27bd0f6aec 100644
--- a/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml
+++ b/detections/cloud/asl_aws_ecr_container_upload_outside_business_hours.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the upload of new containers to AWS Elastic Container Service (ECR) outside of standard business hours through AWS CloudTrail events. It identifies this behavior by monitoring for `PutImage` events occurring before 8 AM or after 8 PM, as well as any uploads on weekends. This activity is significant for a SOC to investigate as it may indicate unauthorized access or malicious deployments, potentially leading to compromised services or data breaches. Identifying and addressing such uploads promptly can mitigate the risk of security incidents and their associated impacts.
+description: The following analytic detects the upload of new containers to AWS Elastic
+  Container Service (ECR) outside of standard business hours through AWS CloudTrail
+  events. It identifies this behavior by monitoring for `PutImage` events occurring
+  before 8 AM or after 8 PM, as well as any uploads on weekends. This activity is
+  significant for a SOC to investigate as it may indicate unauthorized access or malicious
+  deployments, potentially leading to compromised services or data breaches. Identifying
+  and addressing such uploads promptly can mitigate the risk of security incidents
+  and their associated impacts.
 data_source: []
-search: '`amazon_security_lake` api.operation=PutImage | eval hour=strftime(time/pow(10,3), "%H"), weekday=strftime(time/pow(10,3), "%A") | where hour >= 20 OR hour < 8 OR weekday=Saturday OR weekday=Sunday | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent cloud.region | rename actor.user.name as user, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_ecr_container_upload_outside_business_hours_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: When your development is spreaded in different time zones, applying this rule can be difficult.
+search: '`amazon_security_lake` api.operation=PutImage | eval hour=strftime(time/pow(10,3),
+  "%H"), weekday=strftime(time/pow(10,3), "%A") | where hour >= 20 OR hour < 8 OR
+  weekday=Saturday OR weekday=Sunday | fillnull | stats count min(_time) as firstTime
+  max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid
+  http_request.user_agent cloud.region | rename actor.user.name as user, cloud.region
+  as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_ecr_container_upload_outside_business_hours_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: When your development is spreaded in different time zones,
+  applying this rule can be difficult.
 references:
 - https://attack.mitre.org/techniques/T1204/003/
 drilldown_searches:
@@ -18,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml b/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml
index a1359b3999..7c8a06cffd 100644
--- a/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml
+++ b/detections/cloud/asl_aws_ecr_container_upload_unknown_user.yml
@@ -5,10 +5,29 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized container uploads to AWS Elastic Container Service (ECR) by monitoring AWS CloudTrail events. It identifies instances where a new container is uploaded by a user not previously recognized as authorized. This detection is crucial for a SOC as it can indicate a potential compromise or misuse of AWS ECR, which could lead to unauthorized access to sensitive data or the deployment of malicious containers. By identifying and investigating these events, organizations can mitigate the risk of data breaches or other security incidents resulting from unauthorized container uploads. The impact of such an attack could be significant, compromising the integrity and security of the organization's cloud environment.
+description: The following analytic detects unauthorized container uploads to AWS
+  Elastic Container Service (ECR) by monitoring AWS CloudTrail events. It identifies
+  instances where a new container is uploaded by a user not previously recognized
+  as authorized. This detection is crucial for a SOC as it can indicate a potential
+  compromise or misuse of AWS ECR, which could lead to unauthorized access to sensitive
+  data or the deployment of malicious containers. By identifying and investigating
+  these events, organizations can mitigate the risk of data breaches or other security
+  incidents resulting from unauthorized container uploads. The impact of such an attack
+  could be significant, compromising the integrity and security of the organization's
+  cloud environment.
 data_source: []
-search: '`amazon_security_lake` api.operation=PutImage NOT `aws_ecr_users_asl` | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_ecr_container_upload_unknown_user_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
+search: '`amazon_security_lake` api.operation=PutImage NOT `aws_ecr_users_asl` | stats
+  count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_ecr_container_upload_unknown_user_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1204/003/
@@ -18,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_iam_delete_policy.yml b/detections/cloud/asl_aws_iam_delete_policy.yml
index c8562ed159..133bf597ba 100644
--- a/detections/cloud/asl_aws_iam_delete_policy.yml
+++ b/detections/cloud/asl_aws_iam_delete_policy.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies when a policy is deleted in AWS. It leverages Amazon Security Lake logs to detect the DeletePolicy API operation. Monitoring policy deletions is crucial as it can indicate unauthorized attempts to weaken security controls. If confirmed malicious, this activity could allow an attacker to remove critical security policies, potentially leading to privilege escalation or unauthorized access to sensitive resources.
+description: The following analytic identifies when a policy is deleted in AWS. It
+  leverages Amazon Security Lake logs to detect the DeletePolicy API operation. Monitoring
+  policy deletions is crucial as it can indicate unauthorized attempts to weaken security
+  controls. If confirmed malicious, this activity could allow an attacker to remove
+  critical security policies, potentially leading to privilege escalation or unauthorized
+  access to sensitive resources.
 data_source: []
-search: '`amazon_security_lake` api.operation=DeletePolicy | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_delete_policy_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
+search: '`amazon_security_lake` api.operation=DeletePolicy | fillnull | stats count
+  min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_delete_policy_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users. Not every user with AWS access should have permission to
+  delete policies (least privilege). In addition, this may be saved seperately and
+  tuned for failed or success attempts only.
 references:
 - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html
 - https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html
@@ -35,18 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_iam_failure_group_deletion.yml b/detections/cloud/asl_aws_iam_failure_group_deletion.yml
index d8ea3a5757..cc8da37a1e 100644
--- a/detections/cloud/asl_aws_iam_failure_group_deletion.yml
+++ b/detections/cloud/asl_aws_iam_failure_group_deletion.yml
@@ -5,11 +5,32 @@ date: '2024-10-22'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects failed attempts to delete AWS IAM groups, triggered by access denial, conflicts, or non-existent groups. It operates by monitoring CloudTrail logs for specific error codes related to deletion failures. This behavior is significant for a SOC as it may indicate unauthorized attempts to modify access controls or disrupt operations by removing groups. Such actions could be part of a larger attack aiming to escalate privileges or impair security protocols. Identifying these attempts allows for timely investigation and mitigation, preventing potential impact on the organizations security posture.
+description: The following analytic detects failed attempts to delete AWS IAM groups,
+  triggered by access denial, conflicts, or non-existent groups. It operates by monitoring
+  CloudTrail logs for specific error codes related to deletion failures. This behavior
+  is significant for a SOC as it may indicate unauthorized attempts to modify access
+  controls or disrupt operations by removing groups. Such actions could be part of
+  a larger attack aiming to escalate privileges or impair security protocols. Identifying
+  these attempts allows for timely investigation and mitigation, preventing potential
+  impact on the organizations security posture.
 data_source: []
-search: '`amazon_security_lake` api.operation=DeleteGroup api.response.error IN (NoSuchEntityException,DeleteConflictException, AccessDenied) http_request.user_agent!=*.amazonaws.com | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_failure_group_deletion_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+search: '`amazon_security_lake` api.operation=DeleteGroup api.response.error IN (NoSuchEntityException,DeleteConflictException,
+  AccessDenied) http_request.user_agent!=*.amazonaws.com | fillnull | stats count
+  min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_failure_group_deletion_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users. Not every user with AWS access should have permission to
+  delete groups (least privilege).
 references:
 - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
 - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +54,8 @@ tags:
   asset_type: AWS Account
   confidence: 50
   impact: 10
-  message: User $user$ has had mulitple failures while attempting to delete groups from $src_ip$
+  message: User $user$ has had mulitple failures while attempting to delete groups
+    from $src_ip$
   mitre_attack_id:
   - T1098
   observable:
@@ -44,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_iam_successful_group_deletion.yml b/detections/cloud/asl_aws_iam_successful_group_deletion.yml
index 3efca74f61..f444e1f972 100644
--- a/detections/cloud/asl_aws_iam_successful_group_deletion.yml
+++ b/detections/cloud/asl_aws_iam_successful_group_deletion.yml
@@ -5,11 +5,27 @@ date: '2024-10-22'
 author: Patrick Bareiss, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the successful deletion of a group within AWS IAM, leveraging CloudTrail IAM events. This action, while not inherently malicious, can serve as a precursor to more sinister activities, such as unauthorized access or privilege escalation attempts. By monitoring for such deletions, the analytic aids in identifying potential preparatory steps towards an attack, allowing for early detection and mitigation. The identification of this behavior is crucial for a SOC to prevent the potential impact of an attack, which could include unauthorized access to sensitive resources or disruption of AWS environment operations.
+description: The following analytic detects the successful deletion of a group within
+  AWS IAM, leveraging CloudTrail IAM events. This action, while not inherently malicious,
+  can serve as a precursor to more sinister activities, such as unauthorized access
+  or privilege escalation attempts. By monitoring for such deletions, the analytic
+  aids in identifying potential preparatory steps towards an attack, allowing for
+  early detection and mitigation. The identification of this behavior is crucial for
+  a SOC to prevent the potential impact of an attack, which could include unauthorized
+  access to sensitive resources or disruption of AWS environment operations.
 data_source: []
-search: '`amazon_security_lake` api.operation=DeleteGroup status=Success | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_successful_group_deletion_filter`'
-how_to_implement: You must install the Data Lake Federated Analytics App and ingest the logs into Splunk.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+search: '`amazon_security_lake` api.operation=DeleteGroup status=Success | fillnull
+  | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_iam_successful_group_deletion_filter`'
+how_to_implement: You must install the Data Lake Federated Analytics App and ingest
+  the logs into Splunk.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users. Not every user with AWS access should have permission to
+  delete groups (least privilege).
 references:
 - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
 - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
@@ -37,18 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml b/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml
index 3402599b11..599d4cc828 100644
--- a/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml
+++ b/detections/cloud/asl_aws_multi_factor_authentication_disabled.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to disable multi-factor authentication (MFA) for an AWS IAM user. It leverages Amazon Security Lake logs, specifically monitoring for `DeleteVirtualMFADevice` or `DeactivateMFADevice` API operations. This activity is significant as disabling MFA can indicate an adversary attempting to weaken account security to maintain persistence using a compromised account. If confirmed malicious, this action could allow attackers to retain access to the AWS environment without detection, potentially leading to unauthorized access to sensitive resources and prolonged compromise.
+description: The following analytic detects attempts to disable multi-factor authentication
+  (MFA) for an AWS IAM user. It leverages Amazon Security Lake logs, specifically
+  monitoring for `DeleteVirtualMFADevice` or `DeactivateMFADevice` API operations.
+  This activity is significant as disabling MFA can indicate an adversary attempting
+  to weaken account security to maintain persistence using a compromised account.
+  If confirmed malicious, this action could allow attackers to retain access to the
+  AWS environment without detection, potentially leading to unauthorized access to
+  sensitive resources and prolonged compromise.
 data_source: []
-search: '`amazon_security_lake` (api.operation=DeleteVirtualMFADevice OR api.operation=DeactivateMFADevice) | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_multi_factor_authentication_disabled_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: AWS Administrators may disable MFA but it is highly unlikely for this event to occur without prior notice to the company
+search: '`amazon_security_lake` (api.operation=DeleteVirtualMFADevice OR api.operation=DeactivateMFADevice)
+  | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation
+  actor.user.account_uid actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.uid as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_multi_factor_authentication_disabled_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: AWS Administrators may disable MFA but it is highly unlikely
+  for this event to occur without prior notice to the company
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://aws.amazon.com/what-is/mfa/
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml b/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml
index 538c54f2fe..68e392cf75 100644
--- a/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml
+++ b/detections/cloud/asl_aws_new_mfa_method_registered_for_user.yml
@@ -5,11 +5,29 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the registration of a new Multi-Factor Authentication (MFA) method for an AWS account, as logged through Amazon Security Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice` API operation within ASL logs. This behavior is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this activity could allow attackers to secure their access, making it harder to detect and remove their presence from the compromised environment.
+description: The following analytic identifies the registration of a new Multi-Factor
+  Authentication (MFA) method for an AWS account, as logged through Amazon Security
+  Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice`
+  API operation within ASL logs. This behavior is significant because adversaries
+  who gain unauthorized access to an AWS account may register a new MFA method to
+  maintain persistence. If confirmed malicious, this activity could allow attackers
+  to secure their access, making it harder to detect and remove their presence from
+  the compromised environment.
 data_source: []
-search: '`amazon_security_lake` api.operation=CreateVirtualMFADevice | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_new_mfa_method_registered_for_user_filter`'
-how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
-known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
+search: '`amazon_security_lake` api.operation=CreateVirtualMFADevice | fillnull |
+  stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid
+  actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
+  | rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region,
+  http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_new_mfa_method_registered_for_user_filter`'
+how_to_implement: The detection is based on Amazon Security Lake events from Amazon
+  Web Services (AWS), which is a centralized data lake that provides security-related
+  data from AWS services. To use this detection, you must ingest CloudTrail logs from
+  Amazon Security Lake into Splunk. To run this search, ensure that you ingest events
+  using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876)
+  or the Federated Analytics App.
+known_false_positives: Newly onboarded users who are registering an MFA method for
+  the first time will also trigger this detection.
 references:
 - https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/
 - https://attack.mitre.org/techniques/T1556/
@@ -38,18 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - actor.user.account_uid
-  - actor.user.name
-  - actor.user.uid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - cloud.region
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/asl_ocsf_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/asl_ocsf_cloudtrail.json
     sourcetype: aws:cloudtrail:lake
     source: aws_asl
diff --git a/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml b/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml
index b51c4dbe70..ca2d2b11d1 100644
--- a/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml
+++ b/detections/cloud/aws_ami_attribute_modification_for_exfiltration.yml
@@ -7,10 +7,25 @@ status: production
 type: TTP
 data_source:
 - AWS CloudTrail ModifyImageAttribute
-description: The following analytic detects suspicious modifications to AWS AMI attributes, such as sharing an AMI with another AWS account or making it publicly accessible. It leverages AWS CloudTrail logs to identify these changes by monitoring specific API calls. This activity is significant because adversaries can exploit these modifications to exfiltrate sensitive data stored in AWS resources. If confirmed malicious, this could lead to unauthorized access and potential data breaches, compromising the confidentiality and integrity of organizational information.
-search: '`cloudtrail` eventName=ModifyImageAttribute (requestParameters.launchPermission.add.items{}.userId = * OR requestParameters.launchPermission.add.items{}.group = all) | rename requestParameters.launchPermission.add.items{}.group as group_added | rename requestParameters.launchPermission.add.items{}.userId as accounts_added | eval ami_status=if(match(group_added,"all") ,"Public AMI", "Not Public")  | stats count min(_time) as firstTime max(_time) as lastTime  values(group_added) values(accounts_added) as accounts_added values(ami_status) by  src_ip region eventName userAgent user_arn aws_account_id userIdentity.principalId |  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_ami_attribute_modification_for_exfiltration_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with others for  a specific purpose.
+description: The following analytic detects suspicious modifications to AWS AMI attributes,
+  such as sharing an AMI with another AWS account or making it publicly accessible.
+  It leverages AWS CloudTrail logs to identify these changes by monitoring specific
+  API calls. This activity is significant because adversaries can exploit these modifications
+  to exfiltrate sensitive data stored in AWS resources. If confirmed malicious, this
+  could lead to unauthorized access and potential data breaches, compromising the
+  confidentiality and integrity of organizational information.
+search: '`cloudtrail` eventName=ModifyImageAttribute (requestParameters.launchPermission.add.items{}.userId
+  = * OR requestParameters.launchPermission.add.items{}.group = all) | rename requestParameters.launchPermission.add.items{}.group
+  as group_added | rename requestParameters.launchPermission.add.items{}.userId as
+  accounts_added | eval ami_status=if(match(group_added,"all") ,"Public AMI", "Not
+  Public")  | stats count min(_time) as firstTime max(_time) as lastTime  values(group_added)
+  values(accounts_added) as accounts_added values(ami_status) by  src_ip region eventName
+  userAgent user_arn aws_account_id userIdentity.principalId |  `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `aws_ami_attribute_modification_for_exfiltration_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that an AWS admin has legitimately shared a
+  snapshot with others for  a specific purpose.
 references:
 - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
 - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ami/
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +51,8 @@ tags:
   asset_type: EC2 Snapshot
   confidence: 80
   impact: 100
-  message: AWS AMI from account $aws_account_id$ is shared externally with $accounts_added$ from $src_ip$ or AMI made is made Public.
+  message: AWS AMI from account $aws_account_id$ is shared externally with $accounts_added$
+    from $src_ip$ or AMI made is made Public.
   mitre_attack_id:
   - T1537
   observable:
@@ -51,20 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - requestParameters.attributeType
-  - aws_account_id
-  - vendor_region
-  - user_agent
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_ami_shared_public/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_ami_shared_public/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_concurrent_sessions_from_different_ips.yml b/detections/cloud/aws_concurrent_sessions_from_different_ips.yml
index f725e82f47..f1b0bf2479 100644
--- a/detections/cloud/aws_concurrent_sessions_from_different_ips.yml
+++ b/detections/cloud/aws_concurrent_sessions_from_different_ips.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies an AWS IAM account with concurrent sessions originating from more than one unique IP address within a 5-minute window. It leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` event, to detect this behavior. This activity is significant as it may indicate a session hijacking attack, where an adversary uses stolen session cookies to access AWS resources from a different location. If confirmed malicious, this could allow unauthorized access to sensitive corporate resources, leading to potential data breaches or further exploitation within the AWS environment.
+description: The following analytic identifies an AWS IAM account with concurrent
+  sessions originating from more than one unique IP address within a 5-minute window.
+  It leverages AWS CloudTrail logs, specifically the `DescribeEventAggregates` event,
+  to detect this behavior. This activity is significant as it may indicate a session
+  hijacking attack, where an adversary uses stolen session cookies to access AWS resources
+  from a different location. If confirmed malicious, this could allow unauthorized
+  access to sensitive corporate resources, leading to potential data breaches or further
+  exploitation within the AWS environment.
 data_source:
 - AWS CloudTrail DescribeEventAggregates
-search: '`cloudtrail` eventName = DescribeEventAggregates src_ip!="AWS Internal" | bin span=5m _time | stats values(userAgent) values(eventName) values(src_ip) as src_ip  dc(src_ip) as distinct_ip_count by _time user_arn | where distinct_ip_count > 1 | `aws_concurrent_sessions_from_different_ips_filter`'
-how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
+search: '`cloudtrail` eventName = DescribeEventAggregates src_ip!="AWS Internal" |
+  bin span=5m _time | stats values(userAgent) values(eventName) values(src_ip) as
+  src_ip  dc(src_ip) as distinct_ip_count by _time user_arn | where distinct_ip_count
+  > 1 | `aws_concurrent_sessions_from_different_ips_filter`'
+how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: A user with concurrent sessions from different Ips may also
+  represent the legitimate use of more than one device. Filter as needed and/or customize
+  the threshold to fit your environment.
 references:
 - https://attack.mitre.org/techniques/T1185/
 - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 70
-  message: User $user_arn$ has concurrent sessions from more than one unique IP address $src_ip$ in the span of 5 minutes.
+  message: User $user_arn$ has concurrent sessions from more than one unique IP address
+    $src_ip$ in the span of 5 minutes.
   mitre_attack_id:
   - T1185
   observable:
@@ -47,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - user_arn
-  - aws_account_id
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/aws_concurrent_sessions_from_different_ips/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml b/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml
index 08ae8ccd89..0778b8dfbc 100644
--- a/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml
+++ b/detections/cloud/aws_console_login_failed_during_mfa_challenge.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies failed authentication attempts to the AWS Console during the Multi-Factor Authentication (MFA) challenge. It leverages AWS CloudTrail logs, specifically the `additionalEventData` field, to detect when MFA was used but the login attempt still failed. This activity is significant as it may indicate an adversary attempting to access an account with compromised credentials but being thwarted by MFA. If confirmed malicious, this could suggest an ongoing attempt to breach the account, potentially leading to unauthorized access and further attacks if MFA is bypassed.
+description: The following analytic identifies failed authentication attempts to the
+  AWS Console during the Multi-Factor Authentication (MFA) challenge. It leverages
+  AWS CloudTrail logs, specifically the `additionalEventData` field, to detect when
+  MFA was used but the login attempt still failed. This activity is significant as
+  it may indicate an adversary attempting to access an account with compromised credentials
+  but being thwarted by MFA. If confirmed malicious, this could suggest an ongoing
+  attempt to breach the account, potentially leading to unauthorized access and further
+  attacks if MFA is bypassed.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName= ConsoleLogin errorMessage="Failed authentication" additionalEventData.MFAUsed = "Yes" | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion user_name userIdentity.arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_console_login_failed_during_mfa_challenge_filter`'
-how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake.
+search: '`cloudtrail` eventName= ConsoleLogin errorMessage="Failed authentication"
+  additionalEventData.MFAUsed = "Yes" | stats count min(_time) as firstTime max(_time)
+  as lastTime by src eventName eventSource aws_account_id errorCode errorMessage userAgent
+  eventID awsRegion user_name userIdentity.arn | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `aws_console_login_failed_during_mfa_challenge_filter`'
+how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search
+  requires AWS CloudTrail logs.
+known_false_positives: Legitimate users may miss to reply the MFA challenge within
+  the time window or deny it by mistake.
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://aws.amazon.com/what-is/mfa/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_name$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 80
-  message: User $user_name$ failed to pass MFA challenge while logging into console from $src$
+  message: User $user_name$ failed to pass MFA challenge while logging into console
+    from $src$
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -48,22 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src
-  - eventName
-  - eventSource
-  - aws_account_id
-  - errorCode
-  - errorMessage
-  - userAgent
-  - eventID
-  - awsRegion
-  - user_name
-  - userIdentity.arn
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml b/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml
index cf48777e47..f83575c934 100644
--- a/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml
+++ b/detections/cloud/aws_create_policy_version_to_allow_all_resources.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of a new AWS IAM policy version that allows access to all resources. It detects this activity by analyzing AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that grants broad permissions. This behavior is significant because it violates the principle of least privilege, potentially exposing the environment to misuse or abuse. If confirmed malicious, an attacker could gain extensive access to AWS resources, leading to unauthorized actions, data exfiltration, or further compromise of the AWS environment.
+description: The following analytic identifies the creation of a new AWS IAM policy
+  version that allows access to all resources. It detects this activity by analyzing
+  AWS CloudTrail logs for the CreatePolicyVersion event with a policy document that
+  grants broad permissions. This behavior is significant because it violates the principle
+  of least privilege, potentially exposing the environment to misuse or abuse. If
+  confirmed malicious, an attacker could gain extensive access to AWS resources, leading
+  to unauthorized actions, data exfiltration, or further compromise of the AWS environment.
 data_source:
 - AWS CloudTrail CreatePolicyVersion
-search: '`cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success | spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | where key_policy_action_1 = "*" | stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID awsRegion user user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_create_policy_version_to_allow_all_resources_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources and you must verify this activity.
+search: '`cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com
+  errorCode = success | spath input=requestParameters.policyDocument output=key_policy_statements
+  path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements
+  output=key_policy_action_1 path=Action | where key_policy_action_1 = "*" | stats
+  count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements)
+  as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID
+  awsRegion user user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_create_policy_version_to_allow_all_resources_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created a policy to allow a user to access all
+  resources. That said, AWS strongly advises against granting full control to all
+  AWS resources and you must verify this activity.
 references:
 - https://bishopfox.com/blog/privilege-escalation-in-aws
 - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: AWS Account
   confidence: 70
   impact: 70
-  message: User $user$ created a policy version that allows them to access any resource in their account.
+  message: User $user$ created a policy version that allows them to access any resource
+    in their account.
   mitre_attack_id:
   - T1078.004
   - T1078
@@ -42,21 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - eventSource
-  - requestParameters.userName
-  - requestParameters.policyDocument
-  - aws_account_id
-  - awsRegion
-  - eventID
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_createaccesskey.yml b/detections/cloud/aws_createaccesskey.yml
index e3f431adc8..28ce437b95 100644
--- a/detections/cloud/aws_createaccesskey.yml
+++ b/detections/cloud/aws_createaccesskey.yml
@@ -5,12 +5,25 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the creation of AWS IAM access keys by a user for another user, which can indicate privilege escalation. It leverages AWS CloudTrail logs to detect instances where the user creating the access key is different from the user for whom the key is created. This activity is significant because unauthorized access key creation can allow attackers to establish persistence or exfiltrate data via AWS APIs. If confirmed malicious, this could lead to unauthorized access to AWS services, data exfiltration, and long-term persistence in the environment.
+description: The following analytic identifies the creation of AWS IAM access keys
+  by a user for another user, which can indicate privilege escalation. It leverages
+  AWS CloudTrail logs to detect instances where the user creating the access key is
+  different from the user for whom the key is created. This activity is significant
+  because unauthorized access key creation can allow attackers to establish persistence
+  or exfiltrate data via AWS APIs. If confirmed malicious, this could lead to unauthorized
+  access to AWS services, data exfiltration, and long-term persistence in the environment.
 data_source:
 - AWS CloudTrail CreateAccessKey
-search: '`cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName),1,0) | search match=0 | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`aws_createaccesskey_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.
+search: '`cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com
+  errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName),1,0)
+  | search match=0 | stats count min(_time) as firstTime max(_time) as lastTime by
+  requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent
+  eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` |`aws_createaccesskey_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created keys for another user.
 references:
 - https://bishopfox.com/blog/privilege-escalation-in-aws
 - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
@@ -20,7 +33,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 70
-  message: User $user_arn$ is attempting to create access keys for $requestParameters.userName$ from this IP $src$
+  message: User $user_arn$ is attempting to create access keys for $requestParameters.userName$
+    from this IP $src$
   mitre_attack_id:
   - T1136.003
   - T1136
@@ -37,16 +51,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.userName
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_createloginprofile.yml b/detections/cloud/aws_createloginprofile.yml
index 23b4e76e10..36a2aedcb6 100644
--- a/detections/cloud/aws_createloginprofile.yml
+++ b/detections/cloud/aws_createloginprofile.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of a login profile for one AWS user by another, followed by a console login from the same source IP. It uses AWS CloudTrail logs to correlate the `CreateLoginProfile` and `ConsoleLogin` events based on the source IP and user identity. This activity is significant as it may indicate privilege escalation, where an attacker creates a new login profile to gain unauthorized access. If confirmed malicious, this could allow the attacker to escalate privileges and maintain persistent access to the AWS environment.
+description: The following analytic identifies the creation of a login profile for
+  one AWS user by another, followed by a console login from the same source IP. It
+  uses AWS CloudTrail logs to correlate the `CreateLoginProfile` and `ConsoleLogin`
+  events based on the source IP and user identity. This activity is significant as
+  it may indicate privilege escalation, where an attacker creates a new login profile
+  to gain unauthorized access. If confirmed malicious, this could allow the attacker
+  to escalate privileges and maintain persistent access to the AWS environment.
 data_source:
 - AWS CloudTrail CreateLoginProfile AND AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName = CreateLoginProfile | rename requestParameters.userName as new_login_profile | table src_ip eventName new_login_profile userIdentity.userName  | join new_login_profile src_ip [| search `cloudtrail` eventName = ConsoleLogin | rename userIdentity.userName  as new_login_profile | stats count values(eventName) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn new_login_profile src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`] | `aws_createloginprofile_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a login profile for another user.
+search: '`cloudtrail` eventName = CreateLoginProfile | rename requestParameters.userName
+  as new_login_profile | table src_ip eventName new_login_profile userIdentity.userName  |
+  join new_login_profile src_ip [| search `cloudtrail` eventName = ConsoleLogin |
+  rename userIdentity.userName  as new_login_profile | stats count values(eventName)
+  min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode
+  userAgent eventID awsRegion userIdentity.principalId user_arn new_login_profile
+  src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`]
+  | `aws_createloginprofile_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created a login profile for another user.
 references:
 - https://bishopfox.com/blog/privilege-escalation-in-aws
 - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 90
-  message: User $user_arn$ is attempting to create a login profile for $new_login_profile$ and did a console login from this IP $src_ip$
+  message: User $user_arn$ is attempting to create a login profile for $new_login_profile$
+    and did a console login from this IP $src_ip$
   mitre_attack_id:
   - T1136.003
   - T1136
@@ -46,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.userName
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_credential_access_failed_login.yml b/detections/cloud/aws_credential_access_failed_login.yml
index 1910cae811..7503694f7e 100644
--- a/detections/cloud/aws_credential_access_failed_login.yml
+++ b/detections/cloud/aws_credential_access_failed_login.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies unsuccessful login attempts to the AWS Management Console using a specific user identity. It leverages AWS CloudTrail logs to detect failed authentication events associated with the AWS ConsoleLogin action. This activity is significant for a SOC because repeated failed login attempts may indicate a brute force attack or unauthorized access attempts. If confirmed malicious, an attacker could potentially gain access to AWS account services and resources, leading to data breaches, resource manipulation, or further exploitation within the AWS environment.
+description: The following analytic identifies unsuccessful login attempts to the
+  AWS Management Console using a specific user identity. It leverages AWS CloudTrail
+  logs to detect failed authentication events associated with the AWS ConsoleLogin
+  action. This activity is significant for a SOC because repeated failed login attempts
+  may indicate a brute force attack or unauthorized access attempts. If confirmed
+  malicious, an attacker could potentially gain access to AWS account services and
+  resources, leading to data breaches, resource manipulation, or further exploitation
+  within the AWS environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Authentication where Authentication.action = failure Authentication.app=AwsConsoleSignIn Authentication.signature=ConsoleLogin BY Authentication.app Authentication.signature Authentication.dest  Authentication.user Authentication.action Authentication.user_id Authentication.src | `drop_dm_object_name(Authentication)`  | `security_content_ctime(firstTime)`|  `security_content_ctime(lastTime)` | `aws_credential_access_failed_login_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime from
+  datamodel=Authentication where Authentication.action = failure Authentication.app=AwsConsoleSignIn
+  Authentication.signature=ConsoleLogin BY Authentication.app Authentication.signature
+  Authentication.dest  Authentication.user Authentication.action Authentication.user_id
+  Authentication.src | `drop_dm_object_name(Authentication)`  | `security_content_ctime(firstTime)`|  `security_content_ctime(lastTime)`
+  | `aws_credential_access_failed_login_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: Users may genuinely mistype or forget the password.
 references:
 - https://attack.mitre.org/techniques/T1110/001/
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - app
-  - eventSource
-  - action
-  - signature
-  - dest
-  - user
-  - user_id
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/aws_login_failure/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/aws_login_failure/aws_cloudtrail_events.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_credential_access_getpassworddata.yml b/detections/cloud/aws_credential_access_getpassworddata.yml
index ca4db61f75..7647a1b04b 100644
--- a/detections/cloud/aws_credential_access_getpassworddata.yml
+++ b/detections/cloud/aws_credential_access_getpassworddata.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies more than 10 GetPasswordData API calls within a 5-minute window in your AWS account. It leverages AWS CloudTrail logs to detect this activity by counting the distinct instance IDs accessed. This behavior is significant as it may indicate an attempt to retrieve encrypted administrator passwords for running Windows instances, which is a critical security concern. If confirmed malicious, attackers could gain unauthorized access to administrative credentials, potentially leading to full control over the affected instances and further compromise of the AWS environment.
+description: The following analytic identifies more than 10 GetPasswordData API calls
+  within a 5-minute window in your AWS account. It leverages AWS CloudTrail logs to
+  detect this activity by counting the distinct instance IDs accessed. This behavior
+  is significant as it may indicate an attempt to retrieve encrypted administrator
+  passwords for running Windows instances, which is a critical security concern. If
+  confirmed malicious, attackers could gain unauthorized access to administrative
+  credentials, potentially leading to full control over the affected instances and
+  further compromise of the AWS environment.
 data_source:
 - AWS CloudTrail GetPasswordData
-search: '`cloudtrail` eventName=GetPasswordData eventSource = ec2.amazonaws.com |  bin _time span=5m |  stats count values(errorCode) as errorCode dc(requestParameters.instanceId) as distinct_instance_ids values(requestParameters.instanceId) as instance_ids by aws_account_id src_ip user_arn userAgent eventName _time |  where distinct_instance_ids > 10 | `aws_credential_access_getpassworddata_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. We encourage the users to adjust the values of `distinct_instance_ids` and tweak the `span` value according to their environment.
-known_false_positives: Administrator tooling or automated scripts may make these calls but it is highly unlikely to make several calls in a short period of time.
+search: '`cloudtrail` eventName=GetPasswordData eventSource = ec2.amazonaws.com |  bin
+  _time span=5m |  stats count values(errorCode) as errorCode dc(requestParameters.instanceId)
+  as distinct_instance_ids values(requestParameters.instanceId) as instance_ids by
+  aws_account_id src_ip user_arn userAgent eventName _time |  where distinct_instance_ids
+  > 10 | `aws_credential_access_getpassworddata_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs. We encourage the users to adjust the values
+  of `distinct_instance_ids` and tweak the `span` value according to their environment.
+known_false_positives: Administrator tooling or automated scripts may make these calls
+  but it is highly unlikely to make several calls in a short period of time.
 references:
 - https://attack.mitre.org/techniques/T1552/
 - https://stratus-red-team.cloud/attack-techniques/AWS/aws.credential-access.ec2-get-password-data/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 70
   impact: 70
-  message: User $user_arn$ is seen to make mulitple `GetPasswordData` API calls to instance ids $instance_ids$ from IP $src_ip$
+  message: User $user_arn$ is seen to make mulitple `GetPasswordData` API calls to
+    instance ids $instance_ids$ from IP $src_ip$
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -48,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - userIdentity.userName
-  - userAgent
-  - userIdentity.accountId
-  - sourceIPAddress
-  - awsRegion
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/aws_getpassworddata/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/aws_getpassworddata/aws_cloudtrail_events.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_credential_access_rds_password_reset.yml b/detections/cloud/aws_credential_access_rds_password_reset.yml
index c7e864b1da..5dacb7e4d9 100644
--- a/detections/cloud/aws_credential_access_rds_password_reset.yml
+++ b/detections/cloud/aws_credential_access_rds_password_reset.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the resetting of the master user password for an Amazon RDS DB instance. It leverages AWS CloudTrail logs to identify events where the `ModifyDBInstance` API call includes a new `masterUserPassword` parameter. This activity is significant because unauthorized password resets can grant attackers access to sensitive data stored in production databases, such as credit card information, PII, and healthcare data. If confirmed malicious, this could lead to data breaches, regulatory non-compliance, and significant reputational damage. Immediate investigation is required to determine the legitimacy of the password reset.
+description: The following analytic detects the resetting of the master user password
+  for an Amazon RDS DB instance. It leverages AWS CloudTrail logs to identify events
+  where the `ModifyDBInstance` API call includes a new `masterUserPassword` parameter.
+  This activity is significant because unauthorized password resets can grant attackers
+  access to sensitive data stored in production databases, such as credit card information,
+  PII, and healthcare data. If confirmed malicious, this could lead to data breaches,
+  regulatory non-compliance, and significant reputational damage. Immediate investigation
+  is required to determine the legitimacy of the password reset.
 data_source:
 - AWS CloudTrail ModifyDBInstance
-search: '`cloudtrail` eventSource="rds.amazonaws.com" eventName=ModifyDBInstance "requestParameters.masterUserPassword"=* | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.dBInstanceIdentifier) as database_id by src awsRegion eventName userAgent user_arn| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_credential_access_rds_password_reset_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '`cloudtrail` eventSource="rds.amazonaws.com" eventName=ModifyDBInstance "requestParameters.masterUserPassword"=*
+  | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.dBInstanceIdentifier)
+  as database_id by src awsRegion eventName userAgent user_arn| `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `aws_credential_access_rds_password_reset_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: Users may genuinely reset the RDS password.
 references:
 - https://aws.amazon.com/premiumsupport/knowledge-center/reset-master-user-password-rds
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$database_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$database_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$database_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - requestParameters.dBInstanceIdentifier
-  - userAgent
-  - sourceIPAddress
-  - awsRegion
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.002/aws_rds_password_reset/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.002/aws_rds_password_reset/aws_cloudtrail_events.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml b/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml
index 7ad062288d..b0d17ab92a 100644
--- a/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml
+++ b/detections/cloud/aws_cross_account_activity_from_previously_unseen_account.yml
@@ -5,12 +5,36 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies AssumeRole events where an IAM role in a different AWS account is accessed for the first time. It detects this activity by analyzing authentication logs and comparing the requesting and requested account IDs, flagging new cross-account activities. This behavior is significant because unauthorized cross-account access can indicate potential lateral movement or privilege escalation attempts. If confirmed malicious, an attacker could gain unauthorized access to resources in another account, potentially leading to data exfiltration, service disruption, or further compromise of the AWS environment.
+description: The following analytic identifies AssumeRole events where an IAM role
+  in a different AWS account is accessed for the first time. It detects this activity
+  by analyzing authentication logs and comparing the requesting and requested account
+  IDs, flagging new cross-account activities. This behavior is significant because
+  unauthorized cross-account access can indicate potential lateral movement or privilege
+  escalation attempts. If confirmed malicious, an attacker could gain unauthorized
+  access to resources in another account, potentially leading to data exfiltration,
+  service disruption, or further compromise of the AWS environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)` | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") |  where status = "New Cross Account Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_cross_account_activity_from_previously_unseen_account_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro.
-known_false_positives: Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request.
+search: '| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication
+  where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user
+  Authentication.src Authentication.user_role | `drop_dm_object_name(Authentication)`
+  | rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" | where vendor_account
+  != dest_account | rename vendor_account as requestingAccountId dest_account as requestedAccountId
+  | lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId,
+  OUTPUTNEW firstTime | eval status = if(firstTime > relative_time(now(), "-24h@h"),"New
+  Cross Account Activity","Previously Seen") |  where status = "New Cross Account
+  Activity" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `aws_cross_account_activity_from_previously_unseen_account_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen AWS Cross Account
+  Activity - Initial` to build the initial table of source IP address, geographic
+  locations, and times. You must also enable the second baseline search `Previously
+  Seen AWS Cross Account Activity - Update` to keep this table up to date and to age
+  out old data. You can also provide additional filtering for this search by customizing
+  the `aws_cross_account_activity_from_previously_unseen_account_filter` macro.
+known_false_positives: Using multiple AWS accounts and roles is perfectly valid behavior.
+  It's suspicious when an account requests privileges of an account it hasn't before.
+  You should validate with the account owner that this is a legitimate request.
 references: []
 tags:
   analytic_story:
@@ -18,7 +42,8 @@ tags:
   asset_type: AWS Instance
   confidence: 50
   impact: 30
-  message: AWS account $requestingAccountId$ is trying to access resource from some other account $requestedAccountId$, for the first time.
+  message: AWS account $requestingAccountId$ is trying to access resource from some
+    other account $requestedAccountId$, for the first time.
   observable:
   - name: requestingAccountId
     type: Other
@@ -32,17 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.vendor_account
-  - Authentication.user
-  - Authentication.user_role
-  - Authentication.src
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml b/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml
index 72fe47c526..97d59d4c82 100644
--- a/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml
+++ b/detections/cloud/aws_defense_evasion_delete_cloudtrail.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of AWS CloudTrail logs by identifying `DeleteTrail` events within CloudTrail logs. This detection leverages CloudTrail data to monitor for successful `DeleteTrail` actions, excluding those initiated from the AWS console. This activity is significant because adversaries may delete CloudTrail logs to evade detection and operate stealthily within the compromised environment. If confirmed malicious, this action could allow attackers to cover their tracks, making it difficult to trace their activities and potentially leading to prolonged unauthorized access and further exploitation.
+description: The following analytic detects the deletion of AWS CloudTrail logs by
+  identifying `DeleteTrail` events within CloudTrail logs. This detection leverages
+  CloudTrail data to monitor for successful `DeleteTrail` actions, excluding those
+  initiated from the AWS console. This activity is significant because adversaries
+  may delete CloudTrail logs to evade detection and operate stealthily within the
+  compromised environment. If confirmed malicious, this action could allow attackers
+  to cover their tracks, making it difficult to trace their activities and potentially
+  leading to prolonged unauthorized access and further exploitation.
 data_source:
 - AWS CloudTrail DeleteTrail
-search: '`cloudtrail` eventName = DeleteTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.name) as deleted_cloudtrail_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudtrail_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudTrail logging. Please investigate this activity.
+search: '`cloudtrail` eventName = DeleteTrail eventSource = cloudtrail.amazonaws.com
+  userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as
+  firstTime max(_time) as lastTime values(requestParameters.name) as deleted_cloudtrail_name
+  by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudtrail_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in
+  your AWS Environment.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has stopped cloudTrail logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 100
-  message: User $user_arn$ has delete a CloudTrail logging for account id $aws_account_id$ from IP $src$
+  message: User $user_arn$ has delete a CloudTrail logging for account id $aws_account_id$
+    from IP $src$
   mitre_attack_id:
   - T1562.008
   - T1562
@@ -45,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - requestParameters.name
-  - userAgent
-  - aws_account_id
-  - src
-  - region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml b/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml
index 9807d2a8c4..8106740585 100644
--- a/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml
+++ b/detections/cloud/aws_defense_evasion_delete_cloudwatch_log_group.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of CloudWatch log groups in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This detection leverages CloudTrail data to monitor for successful log group deletions, excluding console-based actions. This activity is significant as it indicates potential attempts to evade logging and monitoring, which is crucial for maintaining visibility into AWS activities. If confirmed malicious, this could allow attackers to hide their tracks, making it difficult to detect further malicious actions or investigate incidents within the compromised AWS environment.
+description: The following analytic detects the deletion of CloudWatch log groups
+  in AWS, identified through `DeleteLogGroup` events in CloudTrail logs. This detection
+  leverages CloudTrail data to monitor for successful log group deletions, excluding
+  console-based actions. This activity is significant as it indicates potential attempts
+  to evade logging and monitoring, which is crucial for maintaining visibility into
+  AWS activities. If confirmed malicious, this could allow attackers to hide their
+  tracks, making it difficult to detect further malicious actions or investigate incidents
+  within the compromised AWS environment.
 data_source:
 - AWS CloudTrail DeleteLogGroup
-search: '`cloudtrail` eventName = DeleteLogGroup eventSource = logs.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.logGroupName) as log_group_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudwatch_log_group_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has deleted CloudWatch logging. Please investigate this activity.
+search: '`cloudtrail` eventName = DeleteLogGroup eventSource = logs.amazonaws.com
+  userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as
+  firstTime max(_time) as lastTime values(requestParameters.logGroupName) as log_group_name
+  by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `aws_defense_evasion_delete_cloudwatch_log_group_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in
+  your AWS Environment.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has deleted CloudWatch logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 100
-  message: User $user_arn$ has deleted a CloudWatch logging group for account id $aws_account_id$ from IP $src$
+  message: User $user_arn$ has deleted a CloudWatch logging group for account id $aws_account_id$
+    from IP $src$
   mitre_attack_id:
   - T1562
   - T1562.008
@@ -45,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - requestParameters.name
-  - userAgent
-  - aws_account_id
-  - src
-  - region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/delete_cloudwatch_log_group/aws_cloudtrail_events.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_defense_evasion_impair_security_services.yml b/detections/cloud/aws_defense_evasion_impair_security_services.yml
index 78333249f4..601d1da0a3 100644
--- a/detections/cloud/aws_defense_evasion_impair_security_services.yml
+++ b/detections/cloud/aws_defense_evasion_impair_security_services.yml
@@ -5,7 +5,14 @@ date: '2024-10-17'
 author: Bhavin Patel, Gowthamaraj Rajendran, Splunk
 status: production
 type: Hunting
-description: The following analytic detects attempts to delete critical AWS security service configurations, such as CloudWatch alarms, GuardDuty detectors, and Web Application Firewall rules. It leverages CloudTrail logs to identify specific API calls like "DeleteLogStream" and "DeleteDetector." This activity is significant because it indicates potential efforts to disable security monitoring and evade detection. If confirmed malicious, this could allow attackers to operate undetected, escalate privileges, or exfiltrate data without triggering security alerts, severely compromising the security posture of the AWS environment.
+description: The following analytic detects attempts to delete critical AWS security
+  service configurations, such as CloudWatch alarms, GuardDuty detectors, and Web
+  Application Firewall rules. It leverages CloudTrail logs to identify specific API
+  calls like "DeleteLogStream" and "DeleteDetector." This activity is significant
+  because it indicates potential efforts to disable security monitoring and evade
+  detection. If confirmed malicious, this could allow attackers to operate undetected,
+  escalate privileges, or exfiltrate data without triggering security alerts, severely
+  compromising the security posture of the AWS environment.
 data_source:
 - AWS CloudTrail DeleteLogStream
 - AWS CloudTrail DeleteDetector
@@ -15,9 +22,16 @@ data_source:
 - AWS CloudTrail DeleteRuleGroup
 - AWS CloudTrail DeleteLoggingConfiguration
 - AWS CloudTrail DeleteAlarms
-search: '`cloudtrail` eventName IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms") | stats count min(_time) as firstTime max(_time) as lastTime values(eventName)  as eventName values(eventSource) as eventSource values(requestParameters.*) as * by src region user_arn aws_account_id user_type user_agent errorCode| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|  `aws_defense_evasion_impair_security_services_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
-known_false_positives: While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names.
+search: '`cloudtrail` eventName IN ("DeleteLogStream","DeleteDetector","DeleteIPSet","DeleteWebACL","DeleteRule","DeleteRuleGroup","DeleteLoggingConfiguration","DeleteAlarms")
+  | stats count min(_time) as firstTime max(_time) as lastTime values(eventName)  as
+  eventName values(eventSource) as eventSource values(requestParameters.*) as * by
+  src region user_arn aws_account_id user_type user_agent errorCode| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`|  `aws_defense_evasion_impair_security_services_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in
+  your AWS Environment.
+known_false_positives: While this search has no known false positives, it is possible
+  that it is a legitimate admin activity. Please consider filtering out these noisy
+  events using userAgent, user_arn field names.
 references:
 - https://docs.aws.amazon.com/cli/latest/reference/guardduty/index.html
 - https://docs.aws.amazon.com/cli/latest/reference/waf/index.html
@@ -28,7 +42,8 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 70
-  message: User $user_arn$ has made potentially risky api calls $eventName$ that could impair AWS security services for account id $aws_account_id$
+  message: User $user_arn$ has made potentially risky api calls $eventName$ that could
+    impair AWS security services for account id $aws_account_id$
   mitre_attack_id:
   - T1562.008
   - T1562
@@ -45,20 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - user_agent
-  - user_type
-  - aws_account_id
-  - src
-  - region
-  - errorCode
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/aws_delete_security_services/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml b/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml
index e9baec2dbc..677f8d8635 100644
--- a/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml
+++ b/detections/cloud/aws_defense_evasion_putbucketlifecycle.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: Bhavin Patel
 status: production
 type: Hunting
-description: The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This detection leverages CloudTrail logs to identify suspicious lifecycle configurations. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively.
+description: The following analytic detects `PutBucketLifecycle` events in AWS CloudTrail
+  logs where a user sets a lifecycle rule for an S3 bucket with an expiration period
+  of fewer than three days. This detection leverages CloudTrail logs to identify suspicious
+  lifecycle configurations. This activity is significant because attackers may use
+  it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic
+  investigations. If confirmed malicious, this could allow attackers to cover their
+  tracks, making it difficult to trace their actions and respond to the breach effectively.
 data_source:
 - AWS CloudTrail PutBucketLifecycle
-search: '`cloudtrail` eventName=PutBucketLifecycle user_type=IAMUser errorCode=success |  spath path=requestParameters{}.LifecycleConfiguration{}.Rule{}.Expiration{}.Days output=expiration_days |  spath path=requestParameters{}.bucketName output=bucket_name | stats count min(_time) as firstTime max(_time) as lastTime  by src region eventName userAgent user_arn aws_account_id expiration_days  bucket_name user_type| `security_content_ctime(firstTime)` |  `security_content_ctime(lastTime)` | where expiration_days < 3 | `aws_defense_evasion_putbucketlifecycle_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment. We recommend our users to set the expiration days value according to your company's log retention policies.
-known_false_positives: While this search has no known false positives, it is possible that it is a legitimate admin activity. Please consider filtering out these noisy events using userAgent, user_arn field names.
+search: '`cloudtrail` eventName=PutBucketLifecycle user_type=IAMUser errorCode=success
+  |  spath path=requestParameters{}.LifecycleConfiguration{}.Rule{}.Expiration{}.Days
+  output=expiration_days |  spath path=requestParameters{}.bucketName output=bucket_name
+  | stats count min(_time) as firstTime max(_time) as lastTime  by src region eventName
+  userAgent user_arn aws_account_id expiration_days  bucket_name user_type| `security_content_ctime(firstTime)`
+  |  `security_content_ctime(lastTime)` | where expiration_days < 3 | `aws_defense_evasion_putbucketlifecycle_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in
+  your AWS Environment. We recommend our users to set the expiration days value according
+  to your company's log retention policies.
+known_false_positives: While this search has no known false positives, it is possible
+  that it is a legitimate admin activity. Please consider filtering out these noisy
+  events using userAgent, user_arn field names.
 references:
 - https://stratus-red-team.cloud/attack-techniques/AWS/aws.defense-evasion.cloudtrail-lifecycle-rule/
 tags:
@@ -19,7 +34,8 @@ tags:
   asset_type: AWS Account
   confidence: 40
   impact: 50
-  message: User $user_arn$ has created a new rule to on an S3 bucket $bucket_name$ with short expiration days
+  message: User $user_arn$ has created a new rule to on an S3 bucket $bucket_name$
+    with short expiration days
   mitre_attack_id:
   - T1562.008
   - T1562
@@ -38,21 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - requestParameters.name
-  - userAgent
-  - aws_account_id
-  - src
-  - region
-  - requestParameters{}.LifecycleConfiguration{}.Rule{}.Expiration{}.Days
-  - requestParameters{}.bucketName
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/put_bucketlifecycle/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/put_bucketlifecycle/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml b/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml
index bbc6c713a8..2562a40bff 100644
--- a/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml
+++ b/detections/cloud/aws_defense_evasion_stop_logging_cloudtrail.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects `StopLogging` events in AWS CloudTrail logs. It leverages CloudTrail event data to identify when logging is intentionally stopped, excluding console-based actions and focusing on successful attempts. This activity is significant because adversaries may stop logging to evade detection and operate stealthily within the compromised environment. If confirmed malicious, this action could allow attackers to perform further activities without being logged, hindering incident response and forensic investigations, and potentially leading to unauthorized access or data exfiltration.
+description: The following analytic detects `StopLogging` events in AWS CloudTrail
+  logs. It leverages CloudTrail event data to identify when logging is intentionally
+  stopped, excluding console-based actions and focusing on successful attempts. This
+  activity is significant because adversaries may stop logging to evade detection
+  and operate stealthily within the compromised environment. If confirmed malicious,
+  this action could allow attackers to perform further activities without being logged,
+  hindering incident response and forensic investigations, and potentially leading
+  to unauthorized access or data exfiltration.
 data_source:
 - AWS CloudTrail StopLogging
-search: '`cloudtrail` eventName = StopLogging eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.name) as stopped_cloudtrail_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_defense_evasion_stop_logging_cloudtrail_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in your AWS Environment.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has stopped cloudtrail logging. Please investigate this activity.
+search: '`cloudtrail` eventName = StopLogging eventSource = cloudtrail.amazonaws.com
+  userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as
+  firstTime max(_time) as lastTime values(requestParameters.name) as stopped_cloudtrail_name
+  by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `aws_defense_evasion_stop_logging_cloudtrail_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in
+  your AWS Environment.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has stopped cloudtrail logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 100
-  message: User $user_arn$ has stopped Cloudtrail logging for account id $aws_account_id$ from IP $src$
+  message: User $user_arn$ has stopped Cloudtrail logging for account id $aws_account_id$
+    from IP $src$
   mitre_attack_id:
   - T1562.008
   - T1562
@@ -45,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - requestParameters.name
-  - userAgent
-  - aws_account_id
-  - src
-  - region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/stop_delete_cloudtrail/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_defense_evasion_update_cloudtrail.yml b/detections/cloud/aws_defense_evasion_update_cloudtrail.yml
index f8dc84ee28..3b3dfed38a 100644
--- a/detections/cloud/aws_defense_evasion_update_cloudtrail.yml
+++ b/detections/cloud/aws_defense_evasion_update_cloudtrail.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects `UpdateTrail` events in AWS CloudTrail logs. It identifies attempts to modify CloudTrail settings, potentially to evade logging. The detection leverages CloudTrail logs, focusing on `UpdateTrail` events where the user agent is not the AWS console and the operation is successful. This activity is significant because altering CloudTrail settings can disable or limit logging, hindering visibility into AWS account activities. If confirmed malicious, this could allow attackers to operate undetected, compromising the integrity and security of the AWS environment.
+description: The following analytic detects `UpdateTrail` events in AWS CloudTrail
+  logs. It identifies attempts to modify CloudTrail settings, potentially to evade
+  logging. The detection leverages CloudTrail logs, focusing on `UpdateTrail` events
+  where the user agent is not the AWS console and the operation is successful. This
+  activity is significant because altering CloudTrail settings can disable or limit
+  logging, hindering visibility into AWS account activities. If confirmed malicious,
+  this could allow attackers to operate undetected, compromising the integrity and
+  security of the AWS environment.
 data_source:
 - AWS CloudTrail UpdateTrail
-search: '`cloudtrail` eventName = UpdateTrail eventSource = cloudtrail.amazonaws.com userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.name) as cloudtrail_name by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `aws_defense_evasion_update_cloudtrail_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in your AWS Environment.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has updated cloudtrail logging. Please investigate this activity.
+search: '`cloudtrail` eventName = UpdateTrail eventSource = cloudtrail.amazonaws.com
+  userAgent !=console.amazonaws.com errorCode = success| stats count min(_time) as
+  firstTime max(_time) as lastTime values(requestParameters.name) as cloudtrail_name
+  by src region eventName userAgent user_arn aws_account_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `aws_defense_evasion_update_cloudtrail_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable CloudTrail logs in
+  your AWS Environment.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has updated cloudtrail logging. Please investigate this activity.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 100
-  message: User $user_arn$ has updated a cloudtrail logging for account id $aws_account_id$ from IP $src$
+  message: User $user_arn$ has updated a cloudtrail logging for account id $aws_account_id$
+    from IP $src$
   mitre_attack_id:
   - T1562
   - T1562.008
@@ -45,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - requestParameters.name
-  - userAgent
-  - aws_account_id
-  - src
-  - region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/update_cloudtrail/aws_cloudtrail_events.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_detect_attach_to_role_policy.yml b/detections/cloud/aws_detect_attach_to_role_policy.yml
index 52223fcad7..1b860a29d2 100644
--- a/detections/cloud/aws_detect_attach_to_role_policy.yml
+++ b/detections/cloud/aws_detect_attach_to_role_policy.yml
@@ -5,11 +5,24 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies a user attaching a policy to a different role's trust policy in AWS. It leverages CloudWatch logs to detect the `attach policy` event, extracting relevant fields such as `policyArn`, `sourceIPAddress`, and `userIdentity`. This activity is significant as it can indicate attempts at lateral movement or privilege escalation within the AWS environment. If confirmed malicious, an attacker could gain elevated permissions, potentially compromising sensitive resources and data within the AWS infrastructure.
+description: The following analytic identifies a user attaching a policy to a different
+  role's trust policy in AWS. It leverages CloudWatch logs to detect the `attach policy`
+  event, extracting relevant fields such as `policyArn`, `sourceIPAddress`, and `userIdentity`.
+  This activity is significant as it can indicate attempts at lateral movement or
+  privilege escalation within the AWS environment. If confirmed malicious, an attacker
+  could gain elevated permissions, potentially compromising sensitive resources and
+  data within the AWS infrastructure.
 data_source: []
-search: '`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate  | `aws_detect_attach_to_role_policy_filter`'
-how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
-known_false_positives: Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies.
+search: '`aws_cloudwatchlogs_eks` attach policy| spath requestParameters.policyArn
+  | table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn
+  eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated
+  userIdentity.sessionContext.attributes.creationDate  | `aws_detect_attach_to_role_policy_filter`'
+how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Attach to policy can create a lot of noise. This search can
+  be adjusted to provide specific values to identify cases of abuse (i.e status=failure).
+  The search can provide context for common users attaching themselves to higher privilege
+  policies or even newly created policies.
 references: []
 tags:
   analytic_story:
@@ -29,7 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - requestParameters.policyArn
   security_domain: threat
diff --git a/detections/cloud/aws_detect_permanent_key_creation.yml b/detections/cloud/aws_detect_permanent_key_creation.yml
index 9554b96ae7..f42cc501fd 100644
--- a/detections/cloud/aws_detect_permanent_key_creation.yml
+++ b/detections/cloud/aws_detect_permanent_key_creation.yml
@@ -5,11 +5,21 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects the creation of permanent access keys in AWS accounts. It leverages CloudWatch logs to identify events where the `CreateAccessKey` action is performed by IAM users. Monitoring the creation of permanent keys is crucial as they are not created by default and are typically used for programmatic access. If confirmed malicious, this activity could allow attackers to gain persistent access to AWS resources, potentially leading to unauthorized actions and data exfiltration.
+description: The following analytic detects the creation of permanent access keys
+  in AWS accounts. It leverages CloudWatch logs to identify events where the `CreateAccessKey`
+  action is performed by IAM users. Monitoring the creation of permanent keys is crucial
+  as they are not created by default and are typically used for programmatic access.
+  If confirmed malicious, this activity could allow attackers to gain persistent access
+  to AWS resources, potentially leading to unauthorized actions and data exfiltration.
 data_source: []
-search: '`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
-known_false_positives: Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context.
+search: '`aws_cloudwatchlogs_eks` CreateAccessKey | spath eventName | search eventName=CreateAccessKey
+  "userIdentity.type"=IAMUser | table sourceIPAddress userName userIdentity.type userAgent
+  action status responseElements.accessKey.createDate responseElements.accessKey.status
+  responseElements.accessKey.accessKeyId |`aws_detect_permanent_key_creation_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Not all permanent key creations are malicious. If there is
+  a policy of rotating keys this search can be adjusted to provide better context.
 references: []
 tags:
   analytic_story:
@@ -29,16 +39,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.type
-  - sourceIPAddress
-  - userName userIdentity.type
-  - userAgent
-  - action
-  - status
-  - responseElements.accessKey.createDate
-  - esponseElements.accessKey.status
-  - responseElements.accessKey.accessKeyId
   security_domain: threat
diff --git a/detections/cloud/aws_detect_role_creation.yml b/detections/cloud/aws_detect_role_creation.yml
index 7f9c91cb39..7f835c849b 100644
--- a/detections/cloud/aws_detect_role_creation.yml
+++ b/detections/cloud/aws_detect_role_creation.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies the creation of new IAM roles by users in AWS. It leverages CloudWatch logs to detect events where the `CreateRole` action is performed, focusing on roles with specific trust policies. This activity is significant as unauthorized role creation can facilitate lateral movement and privilege escalation within the AWS environment. If confirmed malicious, attackers could gain elevated permissions, potentially compromising sensitive resources and data.
+description: The following analytic identifies the creation of new IAM roles by users
+  in AWS. It leverages CloudWatch logs to detect events where the `CreateRole` action
+  is performed, focusing on roles with specific trust policies. This activity is significant
+  as unauthorized role creation can facilitate lateral movement and privilege escalation
+  within the AWS environment. If confirmed malicious, attackers could gain elevated
+  permissions, potentially compromising sensitive resources and data.
 data_source: []
-search: '`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate | `aws_detect_role_creation_filter`'
-how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
-known_false_positives: CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases.
+search: '`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole
+  requestParameters.description=Allows* | table sourceIPAddress userIdentity.principalId
+  userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName
+  requestParameters.description responseElements.role.arn responseElements.role.createDate
+  | `aws_detect_role_creation_filter`'
+how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: CreateRole is not very common in common users. This search
+  can be adjusted to provide specific values to identify cases of abuse. In general
+  AWS provides plenty of trust policies that fit most use cases.
 references: []
 tags:
   analytic_story:
@@ -29,23 +41,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event_name
-  - action
-  - userIdentity.type
-  - requestParameters.description
-  - sourceIPAddress
-  - userIdentity.principalId
-  - userIdentity.arn
-  - action
-  - event_name
-  - awsRegion
-  - http_user_agent
-  - mfa_auth
-  - msg
-  - requestParameters.roleName
-  - requestParameters.description
-  - responseElements.role.arn
-  - responseElements.role.createDate
   security_domain: threat
diff --git a/detections/cloud/aws_detect_sts_assume_role_abuse.yml b/detections/cloud/aws_detect_sts_assume_role_abuse.yml
index 2ee22c5038..f22fa052ea 100644
--- a/detections/cloud/aws_detect_sts_assume_role_abuse.yml
+++ b/detections/cloud/aws_detect_sts_assume_role_abuse.yml
@@ -5,11 +5,24 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies suspicious use of the AWS STS AssumeRole action. It leverages AWS CloudTrail logs to detect instances where roles are assumed, focusing on specific fields like source IP address, user ARN, and role names. This activity is significant because attackers can use assumed roles to move laterally within the AWS environment and escalate privileges. If confirmed malicious, this could allow attackers to gain unauthorized access to sensitive resources, execute code, or further entrench themselves within the environment, leading to potential data breaches or service disruptions.
+description: The following analytic identifies suspicious use of the AWS STS AssumeRole
+  action. It leverages AWS CloudTrail logs to detect instances where roles are assumed,
+  focusing on specific fields like source IP address, user ARN, and role names. This
+  activity is significant because attackers can use assumed roles to move laterally
+  within the AWS environment and escalate privileges. If confirmed malicious, this
+  could allow attackers to gain unauthorized access to sensitive resources, execute
+  code, or further entrench themselves within the environment, leading to potential
+  data breaches or service disruptions.
 data_source: []
-search: '`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role | table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate | `aws_detect_sts_assume_role_abuse_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
-known_false_positives: Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse.
+search: '`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role
+  | table sourceIPAddress userIdentity.arn user_agent user_access_key status action
+  requestParameters.roleName responseElements.role.roleName responseElements.role.createDate
+  | `aws_detect_sts_assume_role_abuse_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs
+known_false_positives: Sts:AssumeRole can be very noisy as it is a standard mechanism
+  to provide cross account and cross resources access. This search can be adjusted
+  to provide specific values to identify cases of abuse.
 references: []
 tags:
   analytic_story:
@@ -29,17 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user_type
-  - userIdentity.sessionContext.sessionIssuer.type
-  - sourceIPAddress
-  - userIdentity.arn
-  - user_agent
-  - user_access_key
-  - status
-  - action
-  - requestParameters.roleName
-  - esponseElements.role.roleName
-  - esponseElements.role.createDate
   security_domain: threat
diff --git a/detections/cloud/aws_detect_sts_get_session_token_abuse.yml b/detections/cloud/aws_detect_sts_get_session_token_abuse.yml
index ef0a4b74ee..9b19860c1c 100644
--- a/detections/cloud/aws_detect_sts_get_session_token_abuse.yml
+++ b/detections/cloud/aws_detect_sts_get_session_token_abuse.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies the suspicious use of the AWS STS GetSessionToken API call. It leverages CloudWatch logs to detect instances where this API is invoked, focusing on fields such as source IP address, event time, user identity, and status. This activity is significant because attackers can use these tokens to move laterally within the AWS environment and escalate privileges. If confirmed malicious, this could lead to unauthorized access and control over AWS resources, potentially compromising sensitive data and critical infrastructure.
+description: The following analytic identifies the suspicious use of the AWS STS GetSessionToken
+  API call. It leverages CloudWatch logs to detect instances where this API is invoked,
+  focusing on fields such as source IP address, event time, user identity, and status.
+  This activity is significant because attackers can use these tokens to move laterally
+  within the AWS environment and escalate privileges. If confirmed malicious, this
+  could lead to unauthorized access and control over AWS resources, potentially compromising
+  sensitive data and critical infrastructure.
 data_source: []
-search: '`aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser| spath eventName | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter`'
-how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
-known_false_positives: Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used.
+search: '`aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser| spath eventName
+  | search eventName=GetSessionToken | table sourceIPAddress eventTime userIdentity.arn
+  userName userAgent user_type status region | `aws_detect_sts_get_session_token_abuse_filter`'
+how_to_implement: You must install splunk AWS add-on and Splunk App for AWS. This
+  search works with cloudwatch logs
+known_false_positives: Sts:GetSessionToken can be very noisy as in certain environments
+  numerous calls of this type can be executed. This search can be adjusted to provide
+  specific values to identify cases of abuse. In specific environments the use of
+  field requestParameters.serialNumber will need to be used.
 references: []
 tags:
   analytic_story:
@@ -29,16 +41,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.type
-  - eventName
-  - sourceIPAddress
-  - eventTime
-  - userIdentity.arn
-  - userName
-  - userAgent
-  - user_type
-  - status
-  - region
   security_domain: threat
diff --git a/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml b/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml
index a0a6bf7fdb..5c446a35b8 100644
--- a/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml
+++ b/detections/cloud/aws_detect_users_creating_keys_with_encrypt_policy_without_mfa.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Rod Soto, Patrick Bareiss Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of AWS KMS keys with an encryption policy accessible to everyone, including external entities. It leverages AWS CloudTrail logs to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action is granted to all principals. This activity is significant as it may indicate a compromised account, allowing an attacker to misuse the encryption key to target other organizations. If confirmed malicious, this could lead to unauthorized data encryption, potentially disrupting operations and compromising sensitive information across multiple entities.
+description: The following analytic detects the creation of AWS KMS keys with an encryption
+  policy accessible to everyone, including external entities. It leverages AWS CloudTrail
+  logs to identify `CreateKey` or `PutKeyPolicy` events where the `kms:Encrypt` action
+  is granted to all principals. This activity is significant as it may indicate a
+  compromised account, allowing an attacker to misuse the encryption key to target
+  other organizations. If confirmed malicious, this could lead to unauthorized data
+  encryption, potentially disrupting operations and compromising sensitive information
+  across multiple entities.
 data_source:
 - AWS CloudTrail CreateKey
 - AWS CloudTrail PutKeyPolicy
-search: '`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy output=key_policy_statements path=Statement{} | mvexpand key_policy_statements | spath input=key_policy_statements output=key_policy_action_1 path=Action | spath input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*" | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
+search: '`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy | spath input=requestParameters.policy
+  output=key_policy_statements path=Statement{} | mvexpand key_policy_statements |
+  spath input=key_policy_statements output=key_policy_action_1 path=Action | spath
+  input=key_policy_statements output=key_policy_action_2 path=Action{} | eval key_policy_action=mvappend(key_policy_action_1,
+  key_policy_action_2) | spath input=key_policy_statements output=key_policy_principal
+  path=Principal.AWS | search key_policy_action="kms:Encrypt" AND key_policy_principal="*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource
+  eventID awsRegion userIdentity.principalId user | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` |`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs
 known_false_positives: unknown
 references:
 - https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
@@ -22,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: AWS Account
   confidence: 50
   impact: 50
-  message: AWS account is potentially compromised and user $user$ is trying to compromise other accounts.
+  message: AWS account is potentially compromised and user $user$ is trying to compromise
+    other accounts.
   mitre_attack_id:
   - T1486
   observable:
@@ -43,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - eventID
-  - awsRegion
-  - requestParameters.policy
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml b/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml
index d613fad396..5c243b0c88 100644
--- a/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml
+++ b/detections/cloud/aws_detect_users_with_kms_keys_performing_encryption_s3.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Rod Soto, Patrick Bareiss Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies users with KMS keys performing encryption operations on S3 buckets. It leverages AWS CloudTrail logs to detect the `CopyObject` event where server-side encryption with AWS KMS is specified. This activity is significant as it may indicate unauthorized or suspicious encryption of data, potentially masking exfiltration or tampering efforts. If confirmed malicious, an attacker could be encrypting sensitive data to evade detection or preparing it for exfiltration, posing a significant risk to data integrity and confidentiality.
+description: The following analytic identifies users with KMS keys performing encryption
+  operations on S3 buckets. It leverages AWS CloudTrail logs to detect the `CopyObject`
+  event where server-side encryption with AWS KMS is specified. This activity is significant
+  as it may indicate unauthorized or suspicious encryption of data, potentially masking
+  exfiltration or tampering efforts. If confirmed malicious, an attacker could be
+  encrypting sensitive data to evade detection or preparing it for exfiltration, posing
+  a significant risk to data integrity and confidentiality.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucketName, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(bucketName) as bucketName values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`'
-how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
+search: '`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms"
+  | rename requestParameters.bucketName AS bucketName, requestParameters.x-amz-copy-source
+  AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime
+  max(_time) as lastTime values(bucketName) as bucketName values(src_file) AS src_file
+  values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS
+  region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`'
+how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs
 known_false_positives: There maybe buckets provisioned with S3 encryption
 references:
 - https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: S3 Bucket
   confidence: 50
   impact: 30
-  message: User $user$ with KMS keys is performing encryption, against S3 buckets on these files $dest_file$
+  message: User $user$ with KMS keys is performing encryption, against S3 buckets
+    on these files $dest_file$
   mitre_attack_id:
   - T1486
   observable:
@@ -42,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - requestParameters.x-amz-server-side-encryption
-  - requestParameters.bucketName
-  - requestParameters.x-amz-copy-source
-  - requestParameters.key
-  - userAgent
-  - region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_disable_bucket_versioning.yml b/detections/cloud/aws_disable_bucket_versioning.yml
index d1e61c5635..f01c50608a 100644
--- a/detections/cloud/aws_disable_bucket_versioning.yml
+++ b/detections/cloud/aws_disable_bucket_versioning.yml
@@ -7,10 +7,22 @@ status: production
 type: Anomaly
 data_source:
 - AWS CloudTrail PutBucketVersioning
-description: The following analytic detects when AWS S3 bucket versioning is suspended by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant because disabling versioning can prevent recovery of deleted or modified data, which is a common tactic in ransomware attacks. If confirmed malicious, this action could lead to data loss and hinder recovery efforts, severely impacting data integrity and availability.
-search: '`cloudtrail` eventName= PutBucketVersioning "requestParameters.VersioningConfiguration.Status"=Suspended |  stats count values(requestParameters.bucketName) as bucket_name values(resources{}.ARN) as resource_arn by src_ip aws_account_id awsRegion eventName userAgent user_arn userIdentity.principalId  errorCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `aws_disable_bucket_versioning_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that an AWS Administrator has legitimately disabled versioning on certain buckets to avoid costs.
+description: The following analytic detects when AWS S3 bucket versioning is suspended
+  by a user. It leverages AWS CloudTrail logs to identify `PutBucketVersioning` events
+  with the `VersioningConfiguration.Status` set to `Suspended`. This activity is significant
+  because disabling versioning can prevent recovery of deleted or modified data, which
+  is a common tactic in ransomware attacks. If confirmed malicious, this action could
+  lead to data loss and hinder recovery efforts, severely impacting data integrity
+  and availability.
+search: '`cloudtrail` eventName= PutBucketVersioning "requestParameters.VersioningConfiguration.Status"=Suspended
+  |  stats count values(requestParameters.bucketName) as bucket_name values(resources{}.ARN)
+  as resource_arn by src_ip aws_account_id awsRegion eventName userAgent user_arn
+  userIdentity.principalId  errorCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `aws_disable_bucket_versioning_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that an AWS Administrator has legitimately disabled
+  versioning on certain buckets to avoid costs.
 references:
 - https://invictus-ir.medium.com/ransomware-in-the-cloud-7f14805bbe82
 - https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 80
-  message: Bucket Versioning is suspended for S3 buckets- $bucket_name$ by user $user_arn$ from IP address $src_ip$
+  message: Bucket Versioning is suspended for S3 buckets- $bucket_name$ by user $user_arn$
+    from IP address $src_ip$
   mitre_attack_id:
   - T1490
   observable:
@@ -50,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - aws_account_id
-  - destinationLocationArn
-  - sourceLocationArn
-  - userAgent
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/aws_bucket_version/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/aws_bucket_version/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_ec2_snapshot_shared_externally.yml b/detections/cloud/aws_ec2_snapshot_shared_externally.yml
index 13e27e5bf6..2f4b92b25a 100644
--- a/detections/cloud/aws_ec2_snapshot_shared_externally.yml
+++ b/detections/cloud/aws_ec2_snapshot_shared_externally.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects when an EC2 snapshot is shared with an external AWS account by analyzing AWS CloudTrail events. This detection method leverages CloudTrail logs to identify modifications in snapshot permissions, specifically when the snapshot is shared outside the originating AWS account. This activity is significant as it may indicate an attempt to exfiltrate sensitive data stored in the snapshot. If confirmed malicious, an attacker could gain unauthorized access to the snapshot's data, potentially leading to data breaches or further exploitation of the compromised information.
+description: The following analytic detects when an EC2 snapshot is shared with an
+  external AWS account by analyzing AWS CloudTrail events. This detection method leverages
+  CloudTrail logs to identify modifications in snapshot permissions, specifically
+  when the snapshot is shared outside the originating AWS account. This activity is
+  significant as it may indicate an attempt to exfiltrate sensitive data stored in
+  the snapshot. If confirmed malicious, an attacker could gain unauthorized access
+  to the snapshot's data, potentially leading to data breaches or further exploitation
+  of the compromised information.
 data_source:
 - AWS CloudTrail ModifySnapshotAttribute
-search: '`cloudtrail` eventName=ModifySnapshotAttribute | rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id | search requested_account_id != NULL | eval match=if(requested_account_id==aws_account_id,"Match","No Match") | table _time user_arn src_ip requestParameters.attributeType requested_account_id aws_account_id match vendor_region user_agent userIdentity.principalId | where match = "No Match" | `aws_ec2_snapshot_shared_externally_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with others for  a specific purpose.
+search: '`cloudtrail` eventName=ModifySnapshotAttribute | rename requestParameters.createVolumePermission.add.items{}.userId
+  as requested_account_id | search requested_account_id != NULL | eval match=if(requested_account_id==aws_account_id,"Match","No
+  Match") | table _time user_arn src_ip requestParameters.attributeType requested_account_id
+  aws_account_id match vendor_region user_agent userIdentity.principalId | where match
+  = "No Match" | `aws_ec2_snapshot_shared_externally_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that an AWS admin has legitimately shared a
+  snapshot with others for  a specific purpose.
 references:
 - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
 - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: EC2 Snapshot
   confidence: 80
   impact: 60
-  message: AWS EC2 snapshot from account $aws_account_id$ is shared with $requested_account_id$ by user $user_arn$ from $src_ip$
+  message: AWS EC2 snapshot from account $aws_account_id$ is shared with $requested_account_id$
+    by user $user_arn$ from $src_ip$
   mitre_attack_id:
   - T1537
   observable:
@@ -51,20 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - requestParameters.attributeType
-  - aws_account_id
-  - vendor_region
-  - user_agent
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_ecr_container_scanning_findings_high.yml b/detections/cloud/aws_ecr_container_scanning_findings_high.yml
index f420708bbe..8abd9c4cf0 100644
--- a/detections/cloud/aws_ecr_container_scanning_findings_high.yml
+++ b/detections/cloud/aws_ecr_container_scanning_findings_high.yml
@@ -5,11 +5,28 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic identifies high-severity findings from AWS Elastic Container Registry (ECR) image scans. It detects these activities by analyzing AWS CloudTrail logs for the DescribeImageScanFindings event, specifically filtering for findings with a high severity level. This activity is significant for a SOC because high-severity vulnerabilities in container images can lead to potential exploitation if not addressed. If confirmed malicious, attackers could exploit these vulnerabilities to gain unauthorized access, execute arbitrary code, or escalate privileges within the container environment, posing a significant risk to the overall security posture.
+description: The following analytic identifies high-severity findings from AWS Elastic
+  Container Registry (ECR) image scans. It detects these activities by analyzing AWS
+  CloudTrail logs for the DescribeImageScanFindings event, specifically filtering
+  for findings with a high severity level. This activity is significant for a SOC
+  because high-severity vulnerabilities in container images can lead to potential
+  exploitation if not addressed. If confirmed malicious, attackers could exploit these
+  vulnerabilities to gain unauthorized access, execute arbitrary code, or escalate
+  privileges within the container environment, posing a significant risk to the overall
+  security posture.
 data_source:
 - AWS CloudTrail DescribeImageScanFindings
-search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand findings | spath input=findings | search severity=HIGH | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository, userIdentity.principalId as user | eval finding = finding_name.", ".finding_description | eval phase="release" | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repository, user, src_ip, finding, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_high_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings
+  | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand
+  findings | spath input=findings | search severity=HIGH | rename name as finding_name,
+  description as finding_description, requestParameters.imageId.imageDigest as imageDigest,
+  requestParameters.repositoryName as repository, userIdentity.principalId as user
+  | eval finding = finding_name.", ".finding_description | eval phase="release" |
+  eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion,
+  eventName, eventSource, imageDigest, repository, user, src_ip, finding, phase, severity
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_high_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: unknown
 references:
 - https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$repository$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - eventSource
-  - eventName
-  - responseElements.imageScanFindings.findings{}
-  - awsRegion
-  - requestParameters.imageId.imageDigest
-  - requestParameters.repositoryName
-  - user
-  - userName
-  - src_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml b/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml
index 4e56826322..e700d5935e 100644
--- a/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml
+++ b/detections/cloud/aws_ecr_container_scanning_findings_low_informational_unknown.yml
@@ -5,11 +5,28 @@ date: '2024-09-30'
 author: Patrick Bareiss, Eric McGinnis Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies low, informational, or unknown severity findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically the DescribeImageScanFindings event, to detect these findings. This activity is significant for a SOC as it helps in early identification of potential vulnerabilities or misconfigurations in container images, which could be exploited if left unaddressed. If confirmed malicious, these findings could lead to unauthorized access, data breaches, or further exploitation within the containerized environment.
+description: The following analytic identifies low, informational, or unknown severity
+  findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS
+  CloudTrail logs, specifically the DescribeImageScanFindings event, to detect these
+  findings. This activity is significant for a SOC as it helps in early identification
+  of potential vulnerabilities or misconfigurations in container images, which could
+  be exploited if left unaddressed. If confirmed malicious, these findings could lead
+  to unauthorized access, data breaches, or further exploitation within the containerized
+  environment.
 data_source:
 - AWS CloudTrail DescribeImageScanFindings
-search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand findings | spath input=findings| search severity IN ("LOW", "INFORMATIONAL", "UNKNOWN") | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository, userIdentity.principalId as user | eval finding = finding_name.", ".finding_description | eval phase="release" | eval severity="low" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repository, user, src_ip, finding, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_low_informational_unknown_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings
+  | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand
+  findings | spath input=findings| search severity IN ("LOW", "INFORMATIONAL", "UNKNOWN")
+  | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest
+  as imageDigest, requestParameters.repositoryName as repository, userIdentity.principalId
+  as user | eval finding = finding_name.", ".finding_description | eval phase="release"
+  | eval severity="low" | stats min(_time) as firstTime max(_time) as lastTime by
+  awsRegion, eventName, eventSource, imageDigest, repository, user, src_ip, finding,
+  phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `aws_ecr_container_scanning_findings_low_informational_unknown_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: unknown
 references:
 - https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$repository$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - eventSource
-  - eventName
-  - responseElements.imageScanFindings.findings{}
-  - awsRegion
-  - requestParameters.imageId.imageDigest
-  - requestParameters.repositoryName
-  - user
-  - userName
-  - src_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_ecr_container_scanning_findings_medium.yml b/detections/cloud/aws_ecr_container_scanning_findings_medium.yml
index eeca279e83..effa97fd81 100644
--- a/detections/cloud/aws_ecr_container_scanning_findings_medium.yml
+++ b/detections/cloud/aws_ecr_container_scanning_findings_medium.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies medium-severity findings from AWS Elastic Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically the DescribeImageScanFindings event, to detect vulnerabilities in container images. This activity is significant for a SOC as it highlights potential security risks in containerized applications, which could be exploited if not addressed. If confirmed malicious, these vulnerabilities could lead to unauthorized access, data breaches, or further exploitation within the container environment, compromising the overall security posture.
+description: The following analytic identifies medium-severity findings from AWS Elastic
+  Container Registry (ECR) image scans. It leverages AWS CloudTrail logs, specifically
+  the DescribeImageScanFindings event, to detect vulnerabilities in container images.
+  This activity is significant for a SOC as it highlights potential security risks
+  in containerized applications, which could be exploited if not addressed. If confirmed
+  malicious, these vulnerabilities could lead to unauthorized access, data breaches,
+  or further exploitation within the container environment, compromising the overall
+  security posture.
 data_source:
 - AWS CloudTrail DescribeImageScanFindings
-search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand findings | spath input=findings| search severity=MEDIUM | rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repository, userIdentity.principalId as user| eval finding = finding_name.", ".finding_description | eval phase="release" | eval severity="medium" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repository, user, src_ip, finding, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_medium_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings
+  | spath path=responseElements.imageScanFindings.findings{} output=findings | mvexpand
+  findings | spath input=findings| search severity=MEDIUM | rename name as finding_name,
+  description as finding_description, requestParameters.imageId.imageDigest as imageDigest,
+  requestParameters.repositoryName as repository, userIdentity.principalId as user|
+  eval finding = finding_name.", ".finding_description | eval phase="release" | eval
+  severity="medium" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion,
+  eventName, eventSource, imageDigest, repository, user, src_ip, finding, phase, severity
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_scanning_findings_medium_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: unknown
 references:
 - https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$repository$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - eventSource
-  - eventName
-  - responseElements.imageScanFindings.findings{}
-  - awsRegion
-  - requestParameters.imageId.imageDigest
-  - requestParameters.repositoryName
-  - user
-  - userName
-  - src_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_image_scanning/aws_ecr_scanning_findings_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml b/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml
index c40ba491ef..e48f821207 100644
--- a/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml
+++ b/detections/cloud/aws_ecr_container_upload_outside_business_hours.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the upload of a new container image to AWS Elastic Container Registry (ECR) outside of standard business hours. It leverages AWS CloudTrail logs to identify `PutImage` events occurring between 8 PM and 8 AM or on weekends. This activity is significant because container uploads outside business hours can indicate unauthorized or suspicious activity, potentially pointing to a compromised account or insider threat. If confirmed malicious, this could allow an attacker to deploy unauthorized or malicious containers, leading to potential data breaches or service disruptions.
+description: The following analytic detects the upload of a new container image to
+  AWS Elastic Container Registry (ECR) outside of standard business hours. It leverages
+  AWS CloudTrail logs to identify `PutImage` events occurring between 8 PM and 8 AM
+  or on weekends. This activity is significant because container uploads outside business
+  hours can indicate unauthorized or suspicious activity, potentially pointing to
+  a compromised account or insider threat. If confirmed malicious, this could allow
+  an attacker to deploy unauthorized or malicious containers, leading to potential
+  data breaches or service disruptions.
 data_source:
 - AWS CloudTrail PutImage
-search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage date_hour>=20 OR date_hour<8 OR date_wday=saturday OR date_wday=sunday | rename requestParameters.* as * | rename repositoryName AS repository | eval phase="release" | eval severity="medium" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, user, userName, src_ip, imageTag, registryId, repository, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_upload_outside_business_hours_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: When your development is spreaded in different time zones, applying this rule can be difficult.
+search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage date_hour>=20
+  OR date_hour<8 OR date_wday=saturday OR date_wday=sunday | rename requestParameters.*
+  as * | rename repositoryName AS repository | eval phase="release" | eval severity="medium"
+  | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName,
+  eventSource, user, userName, src_ip, imageTag, registryId, repository, phase, severity
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_upload_outside_business_hours_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: When your development is spreaded in different time zones,
+  applying this rule can be difficult.
 references:
 - https://attack.mitre.org/techniques/T1204/003/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - eventSource
-  - eventName
-  - awsRegion
-  - requestParameters.imageTag
-  - requestParameters.registryId
-  - requestParameters.repositoryName
-  - user
-  - userName
-  - src_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_ecr_container_upload_unknown_user.yml b/detections/cloud/aws_ecr_container_upload_unknown_user.yml
index 993f73b6cf..8e1a2fed61 100644
--- a/detections/cloud/aws_ecr_container_upload_unknown_user.yml
+++ b/detections/cloud/aws_ecr_container_upload_unknown_user.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the upload of a new container image to AWS Elastic Container Registry (ECR) by an unknown user. It leverages AWS CloudTrail logs to identify `PutImage` events from the ECR service, filtering out known users. This activity is significant because container uploads should typically be performed by a limited set of authorized users. If confirmed malicious, this could indicate unauthorized access, potentially leading to the deployment of malicious containers, data exfiltration, or further compromise of the AWS environment.
+description: The following analytic detects the upload of a new container image to
+  AWS Elastic Container Registry (ECR) by an unknown user. It leverages AWS CloudTrail
+  logs to identify `PutImage` events from the ECR service, filtering out known users.
+  This activity is significant because container uploads should typically be performed
+  by a limited set of authorized users. If confirmed malicious, this could indicate
+  unauthorized access, potentially leading to the deployment of malicious containers,
+  data exfiltration, or further compromise of the AWS environment.
 data_source:
 - AWS CloudTrail PutImage
-search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage NOT `aws_ecr_users` | rename requestParameters.* as * | rename repositoryName AS image | eval phase="release" | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, user, userName, src_ip, imageTag, registryId, image, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_ecr_container_upload_unknown_user_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '`cloudtrail` eventSource=ecr.amazonaws.com eventName=PutImage NOT `aws_ecr_users`
+  | rename requestParameters.* as * | rename repositoryName AS image | eval phase="release"
+  | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime by
+  awsRegion, eventName, eventSource, user, userName, src_ip, imageTag, registryId,
+  image, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `aws_ecr_container_upload_unknown_user_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1204/003/
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - eventSource
-  - eventName
-  - awsRegion
-  - requestParameters.imageTag
-  - requestParameters.registryId
-  - requestParameters.repositoryName
-  - user
-  - userName
-  - src_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.003/aws_ecr_container_upload/aws_ecr_container_upload.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_excessive_security_scanning.yml b/detections/cloud/aws_excessive_security_scanning.yml
index 7635262377..36320d794d 100644
--- a/detections/cloud/aws_excessive_security_scanning.yml
+++ b/detections/cloud/aws_excessive_security_scanning.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic identifies excessive security scanning activities in AWS by detecting a high number of Describe, List, or Get API calls from a single user. It leverages AWS CloudTrail logs to count distinct event names and flags users with more than 50 such events. This behavior is significant as it may indicate reconnaissance activities by an attacker attempting to map out your AWS environment. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further exploitation of your cloud infrastructure.
+description: The following analytic identifies excessive security scanning activities
+  in AWS by detecting a high number of Describe, List, or Get API calls from a single
+  user. It leverages AWS CloudTrail logs to count distinct event names and flags users
+  with more than 50 such events. This behavior is significant as it may indicate reconnaissance
+  activities by an attacker attempting to map out your AWS environment. If confirmed
+  malicious, this could lead to unauthorized access, data exfiltration, or further
+  exploitation of your cloud infrastructure.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get*  | stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as command values(src) as src values(userAgent) as userAgent by user userIdentity.arn | where dc_events > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
+search: '`cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get*  |
+  stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime
+  values(eventName) as command values(src) as src values(userAgent) as userAgent by
+  user userIdentity.arn | where dc_events > 50 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
 known_false_positives: While this search has no known false positives.
 references:
 - https://github.com/aquasecurity/cloudsploit
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +44,8 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 30
-  message: User $user$ has excessive number of api calls $dc_events$ from these IP addresses $src$, violating the threshold of 50, using the following commands $command$.
+  message: User $user$ has excessive number of api calls $dc_events$ from these IP
+    addresses $src$, violating the threshold of 50, using the following commands $command$.
   mitre_attack_id:
   - T1526
   observable:
@@ -44,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - src
-  - userAgent
-  - user
-  - userIdentity.arn
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml b/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml
index 484d4db668..4b41f985a5 100644
--- a/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml
+++ b/detections/cloud/aws_exfiltration_via_anomalous_getobject_api_activity.yml
@@ -7,10 +7,22 @@ status: production
 type: Anomaly
 data_source:
 - AWS CloudTrail GetObject
-description: The following analytic identifies anomalous GetObject API activity in AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail logs and uses the `anomalydetection` command to detect unusual patterns in the frequency of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" within a 10-minute window. This activity is significant as it may indicate unauthorized data access or exfiltration from S3 buckets. If confirmed malicious, attackers could exfiltrate sensitive data, leading to data breaches and compliance violations.
-search: '`cloudtrail` eventName=GetObject | bin _time span=10m |  stats count values(requestParameters.bucketName) as bucketName by _time src_ip aws_account_id user_type user_arn userIdentity.principalId | anomalydetection "count" "user_type" "user_arn" action=annotate | search probable_cause=* |`aws_exfiltration_via_anomalous_getobject_api_activity_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that a user downloaded these files to use them locally and there are AWS services in configured that perform these activities for a legitimate reason. Filter is needed.
+description: The following analytic identifies anomalous GetObject API activity in
+  AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail
+  logs and uses the `anomalydetection` command to detect unusual patterns in the frequency
+  of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn"
+  within a 10-minute window. This activity is significant as it may indicate unauthorized
+  data access or exfiltration from S3 buckets. If confirmed malicious, attackers could
+  exfiltrate sensitive data, leading to data breaches and compliance violations.
+search: '`cloudtrail` eventName=GetObject | bin _time span=10m |  stats count values(requestParameters.bucketName)
+  as bucketName by _time src_ip aws_account_id user_type user_arn userIdentity.principalId
+  | anomalydetection "count" "user_type" "user_arn" action=annotate | search probable_cause=*
+  |`aws_exfiltration_via_anomalous_getobject_api_activity_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that a user downloaded these files to use them
+  locally and there are AWS services in configured that perform these activities for
+  a legitimate reason. Filter is needed.
 references:
 - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
 - https://docs.splunk.com/Documentation/Splunk/9.0.4/SearchReference/Anomalydetection
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - aws_account_id
-  - userAgent
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_exfil_high_no_getobject/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_exfil_high_no_getobject/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_exfiltration_via_batch_service.yml b/detections/cloud/aws_exfiltration_via_batch_service.yml
index 1ed9241f12..4b4413968b 100644
--- a/detections/cloud/aws_exfiltration_via_batch_service.yml
+++ b/detections/cloud/aws_exfiltration_via_batch_service.yml
@@ -7,10 +7,21 @@ status: production
 type: TTP
 data_source:
 - AWS CloudTrail JobCreated
-description: The following analytic identifies the creation of AWS Batch jobs that could potentially abuse the AWS Bucket Replication feature on S3 buckets. It leverages AWS CloudTrail logs to detect the `JobCreated` event, analyzing job details and their status. This activity is significant because attackers can exploit this feature to exfiltrate data by creating malicious batch jobs. If confirmed malicious, this could lead to unauthorized data transfer between S3 buckets, resulting in data breaches and loss of sensitive information.
-search: '`cloudtrail` eventName = JobCreated |  stats count min(_time) as firstTime max(_time) as lastTime values(serviceEventDetails.jobArn) as job_arn values(serviceEventDetails.status) as status by src_ip  aws_account_id eventName errorCode userAgent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_batch_service_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that an AWS Administrator or a user has legitimately created this job for some tasks.
+description: The following analytic identifies the creation of AWS Batch jobs that
+  could potentially abuse the AWS Bucket Replication feature on S3 buckets. It leverages
+  AWS CloudTrail logs to detect the `JobCreated` event, analyzing job details and
+  their status. This activity is significant because attackers can exploit this feature
+  to exfiltrate data by creating malicious batch jobs. If confirmed malicious, this
+  could lead to unauthorized data transfer between S3 buckets, resulting in data breaches
+  and loss of sensitive information.
+search: '`cloudtrail` eventName = JobCreated |  stats count min(_time) as firstTime
+  max(_time) as lastTime values(serviceEventDetails.jobArn) as job_arn values(serviceEventDetails.status)
+  as status by src_ip  aws_account_id eventName errorCode userAgent | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_exfiltration_via_batch_service_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that an AWS Administrator or a user has legitimately
+  created this job for some tasks.
 references:
 - https://hackingthe.cloud/aws/exploitation/s3-bucket-replication-exfiltration/
 - https://bleemb.medium.com/data-exfiltration-with-native-aws-s3-features-c94ae4d13436
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - aws_account_id
-  - userAgent
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_exfiltration_via_bucket_replication.yml b/detections/cloud/aws_exfiltration_via_bucket_replication.yml
index e07a1a7d89..4dca2f3395 100644
--- a/detections/cloud/aws_exfiltration_via_bucket_replication.yml
+++ b/detections/cloud/aws_exfiltration_via_bucket_replication.yml
@@ -7,19 +7,38 @@ status: production
 type: TTP
 data_source:
 - AWS CloudTrail PutBucketReplication
-description: The following analytic detects API calls to enable S3 bucket replication services. It leverages AWS CloudTrail logs to identify `PutBucketReplication` events, focusing on fields like `bucketName`, `ReplicationConfiguration.Rule.Destination.Bucket`, and user details. This activity is significant as it can indicate unauthorized data replication, potentially leading to data exfiltration. If confirmed malicious, attackers could replicate sensitive data to external accounts, leading to data breaches and compliance violations.
-search: '`cloudtrail`  eventName = PutBucketReplication eventSource = s3.amazonaws.com | rename requestParameters.* as * | stats count values(bucketName) as source_bucket values(ReplicationConfiguration.Rule.ID) as rule_id values(ReplicationConfiguration.Rule.Destination.Bucket) as destination_bucket by _time user_arn userName user_type src_ip aws_account_id userIdentity.principalId user_agent | `aws_exfiltration_via_bucket_replication_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that an AWS admin has legitimately implemented data replication to ensure data availability and improve data protection/backup strategies.
+description: The following analytic detects API calls to enable S3 bucket replication
+  services. It leverages AWS CloudTrail logs to identify `PutBucketReplication` events,
+  focusing on fields like `bucketName`, `ReplicationConfiguration.Rule.Destination.Bucket`,
+  and user details. This activity is significant as it can indicate unauthorized data
+  replication, potentially leading to data exfiltration. If confirmed malicious, attackers
+  could replicate sensitive data to external accounts, leading to data breaches and
+  compliance violations.
+search: '`cloudtrail`  eventName = PutBucketReplication eventSource = s3.amazonaws.com
+  | rename requestParameters.* as * | stats count values(bucketName) as source_bucket
+  values(ReplicationConfiguration.Rule.ID) as rule_id values(ReplicationConfiguration.Rule.Destination.Bucket)
+  as destination_bucket by _time user_arn userName user_type src_ip aws_account_id
+  userIdentity.principalId user_agent | `aws_exfiltration_via_bucket_replication_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that an AWS admin has legitimately implemented
+  data replication to ensure data availability and improve data protection/backup
+  strategies.
 references:
 - https://hackingthe.cloud/aws/exploitation/s3-bucket-replication-exfiltration/
 drilldown_searches:
 - name: View the detection results for - "$user_arn$" and "$aws_account_id$"
-  search: '%original_detection_search% | search  user_arn = "$user_arn$" aws_account_id = "$aws_account_id$"'
+  search: '%original_detection_search% | search  user_arn = "$user_arn$" aws_account_id
+    = "$aws_account_id$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$" and "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$", "$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$",
+    "$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: EC2 Snapshot
   confidence: 80
   impact: 80
-  message: AWS Bucket Replication rule $rule_id$ added on $source_bucket$ to $destination_bucket$ by user $user_arn$ from IP Address - $src_ip$
+  message: AWS Bucket Replication rule $rule_id$ added on $source_bucket$ to $destination_bucket$
+    by user $user_arn$ from IP Address - $src_ip$
   mitre_attack_id:
   - T1537
   observable:
@@ -49,21 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - eventSource
-  - requestParameters.*
-  - aws_account_id
-  - vendor_region
-  - user_agent
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_exfiltration_via_datasync_task.yml b/detections/cloud/aws_exfiltration_via_datasync_task.yml
index b1aad088f6..06abc150fe 100644
--- a/detections/cloud/aws_exfiltration_via_datasync_task.yml
+++ b/detections/cloud/aws_exfiltration_via_datasync_task.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - AWS CloudTrail CreateTask
-description: The following analytic detects the creation of an AWS DataSync task, which could indicate potential data exfiltration. It leverages AWS CloudTrail logs to identify the `CreateTask` event from the DataSync service. This activity is significant because attackers can misuse DataSync to transfer sensitive data from a private AWS location to a public one, leading to data compromise. If confirmed malicious, this could result in unauthorized access to sensitive information, causing severe data breaches and compliance violations.
-search: '`cloudtrail` eventName = CreateTask eventSource="datasync.amazonaws.com" | rename  requestParameters.*  as *  | stats count min(_time) as firstTime max(_time) as lastTime by src_ip aws_account_id awsRegion eventName destinationLocationArn sourceLocationArn userAgent user_arn userIdentity.principalId  errorCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_exfiltration_via_datasync_task_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: It is possible that an AWS Administrator has legitimately created this task for creating backup. Please check the `sourceLocationArn` and `destinationLocationArn` of this task
+description: The following analytic detects the creation of an AWS DataSync task,
+  which could indicate potential data exfiltration. It leverages AWS CloudTrail logs
+  to identify the `CreateTask` event from the DataSync service. This activity is significant
+  because attackers can misuse DataSync to transfer sensitive data from a private
+  AWS location to a public one, leading to data compromise. If confirmed malicious,
+  this could result in unauthorized access to sensitive information, causing severe
+  data breaches and compliance violations.
+search: '`cloudtrail` eventName = CreateTask eventSource="datasync.amazonaws.com"
+  | rename  requestParameters.*  as *  | stats count min(_time) as firstTime max(_time)
+  as lastTime by src_ip aws_account_id awsRegion eventName destinationLocationArn
+  sourceLocationArn userAgent user_arn userIdentity.principalId  errorCode | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_exfiltration_via_datasync_task_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: It is possible that an AWS Administrator has legitimately created
+  this task for creating backup. Please check the `sourceLocationArn` and `destinationLocationArn`
+  of this task
 references:
 - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
 - https://www.shehackske.com/how-to/data-exfiltration-on-cloud-1606/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 80
-  message: DataSync task created on account id - $aws_account_id$ by user $user_arn$ from src_ip $src_ip$
+  message: DataSync task created on account id - $aws_account_id$ by user $user_arn$
+    from src_ip $src_ip$
   mitre_attack_id:
   - T1119
   observable:
@@ -50,20 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - aws_account_id
-  - destinationLocationArn
-  - sourceLocationArn
-  - userAgent
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1119/aws_exfil_datasync/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml b/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml
index eac5e85279..8767c1261b 100644
--- a/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml
+++ b/detections/cloud/aws_exfiltration_via_ec2_snapshot.yml
@@ -10,10 +10,27 @@ data_source:
 - AWS CloudTrail DescribeSnapshotAttribute
 - AWS CloudTrail ModifySnapshotAttribute
 - AWS CloudTrail DeleteSnapshot
-description: The following analytic detects a series of AWS API calls related to EC2 snapshots within a short time window, indicating potential exfiltration via EC2 Snapshot modifications. It leverages AWS CloudTrail logs to identify actions such as creating, describing, and modifying snapshot attributes. This activity is significant as it may indicate an attacker attempting to exfiltrate data by sharing EC2 snapshots externally. If confirmed malicious, the attacker could gain access to sensitive information stored in the snapshots, leading to data breaches and potential compliance violations.
-search: '`cloudtrail` eventName IN ("CreateSnapshot", "DescribeSnapshotAttribute", "ModifySnapshotAttribute", "DeleteSnapshot") src_ip !="guardduty.amazonaws.com" |  bin _time span=5m |  stats count dc(eventName) as distinct_api_calls values(eventName)  values(requestParameters.attributeType) as attributeType values(requestParameters.createVolumePermission.add.items{}.userId) as aws_account_id_added values(userAgent) as userAgent by _time userName src_ip aws_account_id | where distinct_api_calls >= 2 | `aws_exfiltration_via_ec2_snapshot_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs. We have intentionally removed `guardduty.amazonaws.com` from src_ip to remove false positives caused by guard duty. We recommend you adjust the time window as per your environment.
-known_false_positives: It is possible that an AWS admin has legitimately shared a snapshot with an other account for a specific purpose. Please check any recent change requests filed in your organization.
+description: The following analytic detects a series of AWS API calls related to EC2
+  snapshots within a short time window, indicating potential exfiltration via EC2
+  Snapshot modifications. It leverages AWS CloudTrail logs to identify actions such
+  as creating, describing, and modifying snapshot attributes. This activity is significant
+  as it may indicate an attacker attempting to exfiltrate data by sharing EC2 snapshots
+  externally. If confirmed malicious, the attacker could gain access to sensitive
+  information stored in the snapshots, leading to data breaches and potential compliance
+  violations.
+search: '`cloudtrail` eventName IN ("CreateSnapshot", "DescribeSnapshotAttribute",
+  "ModifySnapshotAttribute", "DeleteSnapshot") src_ip !="guardduty.amazonaws.com"
+  |  bin _time span=5m |  stats count dc(eventName) as distinct_api_calls values(eventName)  values(requestParameters.attributeType)
+  as attributeType values(requestParameters.createVolumePermission.add.items{}.userId)
+  as aws_account_id_added values(userAgent) as userAgent by _time userName src_ip
+  aws_account_id | where distinct_api_calls >= 2 | `aws_exfiltration_via_ec2_snapshot_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs. We have intentionally removed `guardduty.amazonaws.com`
+  from src_ip to remove false positives caused by guard duty. We recommend you adjust
+  the time window as per your environment.
+known_false_positives: It is possible that an AWS admin has legitimately shared a
+  snapshot with an other account for a specific purpose. Please check any recent change
+  requests filed in your organization.
 references:
 - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
 - https://docs.aws.amazon.com/AWSEC2/latest/APIReference/API_ModifySnapshotAttribute.html
@@ -25,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +57,8 @@ tags:
   asset_type: EC2 Snapshot
   confidence: 80
   impact: 80
-  message: Potential AWS EC2 Exfiltration detected on account id - $aws_account_id$ by user $userName$ from src_ip $src_ip$
+  message: Potential AWS EC2 Exfiltration detected on account id - $aws_account_id$
+    by user $userName$ from src_ip $src_ip$
   mitre_attack_id:
   - T1537
   observable:
@@ -55,21 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - user_arn
-  - src_ip
-  - requestParameters.attributeType
-  - aws_account_id
-  - vendor_region
-  - user_agent
-  - userIdentity.principalId
-  - requestParameters.createVolumePermission.add.items{}.userId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml b/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml
index 695778edb1..fa72fab85b 100644
--- a/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml
+++ b/detections/cloud/aws_high_number_of_failed_authentications_for_user.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects an AWS account experiencing more than 20 failed authentication attempts within a 5-minute window. It leverages AWS CloudTrail logs to identify multiple failed ConsoleLogin events. This behavior is significant as it may indicate a brute force attack targeting the account. If confirmed malicious, the attacker could potentially gain unauthorized access, leading to data breaches or further exploitation of the AWS environment. Security teams should consider adjusting the threshold based on their specific environment to reduce false positives.
+description: The following analytic detects an AWS account experiencing more than
+  20 failed authentication attempts within a 5-minute window. It leverages AWS CloudTrail
+  logs to identify multiple failed ConsoleLogin events. This behavior is significant
+  as it may indicate a brute force attack targeting the account. If confirmed malicious,
+  the attacker could potentially gain unauthorized access, leading to data breaches
+  or further exploitation of the AWS environment. Security teams should consider adjusting
+  the threshold based on their specific environment to reduce false positives.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time | stats dc(_raw) AS failed_attempts values(src_ip) as src_ip values(user_agent) by _time, user_name, eventName, eventSource aws_account_id | where failed_attempts > 20 | `aws_high_number_of_failed_authentications_for_user_filter`'
-how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: A user with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
+search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time
+  | stats dc(_raw) AS failed_attempts values(src_ip) as src_ip values(user_agent)
+  by _time, user_name, eventName, eventSource aws_account_id | where failed_attempts
+  > 20 | `aws_high_number_of_failed_authentications_for_user_filter`'
+how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: A user with more than 20 failed authentication attempts in
+  the span of 5 minutes may also be triggered by a broken application.
 references:
 - https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/IAM/password-policy.html
 drilldown_searches:
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_name$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +45,8 @@ tags:
   asset_type: AWS Account
   confidence: 70
   impact: 50
-  message: User $user_name$ failed to authenticate more than 20 times in the span of 5 minutes for AWS Account $aws_account_id$
+  message: User $user_name$ failed to authenticate more than 20 times in the span
+    of 5 minutes for AWS Account $aws_account_id$
   mitre_attack_id:
   - T1201
   observable:
@@ -41,20 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.userName
-  - eventSource
-  - user_arn
-  - aws_account_id
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_multiple_login_fail_per_user/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_multiple_login_fail_per_user/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml b/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml
index ef2e87a9cc..479651e288 100644
--- a/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml
+++ b/detections/cloud/aws_high_number_of_failed_authentications_from_ip.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects an IP address with 20 or more failed authentication attempts to the AWS Web Console within a 5-minute window. This detection leverages CloudTrail logs, aggregating failed login events by IP address and time span. This activity is significant as it may indicate a brute force attack aimed at gaining unauthorized access or escalating privileges within an AWS environment. If confirmed malicious, this could lead to unauthorized access, data breaches, or further exploitation of AWS resources.
+description: The following analytic detects an IP address with 20 or more failed authentication
+  attempts to the AWS Web Console within a 5-minute window. This detection leverages
+  CloudTrail logs, aggregating failed login events by IP address and time span. This
+  activity is significant as it may indicate a brute force attack aimed at gaining
+  unauthorized access or escalating privileges within an AWS environment. If confirmed
+  malicious, this could lead to unauthorized access, data breaches, or further exploitation
+  of AWS resources.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=5m _time | stats dc(_raw) AS failed_attempts values(user_name) as tried_accounts values(user_agent) by _time, src_ip, eventName, eventSource aws_account_id | where failed_attempts > 20 |  `aws_high_number_of_failed_authentications_from_ip_filter`'
-how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the tried account threshold to tune this search according to their environment.
-known_false_positives: An Ip address with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
+search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=5m _time
+  | stats dc(_raw) AS failed_attempts values(user_name) as tried_accounts values(user_agent)
+  by _time, src_ip, eventName, eventSource aws_account_id | where failed_attempts
+  > 20 |  `aws_high_number_of_failed_authentications_from_ip_filter`'
+how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail.
+  We recommend the users to try different combinations of the bucket span time and
+  the tried account threshold to tune this search according to their environment.
+known_false_positives: An Ip address with more than 20 failed authentication attempts
+  in the span of 5 minutes may also be triggered by a broken application.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$tried_accounts$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 60
-  message: 'Multiple failed console login attempts (Count: $failed_attempts$) against users from IP Address - $src_ip$'
+  message: 'Multiple failed console login attempts (Count: $failed_attempts$) against
+    users from IP Address - $src_ip$'
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -45,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - action
-  - eventName
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_iam_accessdenied_discovery_events.yml b/detections/cloud/aws_iam_accessdenied_discovery_events.yml
index 7429b38516..ef6e9da0e4 100644
--- a/detections/cloud/aws_iam_accessdenied_discovery_events.yml
+++ b/detections/cloud/aws_iam_accessdenied_discovery_events.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation.
+description: The following analytic identifies excessive AccessDenied events within
+  an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect
+  multiple failed access attempts from the same source IP and user identity. This
+  activity is significant as it may indicate that an access key has been compromised
+  and is being misused for unauthorized discovery actions. If confirmed malicious,
+  this could allow attackers to gather information about the AWS environment, potentially
+  leading to further exploitation or privilege escalation.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) | bucket _time span=1h | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources by src_ip, userIdentity.arn, _time | where failures >= 5 and methods >= 1 and sources >= 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_accessdenied_discovery_events_filter`'
-how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.
+search: '`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com)
+  | bucket _time span=1h | stats count as failures min(_time) as firstTime max(_time)
+  as lastTime, dc(eventName) as methods, dc(eventSource) as sources by src_ip, userIdentity.arn,
+  _time | where failures >= 5 and methods >= 1 and sources >= 1 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_iam_accessdenied_discovery_events_filter`'
+how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize
+  this data. The search requires AWS CloudTrail logs.
+known_false_positives: It is possible to start this detection will need to be tuned
+  by source IP or user. In addition, change the count values to an upper threshold
+  to restrict false positives.
 references:
 - https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$userIdentity.arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userIdentity.arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userIdentity.arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: AWS Account
   confidence: 50
   impact: 20
-  message: User $userIdentity.arn$ is seen to perform excessive number of discovery related api calls- $failures$, within an hour where the access was denied.
+  message: User $userIdentity.arn$ is seen to perform excessive number of discovery
+    related api calls- $failures$, within an hour where the access was denied.
   mitre_attack_id:
   - T1580
   observable:
@@ -44,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventSource
-  - userAgent
-  - errorCode
-  - userIdentity.type
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_iam_assume_role_policy_brute_force.yml b/detections/cloud/aws_iam_assume_role_policy_brute_force.yml
index d5390fb660..bc3fd8e076 100644
--- a/detections/cloud/aws_iam_assume_role_policy_brute_force.yml
+++ b/detections/cloud/aws_iam_assume_role_policy_brute_force.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects multiple failed attempts to assume an AWS IAM role, indicating a potential brute force attack. It leverages AWS CloudTrail logs to identify `MalformedPolicyDocumentException` errors with a status of `failure` and filters out legitimate AWS services. This activity is significant as repeated failures to assume roles can indicate an adversary attempting to guess role names, which is a precursor to unauthorized access. If confirmed malicious, this could lead to unauthorized access to AWS resources, potentially compromising sensitive data and services.
+description: The following analytic detects multiple failed attempts to assume an
+  AWS IAM role, indicating a potential brute force attack. It leverages AWS CloudTrail
+  logs to identify `MalformedPolicyDocumentException` errors with a status of `failure`
+  and filters out legitimate AWS services. This activity is significant as repeated
+  failures to assume roles can indicate an adversary attempting to guess role names,
+  which is a precursor to unauthorized access. If confirmed malicious, this could
+  lead to unauthorized access to AWS resources, potentially compromising sensitive
+  data and services.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn | where count >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter`'
-how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.
+search: '`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure
+  (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as
+  lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource
+  aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion
+  userIdentity.principalId user_arn | where count >= 2 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter`'
+how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize
+  this data. The search requires AWS CloudTrail logs. Set the `where count` greater
+  than a value to identify suspicious activity in your environment.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users.
 references:
 - https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities/
 - https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: AWS Account
   confidence: 70
   impact: 40
-  message: User $user_arn$ has caused multiple failures with errorCode $errorCode$, which potentially means adversary is attempting to identify a role name.
+  message: User $user_arn$ has caused multiple failures with errorCode $errorCode$,
+    which potentially means adversary is attempting to identify a role name.
   mitre_attack_id:
   - T1580
   - T1110
@@ -47,16 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.policyName
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_iam_delete_policy.yml b/detections/cloud/aws_iam_delete_policy.yml
index 9255247a09..f47b5cca6c 100644
--- a/detections/cloud/aws_iam_delete_policy.yml
+++ b/detections/cloud/aws_iam_delete_policy.yml
@@ -5,12 +5,28 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the deletion of an IAM policy in AWS. It leverages AWS CloudTrail logs to identify `DeletePolicy` events, excluding those from AWS internal services. This activity is significant as unauthorized policy deletions can disrupt access controls and weaken security postures. If confirmed malicious, an attacker could remove critical security policies, potentially leading to privilege escalation, unauthorized access, or data exfiltration. Monitoring this behavior helps ensure that only authorized changes are made to IAM policies, maintaining the integrity and security of the AWS environment.
+description: The following analytic detects the deletion of an IAM policy in AWS.
+  It leverages AWS CloudTrail logs to identify `DeletePolicy` events, excluding those
+  from AWS internal services. This activity is significant as unauthorized policy
+  deletions can disrupt access controls and weaken security postures. If confirmed
+  malicious, an attacker could remove critical security policies, potentially leading
+  to privilege escalation, unauthorized access, or data exfiltration. Monitoring this
+  behavior helps ensure that only authorized changes are made to IAM policies, maintaining
+  the integrity and security of the AWS environment.
 data_source:
 - AWS CloudTrail DeletePolicy
-search: '`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src user_arn eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_delete_policy_filter`'
-how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
+search: '`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) | stats
+  count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn)
+  as policyArn by src user_arn eventName eventSource aws_account_id errorCode errorMessage
+  userAgent eventID awsRegion userIdentity.principalId | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_iam_delete_policy_filter`'
+how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize
+  this data. The search requires AWS CloudTrail logs.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users. Not every user with AWS access should have permission to
+  delete policies (least privilege). In addition, this may be saved seperately and
+  tuned for failed or success attempts only.
 references:
 - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html
 - https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html
@@ -20,7 +36,8 @@ tags:
   asset_type: AWS Account
   confidence: 50
   impact: 20
-  message: User $user_arn$ has deleted AWS Policies from IP address $src$ by executing the following command $eventName$
+  message: User $user_arn$ has deleted AWS Policies from IP address $src$ by executing
+    the following command $eventName$
   mitre_attack_id:
   - T1098
   observable:
@@ -36,16 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.policyArn
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_iam_failure_group_deletion.yml b/detections/cloud/aws_iam_failure_group_deletion.yml
index d6c15531cb..f38884e386 100644
--- a/detections/cloud/aws_iam_failure_group_deletion.yml
+++ b/detections/cloud/aws_iam_failure_group_deletion.yml
@@ -5,12 +5,28 @@ date: '2024-10-22'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies failed attempts to delete AWS IAM groups. It leverages AWS CloudTrail logs to detect events where the DeleteGroup action fails due to errors like NoSuchEntityException, DeleteConflictException, or AccessDenied. This activity is significant as it may indicate unauthorized attempts to modify IAM group configurations, which could be a precursor to privilege escalation or other malicious actions. If confirmed malicious, this could allow an attacker to disrupt IAM policies, potentially leading to unauthorized access or denial of service within the AWS environment.
+description: The following analytic identifies failed attempts to delete AWS IAM groups.
+  It leverages AWS CloudTrail logs to detect events where the DeleteGroup action fails
+  due to errors like NoSuchEntityException, DeleteConflictException, or AccessDenied.
+  This activity is significant as it may indicate unauthorized attempts to modify
+  IAM group configurations, which could be a precursor to privilege escalation or
+  other malicious actions. If confirmed malicious, this could allow an attacker to
+  disrupt IAM policies, potentially leading to unauthorized access or denial of service
+  within the AWS environment.
 data_source:
 - AWS CloudTrail DeleteGroup
-search: '`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_failure_group_deletion_filter`'
-how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+search: '`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode
+  IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com)
+  | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName)
+  as group_name by src eventName eventSource aws_account_id errorCode errorMessage
+  userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_iam_failure_group_deletion_filter`'
+how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize
+  this data. The search requires AWS CloudTrail logs.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users. Not every user with AWS access should have permission to
+  delete groups (least privilege).
 references:
 - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
 - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: AWS Account
   confidence: 50
   impact: 10
-  message: User $user_arn$ has had mulitple failures while attempting to delete groups from $src$
+  message: User $user_arn$ has had mulitple failures while attempting to delete groups
+    from $src$
   mitre_attack_id:
   - T1098
   observable:
@@ -45,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.groupName
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_iam_successful_group_deletion.yml b/detections/cloud/aws_iam_successful_group_deletion.yml
index 0df1f5435a..10404379ae 100644
--- a/detections/cloud/aws_iam_successful_group_deletion.yml
+++ b/detections/cloud/aws_iam_successful_group_deletion.yml
@@ -5,12 +5,27 @@ date: '2024-10-22'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the successful deletion of an IAM group in AWS. It leverages CloudTrail logs to detect `DeleteGroup` events with a success status. This activity is significant as it could indicate potential changes in user permissions or access controls, which may be a precursor to further unauthorized actions. If confirmed malicious, an attacker could disrupt access management, potentially leading to privilege escalation or unauthorized access to sensitive resources. Analysts should review related IAM events, such as recent user additions or new group creations, to assess the broader context.
+description: The following analytic identifies the successful deletion of an IAM group
+  in AWS. It leverages CloudTrail logs to detect `DeleteGroup` events with a success
+  status. This activity is significant as it could indicate potential changes in user
+  permissions or access controls, which may be a precursor to further unauthorized
+  actions. If confirmed malicious, an attacker could disrupt access management, potentially
+  leading to privilege escalation or unauthorized access to sensitive resources. Analysts
+  should review related IAM events, such as recent user additions or new group creations,
+  to assess the broader context.
 data_source:
 - AWS CloudTrail DeleteGroup
-search: '`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_deleted by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_successful_group_deletion_filter`'
-how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+search: '`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success
+  (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as
+  lastTime values(requestParameters.groupName) as group_deleted by src eventName eventSource
+  errorCode user_agent awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_iam_successful_group_deletion_filter`'
+how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize
+  this data. The search requires AWS CloudTrail logs.
+known_false_positives: This detection will require tuning to provide high fidelity
+  detection capabilties. Tune based on src addresses (corporate offices, VPN terminations)
+  or by groups of users. Not every user with AWS access should have permission to
+  delete groups (least privilege).
 references:
 - https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
 - https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
@@ -20,7 +35,8 @@ tags:
   asset_type: AWS Account
   confidence: 50
   impact: 10
-  message: User $user_arn$ has sucessfully deleted mulitple groups $group_deleted$ from $src$
+  message: User $user_arn$ has sucessfully deleted mulitple groups $group_deleted$
+    from $src$
   mitre_attack_id:
   - T1069.003
   - T1098
@@ -42,16 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.groupName
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_lambda_updatefunctioncode.yml b/detections/cloud/aws_lambda_updatefunctioncode.yml
index a0e839955b..c6b85e6451 100644
--- a/detections/cloud/aws_lambda_updatefunctioncode.yml
+++ b/detections/cloud/aws_lambda_updatefunctioncode.yml
@@ -5,12 +5,23 @@ date: '2024-10-22'
 author: Bhavin Patel, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies IAM users attempting to update or modify AWS Lambda code via the AWS CLI. It leverages CloudTrail logs to detect successful `UpdateFunctionCode` events initiated by IAM users. This activity is significant as it may indicate an attempt to gain persistence, further access, or plant backdoors within your AWS environment. If confirmed malicious, an attacker could upload and execute malicious code automatically when the Lambda function is triggered, potentially compromising the integrity and security of your AWS infrastructure.
+description: The following analytic identifies IAM users attempting to update or modify
+  AWS Lambda code via the AWS CLI. It leverages CloudTrail logs to detect successful
+  `UpdateFunctionCode` events initiated by IAM users. This activity is significant
+  as it may indicate an attempt to gain persistence, further access, or plant backdoors
+  within your AWS environment. If confirmed malicious, an attacker could upload and
+  execute malicious code automatically when the Lambda function is triggered, potentially
+  compromising the integrity and security of your AWS infrastructure.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` eventSource=lambda.amazonaws.com eventName=UpdateFunctionCode*  errorCode = success  user_type=IAMUser | stats  count min(_time) as firstTime max(_time) as lastTime  values(requestParameters.functionName) as function_updated by src_ip user_arn user_agent user_type eventName aws_account_id |`aws_lambda_updatefunctioncode_filter`'
-how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in your AWS Environment.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin or an autorized IAM user has updated the lambda fuction code legitimately.
+search: '`cloudtrail` eventSource=lambda.amazonaws.com eventName=UpdateFunctionCode*  errorCode
+  = success  user_type=IAMUser | stats  count min(_time) as firstTime max(_time) as
+  lastTime  values(requestParameters.functionName) as function_updated by src_ip user_arn
+  user_agent user_type eventName aws_account_id |`aws_lambda_updatefunctioncode_filter`'
+how_to_implement: You must install Splunk AWS Add on and enable Cloudtrail logs in
+  your AWS Environment.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin or an autorized IAM user has updated the lambda fuction code legitimately.
 references:
 - http://detectioninthe.cloud/execution/modify_lambda_function_code/
 - https://sysdig.com/blog/exploit-mitigate-aws-lambdas-mitre/
@@ -20,7 +31,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 70
-  message: User $user_arn$ is attempting to update the lambda function code of $function_updated$ from this IP $src_ip$
+  message: User $user_arn$ is attempting to update the lambda function code of $function_updated$
+    from this IP $src_ip$
   mitre_attack_id:
   - T1204
   observable:
@@ -36,15 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/aws_updatelambdafunctioncode/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/aws_updatelambdafunctioncode/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_multi_factor_authentication_disabled.yml b/detections/cloud/aws_multi_factor_authentication_disabled.yml
index 5ebe530196..7702631922 100644
--- a/detections/cloud/aws_multi_factor_authentication_disabled.yml
+++ b/detections/cloud/aws_multi_factor_authentication_disabled.yml
@@ -5,23 +5,40 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to disable multi-factor authentication (MFA) for an AWS IAM user. It leverages AWS CloudTrail logs to identify events where MFA devices are deleted or deactivated. This activity is significant because disabling MFA can indicate an adversary attempting to weaken account security, potentially to maintain persistence using a compromised account. If confirmed malicious, this action could allow attackers to retain access to the AWS environment without detection, posing a significant risk to the security and integrity of the cloud infrastructure.
+description: The following analytic detects attempts to disable multi-factor authentication
+  (MFA) for an AWS IAM user. It leverages AWS CloudTrail logs to identify events where
+  MFA devices are deleted or deactivated. This activity is significant because disabling
+  MFA can indicate an adversary attempting to weaken account security, potentially
+  to maintain persistence using a compromised account. If confirmed malicious, this
+  action could allow attackers to retain access to the AWS environment without detection,
+  posing a significant risk to the security and integrity of the cloud infrastructure.
 data_source:
 - AWS CloudTrail DeleteVirtualMFADevice
 - AWS CloudTrail DeactivateMFADevice
-search: '`cloudtrail` (eventName= DeleteVirtualMFADevice OR eventName=DeactivateMFADevice) | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource aws_account_id userAgent eventID awsRegion user_name userIdentity.arn status | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_multi_factor_authentication_disabled_filter`'
-how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: AWS Administrators may disable MFA but it is highly unlikely for this event to occur without prior notice to the company
+search: '`cloudtrail` (eventName= DeleteVirtualMFADevice OR eventName=DeactivateMFADevice)
+  | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource
+  aws_account_id userAgent eventID awsRegion user_name userIdentity.arn status | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_multi_factor_authentication_disabled_filter`'
+how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search
+  requires AWS CloudTrail logs.
+known_false_positives: AWS Administrators may disable MFA but it is highly unlikely
+  for this event to occur without prior notice to the company
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://aws.amazon.com/what-is/mfa/
 drilldown_searches:
 - name: View the detection results for - "$aws_account_id$" and "$user_name$"
-  search: '%original_detection_search% | search  aws_account_id = "$aws_account_id$" user_name = "$user_name$"'
+  search: '%original_detection_search% | search  aws_account_id = "$aws_account_id$"
+    user_name = "$user_name$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$aws_account_id$" and "$user_name$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$", "$user_name$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$aws_account_id$",
+    "$user_name$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 80
-  message: User $user_name$ has disabled Multi-Factor authentication for AWS account $aws_account_id$
+  message: User $user_name$ has disabled Multi-Factor authentication for AWS account
+    $aws_account_id$
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -54,22 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src
-  - eventName
-  - eventSource
-  - aws_account_id
-  - errorCode
-  - errorMessage
-  - userAgent
-  - eventID
-  - awsRegion
-  - user_name
-  - userIdentity.arn
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_mfa_disabled/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml
index fd8d29ab97..1ff244fe77 100644
--- a/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml
+++ b/detections/cloud/aws_multiple_failed_mfa_requests_for_user.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Bhavin Patel
 status: production
 type: Anomaly
-description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests to an AWS Console for a single user. It leverages AWS CloudTrail logs, specifically the `additionalEventData` field, to detect more than 10 failed MFA prompts within 5 minutes. This activity is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this could lead to unauthorized access to the AWS environment, potentially compromising sensitive data and resources.
+description: The following analytic identifies multiple failed multi-factor authentication
+  (MFA) requests to an AWS Console for a single user. It leverages AWS CloudTrail
+  logs, specifically the `additionalEventData` field, to detect more than 10 failed
+  MFA prompts within 5 minutes. This activity is significant as it may indicate an
+  adversary attempting to bypass MFA by bombarding the user with repeated authentication
+  requests. If confirmed malicious, this could lead to unauthorized access to the
+  AWS environment, potentially compromising sensitive data and resources.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName= ConsoleLogin "additionalEventData.MFAUsed"=Yes errorMessage="Failed authentication" | bucket span=5m _time |  stats dc(_raw) as mfa_prompts values(userAgent) as userAgent values(src) as src by _time user_name user_arn aws_account_id eventName errorMessage | where mfa_prompts > 10| `aws_multiple_failed_mfa_requests_for_user_filter`'
-how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
+search: '`cloudtrail` eventName= ConsoleLogin "additionalEventData.MFAUsed"=Yes errorMessage="Failed
+  authentication" | bucket span=5m _time |  stats dc(_raw) as mfa_prompts values(userAgent)
+  as userAgent values(src) as src by _time user_name user_arn aws_account_id eventName
+  errorMessage | where mfa_prompts > 10| `aws_multiple_failed_mfa_requests_for_user_filter`'
+how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search
+  requires AWS CloudTrail logs.
+known_false_positives: Multiple Failed MFA requests may also be a sign of authentication
+  or application issues. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://aws.amazon.com/what-is/mfa/
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_name$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +45,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 80
-  message: User $user_name$ is seen to have high number of MFA prompt failures within a short period of time.
+  message: User $user_name$ is seen to have high number of MFA prompt failures within
+    a short period of time.
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -47,22 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src
-  - eventName
-  - eventSource
-  - aws_account_id
-  - errorCode
-  - errorMessage
-  - userAgent
-  - eventID
-  - awsRegion
-  - user_name
-  - userIdentity.arn
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/aws_failed_mfa/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml
index ae1789f08c..0141daa586 100644
--- a/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml
+++ b/detections/cloud/aws_multiple_users_failing_to_authenticate_from_ip.yml
@@ -5,13 +5,19 @@ date: '2024-10-16'
 author: Bhavin Patel
 status: production
 type: Anomaly
-description: The following analytic identifies a single source IP failing to authenticate into the AWS Console with 30 unique valid users within 10 minutes. It leverages CloudTrail logs to detect multiple failed login attempts from the same IP address. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain unauthorized access or elevate privileges by trying common passwords across many accounts. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or further exploitation within the AWS environment.
+description: The following analytic identifies a single source IP failing to authenticate
+  into the AWS Console with 30 unique valid users within 10 minutes. It leverages
+  CloudTrail logs to detect multiple failed login attempts from the same IP address.
+  This behavior is significant as it may indicate a Password Spraying attack, where
+  an adversary attempts to gain unauthorized access or elevate privileges by trying
+  common passwords across many accounts. If confirmed malicious, this activity could
+  lead to unauthorized access, data breaches, or further exploitation within the AWS
+  environment.
 data_source:
 - AWS CloudTrail ConsoleLogin
 search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time
-  | stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts by _time, src_ip 
-  | where unique_accounts>30
-  | `aws_multiple_users_failing_to_authenticate_from_ip_filter`'
+  | stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts by
+  _time, src_ip | where unique_accounts>30 | `aws_multiple_users_failing_to_authenticate_from_ip_filter`'
 how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail.
   We recommend the users to try different combinations of the bucket span time and
   the tried account threshold to tune this search according to their environment.
@@ -27,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$tried_accounts$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 60
-  message: 'Multiple failed console login attempts (Count: $unique_accounts$) against users from IP Address - $src_ip$'
+  message: 'Multiple failed console login attempts (Count: $unique_accounts$) against
+    users from IP Address - $src_ip$'
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -55,17 +67,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - action
-  - eventName
-  - src_ip
   security_domain: threat
-  manual_test: This search needs a specific number of events in a time window for the alert to trigger and events split up in CI testing while updating timestamp.
+  manual_test: This search needs a specific number of events in a time window for
+    the alert to trigger and events split up in CI testing while updating timestamp.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml b/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml
index b5a46d3b12..e6bde7ed72 100644
--- a/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml
+++ b/detections/cloud/aws_network_access_control_list_created_with_all_open_ports.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Bhavin Patel, Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of AWS Network Access Control Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry` actions with rules allowing all traffic. This activity is significant because it can expose the network to unauthorized access, increasing the risk of data breaches and other malicious activities. If confirmed malicious, an attacker could exploit this misconfiguration to gain unrestricted access to the network, potentially leading to data exfiltration, service disruption, or further compromise of the AWS environment.
+description: The following analytic detects the creation of AWS Network Access Control
+  Lists (ACLs) with all ports open to a specified CIDR. It leverages AWS CloudTrail
+  events, specifically monitoring for `CreateNetworkAclEntry` or `ReplaceNetworkAclEntry`
+  actions with rules allowing all traffic. This activity is significant because it
+  can expose the network to unauthorized access, increasing the risk of data breaches
+  and other malicious activities. If confirmed malicious, an attacker could exploit
+  this misconfiguration to gain unrestricted access to the network, potentially leading
+  to data exfiltration, service disruption, or further compromise of the AWS environment.
 data_source:
 - AWS CloudTrail CreateNetworkAclEntry
 - AWS CloudTrail ReplaceNetworkAclEntry
-search: '`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 | eval port_range=''requestParameters.portRange.to'' - ''requestParameters.portRange.from'' | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userName user_arn userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail inputs.
-known_false_positives: It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment.
+search: "`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry
+  requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1
+  | append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry
+  requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1
+  | eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from'
+  | where port_range>1024] | fillnull | stats count min(_time) as firstTime max(_time)
+  as lastTime by userName user_arn userIdentity.principalId eventName requestParameters.ruleAction
+  requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to
+  requestParameters.portRange.from src userAgent requestParameters.cidrBlock | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `aws_network_access_control_list_created_with_all_open_ports_filter`"
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS, version 4.4.0 or later, and configure your AWS CloudTrail
+  inputs.
+known_false_positives: It's possible that an admin has created this ACL with all ports
+  open for some legitimate purpose however, this should be scoped and not allowed
+  in production environment.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user_arn$"
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +53,8 @@ tags:
   asset_type: AWS Instance
   confidence: 80
   impact: 60
-  message: User $user_arn$ has created network ACLs with all the ports open to a specified CIDR $requestParameters.cidrBlock$
+  message: User $user_arn$ has created network ACLs with all the ports open to a specified
+    CIDR $requestParameters.cidrBlock$
   mitre_attack_id:
   - T1562.007
   - T1562
@@ -45,22 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - requestParameters.ruleAction
-  - requestParameters.egress
-  - requestParameters.aclProtocol
-  - requestParameters.portRange.to
-  - requestParameters.portRange.from
-  - requestParameters.cidrBlock
-  - userName
-  - userIdentity.principalId
-  - userAgent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_network_access_control_list_deleted.yml b/detections/cloud/aws_network_access_control_list_deleted.yml
index e10fc88848..5c9f196d7e 100644
--- a/detections/cloud/aws_network_access_control_list_deleted.yml
+++ b/detections/cloud/aws_network_access_control_list_deleted.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Bhavin Patel, Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of AWS Network Access Control Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes a network ACL entry. This activity is significant because deleting a network ACL can remove critical access restrictions, potentially allowing unauthorized access to cloud instances. If confirmed malicious, this action could enable attackers to bypass network security controls, leading to unauthorized access, data exfiltration, or further compromise of the cloud environment.
+description: The following analytic detects the deletion of AWS Network Access Control
+  Lists (ACLs). It leverages AWS CloudTrail logs to identify events where a user deletes
+  a network ACL entry. This activity is significant because deleting a network ACL
+  can remove critical access restrictions, potentially allowing unauthorized access
+  to cloud instances. If confirmed malicious, this action could enable attackers to
+  bypass network security controls, leading to unauthorized access, data exfiltration,
+  or further compromise of the cloud environment.
 data_source:
 - AWS CloudTrail DeleteNetworkAclEntry
-search: '`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs.
-known_false_positives: It's possible that a user has legitimately deleted a network ACL.
+search: '`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false
+  | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by user_arn
+  userIdentity.principalId eventName requestParameters.egress src userAgent | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `aws_network_access_control_list_deleted_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail
+  inputs.
+known_false_positives: It's possible that a user has legitimately deleted a network
+  ACL.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user_arn$"
@@ -18,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +44,8 @@ tags:
   asset_type: AWS Instance
   confidence: 50
   impact: 10
-  message: User $user_arn$ from $src$ has sucessfully deleted network ACLs entry (eventName= $eventName$), such that the instance is accessible from anywhere
+  message: User $user_arn$ from $src$ has sucessfully deleted network ACLs entry (eventName=
+    $eventName$), such that the instance is accessible from anywhere
   mitre_attack_id:
   - T1562.007
   - T1562
@@ -44,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - requestParameters.egress
-  - userName
-  - userIdentity.principalId
-  - src
-  - userAgent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_new_mfa_method_registered_for_user.yml b/detections/cloud/aws_new_mfa_method_registered_for_user.yml
index 606726c470..3baab421dd 100644
--- a/detections/cloud/aws_new_mfa_method_registered_for_user.yml
+++ b/detections/cloud/aws_new_mfa_method_registered_for_user.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an AWS account. It leverages AWS CloudTrail logs to identify the `CreateVirtualMFADevice` event. This activity is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this could allow attackers to secure their access, making it difficult to detect and remove their presence, potentially leading to further unauthorized activities and data breaches.
+description: The following analytic detects the registration of a new Multi-Factor
+  Authentication (MFA) method for an AWS account. It leverages AWS CloudTrail logs
+  to identify the `CreateVirtualMFADevice` event. This activity is significant because
+  adversaries who gain unauthorized access to an AWS account may register a new MFA
+  method to maintain persistence. If confirmed malicious, this could allow attackers
+  to secure their access, making it difficult to detect and remove their presence,
+  potentially leading to further unauthorized activities and data breaches.
 data_source:
 - AWS CloudTrail CreateVirtualMFADevice
-search: '`cloudtrail` eventName=CreateVirtualMFADevice | stats count values(requestParameters.virtualMFADeviceName) as virtualMFADeviceName min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_new_mfa_method_registered_for_user_filter`'
-how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This search works when AWS CloudTrail logs.
-known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
+search: '`cloudtrail` eventName=CreateVirtualMFADevice | stats count values(requestParameters.virtualMFADeviceName)
+  as virtualMFADeviceName min(_time) as firstTime max(_time) as lastTime by eventSource
+  aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn
+  src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `aws_new_mfa_method_registered_for_user_filter`'
+how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This
+  search works when AWS CloudTrail logs.
+known_false_positives: Newly onboarded users who are registering an MFA method for
+  the first time will also trigger this detection.
 references:
 - https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/
 - https://attack.mitre.org/techniques/T1556/
@@ -22,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,22 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src_ip
-  - eventName
-  - eventSource
-  - requestParameters.virtualMFADeviceName
-  - errorCode
-  - userIdentity.principalId
-  - userAgent
-  - awsRegion
-  - user_name
-  - userIdentity.arn
-  - _time
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_password_policy_changes.yml b/detections/cloud/aws_password_policy_changes.yml
index 9972bcdfab..35e991ed55 100644
--- a/detections/cloud/aws_password_policy_changes.yml
+++ b/detections/cloud/aws_password_policy_changes.yml
@@ -5,14 +5,28 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: production
 type: Hunting
-description: The following analytic detects successful API calls to view, update, or delete the password policy in an AWS organization. It leverages AWS CloudTrail logs to identify events such as "UpdateAccountPasswordPolicy," "GetAccountPasswordPolicy," and "DeleteAccountPasswordPolicy." This activity is significant because it is uncommon for regular users to perform these actions, and such changes can indicate an adversary attempting to understand or weaken password defenses. If confirmed malicious, this could lead to compromised accounts and increased attack surface, potentially allowing unauthorized access and control over AWS resources.
+description: The following analytic detects successful API calls to view, update,
+  or delete the password policy in an AWS organization. It leverages AWS CloudTrail
+  logs to identify events such as "UpdateAccountPasswordPolicy," "GetAccountPasswordPolicy,"
+  and "DeleteAccountPasswordPolicy." This activity is significant because it is uncommon
+  for regular users to perform these actions, and such changes can indicate an adversary
+  attempting to understand or weaken password defenses. If confirmed malicious, this
+  could lead to compromised accounts and increased attack surface, potentially allowing
+  unauthorized access and control over AWS resources.
 data_source:
 - AWS CloudTrail UpdateAccountPasswordPolicy
 - AWS CloudTrail GetAccountPasswordPolicy
 - AWS CloudTrail DeleteAccountPasswordPolicy
-search: '`cloudtrail` eventName IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy") errorCode=success | stats count values(eventName) as eventName values(userAgent) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode  awsRegion userIdentity.principalId user_arn src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  | `aws_password_policy_changes_filter`'
-how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately triggered an AWS audit tool activity which may trigger this event.
+search: '`cloudtrail` eventName IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy")
+  errorCode=success | stats count values(eventName) as eventName values(userAgent)
+  min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode  awsRegion
+  userIdentity.principalId user_arn src_ip | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)`  | `aws_password_policy_changes_filter`'
+how_to_implement: You must install Splunk AWS Add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately triggered an AWS audit tool activity which may
+  trigger this event.
 references:
 - https://www.trendmicro.com/cloudoneconformity/knowledge-base/aws/IAM/password-policy.html
 tags:
@@ -22,7 +36,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 90
-  message: User $user_arn$ is attempting to $eventName$ the password policy for account id $aws_account_id$
+  message: User $user_arn$ is attempting to $eventName$ the password policy for account
+    id $aws_account_id$
   mitre_attack_id:
   - T1201
   observable:
@@ -38,20 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.userName
-  - eventSource
-  - user_arn
-  - aws_account_id
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/aws_password_policy/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/aws_password_policy/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_s3_exfiltration_behavior_identified.yml b/detections/cloud/aws_s3_exfiltration_behavior_identified.yml
index d8e30f33d7..29c84e9458 100644
--- a/detections/cloud/aws_s3_exfiltration_behavior_identified.yml
+++ b/detections/cloud/aws_s3_exfiltration_behavior_identified.yml
@@ -6,10 +6,30 @@ author: Bhavin Patel, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies potential AWS S3 exfiltration behavior by correlating multiple risk events related to Collection and Exfiltration techniques. It leverages risk events from AWS sources, focusing on instances where two or more unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk object. This activity is significant as it may indicate an ongoing data exfiltration attempt, which is critical for security teams to monitor. If confirmed malicious, this could lead to unauthorized access and theft of sensitive information, compromising the organization's data integrity and confidentiality.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count values(All_Risk.risk_message) as risk_message  from datamodel=Risk.All_Risk where All_Risk.annotations.mitre_attack.mitre_tactic = "collection" OR All_Risk.annotations.mitre_attack.mitre_tactic = "exfiltration" source = *AWS*  by All_Risk.risk_object | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 2 and mitre_tactic_id_count>=2 | `aws_s3_exfiltration_behavior_identified_filter`'
-how_to_implement: You must enable all the detection searches in the Data Exfiltration Analytic story to create risk events in Enterprise Security.
-known_false_positives: alse positives may be present based on automated tooling or system administrators. Filter as needed.
+description: The following analytic identifies potential AWS S3 exfiltration behavior
+  by correlating multiple risk events related to Collection and Exfiltration techniques.
+  It leverages risk events from AWS sources, focusing on instances where two or more
+  unique analytics and distinct MITRE ATT&CK IDs are triggered for a specific risk
+  object. This activity is significant as it may indicate an ongoing data exfiltration
+  attempt, which is critical for security teams to monitor. If confirmed malicious,
+  this could lead to unauthorized access and theft of sensitive information, compromising
+  the organization's data integrity and confidentiality.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count values(All_Risk.risk_message) as risk_message  from datamodel=Risk.All_Risk
+  where All_Risk.annotations.mitre_attack.mitre_tactic = "collection" OR All_Risk.annotations.mitre_attack.mitre_tactic
+  = "exfiltration" source = *AWS*  by All_Risk.risk_object | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 2 and mitre_tactic_id_count>=2 | `aws_s3_exfiltration_behavior_identified_filter`'
+how_to_implement: You must enable all the detection searches in the Data Exfiltration
+  Analytic story to create risk events in Enterprise Security.
+known_false_positives: alse positives may be present based on automated tooling or
+  system administrators. Filter as needed.
 references:
 - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
 - https://stratus-red-team.cloud/attack-techniques/AWS/aws.exfiltration.ec2-share-ebs-snapshot/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 90
-  message: Multiple AWS Exfiltration detections $source$ and techniques $annotations.mitre_attack.mitre_tactic_id$ trigged for risk object $risk_object$
+  message: Multiple AWS Exfiltration detections $source$ and techniques $annotations.mitre_attack.mitre_tactic_id$
+    trigged for risk object $risk_object$
   mitre_attack_id:
   - T1537
   observable:
@@ -42,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - All_Risk.calculated_risk_score
-  - source
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_exfil_risk_events/aws_risk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_exfil_risk_events/aws_risk.log
     sourcetype: stash
     source: aws_exfil
diff --git a/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml b/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml
index 38ee6c7e2c..d5be3be19e 100644
--- a/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml
+++ b/detections/cloud/aws_saml_access_by_provider_user_and_principal.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies specific SAML access events by a service provider, user, and targeted principal within AWS. It leverages AWS CloudTrail logs to detect the `AssumeRoleWithSAML` event, analyzing fields such as `principalArn`, `roleArn`, and `roleSessionName`. This activity is significant as it can indicate abnormal access patterns or potential credential hijacking, especially in federated environments using the SAML protocol. If confirmed malicious, this could allow attackers to assume roles and gain unauthorized access to sensitive AWS resources, leading to data breaches or further exploitation.
+description: The following analytic identifies specific SAML access events by a service
+  provider, user, and targeted principal within AWS. It leverages AWS CloudTrail logs
+  to detect the `AssumeRoleWithSAML` event, analyzing fields such as `principalArn`,
+  `roleArn`, and `roleSessionName`. This activity is significant as it can indicate
+  abnormal access patterns or potential credential hijacking, especially in federated
+  environments using the SAML protocol. If confirmed malicious, this could allow attackers
+  to assume roles and gain unauthorized access to sensitive AWS resources, leading
+  to data breaches or further exploitation.
 data_source:
 - AWS CloudTrail AssumeRoleWithSAML
-search: '`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime max(_time) as lastTime by eventName requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_access_by_provider_user_and_principal_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs
-known_false_positives: Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks.
+search: '`cloudtrail` eventName=Assumerolewithsaml | stats count min(_time) as firstTime
+  max(_time) as lastTime by eventName requestParameters.principalArn requestParameters.roleArn
+  requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress
+  userAgent | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  |`aws_saml_access_by_provider_user_and_principal_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs
+known_false_positives: Attacks using a Golden SAML or SAML assertion hijacks or forgeries
+  are very difficult to detect as accessing cloud providers with these assertions
+  looks exactly like normal access, however things such as source IP sourceIPAddress
+  user, and principal targeted at receiving cloud provider along with endpoint credential
+  access and abuse detection searches can provide the necessary context to detect
+  these attacks.
 references:
 - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
 - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
@@ -22,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$recipientAccountId$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$recipientAccountId$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$recipientAccountId$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: AWS Federated Account
   confidence: 80
   impact: 80
-  message: From IP address $sourceIPAddress$, user agent $userAgent$ has trigged an event $eventName$ for account ID $recipientAccountId$
+  message: From IP address $sourceIPAddress$, user agent $userAgent$ has trigged an
+    event $eventName$ for account ID $recipientAccountId$
   mitre_attack_id:
   - T1078
   observable:
@@ -47,20 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - requestParameters.principalArn
-  - requestParameters.roleArn
-  - requestParameters.roleSessionName
-  - recipientAccountId
-  - responseElements.issuer
-  - sourceIPAddress
-  - userAgent
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_saml_update_identity_provider.yml b/detections/cloud/aws_saml_update_identity_provider.yml
index 76e33c8773..47f9e1c8cf 100644
--- a/detections/cloud/aws_saml_update_identity_provider.yml
+++ b/detections/cloud/aws_saml_update_identity_provider.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic detects updates to the SAML provider in AWS. It leverages AWS CloudTrail logs to identify the `UpdateSAMLProvider` event, analyzing fields such as `sAMLProviderArn`, `sourceIPAddress`, and `userIdentity` details. Monitoring updates to the SAML provider is crucial as it may indicate a perimeter compromise of federated credentials or unauthorized backdoor access set by an attacker. If confirmed malicious, this activity could allow attackers to manipulate identity federation, potentially leading to unauthorized access to cloud resources and sensitive data.
+description: The following analytic detects updates to the SAML provider in AWS. It
+  leverages AWS CloudTrail logs to identify the `UpdateSAMLProvider` event, analyzing
+  fields such as `sAMLProviderArn`, `sourceIPAddress`, and `userIdentity` details.
+  Monitoring updates to the SAML provider is crucial as it may indicate a perimeter
+  compromise of federated credentials or unauthorized backdoor access set by an attacker.
+  If confirmed malicious, this activity could allow attackers to manipulate identity
+  federation, potentially leading to unauthorized access to cloud resources and sensitive
+  data.
 data_source:
 - AWS CloudTrail UpdateSAMLProvider
-search: '`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_saml_update_identity_provider_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored.
+search: '`cloudtrail` eventName=UpdateSAMLProvider | stats count min(_time) as firstTime
+  max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn
+  userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId
+  userIdentity.principalId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  |`aws_saml_update_identity_provider_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: Updating a SAML provider or creating a new one may not necessarily
+  be malicious however it needs to be closely monitored.
 references:
 - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
 - https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$userIdentity.principalId$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userIdentity.principalId$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userIdentity.principalId$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: AWS Federated Account
   confidence: 80
   impact: 80
-  message: User $userIdentity.principalId$ from IP address $sourceIPAddress$ has trigged an event $eventName$ to update the SAML provider to $requestParameters.sAMLProviderArn$
+  message: User $userIdentity.principalId$ from IP address $sourceIPAddress$ has trigged
+    an event $eventName$ to update the SAML provider to $requestParameters.sAMLProviderArn$
   mitre_attack_id:
   - T1078
   observable:
@@ -47,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - eventType
-  - requestParameters.sAMLProviderArn
-  - userIdentity.sessionContext.sessionIssuer.arn
-  - sourceIPAddress
-  - userIdentity.accessKeyId
-  - userIdentity.principalId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_setdefaultpolicyversion.yml b/detections/cloud/aws_setdefaultpolicyversion.yml
index 75070f95cc..36fed96330 100644
--- a/detections/cloud/aws_setdefaultpolicyversion.yml
+++ b/detections/cloud/aws_setdefaultpolicyversion.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects when a user sets a default policy version in AWS. It leverages AWS CloudTrail logs to identify the `SetDefaultPolicyVersion` event from the IAM service. This activity is significant because attackers may exploit this technique for privilege escalation, especially if previous policy versions grant more extensive permissions than the current one. If confirmed malicious, this could allow an attacker to gain elevated access to AWS resources, potentially leading to unauthorized actions and data breaches.
+description: The following analytic detects when a user sets a default policy version
+  in AWS. It leverages AWS CloudTrail logs to identify the `SetDefaultPolicyVersion`
+  event from the IAM service. This activity is significant because attackers may exploit
+  this technique for privilege escalation, especially if previous policy versions
+  grant more extensive permissions than the current one. If confirmed malicious, this
+  could allow an attacker to gain elevated access to AWS resources, potentially leading
+  to unauthorized actions and data breaches.
 data_source:
 - AWS CloudTrail SetDefaultPolicyVersion
-search: '`cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policy_arn by src requestParameters.versionId eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_setdefaultpolicyversion_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately set a default policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources
+search: '`cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com
+  | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn)
+  as policy_arn by src requestParameters.versionId eventName eventSource aws_account_id
+  errorCode userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_setdefaultpolicyversion_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately set a default policy to allow a user to access
+  all resources. That said, AWS strongly advises against granting full control to
+  all AWS resources
 references:
 - https://bishopfox.com/blog/privilege-escalation-in-aws
 - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 50
-  message: From IP address $src$, user $user_arn$ has trigged an event $eventName$ for updating the the default policy version
+  message: From IP address $src$, user $user_arn$ has trigged an event $eventName$
+    for updating the the default policy version
   mitre_attack_id:
   - T1078.004
   - T1078
@@ -46,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.userName
-  - eventSource
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml b/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml
index f58271e6ed..831f79eefd 100644
--- a/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml
+++ b/detections/cloud/aws_successful_console_authentication_from_multiple_ips.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects an AWS account successfully authenticating from multiple unique IP addresses within a 5-minute window. It leverages AWS CloudTrail logs, specifically monitoring `ConsoleLogin` events and counting distinct source IPs. This behavior is significant as it may indicate compromised credentials, potentially from a phishing attack, being used concurrently by an adversary and a legitimate user. If confirmed malicious, this activity could allow unauthorized access to corporate resources, leading to data breaches or further exploitation within the AWS environment.
+description: The following analytic detects an AWS account successfully authenticating
+  from multiple unique IP addresses within a 5-minute window. It leverages AWS CloudTrail
+  logs, specifically monitoring `ConsoleLogin` events and counting distinct source
+  IPs. This behavior is significant as it may indicate compromised credentials, potentially
+  from a phishing attack, being used concurrently by an adversary and a legitimate
+  user. If confirmed malicious, this activity could allow unauthorized access to corporate
+  resources, leading to data breaches or further exploitation within the AWS environment.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName = ConsoleLogin | bin span=5m _time | stats values(userAgent) as userAgent values(eventName) as eventName values(src_ip) as src_ip dc(src_ip) as distinct_ip_count by _time user_arn | where distinct_ip_count>1 | `aws_successful_console_authentication_from_multiple_ips_filter`'
-how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This search works when AWS CloudTrail events are normalized use the Authentication datamodel.
-known_false_positives: A user with successful authentication events from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
+search: '`cloudtrail` eventName = ConsoleLogin | bin span=5m _time | stats values(userAgent)
+  as userAgent values(eventName) as eventName values(src_ip) as src_ip dc(src_ip)
+  as distinct_ip_count by _time user_arn | where distinct_ip_count>1 | `aws_successful_console_authentication_from_multiple_ips_filter`'
+how_to_implement: You must install Splunk AWS add on and Splunk App for AWS. This
+  search works when AWS CloudTrail events are normalized use the Authentication datamodel.
+known_false_positives: A user with successful authentication events from different
+  Ips may also represent the legitimate use of more than one device. Filter as needed
+  and/or customize the threshold to fit your environment.
 references:
 - https://rhinosecuritylabs.com/aws/mfa-phishing-on-aws/
 drilldown_searches:
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +45,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 90
-  message: User $user_arn$ has successfully logged into the AWS Console from different IP addresses $src_ip$ within 5 mins
+  message: User $user_arn$ has successfully logged into the AWS Console from different
+    IP addresses $src_ip$ within 5 mins
   mitre_attack_id:
   - T1586
   - T1535
@@ -46,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - src_ip
-  - user_arn
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/aws_console_login_multiple_ips/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1586.003/aws_console_login_multiple_ips/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_successful_single_factor_authentication.yml b/detections/cloud/aws_successful_single_factor_authentication.yml
index 6f89f6cea7..e329590c56 100644
--- a/detections/cloud/aws_successful_single_factor_authentication.yml
+++ b/detections/cloud/aws_successful_single_factor_authentication.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a successful Console Login authentication event for an AWS IAM user account without Multi-Factor Authentication (MFA) enabled. It leverages AWS CloudTrail logs to detect instances where MFA was not used during login. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to the AWS environment, potentially leading to data exfiltration, resource manipulation, or further privilege escalation.
+description: The following analytic identifies a successful Console Login authentication
+  event for an AWS IAM user account without Multi-Factor Authentication (MFA) enabled.
+  It leverages AWS CloudTrail logs to detect instances where MFA was not used during
+  login. This activity is significant as it may indicate a misconfiguration, policy
+  violation, or potential account takeover attempt. If confirmed malicious, an attacker
+  could gain unauthorized access to the AWS environment, potentially leading to data
+  exfiltration, resource manipulation, or further privilege escalation.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName= ConsoleLogin errorCode=success "additionalEventData.MFAUsed"=No | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource aws_account_id errorCode additionalEventData.MFAUsed userAgent eventID awsRegion user_name userIdentity.arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_successful_single_factor_authentication_filter`'
-how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search requires AWS CloudTrail logs.
-known_false_positives: It is possible that some accounts do not have MFA enabled for the AWS account however its agaisnt the best practices of securing AWS.
+search: '`cloudtrail` eventName= ConsoleLogin errorCode=success "additionalEventData.MFAUsed"=No
+  | stats count min(_time) as firstTime max(_time) as lastTime by src eventName eventSource
+  aws_account_id errorCode additionalEventData.MFAUsed userAgent eventID awsRegion
+  user_name userIdentity.arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `aws_successful_single_factor_authentication_filter`'
+how_to_implement: The Splunk AWS Add-on is required to utilize this data. The search
+  requires AWS CloudTrail logs.
+known_false_positives: It is possible that some accounts do not have MFA enabled for
+  the AWS account however its agaisnt the best practices of securing AWS.
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://attack.mitre.org/techniques/T1078/004/
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_name$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_name$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 80
-  message: User $user_name$ has successfully logged into an AWS Console without Multi-Factor Authentication from $src$
+  message: User $user_name$ has successfully logged into an AWS Console without Multi-Factor
+    Authentication from $src$
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -49,22 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src
-  - eventName
-  - eventSource
-  - aws_account_id
-  - errorCode
-  - additionalEventData.MFAUsed
-  - userAgent
-  - eventID
-  - awsRegion
-  - user_name
-  - userIdentity.arn
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/aws_login_sfa/cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/aws_login_sfa/cloudtrail.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml b/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml
index 95ca16afcd..fb65fdfc95 100644
--- a/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml
+++ b/detections/cloud/aws_unusual_number_of_failed_authentications_from_ip.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a single source IP failing to authenticate into the AWS Console with multiple valid users. It uses CloudTrail logs and calculates the standard deviation for source IP, leveraging the 3-sigma rule to detect unusual numbers of failed authentication attempts. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or further exploitation within the AWS environment.
+description: The following analytic identifies a single source IP failing to authenticate
+  into the AWS Console with multiple valid users. It uses CloudTrail logs and calculates
+  the standard deviation for source IP, leveraging the 3-sigma rule to detect unusual
+  numbers of failed authentication attempts. This behavior is significant as it may
+  indicate a Password Spraying attack, where an adversary attempts to gain initial
+  access or elevate privileges. If confirmed malicious, this activity could lead to
+  unauthorized access, data breaches, or further exploitation within the AWS environment.
 data_source:
 - AWS CloudTrail ConsoleLogin
-search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time | stats  dc(_raw) AS distinct_attempts values(user_name) as tried_accounts by _time, src_ip | eventstats  avg(distinct_attempts) as avg_attempts , stdev(distinct_attempts) as ip_std by _time | eval  upperBound=(avg_attempts+ip_std*3) | eval  isOutlier=if(distinct_attempts > 10 and distinct_attempts >= upperBound, 1, 0) | where isOutlier = 1 |`aws_unusual_number_of_failed_authentications_from_ip_filter`'
-how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail. We recommend the users to try different combinations of the bucket span time and the calculation of the upperBound field to tune this search according to their environment
-known_false_positives: No known false postives for this detection. Please review this alert
+search: '`cloudtrail` eventName=ConsoleLogin action=failure | bucket span=10m _time
+  | stats  dc(_raw) AS distinct_attempts values(user_name) as tried_accounts by _time,
+  src_ip | eventstats  avg(distinct_attempts) as avg_attempts , stdev(distinct_attempts)
+  as ip_std by _time | eval  upperBound=(avg_attempts+ip_std*3) | eval  isOutlier=if(distinct_attempts
+  > 10 and distinct_attempts >= upperBound, 1, 0) | where isOutlier = 1 |`aws_unusual_number_of_failed_authentications_from_ip_filter`'
+how_to_implement: You must install Splunk Add-on for AWS in order to ingest Cloudtrail.
+  We recommend the users to try different combinations of the bucket span time and
+  the calculation of the upperBound field to tune this search according to their environment
+known_false_positives: No known false postives for this detection. Please review this
+  alert
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://www.whiteoaksecurity.com/blog/goawsconsolespray-password-spraying-tool/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$tried_accounts$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 60
-  message: 'Unusual number of failed console login attempts (Count: $distinct_attempts$) against users from IP Address - $src_ip$'
+  message: 'Unusual number of failed console login attempts (Count: $distinct_attempts$)
+    against users from IP Address - $src_ip$'
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -50,16 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - action
-  - eventName
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/aws_mulitple_failed_console_login/aws_cloudtrail.json
     source: aws_cloudtrail
     sourcetype: aws:cloudtrail
diff --git a/detections/cloud/aws_updateloginprofile.yml b/detections/cloud/aws_updateloginprofile.yml
index a192df7d0f..c43528c1d5 100644
--- a/detections/cloud/aws_updateloginprofile.yml
+++ b/detections/cloud/aws_updateloginprofile.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects an AWS CloudTrail event where a user with permissions updates the login profile of another user. It leverages CloudTrail logs to identify instances where the user making the change is different from the user whose profile is being updated. This activity is significant because it can indicate privilege escalation attempts, where an attacker uses a compromised account to gain higher privileges. If confirmed malicious, this could allow the attacker to escalate their privileges, potentially leading to unauthorized access and control over sensitive resources within the AWS environment.
+description: The following analytic detects an AWS CloudTrail event where a user with
+  permissions updates the login profile of another user. It leverages CloudTrail logs
+  to identify instances where the user making the change is different from the user
+  whose profile is being updated. This activity is significant because it can indicate
+  privilege escalation attempts, where an attacker uses a compromised account to gain
+  higher privileges. If confirmed malicious, this could allow the attacker to escalate
+  their privileges, potentially leading to unauthorized access and control over sensitive
+  resources within the AWS environment.
 data_source:
 - AWS CloudTrail UpdateLoginProfile
-search: '`cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName), 1,0) | search match=0 | stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.userName user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_updateloginprofile_filter`'
-how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.
+search: '`cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com
+  errorCode = success | eval match=if(match(userIdentity.userName,requestParameters.userName),
+  1,0) | search match=0 | stats count min(_time) as firstTime max(_time) as lastTime
+  by requestParameters.userName src eventName eventSource aws_account_id errorCode
+  userAgent eventID awsRegion userIdentity.userName user_arn | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `aws_updateloginprofile_filter`'
+how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
+  search works with AWS CloudTrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created keys for another user.
 references:
 - https://bishopfox.com/blog/privilege-escalation-in-aws
 - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,9 @@ tags:
   asset_type: AWS Account
   confidence: 60
   impact: 50
-  message: From IP address $src$, user agent $userAgent$ has trigged an event $eventName$ for updating the existing login profile, potentially giving user $user_arn$ more access privilleges
+  message: From IP address $src$, user agent $userAgent$ has trigged an event $eventName$
+    for updating the existing login profile, potentially giving user $user_arn$ more
+    access privilleges
   mitre_attack_id:
   - T1136.003
   - T1136
@@ -46,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userAgent
-  - errorCode
-  - requestParameters.userName
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/azure_active_directory_high_risk_sign_in.yml b/detections/cloud/azure_active_directory_high_risk_sign_in.yml
index 7b0d51497c..e80bf902ff 100644
--- a/detections/cloud/azure_active_directory_high_risk_sign_in.yml
+++ b/detections/cloud/azure_active_directory_high_risk_sign_in.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects high-risk sign-in attempts against Azure Active Directory, identified by Azure Identity Protection. It leverages the RiskyUsers and UserRiskEvents log categories from Azure AD events ingested via EventHub. This activity is significant as it indicates potentially compromised accounts, flagged by heuristics and machine learning. If confirmed malicious, attackers could gain unauthorized access to sensitive resources, leading to data breaches or further exploitation within the environment.
+description: The following analytic detects high-risk sign-in attempts against Azure
+  Active Directory, identified by Azure Identity Protection. It leverages the RiskyUsers
+  and UserRiskEvents log categories from Azure AD events ingested via EventHub. This
+  activity is significant as it indicates potentially compromised accounts, flagged
+  by heuristics and machine learning. If confirmed malicious, attackers could gain
+  unauthorized access to sensitive resources, leading to data breaches or further
+  exploitation within the environment.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad` category=UserRiskEvents properties.riskLevel=high | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by src_ip, activity, riskLevel, riskEventType, additionalInfo | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_active_directory_high_risk_sign_in_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. Specifically, this analytic leverages the RiskyUsers and UserRiskEvents log category in the azure:monitor:aad sourcetype.
-known_false_positives: Details for the risk calculation algorithm used by Identity Protection are unknown and may be prone to false positives.
+search: '`azure_monitor_aad` category=UserRiskEvents properties.riskLevel=high | rename
+  properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime values(user)
+  as user by src_ip, activity, riskLevel, riskEventType, additionalInfo | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_active_directory_high_risk_sign_in_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. Specifically, this analytic leverages the RiskyUsers and UserRiskEvents
+  log category in the azure:monitor:aad sourcetype.
+known_false_positives: Details for the risk calculation algorithm used by Identity
+  Protection are unknown and may be prone to false positives.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - properties.riskLevel
-  - user
-  - src_ip
-  - properties.activity
-  - properties.riskEventType
-  - properties.additionalInfo
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azuread_highrisk/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azuread_highrisk/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml b/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml
index bf07cb1fa0..313a5f708a 100644
--- a/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml
+++ b/detections/cloud/azure_ad_admin_consent_bypassed_by_service_principal.yml
@@ -7,10 +7,32 @@ data_source:
 - Azure Active Directory Add app role assignment to service principal
 type: TTP
 status: production
-description: The following analytic identifies instances where a service principal in Azure Active Directory assigns app roles without standard admin consent. It uses Entra ID logs from the `azure_monitor_aad` data source, focusing on the "Add app role assignment to service principal" operation. This detection is significant as it highlights potential bypasses of critical administrative consent processes, which could lead to unauthorized privileges being granted. If confirmed malicious, this activity could allow attackers to exploit automation to assign sensitive permissions without proper oversight, potentially compromising the security of the Azure AD environment.
-search: '`azure_monitor_aad` (operationName="Add app role assignment to service principal" OR operationName="Add member to role*") src_user_type=servicePrincipal  | rename properties.* as *   | eval roleId = mvindex(''targetResources{}.modifiedProperties{}.newValue'', 0)  | eval roleValue = mvindex(''targetResources{}.modifiedProperties{}.newValue'', 1)  | eval roleDescription = mvindex(''targetResources{}.modifiedProperties{}.newValue'', 2)  | eval user_id = mvindex(''targetResources{}.id'', 0), user=coalesce(user,mvindex(''targetResources{}.displayName'', 0)) | rename initiatedBy.app.displayName as src_user  | stats count earliest(_time) as firstTime latest(_time) as lastTime by src_user user user_id roleId roleValue roleDescription | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  | `azure_ad_admin_consent_bypassed_by_service_principal_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Auditlog log category
-known_false_positives: Service Principals are sometimes configured to legitimately bypass the consent process for purposes of automation. Filter as needed.
+description: The following analytic identifies instances where a service principal
+  in Azure Active Directory assigns app roles without standard admin consent. It uses
+  Entra ID logs from the `azure_monitor_aad` data source, focusing on the "Add app
+  role assignment to service principal" operation. This detection is significant as
+  it highlights potential bypasses of critical administrative consent processes, which
+  could lead to unauthorized privileges being granted. If confirmed malicious, this
+  activity could allow attackers to exploit automation to assign sensitive permissions
+  without proper oversight, potentially compromising the security of the Azure AD
+  environment.
+search: "`azure_monitor_aad` (operationName=\"Add app role assignment to service principal\"\
+  \ OR operationName=\"Add member to role*\") src_user_type=servicePrincipal  | rename
+  properties.* as *   | eval roleId = mvindex('targetResources{}.modifiedProperties{}.newValue',
+  0)  | eval roleValue = mvindex('targetResources{}.modifiedProperties{}.newValue',
+  1)  | eval roleDescription = mvindex('targetResources{}.modifiedProperties{}.newValue',
+  2)  | eval user_id = mvindex('targetResources{}.id', 0), user=coalesce(user,mvindex('targetResources{}.displayName',
+  0)) | rename initiatedBy.app.displayName as src_user  | stats count earliest(_time)
+  as firstTime latest(_time) as lastTime by src_user user user_id roleId roleValue
+  roleDescription | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`\
+  \  | `azure_ad_admin_consent_bypassed_by_service_principal_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the Auditlog log category
+known_false_positives: Service Principals are sometimes configured to legitimately
+  bypass the consent process for purposes of automation. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 drilldown_searches:
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +56,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 60
   impact: 90
-  message: Service principal $src_user$ bypassed the admin consent process and granted permissions to $user$
+  message: Service principal $src_user$ bypassed the admin consent process and granted
+    permissions to $user$
   mitre_attack_id:
   - T1098.003
   observable:
@@ -45,15 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - targetResources{}.modifiedProperties{}.newValue
-  - targetResources{}.id
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_bypass_admin_consent/azure_ad_bypass_admin_consent.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_bypass_admin_consent/azure_ad_bypass_admin_consent.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_application_administrator_role_assigned.yml b/detections/cloud/azure_ad_application_administrator_role_assigned.yml
index 4e357ef316..d22ac45d99 100644
--- a/detections/cloud/azure_ad_application_administrator_role_assigned.yml
+++ b/detections/cloud/azure_ad_application_administrator_role_assigned.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Add member to role
-description: The following analytic identifies the assignment of the Application Administrator role to an Azure AD user. It leverages Azure Active Directory events, specifically monitoring the "Add member to role" operation. This activity is significant because users in this role can manage all aspects of enterprise applications, including credentials, which can be used to impersonate application identities. If confirmed malicious, an attacker could escalate privileges, manage application settings, and potentially access sensitive resources by impersonating application identities, posing a significant security risk to the Azure AD tenant.
-search: '`azure_monitor_aad`  "operationName"="Add member to role"  "properties.targetResources{}.modifiedProperties{}.newValue"="\"Application Administrator\"" | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime by user initiatedBy, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_application_administrator_role_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Auditlog log category
-known_false_positives: Administrators may legitimately assign the Application Administrator role to a user. Filter as needed.
+description: The following analytic identifies the assignment of the Application Administrator
+  role to an Azure AD user. It leverages Azure Active Directory events, specifically
+  monitoring the "Add member to role" operation. This activity is significant because
+  users in this role can manage all aspects of enterprise applications, including
+  credentials, which can be used to impersonate application identities. If confirmed
+  malicious, an attacker could escalate privileges, manage application settings, and
+  potentially access sensitive resources by impersonating application identities,
+  posing a significant security risk to the Azure AD tenant.
+search: '`azure_monitor_aad`  "operationName"="Add member to role"  "properties.targetResources{}.modifiedProperties{}.newValue"="\"Application
+  Administrator\"" | rename properties.* as * | rename initiatedBy.user.userPrincipalName
+  as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime by user
+  initiatedBy, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_application_administrator_role_assigned_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the Auditlog log category
+known_false_positives: Administrators may legitimately assign the Application Administrator
+  role to a user. Filter as needed.
 references:
 - https://dirkjanm.io/azure-ad-privilege-escalation-application-admin/
 - https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5
@@ -23,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +54,8 @@ tags:
   atomic_guid: []
   confidence: 50
   impact: 70
-  message: The privileged Azure AD role Application Administrator was assigned for User $user$ initiated by $initiatedBy$
+  message: The privileged Azure AD role Application Administrator was assigned for
+    User $user$ initiated by $initiatedBy$
   mitre_attack_id:
   - T1098
   - T1098.003
@@ -50,16 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml b/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml
index d44d323c05..c96d1fb548 100644
--- a/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml
+++ b/detections/cloud/azure_ad_authentication_failed_during_mfa_challenge.yml
@@ -5,14 +5,38 @@ date: '2024-10-31'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk, 0xC0FFEEEE
 status: production
 type: TTP
-description: The following analytic identifies failed authentication attempts against an Azure AD tenant during the Multi-Factor Authentication (MFA) challenge, specifically flagged by error code 500121. It leverages Azure AD SignInLogs to detect these events. This activity is significant as it may indicate an adversary attempting to authenticate using compromised credentials on an account with MFA enabled. If confirmed malicious, this could suggest an ongoing effort to bypass MFA protections, potentially leading to unauthorized access and further compromise of the affected account.
+description: The following analytic identifies failed authentication attempts against
+  an Azure AD tenant during the Multi-Factor Authentication (MFA) challenge, specifically
+  flagged by error code 500121. It leverages Azure AD SignInLogs to detect these events.
+  This activity is significant as it may indicate an adversary attempting to authenticate
+  using compromised credentials on an account with MFA enabled. If confirmed malicious,
+  this could suggest an ongoing effort to bypass MFA protections, potentially leading
+  to unauthorized access and further compromise of the affected account.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad` category=SignInLogs properties.status.errorCode=500121 | rename properties.* as *, authenticationDetails{}.* as * | eval time=strptime(authenticationStepDateTime, "%Y-%m-%dT%H:%M:%S") | eval auth_detail=mvzip(strftime(time, "%Y-%m-%dT%H:%M:%S"),authenticationStepResultDetail," - "), auth_msg=mvappend(''status.additionalDetails'', authenticationStepResultDetail) | eval auth_method=mvmap(authenticationMethod, if(isnull(mvfind(''mfaDetail.authMethod'', authenticationMethod)), authenticationMethod, null())) | stats min(_time) as firstTime, max(_time) as lastTime, values(user) as user, values(src_ip) as src_ip, values(mfaDetail.authMethod) as mfa_method, values(auth_method) as auth_method, values(auth_detail) as auth_detail, values(auth_msg) as auth_msg, values(appDisplayName) as appDisplayName, values(user_agent) as user_agent by tenantId originalRequestId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search NOT auth_msg="MFA successfully completed" | sort 0 - firstTime | `azure_ad_authentication_failed_during_mfa_challenge_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: 'False positives have been minimized by removing attempts that result in ''MFA successfully completed messages'', which were found to be generated when a user opts to use a different MFA method than the default.
-
-  Further reductions in notable events can be achieved through filtering ''MFA denied; duplicate authentication attempt'' messages within the auth_msg field, as they could arguably be considered as false positives.'
+search: "`azure_monitor_aad` category=SignInLogs properties.status.errorCode=500121
+  | rename properties.* as *, authenticationDetails{}.* as * | eval time=strptime(authenticationStepDateTime,
+  \"%Y-%m-%dT%H:%M:%S\") | eval auth_detail=mvzip(strftime(time, \"%Y-%m-%dT%H:%M:%S\"\
+  ),authenticationStepResultDetail,\" - \"), auth_msg=mvappend('status.additionalDetails',
+  authenticationStepResultDetail) | eval auth_method=mvmap(authenticationMethod, if(isnull(mvfind('mfaDetail.authMethod',
+  authenticationMethod)), authenticationMethod, null())) | stats min(_time) as firstTime,
+  max(_time) as lastTime, values(user) as user, values(src_ip) as src_ip, values(mfaDetail.authMethod)
+  as mfa_method, values(auth_method) as auth_method, values(auth_detail) as auth_detail,
+  values(auth_msg) as auth_msg, values(appDisplayName) as appDisplayName, values(user_agent)
+  as user_agent by tenantId originalRequestId | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | search NOT auth_msg=\"MFA successfully completed\"\
+  \ | sort 0 - firstTime | `azure_ad_authentication_failed_during_mfa_challenge_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: "False positives have been minimized by removing attempts that
+  result in 'MFA successfully completed messages', which were found to be generated
+  when a user opts to use a different MFA method than the default.\nFurther reductions
+  in notable events can be achieved through filtering 'MFA denied; duplicate authentication
+  attempt' messages within the auth_msg field, as they could arguably be considered
+  as false positives."
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://attack.mitre.org/techniques/T1078/004/
@@ -24,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,19 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - properties.status.errorCode
-  - user
-  - src_ip
-  - properties.status.additionalDetails
-  - properties.appDisplayName
-  - properties.userAgent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azuread/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azuread/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml b/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml
index 305fde0ca2..ded1dab53d 100644
--- a/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml
+++ b/detections/cloud/azure_ad_block_user_consent_for_risky_apps_disabled.yml
@@ -7,10 +7,30 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Update authorization policy
-description: The following analytic detects when the risk-based step-up consent security setting in Azure AD is disabled. It monitors Azure Active Directory logs for the "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling this feature can expose the organization to OAuth phishing threats by allowing users to grant consent to potentially malicious applications. If confirmed malicious, attackers could gain unauthorized access to user data and sensitive information, leading to data breaches and further compromise within the organization.
-search: '`azure_monitor_aad` operationName="Update authorization policy" | rename properties.* as *  | eval index_number = if(mvfind(''targetResources{}.modifiedProperties{}.displayName'', "AllowUserConsentForRiskyApps") >= 0, mvfind(''targetResources{}.modifiedProperties{}.displayName'', "AllowUserConsentForRiskyApps"), -1) | search index_number >= 0  | eval AllowUserConsentForRiskyApps = mvindex(''targetResources{}.modifiedProperties{}.newValue'',index_number) | search AllowUserConsentForRiskyApps = "[true]" | stats count min(_time) as firstTime max(_time) as lastTime by user, src_ip, operationName, AllowUserConsentForRiskyApps | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_block_user_consent_for_risky_apps_disabled_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting by administrators, perhaps as part of a policy update or security assessment, may trigger this alert, necessitating verification of the change's intent and authorization
+description: The following analytic detects when the risk-based step-up consent security
+  setting in Azure AD is disabled. It monitors Azure Active Directory logs for the
+  "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps"
+  setting. This activity is significant because disabling this feature can expose
+  the organization to OAuth phishing threats by allowing users to grant consent to
+  potentially malicious applications. If confirmed malicious, attackers could gain
+  unauthorized access to user data and sensitive information, leading to data breaches
+  and further compromise within the organization.
+search: "`azure_monitor_aad` operationName=\"Update authorization policy\" | rename
+  properties.* as *  | eval index_number = if(mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"AllowUserConsentForRiskyApps\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"AllowUserConsentForRiskyApps\"), -1) | search index_number >= 0  | eval AllowUserConsentForRiskyApps
+  = mvindex('targetResources{}.modifiedProperties{}.newValue',index_number) | search
+  AllowUserConsentForRiskyApps = \"[true]\" | stats count min(_time) as firstTime
+  max(_time) as lastTime by user, src_ip, operationName, AllowUserConsentForRiskyApps
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_block_user_consent_for_risky_apps_disabled_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLog log category.
+known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting
+  by administrators, perhaps as part of a policy update or security assessment, may
+  trigger this alert, necessitating verification of the change's intent and authorization
 references:
 - https://attack.mitre.org/techniques/T1562/
 - https://goodworkaround.com/2020/10/19/a-look-behind-the-azure-ad-permission-classifications-preview/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.targetResources{}.modifiedProperties{}.displayName
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - user
-  - src_ip
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/azuread_disable_blockconsent_for_riskapps/azuread_disable_blockconsent_for_riskapps.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/azuread_disable_blockconsent_for_riskapps/azuread_disable_blockconsent_for_riskapps.log
     source: Azure Ad
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml b/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml
index ae6ede83a2..00421dea8d 100644
--- a/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml
+++ b/detections/cloud/azure_ad_concurrent_sessions_from_different_ips.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Mauricio Velazco, Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Azure AD account with concurrent sessions originating from multiple unique IP addresses within a 5-minute window. It leverages Azure Active Directory NonInteractiveUserSignInLogs to identify this behavior by analyzing successful authentication events and counting distinct source IPs. This activity is significant as it may indicate session hijacking, where an attacker uses stolen session cookies to access corporate resources from a different location. If confirmed malicious, this could lead to unauthorized access to sensitive information and potential data breaches.
+description: The following analytic detects an Azure AD account with concurrent sessions
+  originating from multiple unique IP addresses within a 5-minute window. It leverages
+  Azure Active Directory NonInteractiveUserSignInLogs to identify this behavior by
+  analyzing successful authentication events and counting distinct source IPs. This
+  activity is significant as it may indicate session hijacking, where an attacker
+  uses stolen session cookies to access corporate resources from a different location.
+  If confirmed malicious, this could lead to unauthorized access to sensitive information
+  and potential data breaches.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad` properties.authenticationDetails{}.succeeded=true category=NonInteractiveUserSignInLogs action=success | rename properties.* as * | bucket span=5m _time | stats count min(_time) as firstTime max(_time) as lastTime dc(src_ip) AS unique_ips dc(location.city) as dc_city values(location.city) as city values(src_ip) as src_ip values(appDisplayName) as appDisplayName values(location.countryOrRegion) by user _time | where unique_ips > 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_concurrent_sessions_from_different_ips_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: A user with concurrent sessions from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment. Also consider the geographic location of the IP addresses and filter out IP space that belong to your organization.
+search: '`azure_monitor_aad` properties.authenticationDetails{}.succeeded=true category=NonInteractiveUserSignInLogs
+  action=success | rename properties.* as * | bucket span=5m _time | stats count min(_time)
+  as firstTime max(_time) as lastTime dc(src_ip) AS unique_ips dc(location.city) as
+  dc_city values(location.city) as city values(src_ip) as src_ip values(appDisplayName)
+  as appDisplayName values(location.countryOrRegion) by user _time | where unique_ips
+  > 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |
+  `azure_ad_concurrent_sessions_from_different_ips_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: A user with concurrent sessions from different Ips may also
+  represent the legitimate use of more than one device. Filter as needed and/or customize
+  the threshold to fit your environment. Also consider the geographic location of
+  the IP addresses and filter out IP space that belong to your organization.
 references:
 - https://attack.mitre.org/techniques/T1185/
 - https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Azure Tenant
   confidence: 60
   impact: 70
-  message: User $user$ has concurrent sessions from more than one unique IP address in the span of 5 minutes.
+  message: User $user$ has concurrent sessions from more than one unique IP address
+    in the span of 5 minutes.
   mitre_attack_id:
   - T1185
   observable:
@@ -47,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - user
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/azure_ad_concurrent_sessions_from_different_ips/azuread.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/azure_ad_concurrent_sessions_from_different_ips/azuread.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_device_code_authentication.yml b/detections/cloud/azure_ad_device_code_authentication.yml
index 067d131ab2..6044cb9b25 100644
--- a/detections/cloud/azure_ad_device_code_authentication.yml
+++ b/detections/cloud/azure_ad_device_code_authentication.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory
-description: The following analytic identifies Azure Device Code Phishing attacks, which can lead to Azure Account Take-Over (ATO). It leverages Azure AD SignInLogs to detect suspicious authentication requests using the device code authentication protocol. This activity is significant as it indicates potential bypassing of Multi-Factor Authentication (MFA) and Conditional Access Policies (CAPs) through phishing emails. If confirmed malicious, attackers could gain unauthorized access to Azure AD, Exchange mailboxes, and Outlook Web Application (OWA), leading to potential data breaches and unauthorized data access.
-search: '`azure_monitor_aad` category=SignInLogs "properties.authenticationProtocol"=deviceCode | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime by user src_ip, appDisplayName, userAgent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_device_code_authentication_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: In most organizations, device code authentication will be used to access common Microsoft service but it may be legitimate for others. Filter as needed.
+description: The following analytic identifies Azure Device Code Phishing attacks,
+  which can lead to Azure Account Take-Over (ATO). It leverages Azure AD SignInLogs
+  to detect suspicious authentication requests using the device code authentication
+  protocol. This activity is significant as it indicates potential bypassing of Multi-Factor
+  Authentication (MFA) and Conditional Access Policies (CAPs) through phishing emails.
+  If confirmed malicious, attackers could gain unauthorized access to Azure AD, Exchange
+  mailboxes, and Outlook Web Application (OWA), leading to potential data breaches
+  and unauthorized data access.
+search: '`azure_monitor_aad` category=SignInLogs "properties.authenticationProtocol"=deviceCode
+  | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime
+  by user src_ip, appDisplayName, userAgent | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_device_code_authentication_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: In most organizations, device code authentication will be used
+  to access common Microsoft service but it may be legitimate for others. Filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1528
 - https://github.com/rvrsh3ll/TokenTactics
@@ -23,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - user
-  - properties.authenticationProtocol
-  - properties.ipAddress
-  - properties.status.additionalDetails
-  - properties.appDisplayName
-  - properties.userAgent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/device_code_authentication/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/device_code_authentication/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_external_guest_user_invited.yml b/detections/cloud/azure_ad_external_guest_user_invited.yml
index 1ea7ea225b..561def2a48 100644
--- a/detections/cloud/azure_ad_external_guest_user_invited.yml
+++ b/detections/cloud/azure_ad_external_guest_user_invited.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the invitation of an external guest user within Azure AD. It leverages Azure AD AuditLogs to identify events where an external user is invited, using fields such as operationName and initiatedBy. Monitoring these invitations is crucial as they can lead to unauthorized access if abused. If confirmed malicious, this activity could allow attackers to gain access to internal resources, potentially leading to data breaches or further exploitation of the environment.
+description: The following analytic detects the invitation of an external guest user
+  within Azure AD. It leverages Azure AD AuditLogs to identify events where an external
+  user is invited, using fields such as operationName and initiatedBy. Monitoring
+  these invitations is crucial as they can lead to unauthorized access if abused.
+  If confirmed malicious, this activity could allow attackers to gain access to internal
+  resources, potentially leading to data breaches or further exploitation of the environment.
 data_source:
 - Azure Active Directory Invite external user
-search: '`azure_monitor_aad` operationName="Invite external user" | rename properties.*  as * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.type as type | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by type, initiatedBy, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_external_guest_user_invited_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: Administrator may legitimately invite external guest users. Filter as needed.
+search: '`azure_monitor_aad` operationName="Invite external user" | rename properties.*  as
+  * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.type
+  as type | stats count min(_time) as firstTime max(_time) as lastTime values(user)
+  as user by type, initiatedBy, result, operationName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_external_guest_user_invited_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLogs log category.
+known_false_positives: Administrator may legitimately invite external guest users.
+  Filter as needed.
 references:
 - https://dirkjanm.io/assets/raw/US-22-Mollema-Backdooring-and-hijacking-Azure-AD-accounts_final.pdf
 - https://www.blackhat.com/us-22/briefings/schedule/#backdooring-and-hijacking-azure-ad-accounts-by-abusing-external-identities-26999
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_external_guest_user_invited/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_external_guest_user_invited/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml b/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml
index 2effb4800a..5b4a7d553b 100644
--- a/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml
+++ b/detections/cloud/azure_ad_fullaccessasapp_permission_assigned.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the assignment of the 'full_access_as_app' permission to an application within Office 365 Exchange Online. This is identified by the GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40' and the ResourceAppId '00000002-0000-0ff1-ce00-000000000000'. The detection leverages the azure_monitor_aad data source, focusing on AuditLogs with the operation name 'Update application'. This activity is significant as it grants broad control over Office 365 operations, including full access to all mailboxes and the ability to send emails as any user. If malicious, this could lead to unauthorized access and data exfiltration.
+description: The following analytic detects the assignment of the 'full_access_as_app'
+  permission to an application within Office 365 Exchange Online. This is identified
+  by the GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40' and the ResourceAppId '00000002-0000-0ff1-ce00-000000000000'.
+  The detection leverages the azure_monitor_aad data source, focusing on AuditLogs
+  with the operation name 'Update application'. This activity is significant as it
+  grants broad control over Office 365 operations, including full access to all mailboxes
+  and the ability to send emails as any user. If malicious, this could lead to unauthorized
+  access and data exfiltration.
 data_source:
 - Azure Active Directory Update application
-search: '`azure_monitor_aad` category=AuditLogs operationName="Update application"  | eval newvalue = mvindex(''properties.targetResources{}.modifiedProperties{}.newValue'',0) | spath input=newvalue  | search "{}.ResourceAppId"="00000002-0000-0ff1-ce00-000000000000"  "{}.RequiredAppPermissions{}.EntitlementId"="dc890d15-9560-4a4c-9b7f-a736ec74ec40" | eval Permissions = ''{}.RequiredAppPermissions{}.EntitlementId'' | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user, object, user_agent, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_fullaccessasapp_permission_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: The full_access_as_app API permission may be assigned to legitimate applications. Filter as needed.
+search: "`azure_monitor_aad` category=AuditLogs operationName=\"Update application\"\
+  \  | eval newvalue = mvindex('properties.targetResources{}.modifiedProperties{}.newValue',0)
+  | spath input=newvalue  | search \"{}.ResourceAppId\"=\"00000002-0000-0ff1-ce00-000000000000\"\
+  \  \"{}.RequiredAppPermissions{}.EntitlementId\"=\"dc890d15-9560-4a4c-9b7f-a736ec74ec40\"\
+  \ | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | stats count
+  earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user,
+  object, user_agent, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_fullaccessasapp_permission_assigned_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLogs log category.
+known_false_positives: The full_access_as_app API permission may be assigned to legitimate
+  applications. Filter as needed.
 references:
 - https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 60
   impact: 80
-  message: User $user$ assigned the full_access_as_app permission to the app registration $object$
+  message: User $user$ assigned the full_access_as_app permission to the app registration
+    $object$
   mitre_attack_id:
   - T1098.002
   - T1098.003
@@ -44,12 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/full_access_as_app_permission_assigned/full_access_as_app_permission_assigned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/full_access_as_app_permission_assigned/full_access_as_app_permission_assigned.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_global_administrator_role_assigned.yml b/detections/cloud/azure_ad_global_administrator_role_assigned.yml
index a2a064d7f1..b1f72a2643 100644
--- a/detections/cloud/azure_ad_global_administrator_role_assigned.yml
+++ b/detections/cloud/azure_ad_global_administrator_role_assigned.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the assignment of the Azure AD Global Administrator role to a user. It leverages Azure Active Directory AuditLogs to identify when the "Add member to role" operation includes the "Global Administrator" role. This activity is significant because the Global Administrator role grants extensive access to data, resources, and settings, similar to a Domain Administrator in traditional AD environments. If confirmed malicious, this could allow an attacker to establish persistence, escalate privileges, and potentially gain control over Azure resources, posing a severe security risk.
+description: The following analytic detects the assignment of the Azure AD Global
+  Administrator role to a user. It leverages Azure Active Directory AuditLogs to identify
+  when the "Add member to role" operation includes the "Global Administrator" role.
+  This activity is significant because the Global Administrator role grants extensive
+  access to data, resources, and settings, similar to a Domain Administrator in traditional
+  AD environments. If confirmed malicious, this could allow an attacker to establish
+  persistence, escalate privileges, and potentially gain control over Azure resources,
+  posing a severe security risk.
 data_source:
 - Azure Active Directory Add member to role
-search: '`azure_monitor_aad`  operationName="Add member to role"  properties.targetResources{}.modifiedProperties{}.newValue="\"Global Administrator\"" | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by initiatedBy, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_global_administrator_role_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: Administrators may legitimately assign the Global Administrator role to a user. Filter as needed.
+search: '`azure_monitor_aad`  operationName="Add member to role"  properties.targetResources{}.modifiedProperties{}.newValue="\"Global
+  Administrator\"" | rename properties.* as * | rename initiatedBy.user.userPrincipalName
+  as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime values(user)
+  as user by initiatedBy, result, operationName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_global_administrator_role_assigned_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLogs log category.
+known_false_positives: Administrators may legitimately assign the Global Administrator
+  role to a user. Filter as needed.
 references:
 - https://o365blog.com/post/admin/
 - https://adsecurity.org/?p=4277
@@ -24,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,16 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_global_administrator/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_global_administrator/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml b/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml
index 583d362bf1..efa00c36b6 100644
--- a/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml
+++ b/detections/cloud/azure_ad_high_number_of_failed_authentications_for_user.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies an Azure AD account experiencing more than 20 failed authentication attempts within a 10-minute window. This detection leverages Azure SignInLogs data, specifically monitoring for error code 50126 and unsuccessful authentication attempts. This behavior is significant as it may indicate a brute force attack targeting the account. If confirmed malicious, an attacker could potentially gain unauthorized access, leading to data breaches or further exploitation within the environment. Security teams should adjust the threshold based on their specific environment to reduce false positives.
+description: The following analytic identifies an Azure AD account experiencing more
+  than 20 failed authentication attempts within a 10-minute window. This detection
+  leverages Azure SignInLogs data, specifically monitoring for error code 50126 and
+  unsuccessful authentication attempts. This behavior is significant as it may indicate
+  a brute force attack targeting the account. If confirmed malicious, an attacker
+  could potentially gain unauthorized access, leading to data breaches or further
+  exploitation within the environment. Security teams should adjust the threshold
+  based on their specific environment to reduce false positives.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad` category= SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=10m _time | stats count min(_time) as firstTime max(_time) as lastTime values(src_ip) as src_ip by user | where count > 20 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_high_number_of_failed_authentications_for_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: A user with more than 20 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
+search: '`azure_monitor_aad` category= SignInLogs properties.status.errorCode=50126
+  properties.authenticationDetails{}.succeeded=false | rename properties.* as * |
+  bucket span=10m _time | stats count min(_time) as firstTime max(_time) as lastTime
+  values(src_ip) as src_ip by user | where count > 20 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_high_number_of_failed_authentications_for_user_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: A user with more than 20 failed authentication attempts in
+  the span of 5 minutes may also be triggered by a broken application.
 references:
 - https://attack.mitre.org/techniques/T1110/
 - https://attack.mitre.org/techniques/T1110/001/
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Azure Tenant
   confidence: 70
   impact: 50
-  message: User $user$ failed to authenticate more than 20 times in the span of 5 minutes.
+  message: User $user$ failed to authenticate more than 20 times in the span of 5
+    minutes.
   mitre_attack_id:
   - T1110
   - T1110.001
@@ -43,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - user
-  - properties.ipAddress
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml b/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml
index b6ebbf4014..ca20e444af 100644
--- a/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml
+++ b/detections/cloud/azure_ad_high_number_of_failed_authentications_from_ip.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects an IP address with 20 or more failed authentication attempts to an Azure AD tenant within 10 minutes. It leverages Azure AD SignInLogs to identify repeated failed logins from the same IP. This behavior is significant as it may indicate a brute force attack aimed at gaining unauthorized access or escalating privileges. If confirmed malicious, the attacker could potentially compromise user accounts, leading to unauthorized access to sensitive information and resources within the Azure environment.
+description: The following analytic detects an IP address with 20 or more failed authentication
+  attempts to an Azure AD tenant within 10 minutes. It leverages Azure AD SignInLogs
+  to identify repeated failed logins from the same IP. This behavior is significant
+  as it may indicate a brute force attack aimed at gaining unauthorized access or
+  escalating privileges. If confirmed malicious, the attacker could potentially compromise
+  user accounts, leading to unauthorized access to sensitive information and resources
+  within the Azure environment.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad` category= SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=10m _time | stats count min(_time) as firstTime max(_time) as lastTime dc(user) AS unique_accounts values(user) as user by src_ip _time | where count > 20 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_high_number_of_failed_authentications_from_ip_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: An Ip address with more than 20 failed authentication attempts in the span of 10 minutes may also be triggered by a broken application.
+search: '`azure_monitor_aad` category= SignInLogs properties.status.errorCode=50126
+  properties.authenticationDetails{}.succeeded=false | rename properties.* as * |
+  bucket span=10m _time | stats count min(_time) as firstTime max(_time) as lastTime
+  dc(user) AS unique_accounts values(user) as user by src_ip _time | where count >
+  20 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |
+  `azure_ad_high_number_of_failed_authentications_from_ip_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: An Ip address with more than 20 failed authentication attempts
+  in the span of 10 minutes may also be triggered by a broken application.
 references:
 - https://attack.mitre.org/techniques/T1110/
 - https://attack.mitre.org/techniques/T1110/001/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +53,8 @@ tags:
   asset_type: Azure Tenant
   confidence: 70
   impact: 50
-  message: $src_ip$ failed to authenticate more than 20 times in the span of 10 minutes minutes.
+  message: $src_ip$ failed to authenticate more than 20 times in the span of 10 minutes
+    minutes.
   mitre_attack_id:
   - T1110
   - T1110.001
@@ -50,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - user
-  - src_ip
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_high_number_of_failed_authentications_for_user/azuread.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml b/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml
index 5a9021eaa8..cbc31e931d 100644
--- a/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml
+++ b/detections/cloud/azure_ad_multi_factor_authentication_disabled.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to disable multi-factor authentication (MFA) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify the "Disable Strong Authentication" operation. This activity is significant because disabling MFA can allow adversaries to maintain persistence using compromised accounts without raising suspicion. If confirmed malicious, this action could enable attackers to bypass an essential security control, potentially leading to unauthorized access and prolonged undetected presence in the environment.
+description: The following analytic detects attempts to disable multi-factor authentication
+  (MFA) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify
+  the "Disable Strong Authentication" operation. This activity is significant because
+  disabling MFA can allow adversaries to maintain persistence using compromised accounts
+  without raising suspicion. If confirmed malicious, this action could enable attackers
+  to bypass an essential security control, potentially leading to unauthorized access
+  and prolonged undetected presence in the environment.
 data_source:
 - Azure Active Directory Disable Strong Authentication
-search: '`azure_monitor_aad` category=AuditLogs operationName="Disable Strong Authentication" | rename properties.* as * | rename targetResources{}.type as type | rename initiatedBy.user.userPrincipalName as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime by user, type, operationName, initiatedBy, result | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multi_factor_authentication_disabled_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: Legitimate use case may require for users to disable MFA. Filter as needed.
+search: '`azure_monitor_aad` category=AuditLogs operationName="Disable Strong Authentication"
+  | rename properties.* as * | rename targetResources{}.type as type | rename initiatedBy.user.userPrincipalName
+  as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime by user,
+  type, operationName, initiatedBy, result | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `azure_ad_multi_factor_authentication_disabled_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLogs log category.
+known_false_positives: Legitimate use case may require for users to disable MFA. Filter
+  as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks
 - https://docs.microsoft.com/en-us/azure/active-directory/authentication/howto-mfa-userstates
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/azuread/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/azuread/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml b/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml
index 8559310a9e..ccb7ea7d9b 100644
--- a/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml
+++ b/detections/cloud/azure_ad_multi_source_failed_authentications_spike.yml
@@ -7,10 +7,40 @@ status: production
 type: Hunting
 data_source:
 - Azure Active Directory
-description: The following analytic detects potential distributed password spraying attacks in an Azure AD environment. It identifies a spike in failed authentication attempts across various user-and-IP combinations from multiple source IPs and countries, using different user agents. This detection leverages Azure AD SignInLogs, focusing on error code 50126 for failed authentications. This activity is significant as it indicates an adversary's attempt to bypass security controls by distributing login attempts. If confirmed malicious, this could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization's infrastructure.
-search: '`azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | eval uniqueIPUserCombo = src_ip . "-" . user | stats count min(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, dc(user_agent) as uniqueUserAgents, dc(location.countryOrRegion) as uniqueCountries values(user) as user, values(src_ip) as ips, values(user_agent) as user_agents, values(location.countryOrRegion) as countries | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 AND uniqueUserAgents = 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multi_source_failed_authentications_spike_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category. The thresholds set within the analytic (such as unique IPs, unique users, etc.) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment.
-known_false_positives: This detection may yield false positives in scenarios where legitimate bulk sign-in activities occur, such as during company-wide system updates or when users are accessing resources from varying locations in a short time frame, such as in the case of VPNs or cloud services that rotate IP addresses. Filter as needed.
+description: The following analytic detects potential distributed password spraying
+  attacks in an Azure AD environment. It identifies a spike in failed authentication
+  attempts across various user-and-IP combinations from multiple source IPs and countries,
+  using different user agents. This detection leverages Azure AD SignInLogs, focusing
+  on error code 50126 for failed authentications. This activity is significant as
+  it indicates an adversary's attempt to bypass security controls by distributing
+  login attempts. If confirmed malicious, this could lead to unauthorized access,
+  data breaches, privilege escalation, and lateral movement within the organization's
+  infrastructure.
+search: '`azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126
+  properties.authenticationDetails{}.succeeded=false | rename properties.* as * |
+  bucket span=5m _time | eval uniqueIPUserCombo = src_ip . "-" . user | stats count
+  min(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations,
+  dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, dc(user_agent) as uniqueUserAgents,
+  dc(location.countryOrRegion) as uniqueCountries values(user) as user, values(src_ip)
+  as ips, values(user_agent) as user_agents, values(location.countryOrRegion) as countries
+  | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 AND
+  uniqueUserAgents = 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_multi_source_failed_authentications_spike_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category. The thresholds set within the
+  analytic (such as unique IPs, unique users, etc.) are initial guidelines and should
+  be customized based on the organization's user behavior and risk profile. Security
+  teams are encouraged to adjust these thresholds to optimize the balance between
+  detecting genuine threats and minimizing false positives, ensuring the detection
+  is tailored to their specific environment.
+known_false_positives: This detection may yield false positives in scenarios where
+  legitimate bulk sign-in activities occur, such as during company-wide system updates
+  or when users are accessing resources from varying locations in a short time frame,
+  such as in the case of VPNs or cloud services that rotate IP addresses. Filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -40,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - properties.authenticationDetails{}.succeeded
-  - properties.location.countryOrRegion
-  - user_agent
-  - src_ip
-  - user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/azure_ad_distributed_spray/azure_ad_distributed_spray.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml b/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml
index 3031d4d1e2..b2d281aada 100644
--- a/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml
+++ b/detections/cloud/azure_ad_multiple_appids_and_useragents_authentication_spike.yml
@@ -7,10 +7,31 @@ status: production
 type: Anomaly
 data_source:
 - Azure Active Directory Sign-in activity
-description: The following analytic detects unusual authentication activity in Azure AD, specifically when a single user account has over 8 authentication attempts using 3+ unique application IDs and 5+ unique user agents within a short period. It leverages Azure AD audit logs, focusing on authentication events and using statistical thresholds. This behavior is significant as it may indicate an adversary probing for MFA requirements. If confirmed malicious, it suggests a compromised account, potentially leading to further exploitation, lateral movement, and data exfiltration. Early detection is crucial to prevent substantial harm.
-search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" (properties.authenticationRequirement="multiFactorAuthentication" AND properties.status.additionalDetails="MFA required in Azure AD") OR (properties.authenticationRequirement=singleFactorAuthentication AND "properties.authenticationDetails{}.succeeded"=true) | bucket span=5m _time | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime dc(appId) as unique_app_ids dc(userAgent) as unique_user_agents values(appDisplayName) values(deviceDetail.operatingSystem) by user, src_ip | where count > 5 and unique_app_ids > 2 and unique_user_agents > 5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_appids_and_useragents_authentication_spike_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: Rapid authentication from the same user using more than 5 different user agents and 3 application IDs is highly unlikely under normal circumstances. However, there are potential scenarios that could lead to false positives.
+description: The following analytic detects unusual authentication activity in Azure
+  AD, specifically when a single user account has over 8 authentication attempts using
+  3+ unique application IDs and 5+ unique user agents within a short period. It leverages
+  Azure AD audit logs, focusing on authentication events and using statistical thresholds.
+  This behavior is significant as it may indicate an adversary probing for MFA requirements.
+  If confirmed malicious, it suggests a compromised account, potentially leading to
+  further exploitation, lateral movement, and data exfiltration. Early detection is
+  crucial to prevent substantial harm.
+search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity"
+  (properties.authenticationRequirement="multiFactorAuthentication" AND properties.status.additionalDetails="MFA
+  required in Azure AD") OR (properties.authenticationRequirement=singleFactorAuthentication
+  AND "properties.authenticationDetails{}.succeeded"=true) | bucket span=5m _time
+  | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime
+  dc(appId) as unique_app_ids dc(userAgent) as unique_user_agents values(appDisplayName)
+  values(deviceDetail.operatingSystem) by user, src_ip | where count > 5 and unique_app_ids
+  > 2 and unique_user_agents > 5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_multiple_appids_and_useragents_authentication_spike_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: Rapid authentication from the same user using more than 5 different
+  user agents and 3 application IDs is highly unlikely under normal circumstances.
+  However, there are potential scenarios that could lead to false positives.
 references:
 - https://attack.mitre.org/techniques/T1078/
 - https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Azure Tenant
   confidence: 80
   impact: 60
-  message: $user$ authenticated in a short periof of time with more than 5 different user agents across 3 or more unique application ids.
+  message: $user$ authenticated in a short periof of time with more than 5 different
+    user agents across 3 or more unique application ids.
   mitre_attack_id:
   - T1078
   observable:
@@ -43,21 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - user
-  - src_ip
-  - operationName
-  - properties.authenticationRequirement
-  - properties.status.additionalDetails
-  - properties.authenticationDetails{}.succeeded
-  - properties.userAgent
-  - properties.appDisplayName
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/azure_ad_multiple_appids_and_useragents_auth/azure_ad_multiple_appids_and_useragents_auth.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/azure_ad_multiple_appids_and_useragents_auth/azure_ad_multiple_appids_and_useragents_auth.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml b/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml
index 215e8e41e7..9c7d354912 100644
--- a/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml
+++ b/detections/cloud/azure_ad_multiple_denied_mfa_requests_for_user.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Sign-in activity
-description: The following analytic detects an unusually high number of denied Multi-Factor Authentication (MFA) requests for a single user within a 10-minute window, specifically when more than nine MFA prompts are declined. It leverages Azure Active Directory (Azure AD) sign-in logs, focusing on "Sign-in activity" events with error code 500121 and additional details indicating "MFA denied; user declined the authentication." This behavior is significant as it may indicate a targeted attack or account compromise attempt, with the user actively declining unauthorized access. If confirmed malicious, it could lead to data exfiltration, lateral movement, or further malicious activities.
-search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" | rename properties.* as * | search status.errorCode=500121 status.additionalDetails="MFA denied; user declined the authentication" | bucket span=10m _time | stats count min(_time) as firstTime max(_time) as lastTime by user, status.additionalDetails, appDisplayName, user_agent | where count > 9 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_denied_mfa_requests_for_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: Multiple denifed MFA requests in a short period of span may also be a sign of authentication errors. Investigate and filter as needed.
+description: The following analytic detects an unusually high number of denied Multi-Factor
+  Authentication (MFA) requests for a single user within a 10-minute window, specifically
+  when more than nine MFA prompts are declined. It leverages Azure Active Directory
+  (Azure AD) sign-in logs, focusing on "Sign-in activity" events with error code 500121
+  and additional details indicating "MFA denied; user declined the authentication."
+  This behavior is significant as it may indicate a targeted attack or account compromise
+  attempt, with the user actively declining unauthorized access. If confirmed malicious,
+  it could lead to data exfiltration, lateral movement, or further malicious activities.
+search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity"
+  | rename properties.* as * | search status.errorCode=500121 status.additionalDetails="MFA
+  denied; user declined the authentication" | bucket span=10m _time | stats count
+  min(_time) as firstTime max(_time) as lastTime by user, status.additionalDetails,
+  appDisplayName, user_agent | where count > 9 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_multiple_denied_mfa_requests_for_user_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: Multiple denifed MFA requests in a short period of span may
+  also be a sign of authentication errors. Investigate and filter as needed.
 references:
 - https://www.mandiant.com/resources/blog/russian-targeting-gov-business
 - https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/
@@ -24,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - properties.status.errorCode
-  - properties.status.additionalDetails
-  - user
-  - properties.appDisplayName
-  - user_agent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azure_ad_multiple_denied_mfa_requests/azure_ad_multiple_denied_mfa_requests.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/azure_ad_multiple_denied_mfa_requests/azure_ad_multiple_denied_mfa_requests.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml
index eed686e047..c17d8270da 100644
--- a/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml
+++ b/detections/cloud/azure_ad_multiple_failed_mfa_requests_for_user.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic identifies multiple failed multi-factor authentication (MFA) requests for a single user within an Azure AD tenant. It leverages Azure AD Sign-in Logs, specifically error code 500121, to detect more than 10 failed MFA attempts within 10 minutes. This behavior is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication prompts. If confirmed malicious, this activity could lead to unauthorized access, allowing attackers to compromise user accounts and potentially escalate their privileges within the environment.
+description: The following analytic identifies multiple failed multi-factor authentication
+  (MFA) requests for a single user within an Azure AD tenant. It leverages Azure AD
+  Sign-in Logs, specifically error code 500121, to detect more than 10 failed MFA
+  attempts within 10 minutes. This behavior is significant as it may indicate an adversary
+  attempting to bypass MFA by bombarding the user with repeated authentication prompts.
+  If confirmed malicious, this activity could lead to unauthorized access, allowing
+  attackers to compromise user accounts and potentially escalate their privileges
+  within the environment.
 data_source:
 - Azure Active Directory Sign-in activity
-search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity" properties.status.errorCode=500121 properties.status.additionalDetails!="MFA denied; user declined the authentication" | rename properties.* as * | bucket span=10m _time | stats count min(_time) as firstTime max(_time) as lastTime by user, status.additionalDetails, appDisplayName, user_agent | where count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_failed_mfa_requests_for_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
+search: '`azure_monitor_aad` category=SignInLogs operationName="Sign-in activity"
+  properties.status.errorCode=500121 properties.status.additionalDetails!="MFA denied;
+  user declined the authentication" | rename properties.* as * | bucket span=10m _time
+  | stats count min(_time) as firstTime max(_time) as lastTime by user, status.additionalDetails,
+  appDisplayName, user_agent | where count > 10 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_multiple_failed_mfa_requests_for_user_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: Multiple Failed MFA requests may also be a sign of authentication
+  or application issues. Filter as needed.
 references:
 - https://www.mandiant.com/resources/blog/russian-targeting-gov-business
 - https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/
@@ -24,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +55,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 90
   impact: 60
-  message: User $user$ failed to complete MFA authentication more than 9 times in a timespan of 10 minutes.
+  message: User $user$ failed to complete MFA authentication more than 9 times in
+    a timespan of 10 minutes.
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -49,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - user
-  - user_agent
-  - operationName
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_requests/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_requests/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml b/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml
index bfa503213e..dd5f814251 100644
--- a/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml
+++ b/detections/cloud/azure_ad_multiple_service_principals_created_by_sp.yml
@@ -7,10 +7,27 @@ data_source:
 - Azure Active Directory Add service principal
 type: Anomaly
 status: production
-description: The following analytic detects when a single service principal in Azure AD creates more than three unique OAuth applications within a 10-minute span. It leverages Azure AD audit logs, specifically monitoring the 'Add service principal' operation initiated by service principals. This behavior is significant as it may indicate an attacker using a compromised or malicious service principal to rapidly establish multiple service principals, potentially staging an attack. If confirmed malicious, this activity could facilitate network infiltration or expansion, allowing the attacker to gain unauthorized access and persist within the environment.
-search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.app.appId=* | rename properties.* as * | bucket span=10m _time | rename targetResources{}.displayName as displayName | rename targetResources{}.type as type | rename initiatedBy.app.displayName as src_user | stats min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps by src_user | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_service_principals_created_by_sp_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed.
+description: The following analytic detects when a single service principal in Azure
+  AD creates more than three unique OAuth applications within a 10-minute span. It
+  leverages Azure AD audit logs, specifically monitoring the 'Add service principal'
+  operation initiated by service principals. This behavior is significant as it may
+  indicate an attacker using a compromised or malicious service principal to rapidly
+  establish multiple service principals, potentially staging an attack. If confirmed
+  malicious, this activity could facilitate network infiltration or expansion, allowing
+  the attacker to gain unauthorized access and persist within the environment.
+search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.app.appId=*
+  | rename properties.* as * | bucket span=10m _time | rename targetResources{}.displayName
+  as displayName | rename targetResources{}.type as type | rename initiatedBy.app.displayName
+  as src_user | stats min(_time) as firstTime max(_time) as lastTime values(displayName)
+  as displayName dc(displayName) as unique_apps by src_user | where unique_apps >
+  3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_service_principals_created_by_sp_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLogs log category.
+known_false_positives: Certain users or applications may create multiple service principals
+  in a short period of time for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1136/003/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +52,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 60
   impact: 70
-  message: Multiple OAuth applications were created by $src_user$ in a short period of time
+  message: Multiple OAuth applications were created by $src_user$ in a short period
+    of time
   mitre_attack_id:
   - T1136.003
   observable:
@@ -42,12 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml b/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml
index 94eeffae9a..166db52bf8 100644
--- a/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml
+++ b/detections/cloud/azure_ad_multiple_service_principals_created_by_user.yml
@@ -7,10 +7,26 @@ data_source:
 - Azure Active Directory Add service principal
 type: Anomaly
 status: production
-description: The following analytic identifies instances where a single user creates more than three unique OAuth applications within a 10-minute timeframe in Azure AD. It detects this activity by monitoring the 'Add service principal' operation and aggregating data in 10-minute intervals. This behavior is significant as it may indicate an adversary rapidly creating multiple service principals to stage an attack or expand their foothold within the network. If confirmed malicious, this activity could allow attackers to establish persistence, escalate privileges, or access sensitive information within the Azure environment.
-search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.user.id=* | rename properties.* as * | bucket span=10m _time | rename targetResources{}.displayName as displayName | stats min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps by src_user | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_service_principals_created_by_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed.
+description: The following analytic identifies instances where a single user creates
+  more than three unique OAuth applications within a 10-minute timeframe in Azure
+  AD. It detects this activity by monitoring the 'Add service principal' operation
+  and aggregating data in 10-minute intervals. This behavior is significant as it
+  may indicate an adversary rapidly creating multiple service principals to stage
+  an attack or expand their foothold within the network. If confirmed malicious, this
+  activity could allow attackers to establish persistence, escalate privileges, or
+  access sensitive information within the Azure environment.
+search: '`azure_monitor_aad` operationName="Add service principal" properties.initiatedBy.user.id=*
+  | rename properties.* as * | bucket span=10m _time | rename targetResources{}.displayName
+  as displayName | stats min(_time) as firstTime max(_time) as lastTime values(displayName)
+  as displayName dc(displayName) as unique_apps by src_user | where unique_apps >
+  3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_service_principals_created_by_user_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLogs log category.
+known_false_positives: Certain users or applications may create multiple service principals
+  in a short period of time for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1136/003/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 60
   impact: 70
-  message: Multiple OAuth applications were created by $src_user$ in a short period of time
+  message: Multiple OAuth applications were created by $src_user$ in a short period
+    of time
   mitre_attack_id:
   - T1136.003
   observable:
@@ -42,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.initiatedBy.user.id
-  - targetResources{}.displayName
-  - src_user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_multiple_service_principals_created/azure_ad_multiple_service_principals_created.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml
index c2106f242a..287d5f41a5 100644
--- a/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml
+++ b/detections/cloud/azure_ad_multiple_users_failing_to_authenticate_from_ip.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a single source IP failing to authenticate with 30 unique valid users within 5 minutes in Azure Active Directory. It leverages Azure AD SignInLogs with error code 50126, indicating invalid passwords. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges by trying common passwords across many accounts. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or privilege escalation within the Azure AD environment.
+description: The following analytic detects a single source IP failing to authenticate
+  with 30 unique valid users within 5 minutes in Azure Active Directory. It leverages
+  Azure AD SignInLogs with error code 50126, indicating invalid passwords. This behavior
+  is significant as it may indicate a Password Spraying attack, where an adversary
+  attempts to gain initial access or elevate privileges by trying common passwords
+  across many accounts. If confirmed malicious, this activity could lead to unauthorized
+  access, data breaches, or privilege escalation within the Azure AD environment.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad`  category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | stats count min(_time) as firstTime max(_time) as lastTime dc(user) AS unique_accounts values(user) as user by src_ip | where unique_accounts > 30 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_multiple_users_failing_to_authenticate_from_ip_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: A source Ip failing to authenticate with multiple users is not a common for legitimate behavior.
+search: '`azure_monitor_aad`  category=SignInLogs properties.status.errorCode=50126
+  properties.authenticationDetails{}.succeeded=false | rename properties.* as * |
+  bucket span=5m _time | stats count min(_time) as firstTime max(_time) as lastTime
+  dc(user) AS unique_accounts values(user) as user by src_ip | where unique_accounts
+  > 30 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_multiple_users_failing_to_authenticate_from_ip_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: A source Ip failing to authenticate with multiple users is
+  not a common for legitimate behavior.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -22,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - user
-  - user_agent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_new_custom_domain_added.yml b/detections/cloud/azure_ad_new_custom_domain_added.yml
index cc6c5b14cd..615b0b208a 100644
--- a/detections/cloud/azure_ad_new_custom_domain_added.yml
+++ b/detections/cloud/azure_ad_new_custom_domain_added.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of a new custom domain within an Azure Active Directory (AD) tenant. It leverages Azure AD AuditLogs to identify successful "Add unverified domain" operations. This activity is significant as it may indicate an adversary attempting to establish persistence by setting up identity federation backdoors, allowing them to impersonate users and bypass authentication mechanisms. If confirmed malicious, this could enable attackers to gain unauthorized access, escalate privileges, and maintain long-term access to the Azure AD environment, posing a severe security risk.
+description: The following analytic detects the addition of a new custom domain within
+  an Azure Active Directory (AD) tenant. It leverages Azure AD AuditLogs to identify
+  successful "Add unverified domain" operations. This activity is significant as it
+  may indicate an adversary attempting to establish persistence by setting up identity
+  federation backdoors, allowing them to impersonate users and bypass authentication
+  mechanisms. If confirmed malicious, this could enable attackers to gain unauthorized
+  access, escalate privileges, and maintain long-term access to the Azure AD environment,
+  posing a severe security risk.
 data_source:
 - Azure Active Directory Add unverified domain
-search: '`azure_monitor_aad`  operationName="Add unverified domain" properties.result=success | rename properties.* as * | rename targetResources{}.displayName as domain | stats count min(_time) as firstTime max(_time) as lastTime by user, domain, result, operationName, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_new_custom_domain_added_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: In most organizations, new customm domains will be updated infrequently. Filter as needed.
+search: '`azure_monitor_aad`  operationName="Add unverified domain" properties.result=success
+  | rename properties.* as * | rename targetResources{}.displayName as domain | stats
+  count min(_time) as firstTime max(_time) as lastTime by user, domain, result, operationName,
+  src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_new_custom_domain_added_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLogs log category.
+known_false_positives: In most organizations, new customm domains will be updated
+  infrequently. Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/active-directory/enterprise-users/domains-manage
 - https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13
@@ -24,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.result
-  - src_ip
-  - properties.targetResources{}.displayName
-  - user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_new_federated_domain_added.yml b/detections/cloud/azure_ad_new_federated_domain_added.yml
index 44010cb9a2..930af77c49 100644
--- a/detections/cloud/azure_ad_new_federated_domain_added.yml
+++ b/detections/cloud/azure_ad_new_federated_domain_added.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of a new federated domain within an Azure Active Directory tenant. It leverages Azure AD AuditLogs to identify successful "Set domain authentication" operations. This activity is significant as it may indicate the use of the Azure AD identity federation backdoor technique, allowing an adversary to establish persistence. If confirmed malicious, the attacker could impersonate any user, bypassing password and MFA requirements, potentially leading to unauthorized access and control over the Azure AD environment.
+description: The following analytic detects the addition of a new federated domain
+  within an Azure Active Directory tenant. It leverages Azure AD AuditLogs to identify
+  successful "Set domain authentication" operations. This activity is significant
+  as it may indicate the use of the Azure AD identity federation backdoor technique,
+  allowing an adversary to establish persistence. If confirmed malicious, the attacker
+  could impersonate any user, bypassing password and MFA requirements, potentially
+  leading to unauthorized access and control over the Azure AD environment.
 data_source:
 - Azure Active Directory Set domain authentication
-search: '`azure_monitor_aad`  operationName="Set domain authentication" "properties.result"=success | rename properties.* as * | rename targetResources{}.displayName as domain | stats count min(_time) as firstTime max(_time) as lastTime by user, domain, result, operationName, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_new_federated_domain_added_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: In most organizations, domain federation settings will be updated infrequently. Filter as needed.
+search: '`azure_monitor_aad`  operationName="Set domain authentication" "properties.result"=success
+  | rename properties.* as * | rename targetResources{}.displayName as domain | stats
+  count min(_time) as firstTime max(_time) as lastTime by user, domain, result, operationName,
+  src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_new_federated_domain_added_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLogs log category.
+known_false_positives: In most organizations, domain federation settings will be updated
+  infrequently. Filter as needed.
 references:
 - https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13
 - https://o365blog.com/post/federation-vulnerability/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.result
-  - src_ip
-  - properties.targetResources{}.displayName
-  - user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.002/new_federated_domain/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_new_mfa_method_registered.yml b/detections/cloud/azure_ad_new_mfa_method_registered.yml
index 04a8261acb..0e88f4c809 100644
--- a/detections/cloud/azure_ad_new_mfa_method_registered.yml
+++ b/detections/cloud/azure_ad_new_mfa_method_registered.yml
@@ -7,10 +7,31 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Update user
-description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a user account in Azure Active Directory. It leverages Azure AD audit logs to identify changes in MFA configurations. This activity is significant because adding a new MFA method can indicate an attacker's attempt to maintain persistence on a compromised account. If confirmed malicious, the attacker could bypass existing security measures, solidify their access, and potentially escalate privileges, access sensitive data, or make unauthorized changes. Immediate verification and remediation are required to secure the affected account.
-search: '`azure_monitor_aad`  operationName="Update user" | rename properties.* as *  | eval propertyName = mvindex(''targetResources{}.modifiedProperties{}.displayName'', 0) | search propertyName = StrongAuthenticationMethod | eval oldvalue = mvindex(''targetResources{}.modifiedProperties{}.oldValue'',0) | eval newvalue = mvindex(''targetResources{}.modifiedProperties{}.newValue'',0) | rex field=newvalue max_match=0 "(?i)(?<new_method_type>\"MethodType\")" | rex field=oldvalue max_match=0 "(?i)(?<old_method_type>\"MethodType\")" | eval count_new_method_type = coalesce(mvcount(new_method_type), 0) | eval count_old_method_type = coalesce(mvcount(old_method_type), 0) | stats earliest(_time) as firstTime latest(_time) as lastTime values(propertyName) by user newvalue oldvalue | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_new_mfa_method_registered_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Users may register MFA methods legitimally, investigate and filter as needed.
+description: The following analytic detects the registration of a new Multi-Factor
+  Authentication (MFA) method for a user account in Azure Active Directory. It leverages
+  Azure AD audit logs to identify changes in MFA configurations. This activity is
+  significant because adding a new MFA method can indicate an attacker's attempt to
+  maintain persistence on a compromised account. If confirmed malicious, the attacker
+  could bypass existing security measures, solidify their access, and potentially
+  escalate privileges, access sensitive data, or make unauthorized changes. Immediate
+  verification and remediation are required to secure the affected account.
+search: "`azure_monitor_aad`  operationName=\"Update user\" | rename properties.*
+  as *  | eval propertyName = mvindex('targetResources{}.modifiedProperties{}.displayName',
+  0) | search propertyName = StrongAuthenticationMethod | eval oldvalue = mvindex('targetResources{}.modifiedProperties{}.oldValue',0)
+  | eval newvalue = mvindex('targetResources{}.modifiedProperties{}.newValue',0) |
+  rex field=newvalue max_match=0 \"(?i)(?<new_method_type>\\\"MethodType\\\")\" |
+  rex field=oldvalue max_match=0 \"(?i)(?<old_method_type>\\\"MethodType\\\")\" |
+  eval count_new_method_type = coalesce(mvcount(new_method_type), 0) | eval count_old_method_type
+  = coalesce(mvcount(old_method_type), 0) | stats earliest(_time) as firstTime latest(_time)
+  as lastTime values(propertyName) by user newvalue oldvalue | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_new_mfa_method_registered_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLog log category.
+known_false_positives: Users may register MFA methods legitimally, investigate and
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/005/
 - https://www.microsoft.com/en-us/security/blog/2023/06/08/detecting-and-mitigating-a-multi-stage-aitm-phishing-and-bec-campaign/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.targetResources{}.modifiedProperties{}.displayName
-  - properties.targetResources{}.modifiedProperties{}.oldValue
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/azure_ad_register_new_mfa_method/azure_ad_register_new_mfa_method.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/azure_ad_register_new_mfa_method/azure_ad_register_new_mfa_method.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml b/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml
index cb5c24a362..3f8a9b0914 100644
--- a/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml
+++ b/detections/cloud/azure_ad_new_mfa_method_registered_for_user.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an Azure AD account. It leverages Azure AD AuditLogs to identify when a user registers new security information. This activity is significant because adversaries who gain unauthorized access to an account may add their own MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security controls, maintain long-term access, and potentially escalate their privileges within the environment.
+description: The following analytic detects the registration of a new Multi-Factor
+  Authentication (MFA) method for an Azure AD account. It leverages Azure AD AuditLogs
+  to identify when a user registers new security information. This activity is significant
+  because adversaries who gain unauthorized access to an account may add their own
+  MFA method to maintain persistence. If confirmed malicious, this could allow attackers
+  to bypass existing security controls, maintain long-term access, and potentially
+  escalate their privileges within the environment.
 data_source:
 - Azure Active Directory User registered security info
-search: '`azure_monitor_aad` category=AuditLogs operationName="User registered security info" properties.operationType=Add | rename properties.* as * | rename targetResources{}.* as * | stats count min(_time) as firstTime max(_time) as lastTime by user, resultDescription, result, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_new_mfa_method_registered_for_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLogs log category.
-known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
+search: '`azure_monitor_aad` category=AuditLogs operationName="User registered security
+  info" properties.operationType=Add | rename properties.* as * | rename targetResources{}.*
+  as * | stats count min(_time) as firstTime max(_time) as lastTime by user, resultDescription,
+  result, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_new_mfa_method_registered_for_user_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLogs log category.
+known_false_positives: Newly onboarded users who are registering an MFA method for
+  the first time will also trigger this detection.
 references:
 - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks
 - https://attack.mitre.org/techniques/T1556/
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - operationName
-  - properties.operationType
-  - user
-  - resultDescription
-  - result
-  - src_ip
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/azure_ad_new_mfa_method_registered_for_user/azuread.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/azure_ad_new_mfa_method_registered_for_user/azuread.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml b/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml
index 00538c3801..72b9ce8467 100644
--- a/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml
+++ b/detections/cloud/azure_ad_oauth_application_consent_granted_by_user.yml
@@ -7,10 +7,30 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Consent to application
-description: The following analytic detects when a user in an Azure AD environment grants consent to an OAuth application. It leverages Azure AD audit logs to identify events where users approve application consents. This activity is significant as it can expose organizational data to third-party applications, a common tactic used by malicious actors to gain unauthorized access. If confirmed malicious, this could lead to unauthorized access to sensitive information and resources. Immediate investigation is required to validate the application's legitimacy, review permissions, and mitigate potential risks.
-search: '`azure_monitor_aad` operationName="Consent to application" properties.result=success | rename properties.* as *  | eval permissions_index = if(mvfind(''targetResources{}.modifiedProperties{}.displayName'', "ConsentAction.Permissions") >= 0, mvfind(''targetResources{}.modifiedProperties{}.displayName'', "ConsentAction.Permissions"), -1) | eval permissions = mvindex(''targetResources{}.modifiedProperties{}.newValue'',permissions_index) | rex field=permissions "Scope: (?<Scope>[^,]+)" | stats count min(_time) as firstTime max(_time) as lastTime by operationName, user, Scope | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_oauth_application_consent_granted_by_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: False positives may occur if users are granting consents as part of legitimate application integrations or setups. It is crucial to review the application and the permissions it requests to ensure they align with organizational policies and security best practices.
+description: The following analytic detects when a user in an Azure AD environment
+  grants consent to an OAuth application. It leverages Azure AD audit logs to identify
+  events where users approve application consents. This activity is significant as
+  it can expose organizational data to third-party applications, a common tactic used
+  by malicious actors to gain unauthorized access. If confirmed malicious, this could
+  lead to unauthorized access to sensitive information and resources. Immediate investigation
+  is required to validate the application's legitimacy, review permissions, and mitigate
+  potential risks.
+search: "`azure_monitor_aad` operationName=\"Consent to application\" properties.result=success
+  | rename properties.* as *  | eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"ConsentAction.Permissions\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"ConsentAction.Permissions\"), -1) | eval permissions = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index)
+  | rex field=permissions \"Scope: (?<Scope>[^,]+)\" | stats count min(_time) as firstTime
+  max(_time) as lastTime by operationName, user, Scope | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_oauth_application_consent_granted_by_user_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLog log category.
+known_false_positives: False positives may occur if users are granting consents as
+  part of legitimate application integrations or setups. It is crucial to review the
+  application and the permissions it requests to ensure they align with organizational
+  policies and security best practices.
 references:
 - https://attack.mitre.org/techniques/T1528/
 - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.targetResources{}.modifiedProperties{}.displayName
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_granted/azure_ad_user_consent_granted.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_granted/azure_ad_user_consent_granted.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_pim_role_assigned.yml b/detections/cloud/azure_ad_pim_role_assigned.yml
index ede5c75f3a..e9efd3cd9e 100644
--- a/detections/cloud/azure_ad_pim_role_assigned.yml
+++ b/detections/cloud/azure_ad_pim_role_assigned.yml
@@ -7,10 +7,25 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory
-description: The following analytic detects the assignment of an Azure AD Privileged Identity Management (PIM) role. It leverages Azure Active Directory events to identify when a user is added as an eligible member to a PIM role. This activity is significant because PIM roles grant elevated privileges, and their assignment should be closely monitored to prevent unauthorized access. If confirmed malicious, an attacker could exploit this to gain privileged access, potentially leading to unauthorized actions, data breaches, or further compromise of the environment.
-search: '`azure_monitor_aad` operationName="Add eligible member to role in PIM completed*" | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user values(targetResources{}.displayName) as displayName  by result, operationName, initiatedBy.user.displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_pim_role_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: As part of legitimate administrative behavior, users may be assigned PIM roles. Filter as needed
+description: The following analytic detects the assignment of an Azure AD Privileged
+  Identity Management (PIM) role. It leverages Azure Active Directory events to identify
+  when a user is added as an eligible member to a PIM role. This activity is significant
+  because PIM roles grant elevated privileges, and their assignment should be closely
+  monitored to prevent unauthorized access. If confirmed malicious, an attacker could
+  exploit this to gain privileged access, potentially leading to unauthorized actions,
+  data breaches, or further compromise of the environment.
+search: '`azure_monitor_aad` operationName="Add eligible member to role in PIM completed*"
+  | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime
+  values(user) as user values(targetResources{}.displayName) as displayName  by result,
+  operationName, initiatedBy.user.displayName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_pim_role_assigned_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: As part of legitimate administrative behavior, users may be
+  assigned PIM roles. Filter as needed
 references:
 - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure
 - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-how-to-activate-role
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties
-  - operationName
-  - user
-  - initiatedBy.user.userPrincipalName
-  - result
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_pim_role_assignment_activated.yml b/detections/cloud/azure_ad_pim_role_assignment_activated.yml
index 93c4cd6e21..951eb6b14a 100644
--- a/detections/cloud/azure_ad_pim_role_assignment_activated.yml
+++ b/detections/cloud/azure_ad_pim_role_assignment_activated.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory
-description: The following analytic detects the activation of an Azure AD Privileged Identity Management (PIM) role. It leverages Azure Active Directory events to identify when a user activates a PIM role assignment, indicated by the "Add member to role completed (PIM activation)" operation. Monitoring this activity is crucial as PIM roles grant elevated privileges, and unauthorized activation could indicate an adversary attempting to gain privileged access. If confirmed malicious, this could lead to unauthorized administrative actions, data breaches, or further compromise of the Azure environment.
-search: '`azure_monitor_aad` operationName="Add member to role completed (PIM activation)" | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user values(targetResources{}.displayName) as displayName by initiatedBy, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_pim_role_assignment_activated_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: As part of legitimate administrative behavior, users may activate PIM roles. Filter as needed
+description: The following analytic detects the activation of an Azure AD Privileged
+  Identity Management (PIM) role. It leverages Azure Active Directory events to identify
+  when a user activates a PIM role assignment, indicated by the "Add member to role
+  completed (PIM activation)" operation. Monitoring this activity is crucial as PIM
+  roles grant elevated privileges, and unauthorized activation could indicate an adversary
+  attempting to gain privileged access. If confirmed malicious, this could lead to
+  unauthorized administrative actions, data breaches, or further compromise of the
+  Azure environment.
+search: '`azure_monitor_aad` operationName="Add member to role completed (PIM activation)"
+  | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy
+  | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user
+  values(targetResources{}.displayName) as displayName by initiatedBy, result, operationName
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_pim_role_assignment_activated_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: As part of legitimate administrative behavior, users may activate
+  PIM roles. Filter as needed
 references:
 - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure
 - https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-how-to-activate-role
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties
-  - operationName
-  - user
-  - initiatedBy.user.userPrincipalName
-  - result
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_pim_role_activated/azure-audit.log
     source: eventhub://researchhub1.servicebus.windows.net/azureadhub;
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml b/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml
index 6641193695..c204ccf3d7 100644
--- a/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml
+++ b/detections/cloud/azure_ad_privileged_authentication_administrator_role_assigned.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Add member to role
-description: The following analytic detects the assignment of the Privileged Authentication Administrator role to an Azure AD user. It leverages Azure Active Directory audit logs to identify when this specific role is assigned. This activity is significant because users in this role can set or reset authentication methods for any user, including those in privileged roles like Global Administrators. If confirmed malicious, an attacker could change credentials and assume the identity and permissions of high-privilege users, potentially leading to unauthorized access to sensitive information and critical configurations.
-search: '`azure_monitor_aad`  "operationName"="Add member to role"  "properties.targetResources{}.modifiedProperties{}.newValue"="\"Privileged Authentication Administrator\"" | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by initiatedBy, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_privileged_authentication_administrator_role_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Administrators may legitimately assign the Privileged Authentication Administrator role as part of administrative tasks. Filter as needed.
+description: The following analytic detects the assignment of the Privileged Authentication
+  Administrator role to an Azure AD user. It leverages Azure Active Directory audit
+  logs to identify when this specific role is assigned. This activity is significant
+  because users in this role can set or reset authentication methods for any user,
+  including those in privileged roles like Global Administrators. If confirmed malicious,
+  an attacker could change credentials and assume the identity and permissions of
+  high-privilege users, potentially leading to unauthorized access to sensitive information
+  and critical configurations.
+search: '`azure_monitor_aad`  "operationName"="Add member to role"  "properties.targetResources{}.modifiedProperties{}.newValue"="\"Privileged
+  Authentication Administrator\"" | rename properties.* as * | rename initiatedBy.user.userPrincipalName
+  as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime values(user)
+  as user by initiatedBy, result, operationName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_privileged_authentication_administrator_role_assigned_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: Administrators may legitimately assign the Privileged Authentication
+  Administrator role as part of administrative tasks. Filter as needed.
 references:
 - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference#privileged-authentication-administrator
 - https://posts.specterops.io/azure-privilege-escalation-via-azure-api-permissions-abuse-74aee1006f48
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 50
   impact: 100
-  message: The privileged Azure AD role Privileged Authentication Administrator was assigned for User $user$ initiated by $initiatedBy$
+  message: The privileged Azure AD role Privileged Authentication Administrator was
+    assigned for User $user$ initiated by $initiatedBy$
   mitre_attack_id:
   - T1003.002
   observable:
@@ -46,16 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml b/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml
index f13acc1e81..2d54577dec 100644
--- a/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml
+++ b/detections/cloud/azure_ad_privileged_graph_api_permission_assigned.yml
@@ -7,10 +7,30 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Update application
-description: The following analytic detects the assignment of high-risk Graph API permissions in Azure AD, specifically Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory. It uses azure_monitor_aad data to scan AuditLogs for 'Update application' operations, identifying when these permissions are assigned. This activity is significant as it grants broad control over Azure AD, including application and directory settings. If confirmed malicious, it could lead to unauthorized modifications and potential security breaches, compromising the integrity and security of the Azure AD environment. Immediate investigation is required.
-search: '`azure_monitor_aad` category=AuditLogs operationName="Update application"  | eval newvalue = mvindex(''properties.targetResources{}.modifiedProperties{}.newValue'',0) | spath input=newvalue  | search "{}.RequiredAppPermissions{}.EntitlementId"="1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9" OR "{}.RequiredAppPermissions{}.EntitlementId"="06b708a9-e830-4db3-a914-8e69da51d44f" OR "{}.RequiredAppPermissions{}.EntitlementId"="9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8"  | eval Permissions = ''{}.RequiredAppPermissions{}.EntitlementId'' | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user, object, user_agent, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`   | `azure_ad_privileged_graph_api_permission_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Privileged Graph API permissions may be assigned for legitimate purposes. Filter as needed.
+description: The following analytic detects the assignment of high-risk Graph API
+  permissions in Azure AD, specifically Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All,
+  and RoleManagement.ReadWrite.Directory. It uses azure_monitor_aad data to scan AuditLogs
+  for 'Update application' operations, identifying when these permissions are assigned.
+  This activity is significant as it grants broad control over Azure AD, including
+  application and directory settings. If confirmed malicious, it could lead to unauthorized
+  modifications and potential security breaches, compromising the integrity and security
+  of the Azure AD environment. Immediate investigation is required.
+search: "`azure_monitor_aad` category=AuditLogs operationName=\"Update application\"\
+  \  | eval newvalue = mvindex('properties.targetResources{}.modifiedProperties{}.newValue',0)
+  | spath input=newvalue  | search \"{}.RequiredAppPermissions{}.EntitlementId\"=\"\
+  1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9\" OR \"{}.RequiredAppPermissions{}.EntitlementId\"\
+  =\"06b708a9-e830-4db3-a914-8e69da51d44f\" OR \"{}.RequiredAppPermissions{}.EntitlementId\"\
+  =\"9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8\"  | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId'
+  | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions)
+  by user, object, user_agent, operationName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`   | `azure_ad_privileged_graph_api_permission_assigned_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: Privileged Graph API permissions may be assigned for legitimate
+  purposes. Filter as needed.
 references:
 - https://cloudbrothers.info/en/azure-attack-paths/
 - https://github.com/mandiant/Mandiant-Azure-AD-Investigator/blob/master/MandiantAzureADInvestigator.json
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - operationName
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - user
-  - object
-  - user_agent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_graph_perm_assigned/azure_ad_privileged_graph_perm_assigned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_graph_perm_assigned/azure_ad_privileged_graph_perm_assigned.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_privileged_role_assigned.yml b/detections/cloud/azure_ad_privileged_role_assigned.yml
index 70bc0d0ac0..2191d017f9 100644
--- a/detections/cloud/azure_ad_privileged_role_assigned.yml
+++ b/detections/cloud/azure_ad_privileged_role_assigned.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the assignment of privileged Azure Active Directory roles to a user. It leverages Azure AD audit logs, specifically monitoring the "Add member to role" operation. This activity is significant as adversaries may assign privileged roles to compromised accounts to maintain persistence within the Azure AD environment. If confirmed malicious, this could allow attackers to escalate privileges, access sensitive information, and maintain long-term control over the Azure AD infrastructure.
+description: The following analytic detects the assignment of privileged Azure Active
+  Directory roles to a user. It leverages Azure AD audit logs, specifically monitoring
+  the "Add member to role" operation. This activity is significant as adversaries
+  may assign privileged roles to compromised accounts to maintain persistence within
+  the Azure AD environment. If confirmed malicious, this could allow attackers to
+  escalate privileges, access sensitive information, and maintain long-term control
+  over the Azure AD infrastructure.
 data_source:
 - Azure Active Directory Add member to role
-search: '`azure_monitor_aad`  "operationName"="Add member to role" | rename properties.*  as * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue  as roles | eval role=mvindex(roles,1) | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by initiatedBy, result, operationName, role | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description | search isprvilegedadrole = True | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_privileged_role_assigned_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Administrators will legitimately assign the privileged roles users as part of administrative tasks. Filter as needed.
+search: '`azure_monitor_aad`  "operationName"="Add member to role" | rename properties.*  as
+  * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue  as
+  roles | eval role=mvindex(roles,1) | stats count min(_time) as firstTime max(_time)
+  as lastTime values(user) as user by initiatedBy, result, operationName, role | lookup
+  privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description
+  | search isprvilegedadrole = True | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_privileged_role_assigned_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: Administrators will legitimately assign the privileged roles
+  users as part of administrative tasks. Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/active-directory/roles/concept-understand-roles
 - https://docs.microsoft.com/en-us/azure/active-directory/roles/permissions-reference
@@ -24,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,16 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_assign_privileged_role/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml b/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml
index 9e71596a85..8b8e4a177a 100644
--- a/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml
+++ b/detections/cloud/azure_ad_privileged_role_assigned_to_service_principal.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the assignment of privileged roles to service principals in Azure Active Directory (AD). It leverages the AuditLogs log category from ingested Azure AD events. This activity is significant because assigning elevated permissions to non-human entities can lead to unauthorized access or malicious activities. If confirmed malicious, attackers could exploit these service principals to gain elevated access to Azure resources, potentially compromising sensitive data and critical infrastructure. Monitoring this behavior helps prevent privilege escalation and ensures the security of Azure environments.
+description: The following analytic detects the assignment of privileged roles to
+  service principals in Azure Active Directory (AD). It leverages the AuditLogs log
+  category from ingested Azure AD events. This activity is significant because assigning
+  elevated permissions to non-human entities can lead to unauthorized access or malicious
+  activities. If confirmed malicious, attackers could exploit these service principals
+  to gain elevated access to Azure resources, potentially compromising sensitive data
+  and critical infrastructure. Monitoring this behavior helps prevent privilege escalation
+  and ensures the security of Azure environments.
 data_source:
 - Azure Active Directory Add member to role
-search: '`azure_monitor_aad`  operationName="Add member to role" | rename properties.* as * | search "targetResources{}.type"=ServicePrincipal | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue  as roles | eval role=mvindex(roles,1) | rename targetResources{}.displayName as apps | eval displayName=mvindex(apps,0) | stats count min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName by initiatedBy, result, operationName, role | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole description | search isprvilegedadrole = True | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_privileged_role_assigned_to_service_principal_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Administrators may legitimately assign the privileged roles to Service Principals as part of administrative tasks. Filter as needed.
+search: '`azure_monitor_aad`  operationName="Add member to role" | rename properties.*
+  as * | search "targetResources{}.type"=ServicePrincipal | rename initiatedBy.user.userPrincipalName
+  as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue  as roles
+  | eval role=mvindex(roles,1) | rename targetResources{}.displayName as apps | eval
+  displayName=mvindex(apps,0) | stats count min(_time) as firstTime max(_time) as
+  lastTime values(displayName) as displayName by initiatedBy, result, operationName,
+  role | lookup privileged_azure_ad_roles azureadrole AS role OUTPUT isprvilegedadrole
+  description | search isprvilegedadrole = True | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_privileged_role_assigned_to_service_principal_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: Administrators may legitimately assign the privileged roles
+  to Service Principals as part of administrative tasks. Filter as needed.
 references:
 - https://posts.specterops.io/azure-privilege-escalation-via-service-principal-abuse-210ae2be2a5
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$initiatedBy$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$initiatedBy$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$initiatedBy$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +54,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 50
   impact: 70
-  message: A privileged Azure AD role was assigned to the Service Principal $displayName$ initiated by $initiatedBy$
+  message: A privileged Azure AD role was assigned to the Service Principal $displayName$
+    initiated by $initiatedBy$
   mitre_attack_id:
   - T1098
   - T1098.003
@@ -42,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.targetResources{}.type
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_role_serviceprincipal/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_privileged_role_serviceprincipal/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_service_principal_authentication.yml b/detections/cloud/azure_ad_service_principal_authentication.yml
index 96e35df9e1..e466053c83 100644
--- a/detections/cloud/azure_ad_service_principal_authentication.yml
+++ b/detections/cloud/azure_ad_service_principal_authentication.yml
@@ -7,10 +7,28 @@ data_source:
 - Azure Active Directory Sign-in activity
 type: TTP
 status: production
-description: The following analytic identifies authentication events of service principals in Azure Active Directory. It leverages the `azure_monitor_aad` data source, specifically targeting "Sign-in activity" within ServicePrincipalSignInLogs. This detection gathers details such as sign-in frequency, timing, source IPs, and accessed resources. Monitoring these events is significant for SOC teams to distinguish between normal application authentication and potential anomalies, which could indicate compromised credentials or malicious activities. If confirmed malicious, attackers could gain unauthorized access to resources, leading to data breaches or further exploitation within the environment.
-search: '`azure_monitor_aad` operationName="Sign-in activity" category=ServicePrincipalSignInLogs | rename properties.* as * | stats count earliest(_time) as firstTime latest(_time) as lastTime by user, user_id, src_ip, resourceDisplayName, resourceId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_service_principal_authentication_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: Service Principals will legitimally authenticate remotely to your tenant. Implementing this detection after establishing a baseline enables a more accurate identification of security threats, ensuring proactive and informed responses to safeguard the Azure AD environment. source ips.
+description: The following analytic identifies authentication events of service principals
+  in Azure Active Directory. It leverages the `azure_monitor_aad` data source, specifically
+  targeting "Sign-in activity" within ServicePrincipalSignInLogs. This detection gathers
+  details such as sign-in frequency, timing, source IPs, and accessed resources. Monitoring
+  these events is significant for SOC teams to distinguish between normal application
+  authentication and potential anomalies, which could indicate compromised credentials
+  or malicious activities. If confirmed malicious, attackers could gain unauthorized
+  access to resources, leading to data breaches or further exploitation within the
+  environment.
+search: '`azure_monitor_aad` operationName="Sign-in activity" category=ServicePrincipalSignInLogs
+  | rename properties.* as * | stats count earliest(_time) as firstTime latest(_time)
+  as lastTime by user, user_id, src_ip, resourceDisplayName, resourceId | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_service_principal_authentication_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: Service Principals will legitimally authenticate remotely to
+  your tenant. Implementing this detection after establishing a baseline enables a
+  more accurate identification of security threats, ensuring proactive and informed
+  responses to safeguard the Azure AD environment. source ips.
 references:
 - https://attack.mitre.org/techniques/T1078/004/
 - https://learn.microsoft.com/en-us/entra/identity/monitoring-health/concept-sign-ins#service-principal-sign-ins
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - category
-  - properties.resourceDisplayName
-  - properties.resourceId
-  - user
-  - src_ip
-  - user_id
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_ad_service_principal_authentication/azure_ad_service_principal_authentication.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_ad_service_principal_authentication/azure_ad_service_principal_authentication.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_service_principal_created.yml b/detections/cloud/azure_ad_service_principal_created.yml
index c2d275db04..aa156fac51 100644
--- a/detections/cloud/azure_ad_service_principal_created.yml
+++ b/detections/cloud/azure_ad_service_principal_created.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a Service Principal in an Azure AD environment. It leverages Azure Active Directory events ingested through EventHub, specifically monitoring the "Add service principal" operation. This activity is significant because Service Principals can be used by adversaries to establish persistence and bypass multi-factor authentication and conditional access policies. If confirmed malicious, this could allow attackers to maintain single-factor access to the Azure AD environment, potentially leading to unauthorized access to resources and prolonged undetected activity.
+description: The following analytic detects the creation of a Service Principal in
+  an Azure AD environment. It leverages Azure Active Directory events ingested through
+  EventHub, specifically monitoring the "Add service principal" operation. This activity
+  is significant because Service Principals can be used by adversaries to establish
+  persistence and bypass multi-factor authentication and conditional access policies.
+  If confirmed malicious, this could allow attackers to maintain single-factor access
+  to the Azure AD environment, potentially leading to unauthorized access to resources
+  and prolonged undetected activity.
 data_source:
 - Azure Active Directory Add service principal
-search: '`azure_monitor_aad`  operationName="Add service principal" properties.initiatedBy.user.id=* | rename properties.* as * | rename targetResources{}.displayName as displayName | rename targetResources{}.type as type | stats count min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName by type, user, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_service_principal_created_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment thorough an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Administrator may legitimately create Service Principal. Filter as needed.
+search: '`azure_monitor_aad`  operationName="Add service principal" properties.initiatedBy.user.id=*
+  | rename properties.* as * | rename targetResources{}.displayName as displayName
+  | rename targetResources{}.type as type | stats count min(_time) as firstTime max(_time)
+  as lastTime values(displayName) as displayName by type, user, result, operationName
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_service_principal_created_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  thorough an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLog log category.
+known_false_positives: Administrator may legitimately create Service Principal. Filter
+  as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/active-directory/develop/app-objects-and-service-principals
 - https://docs.microsoft.com/en-us/powershell/azure/create-azure-service-principal-azureps?view=azps-8.2.0
@@ -23,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$displayName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.targetResources{}.displayName
-  - properties.targetResources{}.type
-  - user
-  - properties.result
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_add_service_principal/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_ad_add_service_principal/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_service_principal_new_client_credentials.yml b/detections/cloud/azure_ad_service_principal_new_client_credentials.yml
index 8d384fad87..70cd31272f 100644
--- a/detections/cloud/azure_ad_service_principal_new_client_credentials.yml
+++ b/detections/cloud/azure_ad_service_principal_new_client_credentials.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of new credentials to Service Principals and Applications in Azure AD. It leverages Azure AD AuditLogs, specifically monitoring the "Update application*Certificates and secrets management" operation. This activity is significant as it may indicate an adversary attempting to maintain persistent access or escalate privileges within the Azure environment. If confirmed malicious, attackers could use these new credentials to log in as the service principal, potentially compromising sensitive accounts and resources, leading to unauthorized access and control over the Azure environment.
+description: The following analytic detects the addition of new credentials to Service
+  Principals and Applications in Azure AD. It leverages Azure AD AuditLogs, specifically
+  monitoring the "Update application*Certificates and secrets management" operation.
+  This activity is significant as it may indicate an adversary attempting to maintain
+  persistent access or escalate privileges within the Azure environment. If confirmed
+  malicious, attackers could use these new credentials to log in as the service principal,
+  potentially compromising sensitive accounts and resources, leading to unauthorized
+  access and control over the Azure environment.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad`  category=AuditLogs operationName="Update application*Certificates and secrets management " | rename properties.* as * | rename  targetResources{}.* as * | stats count min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName by user, modifiedProperties{}.newValue, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_service_principal_new_client_credentials_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: Service Principal client credential modifications may be part of legitimate administrative operations. Filter as needed.
+search: '`azure_monitor_aad`  category=AuditLogs operationName="Update application*Certificates
+  and secrets management " | rename properties.* as * | rename  targetResources{}.*
+  as * | stats count min(_time) as firstTime max(_time) as lastTime values(displayName)
+  as displayName by user, modifiedProperties{}.newValue, src_ip | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_service_principal_new_client_credentials_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the Signin log category.
+known_false_positives: Service Principal client credential modifications may be part
+  of legitimate administrative operations. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/001/
 - https://microsoft.github.io/Azure-Threat-Research-Matrix/Persistence/AZT501/AZT501-2/
@@ -24,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - operationName
-  - user
-  - properties.targetResources{}.displayName
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - src_ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/azure_ad_service_principal_credentials/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/azure_ad_service_principal_credentials/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_service_principal_owner_added.yml b/detections/cloud/azure_ad_service_principal_owner_added.yml
index 67aa2ef5cb..31349e7aaa 100644
--- a/detections/cloud/azure_ad_service_principal_owner_added.yml
+++ b/detections/cloud/azure_ad_service_principal_owner_added.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of a new owner to a Service Principal within an Azure AD tenant. It leverages Azure Active Directory events from the AuditLog log category to identify this activity. This behavior is significant because Service Principals do not support multi-factor authentication or conditional access policies, making them a target for adversaries seeking persistence or privilege escalation. If confirmed malicious, this activity could allow attackers to maintain access to the Azure AD environment with single-factor authentication, potentially leading to unauthorized access and control over critical resources.
+description: The following analytic detects the addition of a new owner to a Service
+  Principal within an Azure AD tenant. It leverages Azure Active Directory events
+  from the AuditLog log category to identify this activity. This behavior is significant
+  because Service Principals do not support multi-factor authentication or conditional
+  access policies, making them a target for adversaries seeking persistence or privilege
+  escalation. If confirmed malicious, this activity could allow attackers to maintain
+  access to the Azure AD environment with single-factor authentication, potentially
+  leading to unauthorized access and control over critical resources.
 data_source:
 - Azure Active Directory Add owner to application
-search: '`azure_monitor_aad`  operationName="Add owner to application" | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.userPrincipalName as newOwner | rename targetResources{}.modifiedProperties{}.newValue as displayName | eval displayName = mvindex(displayName,1) | where initiatedBy!=newOwner | stats count min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName by initiatedBy, result, operationName, newOwner | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_service_principal_owner_added_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: Administrator may legitimately add new owners for Service Principals. Filter as needed.
+search: '`azure_monitor_aad`  operationName="Add owner to application" | rename properties.*
+  as * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.userPrincipalName
+  as newOwner | rename targetResources{}.modifiedProperties{}.newValue as displayName
+  | eval displayName = mvindex(displayName,1) | where initiatedBy!=newOwner | stats
+  count min(_time) as firstTime max(_time) as lastTime values(displayName) as displayName
+  by initiatedBy, result, operationName, newOwner | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_service_principal_owner_added_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLog log category.
+known_false_positives: Administrator may legitimately add new owners for Service Principals.
+  Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 drilldown_searches:
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$displayName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$displayName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.targetResources{}.userPrincipalName
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - properties.result
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_add_serviceprincipal_owner/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_add_serviceprincipal_owner/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml b/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml
index 4a009b549a..1fb1d2dda8 100644
--- a/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml
+++ b/detections/cloud/azure_ad_successful_authentication_from_different_ips.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Azure AD account successfully authenticating from multiple unique IP addresses within a 30-minute window. It leverages Azure AD SignInLogs to identify instances where the same user logs in from different IPs in a short time frame. This behavior is significant as it may indicate compromised credentials being used by an adversary, potentially following a phishing attack. If confirmed malicious, this activity could allow unauthorized access to corporate resources, leading to data breaches or further exploitation within the network.
+description: The following analytic detects an Azure AD account successfully authenticating
+  from multiple unique IP addresses within a 30-minute window. It leverages Azure
+  AD SignInLogs to identify instances where the same user logs in from different IPs
+  in a short time frame. This behavior is significant as it may indicate compromised
+  credentials being used by an adversary, potentially following a phishing attack.
+  If confirmed malicious, this activity could allow unauthorized access to corporate
+  resources, leading to data breaches or further exploitation within the network.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad`  properties.authenticationDetails{}.succeeded=true category=SignInLogs | rename properties.* as * | bucket span=30m _time | stats count min(_time) as firstTime max(_time) as lastTime dc(src_ip) AS unique_ips values(src_ip) as src_ip values(appDisplayName) as appDisplayName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where unique_ips  > 1 | `azure_ad_successful_authentication_from_different_ips_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: A user with successful authentication events from different Ips may also represent the legitimate use of more than one device. Filter as needed and/or customize the threshold to fit your environment.
+search: '`azure_monitor_aad`  properties.authenticationDetails{}.succeeded=true category=SignInLogs
+  | rename properties.* as * | bucket span=30m _time | stats count min(_time) as firstTime
+  max(_time) as lastTime dc(src_ip) AS unique_ips values(src_ip) as src_ip values(appDisplayName)
+  as appDisplayName by user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | where unique_ips  > 1 | `azure_ad_successful_authentication_from_different_ips_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: A user with successful authentication events from different
+  Ips may also represent the legitimate use of more than one device. Filter as needed
+  and/or customize the threshold to fit your environment.
 references:
 - https://attack.mitre.org/techniques/T1110
 - https://attack.mitre.org/techniques/T1110.001
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: Azure Tenant
   confidence: 80
   impact: 70
-  message: User $user$ has had successful authentication events from more than one unique IP address in the span of 30 minutes.
+  message: User $user$ has had successful authentication events from more than one
+    unique IP address in the span of 30 minutes.
   mitre_attack_id:
   - T1110
   - T1110.001
@@ -49,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - user
-  - src_ip
-  - properties.appDisplayName
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_successful_authentication_from_different_ips/azuread.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/azure_ad_successful_authentication_from_different_ips/azuread.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_successful_powershell_authentication.yml b/detections/cloud/azure_ad_successful_powershell_authentication.yml
index 5bde836ce5..188712d36a 100644
--- a/detections/cloud/azure_ad_successful_powershell_authentication.yml
+++ b/detections/cloud/azure_ad_successful_powershell_authentication.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a successful authentication event against an Azure AD tenant using PowerShell cmdlets. This detection leverages Azure AD SignInLogs to identify successful logins where the appDisplayName is "Microsoft Azure PowerShell." This activity is significant because it is uncommon for regular, non-administrative users to authenticate using PowerShell, and it may indicate enumeration and discovery techniques by an attacker. If confirmed malicious, this activity could allow attackers to perform extensive reconnaissance, potentially leading to privilege escalation or further exploitation within the Azure environment.
+description: The following analytic identifies a successful authentication event against
+  an Azure AD tenant using PowerShell cmdlets. This detection leverages Azure AD SignInLogs
+  to identify successful logins where the appDisplayName is "Microsoft Azure PowerShell."
+  This activity is significant because it is uncommon for regular, non-administrative
+  users to authenticate using PowerShell, and it may indicate enumeration and discovery
+  techniques by an attacker. If confirmed malicious, this activity could allow attackers
+  to perform extensive reconnaissance, potentially leading to privilege escalation
+  or further exploitation within the Azure environment.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad`  category=SignInLogs properties.authenticationDetails{}.succeeded=true properties.appDisplayName="Microsoft Azure PowerShell" | rename properties.*  as * | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by src_ip, appDisplayName, user_agent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_successful_powershell_authentication_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: Administrative users will likely use PowerShell commandlets to troubleshoot and maintain the environment. Filter as needed.
+search: '`azure_monitor_aad`  category=SignInLogs properties.authenticationDetails{}.succeeded=true
+  properties.appDisplayName="Microsoft Azure PowerShell" | rename properties.*  as
+  * | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user
+  by src_ip, appDisplayName, user_agent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_successful_powershell_authentication_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: Administrative users will likely use PowerShell commandlets
+  to troubleshoot and maintain the environment. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1078/004/
 - https://docs.microsoft.com/en-us/powershell/module/azuread/connect-azuread?view=azureadps-2.0
@@ -22,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.appDisplayName
-  - category
-  - user
-  - src_ip
-  - properties.appDisplayName
-  - properties.userAgent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread_pws/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread_pws/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_successful_single_factor_authentication.yml b/detections/cloud/azure_ad_successful_single_factor_authentication.yml
index 264ce2b884..ceb47acd48 100644
--- a/detections/cloud/azure_ad_successful_single_factor_authentication.yml
+++ b/detections/cloud/azure_ad_successful_single_factor_authentication.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a successful single-factor authentication event against Azure Active Directory. It leverages Azure SignInLogs data, specifically focusing on events where single-factor authentication succeeded. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to the account, potentially leading to data breaches, privilege escalation, or further exploitation within the environment.
+description: The following analytic identifies a successful single-factor authentication
+  event against Azure Active Directory. It leverages Azure SignInLogs data, specifically
+  focusing on events where single-factor authentication succeeded. This activity is
+  significant as it may indicate a misconfiguration, policy violation, or potential
+  account takeover attempt. If confirmed malicious, an attacker could gain unauthorized
+  access to the account, potentially leading to data breaches, privilege escalation,
+  or further exploitation within the environment.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad` category=SignInLogs properties.authenticationRequirement=singleFactorAuthentication properties.authenticationDetails{}.succeeded=true | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by src_ip, appDisplayName, authenticationRequirement | `azure_ad_successful_single_factor_authentication_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: Although not recommended, certain users may be required without multi-factor authentication. Filter as needed
+search: '`azure_monitor_aad` category=SignInLogs properties.authenticationRequirement=singleFactorAuthentication
+  properties.authenticationDetails{}.succeeded=true | rename properties.* as * | stats
+  count min(_time) as firstTime max(_time) as lastTime values(user) as user by src_ip,
+  appDisplayName, authenticationRequirement | `azure_ad_successful_single_factor_authentication_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: Although not recommended, certain users may be required without
+  multi-factor authentication. Filter as needed
 references:
 - https://attack.mitre.org/techniques/T1078/004/
 - https://docs.microsoft.com/en-us/azure/active-directory/authentication/concept-mfa-howitworks*
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - properties.authenticationRequirement
-  - properties.authenticationDetails
-  - user
-  - src_ip
-  - properties.appDisplayName
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azuread/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml b/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml
index 5cfaaffe72..0399763ad9 100644
--- a/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml
+++ b/detections/cloud/azure_ad_tenant_wide_admin_consent_granted.yml
@@ -7,10 +7,28 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Consent to application
-description: The following analytic identifies instances where admin consent is granted to an application within an Azure AD tenant. It leverages Azure AD audit logs, specifically events related to the admin consent action within the ApplicationManagement category. This activity is significant because admin consent allows applications to access data across the entire tenant, potentially exposing vast amounts of organizational data. If confirmed malicious, an attacker could gain extensive and persistent access to sensitive data, leading to data exfiltration, espionage, further malicious activities, and potential compliance violations.
-search: '`azure_monitor_aad` operationName="Consent to application" | eval new_field=mvindex(''properties.targetResources{}.modifiedProperties{}.newValue'', 4) | rename properties.* as *  | rex field=new_field "ConsentType: (?<ConsentType>[^\,]+)" | rex field=new_field "Scope: (?<Scope>[^\,]+)"  | search  ConsentType = "AllPrincipals"  | stats count min(_time) as firstTime max(_time) as lastTime by operationName, user, targetResources{}.displayName, targetResources{}.id, ConsentType, Scope | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `azure_ad_tenant_wide_admin_consent_granted_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Auditlogs log category.
-known_false_positives: Legitimate applications may be granted tenant wide consent, filter as needed.
+description: The following analytic identifies instances where admin consent is granted
+  to an application within an Azure AD tenant. It leverages Azure AD audit logs, specifically
+  events related to the admin consent action within the ApplicationManagement category.
+  This activity is significant because admin consent allows applications to access
+  data across the entire tenant, potentially exposing vast amounts of organizational
+  data. If confirmed malicious, an attacker could gain extensive and persistent access
+  to sensitive data, leading to data exfiltration, espionage, further malicious activities,
+  and potential compliance violations.
+search: "`azure_monitor_aad` operationName=\"Consent to application\" | eval new_field=mvindex('properties.targetResources{}.modifiedProperties{}.newValue',
+  4) | rename properties.* as *  | rex field=new_field \"ConsentType: (?<ConsentType>[^\\\
+  ,]+)\" | rex field=new_field \"Scope: (?<Scope>[^\\,]+)\"  | search  ConsentType
+  = \"AllPrincipals\"  | stats count min(_time) as firstTime max(_time) as lastTime
+  by operationName, user, targetResources{}.displayName, targetResources{}.id, ConsentType,
+  Scope | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `azure_ad_tenant_wide_admin_consent_granted_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Auditlogs log category.
+known_false_positives: Legitimate applications may be granted tenant wide consent,
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452
@@ -23,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - user
-  - properties.targetResources{}.modifiedProperties{}.newValue
-  - properties.targetResources{}.displayName
-  - properties.targetResources{}.id
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_admin_consent/azure_ad_admin_consent.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/azure_ad_admin_consent/azure_ad_admin_consent.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml b/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml
index 7ad9cc805c..2d1a86386f 100644
--- a/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml
+++ b/detections/cloud/azure_ad_unusual_number_of_failed_authentications_from_ip.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a single source IP failing to authenticate with multiple valid users, potentially indicating a Password Spraying attack against an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers of failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.
+description: The following analytic identifies a single source IP failing to authenticate
+  with multiple valid users, potentially indicating a Password Spraying attack against
+  an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the
+  standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers
+  of failed authentication attempts. This activity is significant as it may signal
+  an adversary attempting to gain initial access or elevate privileges. If confirmed
+  malicious, this could lead to unauthorized access, privilege escalation, and potential
+  compromise of sensitive information.
 data_source:
 - Azure Active Directory
-search: '`azure_monitor_aad`  category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false | rename properties.* as * | bucket span=5m _time | stats  dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName by _time, ipAddress | eventstats  avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std by ipAddress | eval  upperBound=(ip_avg+ip_std*3) | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0) | where isOutlier = 1 | `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
-known_false_positives: A source Ip failing to authenticate with multiple users is not a common for legitimate behavior.
+search: '`azure_monitor_aad`  category=SignInLogs properties.status.errorCode=50126
+  properties.authenticationDetails{}.succeeded=false | rename properties.* as * |
+  bucket span=5m _time | stats  dc(userPrincipalName) AS unique_accounts values(userPrincipalName)
+  as userPrincipalName by _time, ipAddress | eventstats  avg(unique_accounts) as ip_avg,
+  stdev(unique_accounts) as ip_std by ipAddress | eval  upperBound=(ip_avg+ip_std*3)
+  | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0)
+  | where isOutlier = 1 | `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the Signin log category.
+known_false_positives: A source Ip failing to authenticate with multiple users is
+  not a common for legitimate behavior.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$userPrincipalName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userPrincipalName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userPrincipalName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - properties.status.errorCode
-  - category
-  - properties.authenticationDetails
-  - properties.userPrincipalName
-  - properties.ipAddress
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/password_spraying_azuread/azuread_signin.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml b/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml
index 6927ad243d..1405ace7af 100644
--- a/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml
+++ b/detections/cloud/azure_ad_user_consent_blocked_for_risky_application.yml
@@ -7,9 +7,32 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Consent to application
-description: The following analytic detects instances where Azure AD has blocked a user's attempt to grant consent to a risky or potentially malicious application. This detection leverages Azure AD audit logs, focusing on user consent actions and system-driven blocks. Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization. If confirmed malicious, this activity suggests that Azure's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation to understand the context and take preventive measures.
-search: '`azure_monitor_aad` operationName="Consent to application" properties.result=failure | rename properties.* as *  | eval reason_index = if(mvfind(''targetResources{}.modifiedProperties{}.displayName'', "ConsentAction.Reason") >= 0, mvfind(''targetResources{}.modifiedProperties{}.displayName'', "ConsentAction.Reason"), -1) | eval permissions_index = if(mvfind(''targetResources{}.modifiedProperties{}.displayName'', "ConsentAction.Permissions") >= 0, mvfind(''targetResources{}.modifiedProperties{}.displayName'', "ConsentAction.Permissions"), -1) | search reason_index >= 0  | eval reason = mvindex(''targetResources{}.modifiedProperties{}.newValue'',reason_index) | eval permissions = mvindex(''targetResources{}.modifiedProperties{}.newValue'',permissions_index) | search reason = "\"Risky application detected\"" | rex field=permissions "Scope: (?<Scope>[^,]+)" | stats count min(_time) as firstTime max(_time) as lastTime by operationName, user, reason, Scope | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_user_consent_blocked_for_risky_application_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
+description: The following analytic detects instances where Azure AD has blocked a
+  user's attempt to grant consent to a risky or potentially malicious application.
+  This detection leverages Azure AD audit logs, focusing on user consent actions and
+  system-driven blocks. Monitoring these blocked consent attempts is crucial as it
+  highlights potential threats early on, indicating that a user might be targeted
+  or that malicious applications are attempting to infiltrate the organization. If
+  confirmed malicious, this activity suggests that Azure's security measures successfully
+  prevented a harmful application from accessing organizational data, warranting immediate
+  investigation to understand the context and take preventive measures.
+search: "`azure_monitor_aad` operationName=\"Consent to application\" properties.result=failure
+  | rename properties.* as *  | eval reason_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"ConsentAction.Reason\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"ConsentAction.Reason\"), -1) | eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"ConsentAction.Permissions\") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName',
+  \"ConsentAction.Permissions\"), -1) | search reason_index >= 0  | eval reason =
+  mvindex('targetResources{}.modifiedProperties{}.newValue',reason_index) | eval permissions
+  = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) |
+  search reason = \"\\\"Risky application detected\\\"\" | rex field=permissions \"\
+  Scope: (?<Scope>[^,]+)\" | stats count min(_time) as firstTime max(_time) as lastTime
+  by operationName, user, reason, Scope | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_user_consent_blocked_for_risky_application_filter`"
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the AuditLog log category.
 known_false_positives: UPDATE_KNOWN_FALSE_POSITIVES
 references:
 - https://attack.mitre.org/techniques/T1528/
@@ -24,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,8 @@ tags:
   asset_type: Azure Tenant
   confidence: 100
   impact: 30
-  message: Azure AD has blocked $user$ attempt to grant to consent to an application deemed risky.
+  message: Azure AD has blocked $user$ attempt to grant to consent to an application
+    deemed risky.
   mitre_attack_id:
   - T1528
   observable:
@@ -45,16 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.result
-  - properties.targetResources{}.modifiedProperties{}.displayName
-  - properties.targetResources{}.modifiedProperties{}.newValue
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_blocked/azure_ad_user_consent_blocked.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_blocked/azure_ad_user_consent_blocked.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml b/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml
index 7bba96ae74..7074e213f2 100644
--- a/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml
+++ b/detections/cloud/azure_ad_user_consent_denied_for_oauth_application.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Azure Active Directory Sign-in activity
-description: The following analytic identifies instances where a user has denied consent to an OAuth application seeking permissions within the Azure AD environment. This detection leverages Azure AD's audit logs, specifically focusing on user consent actions with error code 65004. Monitoring denied consent actions is significant as it can indicate users recognizing potentially suspicious or untrusted applications. If confirmed malicious, this activity could suggest attempts by unauthorized applications to gain access, potentially leading to data breaches or unauthorized actions within the environment. Understanding these denials helps refine security policies and enhance user awareness.
-search: '`azure_monitor_aad` operationName="Sign-in activity" properties.status.errorCode=65004 | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime by operationName, user, appDisplayName, status.failureReason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_user_consent_denied_for_oauth_application_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.
-known_false_positives: Users may deny consent for legitimate applications by mistake, filter as needed.
+description: The following analytic identifies instances where a user has denied consent
+  to an OAuth application seeking permissions within the Azure AD environment. This
+  detection leverages Azure AD's audit logs, specifically focusing on user consent
+  actions with error code 65004. Monitoring denied consent actions is significant
+  as it can indicate users recognizing potentially suspicious or untrusted applications.
+  If confirmed malicious, this activity could suggest attempts by unauthorized applications
+  to gain access, potentially leading to data breaches or unauthorized actions within
+  the environment. Understanding these denials helps refine security policies and
+  enhance user awareness.
+search: '`azure_monitor_aad` operationName="Sign-in activity" properties.status.errorCode=65004
+  | rename properties.* as * | stats count min(_time) as firstTime max(_time) as lastTime
+  by operationName, user, appDisplayName, status.failureReason | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_user_consent_denied_for_oauth_application_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment
+  through an EventHub. This analytic was written to be used with the azure:monitor:aad
+  sourcetype leveraging the SignInLogs log category.
+known_false_positives: Users may deny consent for legitimate applications by mistake,
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1528/
 - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
@@ -24,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - properties.status.errorCode
-  - user
-  - properties.appDisplayName
-  - status.failureReason
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_declined/azure_ad_user_consent_declined.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/azure_ad_user_consent_declined/azure_ad_user_consent_declined.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_user_enabled_and_password_reset.yml b/detections/cloud/azure_ad_user_enabled_and_password_reset.yml
index 5c02806bd3..28561de0bc 100644
--- a/detections/cloud/azure_ad_user_enabled_and_password_reset.yml
+++ b/detections/cloud/azure_ad_user_enabled_and_password_reset.yml
@@ -5,14 +5,31 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Azure AD user enabling a previously disabled account and resetting its password within 2 minutes. It uses Azure Active Directory events to identify this sequence of actions. This activity is significant because it may indicate an adversary with administrative access attempting to establish a backdoor identity within the Azure AD tenant. If confirmed malicious, this could allow the attacker to maintain persistent access, escalate privileges, and potentially exfiltrate sensitive information from the environment.
+description: The following analytic detects an Azure AD user enabling a previously
+  disabled account and resetting its password within 2 minutes. It uses Azure Active
+  Directory events to identify this sequence of actions. This activity is significant
+  because it may indicate an adversary with administrative access attempting to establish
+  a backdoor identity within the Azure AD tenant. If confirmed malicious, this could
+  allow the attacker to maintain persistent access, escalate privileges, and potentially
+  exfiltrate sensitive information from the environment.
 data_source:
 - Azure Active Directory Enable account
 - Azure Active Directory Reset password (by admin)
 - Azure Active Directory Update user
-search: '`azure_monitor_aad`  (operationName="Enable account" OR operationName="Reset password (by admin)" OR operationName="Update user") | transaction user startsWith=(operationName="Enable account") endsWith=(operationName="Reset password (by admin)") maxspan=2m | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | stats count min(_time) as firstTime max(_time) as lastTime values(operationName) as operationName values(initiatedBy) as initiatedBy by user, result | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_user_enabled_and_password_reset_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: While not common, Administrators may enable accounts and reset their passwords for legitimate reasons. Filter as needed.
+search: '`azure_monitor_aad`  (operationName="Enable account" OR operationName="Reset
+  password (by admin)" OR operationName="Update user") | transaction user startsWith=(operationName="Enable
+  account") endsWith=(operationName="Reset password (by admin)") maxspan=2m | rename
+  properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | stats
+  count min(_time) as firstTime max(_time) as lastTime values(operationName) as operationName
+  values(initiatedBy) as initiatedBy by user, result | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_ad_user_enabled_and_password_reset_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: While not common, Administrators may enable accounts and reset
+  their passwords for legitimate reasons. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 drilldown_searches:
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +52,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 90
   impact: 50
-  message: A user account, $user$, was enabled and its password reset within 2 minutes by $initiatedBy$
+  message: A user account, $user$, was enabled and its password reset within 2 minutes
+    by $initiatedBy$
   mitre_attack_id:
   - T1098
   observable:
@@ -46,16 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName
-  - user
-  - properties.initiatedBy.user.userPrincipalName
-  - properties.result
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_enable_and_reset/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_enable_and_reset/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml b/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml
index 4c1152143c..11fd3574ed 100644
--- a/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml
+++ b/detections/cloud/azure_ad_user_immutableid_attribute_updated.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the modification of the SourceAnchor (ImmutableId) attribute for an Azure Active Directory user. This detection leverages Azure AD audit logs, specifically monitoring the "Update user" operation and changes to the SourceAnchor attribute. This activity is significant as it is a step in setting up an Azure AD identity federation backdoor, allowing an adversary to establish persistence. If confirmed malicious, the attacker could impersonate any user, bypassing password and MFA requirements, leading to unauthorized access and potential data breaches.
+description: The following analytic identifies the modification of the SourceAnchor
+  (ImmutableId) attribute for an Azure Active Directory user. This detection leverages
+  Azure AD audit logs, specifically monitoring the "Update user" operation and changes
+  to the SourceAnchor attribute. This activity is significant as it is a step in setting
+  up an Azure AD identity federation backdoor, allowing an adversary to establish
+  persistence. If confirmed malicious, the attacker could impersonate any user, bypassing
+  password and MFA requirements, leading to unauthorized access and potential data
+  breaches.
 data_source:
 - Azure Active Directory Update user
-search: '`azure_monitor_aad`  operationName="Update user" properties.targetResources{}.modifiedProperties{}.displayName=SourceAnchor | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy | rename targetResources{}.modifiedProperties{}.newValue as modifiedProperties | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user values(modifiedProperties) as modifiedProperties by initiatedBy, src_ip, result, operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_ad_user_immutableid_attribute_updated_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.
-known_false_positives: The SourceAnchor (also called ImmutableId) Azure AD attribute has legitimate uses for directory synchronization. Investigate and filter as needed.
+search: '`azure_monitor_aad`  operationName="Update user" properties.targetResources{}.modifiedProperties{}.displayName=SourceAnchor
+  | rename properties.* as * | rename initiatedBy.user.userPrincipalName as initiatedBy
+  | rename targetResources{}.modifiedProperties{}.newValue as modifiedProperties |
+  stats count min(_time) as firstTime max(_time) as lastTime values(user) as user
+  values(modifiedProperties) as modifiedProperties by initiatedBy, src_ip, result,
+  operationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `azure_ad_user_immutableid_attribute_updated_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase(https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Active Directory events into your Splunk environment.
+  This analytic was written to be used with the azure:monitor:aad sourcetype leveraging
+  the AuditLog log category.
+known_false_positives: The SourceAnchor (also called ImmutableId) Azure AD attribute
+  has legitimate uses for directory synchronization. Investigate and filter as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/active-directory/hybrid/plan-connect-design-concepts
 - https://www.mandiant.com/resources/remediation-and-hardening-strategies-microsoft-365-defend-against-apt29-v13
@@ -24,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +56,8 @@ tags:
   asset_type: Azure Active Directory
   confidence: 90
   impact: 50
-  message: The SourceAnchor or ImmutableID attribute has been modified for user $user$ by $initiatedBy$
+  message: The SourceAnchor or ImmutableID attribute has been modified for user $user$
+    by $initiatedBy$
   mitre_attack_id:
   - T1098
   observable:
@@ -49,12 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_set_immutableid/azure-audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/azure_ad_set_immutableid/azure-audit.log
     source: Azure AD
     sourcetype: azure:monitor:aad
diff --git a/detections/cloud/azure_automation_account_created.yml b/detections/cloud/azure_automation_account_created.yml
index 71d4b80ce0..f5eb8d6339 100644
--- a/detections/cloud/azure_automation_account_created.yml
+++ b/detections/cloud/azure_automation_account_created.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a new Azure Automation account within an Azure tenant. It leverages Azure Audit events, specifically the Azure Activity log category, to identify when an account is created or updated. This activity is significant because Azure Automation accounts can be used to automate tasks and orchestrate actions across Azure and on-premise environments. If an attacker creates an Automation account with elevated privileges, they could maintain persistence, execute malicious runbooks, and potentially escalate privileges or execute code on virtual machines, posing a significant security risk.
+description: The following analytic detects the creation of a new Azure Automation
+  account within an Azure tenant. It leverages Azure Audit events, specifically the
+  Azure Activity log category, to identify when an account is created or updated.
+  This activity is significant because Azure Automation accounts can be used to automate
+  tasks and orchestrate actions across Azure and on-premise environments. If an attacker
+  creates an Automation account with elevated privileges, they could maintain persistence,
+  execute malicious runbooks, and potentially escalate privileges or execute code
+  on virtual machines, posing a significant security risk.
 data_source:
 - Azure Audit Create or Update an Azure Automation account
-search: '`azure_audit` operationName.localizedValue="Create or Update an Azure Automation account" status.value=Succeeded | dedup object | rename claims.ipaddr as src_ip | rename caller as user | stats count min(_time) as firstTime max(_time) as lastTime values(object) as object by user, src_ip, resourceGroupName, object_path | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_automation_account_created_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
-known_false_positives: Administrators may legitimately create Azure Automation accounts. Filter as needed.
+search: '`azure_audit` operationName.localizedValue="Create or Update an Azure Automation
+  account" status.value=Succeeded | dedup object | rename claims.ipaddr as src_ip
+  | rename caller as user | stats count min(_time) as firstTime max(_time) as lastTime
+  values(object) as object by user, src_ip, resourceGroupName, object_path | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_automation_account_created_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Audit events into your Splunk environment. Specifically,
+  this analytic leverages the Azure Activity log category.
+known_false_positives: Administrators may legitimately create Azure Automation accounts.
+  Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/automation/overview
 - https://docs.microsoft.com/en-us/azure/automation/automation-create-standalone-account?tabs=azureportal
@@ -25,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName.localizedValue
-  - status.value
-  - object
-  - caller
-  - claims.ipaddr
-  - resourceGroupName
-  - object_path
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_automation_account/azure-activity.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/azure_automation_account/azure-activity.log
     source: mscs:azure:audit
     sourcetype: mscs:azure:audit
diff --git a/detections/cloud/azure_automation_runbook_created.yml b/detections/cloud/azure_automation_runbook_created.yml
index abf0da73b1..08d67f1a39 100644
--- a/detections/cloud/azure_automation_runbook_created.yml
+++ b/detections/cloud/azure_automation_runbook_created.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a new Azure Automation Runbook within an Azure tenant. It leverages Azure Audit events, specifically the Azure Activity log category, to identify when a new Runbook is created or updated. This activity is significant because adversaries with privileged access can use Runbooks to maintain persistence, escalate privileges, or execute malicious code. If confirmed malicious, this could lead to unauthorized actions such as creating Global Administrators, executing code on VMs, and compromising the entire Azure environment.
+description: The following analytic detects the creation of a new Azure Automation
+  Runbook within an Azure tenant. It leverages Azure Audit events, specifically the
+  Azure Activity log category, to identify when a new Runbook is created or updated.
+  This activity is significant because adversaries with privileged access can use
+  Runbooks to maintain persistence, escalate privileges, or execute malicious code.
+  If confirmed malicious, this could lead to unauthorized actions such as creating
+  Global Administrators, executing code on VMs, and compromising the entire Azure
+  environment.
 data_source:
 - Azure Audit Create or Update an Azure Automation Runbook
-search: '`azure_audit` operationName.localizedValue="Create or Update an Azure Automation Runbook" object!=AzureAutomationTutorial* status.value=Succeeded | dedup object | rename claims.ipaddr as src_ip | rename caller as user | stats count min(_time) as firstTime max(_time) as lastTime by object user, src_ip, resourceGroupName, object_path | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_automation_runbook_created_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
-known_false_positives: Administrators may legitimately create Azure Automation Runbooks. Filter as needed.
+search: '`azure_audit` operationName.localizedValue="Create or Update an Azure Automation
+  Runbook" object!=AzureAutomationTutorial* status.value=Succeeded | dedup object
+  | rename claims.ipaddr as src_ip | rename caller as user | stats count min(_time)
+  as firstTime max(_time) as lastTime by object user, src_ip, resourceGroupName, object_path
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_automation_runbook_created_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Audit events into your Splunk environment. Specifically,
+  this analytic leverages the Azure Activity log category.
+known_false_positives: Administrators may legitimately create Azure Automation Runbooks.
+  Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/automation/overview
 - https://docs.microsoft.com/en-us/azure/automation/automation-runbook-types
@@ -25,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName.localizedValue
-  - status.value
-  - object
-  - caller
-  - claims.ipaddr
-  - resourceGroupName
-  - object_path
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_automation_runbook/azure-activity.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_automation_runbook/azure-activity.log
     source: mscs:azure:audit
     sourcetype: mscs:azure:audit
diff --git a/detections/cloud/azure_runbook_webhook_created.yml b/detections/cloud/azure_runbook_webhook_created.yml
index 4ee511015b..4d13bea992 100644
--- a/detections/cloud/azure_runbook_webhook_created.yml
+++ b/detections/cloud/azure_runbook_webhook_created.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a new Automation Runbook Webhook within an Azure tenant. It leverages Azure Audit events, specifically the "Create or Update an Azure Automation webhook" operation, to identify this activity. This behavior is significant because Webhooks can trigger Automation Runbooks via unauthenticated URLs exposed to the Internet, posing a security risk. If confirmed malicious, an attacker could use this to execute code, create users, or maintain persistence within the environment, potentially leading to unauthorized access and control over Azure resources.
+description: The following analytic detects the creation of a new Automation Runbook
+  Webhook within an Azure tenant. It leverages Azure Audit events, specifically the
+  "Create or Update an Azure Automation webhook" operation, to identify this activity.
+  This behavior is significant because Webhooks can trigger Automation Runbooks via
+  unauthenticated URLs exposed to the Internet, posing a security risk. If confirmed
+  malicious, an attacker could use this to execute code, create users, or maintain
+  persistence within the environment, potentially leading to unauthorized access and
+  control over Azure resources.
 data_source:
 - Azure Audit Create or Update an Azure Automation webhook
-search: '`azure_audit` operationName.localizedValue="Create or Update an Azure Automation webhook" status.value=Succeeded | dedup object | rename claims.ipaddr as src_ip | rename caller as user | stats count min(_time) as firstTime max(_time) as lastTime by object user, src_ip, resourceGroupName, object_path | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `azure_runbook_webhook_created_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
-known_false_positives: Administrators may legitimately create Azure Runbook Webhooks. Filter as needed.
+search: '`azure_audit` operationName.localizedValue="Create or Update an Azure Automation
+  webhook" status.value=Succeeded | dedup object | rename claims.ipaddr as src_ip
+  | rename caller as user | stats count min(_time) as firstTime max(_time) as lastTime
+  by object user, src_ip, resourceGroupName, object_path | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `azure_runbook_webhook_created_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Microsoft
+  Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details).
+  You must be ingesting Azure Audit events into your Splunk environment. Specifically,
+  this analytic leverages the Azure Activity log category.
+known_false_positives: Administrators may legitimately create Azure Runbook Webhooks.
+  Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/azure/automation/overview
 - https://docs.microsoft.com/en-us/azure/automation/automation-runbook-types
@@ -25,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operationName.localizedValue
-  - status.value
-  - object
-  - caller
-  - claims.ipaddr
-  - resourceGroupName
-  - object_path
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_runbook_webhook/azure-activity.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/azure_runbook_webhook/azure-activity.log
     source: mscs:azure:audit
     sourcetype: mscs:azure:audit
diff --git a/detections/cloud/circle_ci_disable_security_job.yml b/detections/cloud/circle_ci_disable_security_job.yml
index 56bdcd3790..78fc8d96b9 100644
--- a/detections/cloud/circle_ci_disable_security_job.yml
+++ b/detections/cloud/circle_ci_disable_security_job.yml
@@ -5,10 +5,23 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the disabling of security jobs in CircleCI pipelines. It leverages CircleCI log data, renaming and extracting fields such as job names, workflow IDs, user information, commit messages, URLs, and branches. The detection identifies mandatory jobs for each workflow and checks if they were executed. This activity is significant because disabling security jobs can allow malicious code to bypass security checks, leading to potential data breaches, system downtime, and reputational damage. If confirmed malicious, this could result in unauthorized code execution and compromised pipeline integrity.
+description: The following analytic detects the disabling of security jobs in CircleCI
+  pipelines. It leverages CircleCI log data, renaming and extracting fields such as
+  job names, workflow IDs, user information, commit messages, URLs, and branches.
+  The detection identifies mandatory jobs for each workflow and checks if they were
+  executed. This activity is significant because disabling security jobs can allow
+  malicious code to bypass security checks, leading to potential data breaches, system
+  downtime, and reputational damage. If confirmed malicious, this could result in
+  unauthorized code execution and compromised pipeline integrity.
 data_source:
 - CircleCI
-search: '`circleci` | rename vcs.committer_name as user vcs.subject as commit_message vcs.url as url workflows.* as *  | stats values(job_name) as job_names by workflow_id workflow_name user commit_message url branch | lookup mandatory_job_for_workflow workflow_name OUTPUTNEW job_name AS mandatory_job | search mandatory_job=* | eval mandatory_job_executed=if(like(job_names, "%".mandatory_job."%"), 1, 0) | where mandatory_job_executed=0 | eval phase="build" | rex field=url "(?<repository>[^\/]*\/[^\/]*)$" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `circle_ci_disable_security_job_filter`'
+search: '`circleci` | rename vcs.committer_name as user vcs.subject as commit_message
+  vcs.url as url workflows.* as *  | stats values(job_name) as job_names by workflow_id
+  workflow_name user commit_message url branch | lookup mandatory_job_for_workflow
+  workflow_name OUTPUTNEW job_name AS mandatory_job | search mandatory_job=* | eval
+  mandatory_job_executed=if(like(job_names, "%".mandatory_job."%"), 1, 0) | where
+  mandatory_job_executed=0 | eval phase="build" | rex field=url "(?<repository>[^\/]*\/[^\/]*)$"
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `circle_ci_disable_security_job_filter`'
 how_to_implement: You must index CircleCI logs.
 known_false_positives: unknown
 references: []
@@ -18,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +45,8 @@ tags:
   asset_type: CircleCI
   confidence: 90
   impact: 80
-  message: Disable security job $mandatory_job$ in workflow $workflow_name$ from user $user$
+  message: Disable security job $mandatory_job$ in workflow $workflow_name$ from user
+    $user$
   mitre_attack_id:
   - T1554
   observable:
@@ -39,12 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _times
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_job/circle_ci_disable_security_job.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_job/circle_ci_disable_security_job.json
     sourcetype: circleci
     source: circleci
diff --git a/detections/cloud/circle_ci_disable_security_step.yml b/detections/cloud/circle_ci_disable_security_step.yml
index 83e12fdfab..1bc4f7a87e 100644
--- a/detections/cloud/circle_ci_disable_security_step.yml
+++ b/detections/cloud/circle_ci_disable_security_step.yml
@@ -5,10 +5,24 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects the disablement of security steps in a CircleCI pipeline. It leverages CircleCI logs, using field renaming, joining, and statistical analysis to identify instances where mandatory security steps are not executed. This activity is significant because disabling security steps can introduce vulnerabilities, unauthorized changes, or malicious code into the pipeline. If confirmed malicious, this could lead to potential attacks, data breaches, or compromised infrastructure. Investigate by reviewing job names, commit details, and user information associated with the disablement, and examine any relevant artifacts and concurrent processes.
+description: The following analytic detects the disablement of security steps in a
+  CircleCI pipeline. It leverages CircleCI logs, using field renaming, joining, and
+  statistical analysis to identify instances where mandatory security steps are not
+  executed. This activity is significant because disabling security steps can introduce
+  vulnerabilities, unauthorized changes, or malicious code into the pipeline. If confirmed
+  malicious, this could lead to potential attacks, data breaches, or compromised infrastructure.
+  Investigate by reviewing job names, commit details, and user information associated
+  with the disablement, and examine any relevant artifacts and concurrent processes.
 data_source:
 - CircleCI
-search: '`circleci` | rename workflows.job_id AS job_id | join job_id [ | search `circleci` | stats values(name) as step_names count by job_id job_name ] | stats count by step_names job_id job_name vcs.committer_name vcs.subject vcs.url owners{} | rename vcs.* as * , owners{} as user | lookup mandatory_step_for_job job_name OUTPUTNEW step_name AS mandatory_step | search mandatory_step=* | eval mandatory_step_executed=if(like(step_names, "%".mandatory_step."%"), 1, 0) | where mandatory_step_executed=0 | rex field=url "(?<repository>[^\/]*\/[^\/]*)$" | eval phase="build"  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `circle_ci_disable_security_step_filter`'
+search: '`circleci` | rename workflows.job_id AS job_id | join job_id [ | search `circleci`
+  | stats values(name) as step_names count by job_id job_name ] | stats count by step_names
+  job_id job_name vcs.committer_name vcs.subject vcs.url owners{} | rename vcs.* as
+  * , owners{} as user | lookup mandatory_step_for_job job_name OUTPUTNEW step_name
+  AS mandatory_step | search mandatory_step=* | eval mandatory_step_executed=if(like(step_names,
+  "%".mandatory_step."%"), 1, 0) | where mandatory_step_executed=0 | rex field=url
+  "(?<repository>[^\/]*\/[^\/]*)$" | eval phase="build"  | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `circle_ci_disable_security_step_filter`'
 how_to_implement: You must index CircleCI logs.
 known_false_positives: unknown
 references: []
@@ -30,12 +44,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _times
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_step/circle_ci_disable_security_step.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1554/circle_ci_disable_security_step/circle_ci_disable_security_step.json
     sourcetype: circleci
     source: circleci
diff --git a/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml b/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml
index 839e6e6e17..3ed98d1f54 100644
--- a/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml
+++ b/detections/cloud/cloud_api_calls_from_previously_unseen_user_roles.yml
@@ -5,11 +5,33 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
+description: The following analytic detects cloud API calls executed by user roles
+  that have not previously run these commands. It leverages the Change data model
+  in Splunk to identify commands executed by users with the user_type of AssumedRole
+  and a status of success. This activity is significant because new commands from
+  different user roles can indicate potential malicious activity or unauthorized actions.
+  If confirmed malicious, this behavior could lead to unauthorized access, data breaches,
+  or other damaging outcomes by exploiting new or unmonitored commands within the
+  cloud environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user, object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cloud_api_calls_from_previously_unseen_user_roles_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter`
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user,
+  All_Changes.command All_Changes.object | `drop_dm_object_name("All_Changes")` |
+  lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command
+  OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data |
+  where enough_data=1 | eval firstTimeSeenUserApiCall=min(firstTimeSeen) | where isnull(firstTimeSeenUserApiCall)
+  OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") | table firstTime, user,
+  object, command |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `cloud_api_calls_from_previously_unseen_user_roles_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider.  You should run the baseline search `Previously Seen Cloud API Calls Per
+  User Role - Initial` to build the initial table of user roles, commands, and times.
+  You must also enable the second baseline search `Previously Seen Cloud API Calls
+  Per User Role - Update` to keep this table up to date and to age out old data. You
+  can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window`
+  macro. You can also provide additional filtering for this search by customizing
+  the `cloud_api_calls_from_previously_unseen_user_roles_filter`
 known_false_positives: None.
 references: []
 tags:
@@ -18,7 +40,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 60
-  message: User $user$ of type AssumedRole attempting to execute new API calls $command$ that have not been seen before
+  message: User $user$ of type AssumedRole attempting to execute new API calls $command$
+    that have not been seen before
   mitre_attack_id:
   - T1078
   observable:
@@ -30,17 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.user
-  - All_Changes.user_type
-  - All_Changes.status
-  - All_Changes.command
-  - All_Changes.object
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml b/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml
index e6af3c137d..dc18f65dc1 100644
--- a/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml
+++ b/detections/cloud/cloud_compute_instance_created_by_previously_unseen_user.yml
@@ -5,12 +5,30 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies the creation of cloud compute instances by users who have not previously created them. It leverages data from the Change data model, focusing on 'create' actions by users, and cross-references with a baseline of known user activities. This activity is significant as it may indicate unauthorized access or misuse of cloud resources by new or compromised accounts. If confirmed malicious, attackers could deploy unauthorized compute instances, leading to potential data exfiltration, increased costs, or further exploitation within the cloud environment.
+description: The following analytic identifies the creation of cloud compute instances
+  by users who have not previously created them. It leverages data from the Change
+  data model, focusing on 'create' actions by users, and cross-references with a baseline
+  of known user activities. This activity is significant as it may indicate unauthorized
+  access or misuse of cloud resources by new or compromised accounts. If confirmed
+  malicious, attackers could deploy unauthorized compute instances, leading to potential
+  data exfiltration, increased costs, or further exploitation within the cloud environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_by_previously_unseen_user_filter`'
-how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users.
-known_false_positives: It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior.
+search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime,
+  latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change
+  where All_Changes.action=created by All_Changes.user All_Changes.vendor_region |
+  `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_compute_creations_by_user
+  user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data)
+  as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen)
+  | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h")
+  | table firstTime, user, dest, count vendor_region | `security_content_ctime(firstTime)`
+  | `cloud_compute_instance_created_by_previously_unseen_user_filter`'
+how_to_implement: You must be ingesting the appropriate cloud-infrastructure logs
+  Run the "Previously Seen Cloud Compute Creations By User" support search to create
+  of baseline of previously seen users.
+known_false_positives: It's possible that a user will start to create compute instances
+  for the first time, for any number of reasons. Verify with the user launching instances
+  that this is the intended behavior.
 references: []
 tags:
   analytic_story:
@@ -35,16 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object
-  - All_Changes.action
-  - All_Changes.user
-  - All_Changes.vendor_region
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml b/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml
index 333f7fff10..980f9f714c 100644
--- a/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml
+++ b/detections/cloud/cloud_compute_instance_created_in_previously_unused_region.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects the creation of a cloud compute instance in a region that has not been previously used within the last hour. It leverages cloud infrastructure logs and compares the regions of newly created instances against a lookup file of historically used regions. This activity is significant because the creation of instances in new regions can indicate unauthorized or suspicious activity, such as an attacker attempting to evade detection or establish a foothold in a less monitored area. If confirmed malicious, this could lead to unauthorized resource usage, data exfiltration, or further compromise of the cloud environment.
+description: The following analytic detects the creation of a cloud compute instance
+  in a region that has not been previously used within the last hour. It leverages
+  cloud infrastructure logs and compares the regions of newly created instances against
+  a lookup file of historically used regions. This activity is significant because
+  the creation of instances in new regions can indicate unauthorized or suspicious
+  activity, such as an attacker attempting to evade detection or establish a foothold
+  in a less monitored area. If confirmed malicious, this could lead to unauthorized
+  resource usage, data exfiltration, or further compromise of the cloud environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_in_previously_unused_region_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro.
-known_false_positives: It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate.
+search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id)
+  as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region,
+  All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_regions
+  vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data | eventstats
+  max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen)
+  | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(),
+  "-24h@h") | table firstTime, user, dest, count , vendor_region | `security_content_ctime(firstTime)`
+  | `cloud_compute_instance_created_in_previously_unused_region_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen Cloud Regions - Initial`
+  to build the initial table of images observed and times. You must also enable the
+  second baseline search `Previously Seen Cloud Regions - Update` to keep this table
+  up to date and to age out old data. You can also provide additional filtering for
+  this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter`
+  macro.
+known_false_positives: It's possible that a user has unknowingly started an instance
+  in a new region. Please verify that this activity is legitimate.
 references: []
 tags:
   analytic_story:
@@ -18,7 +39,8 @@ tags:
   asset_type: Cloud Compute Instance
   confidence: 60
   impact: 70
-  message: User $user$ is creating an instance $dest$ in a new region for the first time
+  message: User $user$ is creating an instance $dest$ in a new region for the first
+    time
   mitre_attack_id:
   - T1535
   observable:
@@ -34,16 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.vendor_region
-  - All_Changes.user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml
index 1eca02d33e..c0021b5ea8 100644
--- a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml
+++ b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_image.yml
@@ -5,12 +5,34 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
+description: The following analytic detects the creation of cloud compute instances
+  using previously unseen image IDs. It leverages cloud infrastructure logs to identify
+  new image IDs that have not been observed before. This activity is significant because
+  it may indicate unauthorized or suspicious activity, such as the deployment of malicious
+  payloads or unauthorized access to sensitive information. If confirmed malicious,
+  this could lead to data breaches, unauthorized access, or further compromise of
+  the cloud environment. Immediate investigation is required to determine the legitimacy
+  of the instance creation and to mitigate potential threats.
 data_source:
 - AWS CloudTrail
-search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user, image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro.
-known_false_positives: After a new image is created, the first systems created with that image will cause this alert to fire.  Verify that the image being used was created by a legitimate user.
+search: '| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id)
+  as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id,
+  All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")`
+  | where image_id != "unknown" | lookup previously_seen_cloud_compute_images image_id
+  as image_id OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data
+  | where enough_data=1 | eval firstTimeSeenImage=min(firstTimeSeen) | where isnull(firstTimeSeenImage)
+  OR firstTimeSeenImage > relative_time(now(), "-24h@h") | table firstTime, user,
+  image_id, count, dest | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_image_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen Cloud Compute Images
+  - Initial` to build the initial table of images observed and times. You must also
+  enable the second baseline search `Previously Seen Cloud Compute Images - Update`
+  to keep this table up to date and to age out old data. You can also provide additional
+  filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter`
+  macro.
+known_false_positives: After a new image is created, the first systems created with
+  that image will cause this alert to fire.  Verify that the image being used was
+  created by a legitimate user.
 references: []
 tags:
   analytic_story:
@@ -18,7 +40,8 @@ tags:
   asset_type: Cloud Compute Instance
   confidence: 60
   impact: 60
-  message: User $user$ is creating an instance $dest$ with an image that has not been previously seen.
+  message: User $user$ is creating an instance $dest$ with an image that has not been
+    previously seen.
   observable:
   - name: user
     type: User
@@ -32,16 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.Instance_Changes.image_id
-  - All_Changes.user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml
index 983ca590be..83c5ecec22 100644
--- a/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml
+++ b/detections/cloud/cloud_compute_instance_created_with_previously_unseen_instance_type.yml
@@ -5,12 +5,35 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects the creation of EC2 instances with previously unseen instance types. It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded. This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes. If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation.
+description: The following analytic detects the creation of EC2 instances with previously
+  unseen instance types. It leverages Splunk's tstats command to analyze data from
+  the Change data model, identifying instance types that have not been previously
+  recorded. This activity is significant for a SOC because it may indicate unauthorized
+  or suspicious activity, such as an attacker attempting to create instances for malicious
+  purposes. If confirmed malicious, this could lead to unauthorized access, data exfiltration,
+  system compromise, or service disruption. Immediate investigation is required to
+  determine the legitimacy of the instance creation.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")` | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen) | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)` | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro.
-known_false_positives: It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type.
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id)
+  as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type,
+  All_Changes.user | `drop_dm_object_name("All_Changes")` | `drop_dm_object_name("Instance_Changes")`
+  | where instance_type != "unknown" | lookup previously_seen_cloud_compute_instance_types
+  instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data | eventstats
+  max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenInstanceType=min(firstTimeSeen)
+  | where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(),
+  "-24h@h") | table firstTime, user, dest, count, instance_type | `security_content_ctime(firstTime)`
+  | `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider. You should run the baseline search `Previously Seen Cloud Compute Instance
+  Types - Initial` to build the initial table of instance types observed and times.
+  You must also enable the second baseline search `Previously Seen Cloud Compute Instance
+  Types - Update` to keep this table up to date and to age out old data. You can also
+  provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`
+  macro.
+known_false_positives: It is possible that an admin will create a new system using
+  a new instance type that has never been used before. Verify with the creator that
+  they intended to create the system with the new instance type.
 references: []
 tags:
   analytic_story:
@@ -18,7 +41,8 @@ tags:
   asset_type: Cloud Compute Instance
   confidence: 60
   impact: 50
-  message: User $user$ is creating an instance $dest$ with an instance type $instance_type$ that has not been previously seen.
+  message: User $user$ is creating an instance $dest$ with an instance type $instance_type$
+    that has not been previously seen.
   observable:
   - name: user
     type: User
@@ -32,16 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.Instance_Changes.instance_type
-  - All_Changes.user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml b/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml
index ddf9380220..29d85053f8 100644
--- a/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml
+++ b/detections/cloud/cloud_instance_modified_by_previously_unseen_user.yml
@@ -5,12 +5,31 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies cloud instances being modified by users who have not previously modified them. It leverages data from the Change data model, focusing on successful modifications of EC2 instances. This activity is significant because it can indicate unauthorized or suspicious changes by potentially compromised or malicious users. If confirmed malicious, this could lead to unauthorized access, configuration changes, or potential disruption of cloud services, posing a significant risk to the organization's cloud infrastructure.
+description: The following analytic identifies cloud instances being modified by users
+  who have not previously modified them. It leverages data from the Change data model,
+  focusing on successful modifications of EC2 instances. This activity is significant
+  because it can indicate unauthorized or suspicious changes by potentially compromised
+  or malicious users. If confirmed malicious, this could lead to unauthorized access,
+  configuration changes, or potential disruption of cloud services, posing a significant
+  risk to the organization's cloud infrastructure.
 data_source:
 - AWS CloudTrail
-search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter`'
-how_to_implement: This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work.
-known_false_positives: It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.
+search: '| tstats `security_content_summariesonly` count earliest(_time) as firstTime,
+  latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command)
+  as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2
+  All_Changes.status=success by All_Changes.user | `drop_dm_object_name("All_Changes")`
+  | lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW
+  firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where
+  enough_data=1 | eval firstTimeSeenUser=min(firstTimeSeen) | where isnull(firstTimeSeenUser)
+  OR firstTimeSeenUser > relative_time(now(), "-24h@h") | table firstTime user command
+  object_id count | `security_content_ctime(firstTime)` | `cloud_instance_modified_by_previously_unseen_user_filter`'
+how_to_implement: This search has a dependency on other searches to create and update
+  a baseline of users observed to be associated with this activity. The search "Previously
+  Seen Cloud Instance Modifications By User - Update" should be enabled for this detection
+  to properly work.
+known_false_positives: It's possible that a new user will start to modify EC2 instances
+  when they haven't before for any number of reasons. Verify with the user that is
+  modifying instances that this is the intended behavior.
 references: []
 tags:
   analytic_story:
@@ -31,18 +50,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.command
-  - All_Changes.action
-  - All_Changes.change_type
-  - All_Changes.status
-  - All_Changes.user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml
index 49e1ff7686..9ced3ae7db 100644
--- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml
+++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_city.yml
@@ -5,14 +5,45 @@ date: '2024-09-30'
 author: Rico Valdez, Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects cloud provisioning activities originating from previously unseen cities. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
+description: The following analytic detects cloud provisioning activities originating
+  from previously unseen cities. It leverages cloud infrastructure logs and compares
+  the geographic location of the source IP address against a baseline of known locations.
+  This activity is significant as it may indicate unauthorized access or misuse of
+  cloud resources from an unexpected location. If confirmed malicious, this could
+  lead to unauthorized resource creation, potential data exfiltration, or further
+  compromise of cloud infrastructure.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) | lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | `security_content_ctime(firstTime)` | table firstTime, src, City, user, object, command | `cloud_provisioning_activity_from_previously_unseen_city_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro.
-known_false_positives: 'This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you''re searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
-
-  This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.'
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
+  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
+  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(City) |
+  lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen,
+  enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 |
+  eval firstTimeSeenCity=min(firstTimeSeen) | where isnull(firstTimeSeenCity) OR firstTimeSeenCity
+  > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) |
+  `security_content_ctime(firstTime)` | table firstTime, src, City, user, object,
+  command | `cloud_provisioning_activity_from_previously_unseen_city_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider.  You should run the baseline search `Previously Seen Cloud Provisioning
+  Activity Sources - Initial` to build the initial table of source IP address, geographic
+  locations, and times. You must also enable the second baseline search `Previously
+  Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date
+  and to age out old data. You can adjust the time window for this search by updating
+  the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
+  additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter`
+  macro.
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  IP address is seen in the **GeoIP** database for any kind of provisioning activity.
+  If you typically do all provisioning from tools inside of your country, there should
+  be few false positives. If you are located in countries where the free version of
+  **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly
+  small countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$object$"
@@ -20,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$object$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +65,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 30
-  message: User $user$ is starting or creating an instance $object$ for the first time in City $City$ from IP address $src$
+  message: User $user$ is starting or creating an instance $object$ for the first
+    time in City $City$ from IP address $src$
   mitre_attack_id:
   - T1078
   observable:
@@ -49,19 +86,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.src
-  - All_Changes.user
-  - All_Changes.object
-  - All_Changes.command
   security_domain: threat
   manual_test: This search needs the baseline to be run first to create a lookup
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml
index a1d53ae71c..1099c7398d 100644
--- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml
+++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_country.yml
@@ -5,14 +5,44 @@ date: '2024-09-30'
 author: Rico Valdez, Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects cloud provisioning activities originating from previously unseen countries. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or potential compromise of cloud resources. If confirmed malicious, an attacker could gain control over cloud assets, leading to data breaches, service disruptions, or further infiltration into the network.
+description: The following analytic detects cloud provisioning activities originating
+  from previously unseen countries. It leverages cloud infrastructure logs and compares
+  the geographic location of the source IP address against a baseline of known locations.
+  This activity is significant as it may indicate unauthorized access or potential
+  compromise of cloud resources. If confirmed malicious, an attacker could gain control
+  over cloud assets, leading to data breaches, service disruptions, or further infiltration
+  into the network.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country) | lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | `security_content_ctime(firstTime)` | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro.
-known_false_positives: 'This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you''re searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
-
-  This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.'
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
+  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
+  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Country)
+  | lookup previously_seen_cloud_provisioning_activity_sources Country as Country
+  OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data |
+  where enough_data=1 | eval firstTimeSeenCountry=min(firstTimeSeen) | where isnull(firstTimeSeenCountry)
+  OR firstTimeSeenCountry > relative_time(now(), "-24h@h") | `security_content_ctime(firstTime)`
+  | table firstTime, src, Country, user, object, command | `cloud_provisioning_activity_from_previously_unseen_country_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider.  You should run the baseline search `Previously Seen Cloud Provisioning
+  Activity Sources - Initial` to build the initial table of source IP address, geographic
+  locations, and times. You must also enable the second baseline search `Previously
+  Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date
+  and to age out old data. You can adjust the time window for this search by updating
+  the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
+  additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter`
+  macro.
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  IP address is seen in the **GeoIP** database for any kind of provisioning activity.
+  If you typically do all provisioning from tools inside of your country, there should
+  be few false positives. If you are located in countries where the free version of
+  **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly
+  small countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 drilldown_searches:
 - name: View the detection results for - "$object$"
@@ -20,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +64,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 70
-  message: User $user$ is starting or creating an instance $object$ for the first time in Country $Country$ from IP address $src$
+  message: User $user$ is starting or creating an instance $object$ for the first
+    time in Country $Country$ from IP address $src$
   mitre_attack_id:
   - T1078
   observable:
@@ -49,19 +85,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.src
-  - All_Changes.user
-  - All_Changes.object
-  - All_Changes.command
   security_domain: threat
   manual_test: This search needs the baseline to be run first to create a lookup
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml
index aea0e816d4..b0eea5c0f0 100644
--- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml
+++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_ip_address.yml
@@ -5,14 +5,44 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs.
+description: The following analytic detects cloud provisioning activities originating
+  from previously unseen IP addresses. It leverages cloud infrastructure logs to identify
+  events where resources are created or started, and cross-references these with a
+  baseline of known IP addresses. This activity is significant as it may indicate
+  unauthorized access or potential misuse of cloud resources. If confirmed malicious,
+  an attacker could gain unauthorized control over cloud resources, leading to data
+  breaches, service disruptions, or increased operational costs.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | `security_content_ctime(firstTime)` | table firstTime, src, user, object_id, command | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro.
-known_false_positives: 'This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you''re searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
-
-  This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.'
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id)
+  as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created)
+  All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command
+  | `drop_dm_object_name("All_Changes")` | lookup previously_seen_cloud_provisioning_activity_sources
+  src as src OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data
+  | where enough_data=1 | eval firstTimeSeenSrc=min(firstTimeSeen) | where isnull(firstTimeSeenSrc)
+  OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`)
+  | `security_content_ctime(firstTime)` | table firstTime, src, user, object_id, command
+  | `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider.  You should run the baseline search `Previously Seen Cloud Provisioning
+  Activity Sources - Initial` to build the initial table of source IP address, geographic
+  locations, and times. You must also enable the second baseline search `Previously
+  Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date
+  and to age out old data. You can adjust the time window for this search by updating
+  the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
+  additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter`
+  macro.
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  IP address is seen in the **GeoIP** database for any kind of provisioning activity.
+  If you typically do all provisioning from tools inside of your country, there should
+  be few false positives. If you are located in countries where the free version of
+  **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly
+  small countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 drilldown_searches:
 - name: View the detection results for - "$object_id$"
@@ -20,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$object_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +64,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 70
-  message: User $user$ is starting or creating an instance $object_id$ for the first time from IP address $src$
+  message: User $user$ is starting or creating an instance $object_id$ for the first
+    time from IP address $src$
   mitre_attack_id:
   - T1078
   observable:
@@ -49,19 +85,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.src
-  - All_Changes.user
-  - All_Changes.command
   security_domain: threat
   manual_test: This search needs the baseline to be run first to create a lookup
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml
index 44cc801c62..e52773682d 100644
--- a/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml
+++ b/detections/cloud/cloud_provisioning_activity_from_previously_unseen_region.yml
@@ -5,14 +5,45 @@ date: '2024-09-30'
 author: Rico Valdez, Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects cloud provisioning activities originating from previously unseen regions. It leverages cloud infrastructure logs to identify events where resources are started or created, and cross-references these with a baseline of known regions. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
+description: The following analytic detects cloud provisioning activities originating
+  from previously unseen regions. It leverages cloud infrastructure logs to identify
+  events where resources are started or created, and cross-references these with a
+  baseline of known regions. This activity is significant as it may indicate unauthorized
+  access or misuse of cloud resources from unfamiliar locations. If confirmed malicious,
+  this could lead to unauthorized resource creation, potential data exfiltration,
+  or further compromise of cloud infrastructure.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command | `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region) | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) | `security_content_ctime(firstTime)` | table firstTime, src, Region, user, object, command | `cloud_provisioning_activity_from_previously_unseen_region_filter`'
-how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro.
-known_false_positives: 'This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you''re searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.
-
-  This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.'
+search: '| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change
+  where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success
+  by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command |
+  `drop_dm_object_name("All_Changes")` | iplocation src | where isnotnull(Region)
+  | lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT
+  firstTimeSeen, enough_data | eventstats max(enough_data) as enough_data | where
+  enough_data=1 | eval firstTimeSeenRegion=min(firstTimeSeen) | where isnull(firstTimeSeenRegion)
+  OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`)
+  | `security_content_ctime(firstTime)` | table firstTime, src, Region, user, object,
+  command | `cloud_provisioning_activity_from_previously_unseen_region_filter`'
+how_to_implement: You must be ingesting your cloud infrastructure logs from your cloud
+  provider.  You should run the baseline search `Previously Seen Cloud Provisioning
+  Activity Sources - Initial` to build the initial table of source IP address, geographic
+  locations, and times. You must also enable the second baseline search `Previously
+  Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date
+  and to age out old data. You can adjust the time window for this search by updating
+  the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide
+  additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter`
+  macro.
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  IP address is seen in the **GeoIP** database for any kind of provisioning activity.
+  If you typically do all provisioning from tools inside of your country, there should
+  be few false positives. If you are located in countries where the free version of
+  **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly
+  small countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 drilldown_searches:
 - name: View the detection results for - "$object$"
@@ -20,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +65,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 70
-  message: User $user$ is starting or creating an instance $object$ for the first time in region $Region$ from IP address $src$
+  message: User $user$ is starting or creating an instance $object$ for the first
+    time in region $Region$ from IP address $src$
   mitre_attack_id:
   - T1078
   observable:
@@ -49,19 +86,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.src
-  - All_Changes.user
-  - All_Changes.object
-  - All_Changes.command
   security_domain: threat
   manual_test: This search needs the baseline to be run first to create a lookup
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/cloud_security_groups_modifications_by_user.yml b/detections/cloud/cloud_security_groups_modifications_by_user.yml
index 9513dfe4a8..ce7f2cb0b7 100644
--- a/detections/cloud/cloud_security_groups_modifications_by_user.yml
+++ b/detections/cloud/cloud_security_groups_modifications_by_user.yml
@@ -7,10 +7,29 @@ data_source:
 - AWS CloudTrail
 type: Anomaly
 status: production
-description: The following analytic identifies unusual modifications to security groups in your cloud environment by users, focusing on actions such as modifications, deletions, or creations over 30-minute intervals. It leverages cloud infrastructure logs and calculates the standard deviation for each user, using the 3-sigma rule to detect anomalies. This activity is significant as it may indicate a compromised account or insider threat. If confirmed malicious, attackers could alter security group configurations, potentially exposing sensitive resources or disrupting services.
-search: '| tstats dc(All_Changes.object) as unique_security_groups values(All_Changes.src) as src values(All_Changes.user_type) as user_type values(All_Changes.object_category) as object_category values(All_Changes.object) as objects values(All_Changes.action) as action  values(All_Changes.user_agent) as user_agent values(All_Changes.command) as command from datamodel=Change WHERE All_Changes.object_category = "security_group" (All_Changes.action = modified OR All_Changes.action = deleted OR All_Changes.action = created)  by All_Changes.user  _time span=30m |  `drop_dm_object_name("All_Changes")` | eventstats avg(unique_security_groups) as avg_changes , stdev(unique_security_groups) as std_changes by user | eval upperBound=(avg_changes+std_changes*3) | eval isOutlier=if(unique_security_groups > 2 and unique_security_groups >= upperBound, 1, 0) | where isOutlier=1| `cloud_security_groups_modifications_by_user_filter`'
-how_to_implement: This search requries the Cloud infrastructure logs such as AWS Cloudtrail, GCP Pubsub Message logs, Azure Audit logs to be ingested into an accelerated Change datamodel. It is also recommended that users can try different combinations of the `bucket` span time and outlier conditions to better suit with their environment.
-known_false_positives: It is possible that legitimate user/admin may modify a number of security groups
+description: The following analytic identifies unusual modifications to security groups
+  in your cloud environment by users, focusing on actions such as modifications, deletions,
+  or creations over 30-minute intervals. It leverages cloud infrastructure logs and
+  calculates the standard deviation for each user, using the 3-sigma rule to detect
+  anomalies. This activity is significant as it may indicate a compromised account
+  or insider threat. If confirmed malicious, attackers could alter security group
+  configurations, potentially exposing sensitive resources or disrupting services.
+search: '| tstats dc(All_Changes.object) as unique_security_groups values(All_Changes.src)
+  as src values(All_Changes.user_type) as user_type values(All_Changes.object_category)
+  as object_category values(All_Changes.object) as objects values(All_Changes.action)
+  as action  values(All_Changes.user_agent) as user_agent values(All_Changes.command)
+  as command from datamodel=Change WHERE All_Changes.object_category = "security_group"
+  (All_Changes.action = modified OR All_Changes.action = deleted OR All_Changes.action
+  = created)  by All_Changes.user  _time span=30m |  `drop_dm_object_name("All_Changes")`
+  | eventstats avg(unique_security_groups) as avg_changes , stdev(unique_security_groups)
+  as std_changes by user | eval upperBound=(avg_changes+std_changes*3) | eval isOutlier=if(unique_security_groups
+  > 2 and unique_security_groups >= upperBound, 1, 0) | where isOutlier=1| `cloud_security_groups_modifications_by_user_filter`'
+how_to_implement: This search requries the Cloud infrastructure logs such as AWS Cloudtrail,
+  GCP Pubsub Message logs, Azure Audit logs to be ingested into an accelerated Change
+  datamodel. It is also recommended that users can try different combinations of the
+  `bucket` span time and outlier conditions to better suit with their environment.
+known_false_positives: It is possible that legitimate user/admin may modify a number
+  of security groups
 references:
 - https://attack.mitre.org/techniques/T1578/005/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.object_id
-  - All_Changes.action
-  - All_Changes.status
-  - All_Changes.object_category
-  - All_Changes.user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1578.005/aws_authorize_security_group/aws_authorize_security_group.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1578.005/aws_authorize_security_group/aws_authorize_security_group.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_aws_console_login_by_new_user.yml b/detections/cloud/detect_aws_console_login_by_new_user.yml
index 910ed6e79a..33962173f6 100644
--- a/detections/cloud/detect_aws_console_login_by_new_user.yml
+++ b/detections/cloud/detect_aws_console_login_by_new_user.yml
@@ -5,12 +5,32 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects AWS console login events by new users. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen users based on ARN values. This detection is significant because a new user logging into the AWS console could indicate the creation of new accounts or potential unauthorized access. If confirmed malicious, this activity could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the cloud environment.
+description: The following analytic detects AWS console login events by new users.
+  It leverages AWS CloudTrail events and compares them against a lookup file of previously
+  seen users based on ARN values. This detection is significant because a new user
+  logging into the AWS console could indicate the creation of new accounts or potential
+  unauthorized access. If confirmed malicious, this activity could lead to unauthorized
+  access to AWS resources, data exfiltration, or further exploitation within the cloud
+  environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)` | join user type=outer [ | inputlookup previously_seen_users_console_logins | stats min(firstTime) as earliestseen by user] | eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "First Time Logging into AWS Console", "Previously Seen User") | where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_aws_console_login_by_new_user_filter`'
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.
-known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
+search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
+  where Authentication.signature=ConsoleLogin by Authentication.user | `drop_dm_object_name(Authentication)`
+  | join user type=outer [ | inputlookup previously_seen_users_console_logins | stats
+  min(firstTime) as earliestseen by user] | eval userStatus=if(earliestseen >= relative_time(now(),
+  "-24h@h") OR isnull(earliestseen), "First Time Logging into AWS Console", "Previously
+  Seen User") | where userStatus="First Time Logging into AWS Console" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_aws_console_login_by_new_user_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
 references: []
 tags:
   analytic_story:
@@ -33,14 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml
index 0c08032447..63455d61da 100644
--- a/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml
+++ b/detections/cloud/detect_aws_console_login_by_user_from_new_city.yml
@@ -5,12 +5,37 @@ date: '2024-10-17'
 author: Bhavin Patel, Eric McGinnis Splunk
 status: production
 type: Hunting
-description: The following analytic identifies AWS console login events by users from a new city within the last hour. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen user locations. This activity is significant for a SOC as it may indicate unauthorized access or credential compromise, especially if the login originates from an unusual location. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the cloud environment.
+description: The following analytic identifies AWS console login events by users from
+  a new city within the last hour. It leverages AWS CloudTrail events and compares
+  them against a lookup file of previously seen user locations. This activity is significant
+  for a SOC as it may indicate unauthorized access or credential compromise, especially
+  if the login originates from an unusual location. If confirmed malicious, this could
+  lead to unauthorized access to AWS resources, data exfiltration, or further exploitation
+  within the cloud environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename City as justSeenCity | table firstTime lastTime user justSeenCity | join user type=outer [| inputlookup previously_seen_users_console_logins | rename City as previouslySeenCity | stats min(firstTime) AS earliestseen by user previouslySeenCity | fields earliestseen user previouslySeenCity] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") | where userCity = "New City" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenCity justSeenCity userCity | `detect_aws_console_login_by_user_from_new_city_filter`'
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro.
-known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
+search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
+  where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
+  | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename
+  City as justSeenCity | table firstTime lastTime user justSeenCity | join user type=outer
+  [| inputlookup previously_seen_users_console_logins | rename City as previouslySeenCity
+  | stats min(firstTime) AS earliestseen by user previouslySeenCity | fields earliestseen
+  user previouslySeenCity] | eval userCity=if(firstTime >= relative_time(now(), "-24h@h"),
+  "New City","Previously Seen City") | where userCity = "New City" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenCity
+  justSeenCity userCity | `detect_aws_console_login_by_user_from_new_city_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in AWS CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines. You can also provide additional filtering for
+  this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter`
+  macro.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
 references: []
 tags:
   analytic_story:
@@ -21,7 +46,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 30
-  message: User $user$ is logging into the AWS console from City $City$ for the first time
+  message: User $user$ is logging into the AWS console from City $City$ for the first
+    time
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -35,16 +61,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.user
-  - Authentication.src
   security_domain: threat
-  manual_test: This search needs the baseline to be run first to create a lookup. It also requires that the timestamps in the dataset be updated.
+  manual_test: This search needs the baseline to be run first to create a lookup.
+    It also requires that the timestamps in the dataset be updated.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml
index 821253a524..b9fc52c316 100644
--- a/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml
+++ b/detections/cloud/detect_aws_console_login_by_user_from_new_country.yml
@@ -5,12 +5,37 @@ date: '2024-10-17'
 author: Bhavin Patel, Eric McGinnis Splunk
 status: production
 type: Hunting
-description: The following analytic identifies AWS console login events by users from a new country. It leverages AWS CloudTrail events and compares them against a lookup file of previously seen users and their login locations. This activity is significant because logins from new countries can indicate potential unauthorized access or compromised accounts. If confirmed malicious, this could lead to unauthorized access to AWS resources, data exfiltration, or further exploitation within the AWS environment.
+description: The following analytic identifies AWS console login events by users from
+  a new country. It leverages AWS CloudTrail events and compares them against a lookup
+  file of previously seen users and their login locations. This activity is significant
+  because logins from new countries can indicate potential unauthorized access or
+  compromised accounts. If confirmed malicious, this could lead to unauthorized access
+  to AWS resources, data exfiltration, or further exploitation within the AWS environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename Country as justSeenCountry | table firstTime lastTime user justSeenCountry | join user type=outer [| inputlookup previously_seen_users_console_logins | rename Country as previouslySeenCountry | stats min(firstTime) AS earliestseen by user previouslySeenCountry | fields earliestseen user previouslySeenCountry] | eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | where userCountry = "New Country" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenCountry justSeenCountry userCountry | `detect_aws_console_login_by_user_from_new_country_filter`'
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro.
-known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
+search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
+  where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
+  | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename
+  Country as justSeenCountry | table firstTime lastTime user justSeenCountry | join
+  user type=outer [| inputlookup previously_seen_users_console_logins | rename Country
+  as previouslySeenCountry | stats min(firstTime) AS earliestseen by user previouslySeenCountry
+  | fields earliestseen user previouslySeenCountry] | eval userCountry=if(firstTime
+  >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") | where
+  userCountry = "New Country" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | table firstTime lastTime user previouslySeenCountry justSeenCountry userCountry
+  | `detect_aws_console_login_by_user_from_new_country_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in AWS CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines. You can also provide additional filtering for
+  this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter`
+  macro.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
 references: []
 tags:
   analytic_story:
@@ -21,7 +46,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 70
-  message: User $user$ is logging into the AWS console from Country $Country$ for the first time
+  message: User $user$ is logging into the AWS console from Country $Country$ for
+    the first time
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -35,16 +61,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.user
-  - Authentication.src
   security_domain: threat
-  manual_test: This search needs the baseline to be run first to create a lookup. It also requires that the timestamps in the dataset be updated.
+  manual_test: This search needs the baseline to be run first to create a lookup.
+    It also requires that the timestamps in the dataset be updated.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml b/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml
index de115f7510..8afa8f0e20 100644
--- a/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml
+++ b/detections/cloud/detect_aws_console_login_by_user_from_new_region.yml
@@ -5,12 +5,38 @@ date: '2024-10-17'
 author: Bhavin Patel, Eric McGinnis Splunk
 status: production
 type: Hunting
-description: The following analytic identifies AWS console login attempts by users from a new region. It leverages AWS CloudTrail events and compares current login regions against a baseline of previously seen regions for each user. This activity is significant as it may indicate unauthorized access attempts or compromised credentials. If confirmed malicious, an attacker could gain unauthorized access to AWS resources, potentially leading to data breaches, resource manipulation, or further lateral movement within the cloud environment.
+description: The following analytic identifies AWS console login attempts by users
+  from a new region. It leverages AWS CloudTrail events and compares current login
+  regions against a baseline of previously seen regions for each user. This activity
+  is significant as it may indicate unauthorized access attempts or compromised credentials.
+  If confirmed malicious, an attacker could gain unauthorized access to AWS resources,
+  potentially leading to data breaches, resource manipulation, or further lateral
+  movement within the cloud environment.
 data_source:
 - AWS CloudTrail
-search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename Region as justSeenRegion | table firstTime lastTime user justSeenRegion | join user type=outer [| inputlookup previously_seen_users_console_logins | rename Region as previouslySeenRegion | stats min(firstTime) AS earliestseen by user previouslySeenRegion | fields earliestseen user previouslySeenRegion] | eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | where userRegion= "New Region" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime lastTime user previouslySeenRegion justSeenRegion userRegion | `detect_aws_console_login_by_user_from_new_region_filter`'
-how_to_implement: You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro.
-known_false_positives: When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
+search: '| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication
+  where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src
+  | iplocation Authentication.src | `drop_dm_object_name(Authentication)` | rename
+  Region as justSeenRegion | table firstTime lastTime user justSeenRegion | join user
+  type=outer [| inputlookup previously_seen_users_console_logins | rename Region as
+  previouslySeenRegion | stats min(firstTime) AS earliestseen by user previouslySeenRegion
+  | fields earliestseen user previouslySeenRegion] | eval userRegion=if(firstTime
+  >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") | where
+  userRegion= "New Region" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | table firstTime lastTime user previouslySeenRegion justSeenRegion userRegion |
+  `detect_aws_console_login_by_user_from_new_region_filter`'
+how_to_implement: You must install and configure the Splunk Add-on for AWS (version
+  5.1.0 or later) and Enterprise Security 6.2, which contains the required updates
+  to the Authentication data model for cloud use cases. Run the `Previously Seen Users
+  in AWS CloudTrail - Initial` support search only once to create a baseline of previously
+  seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail
+  - Update` hourly (or more frequently depending on how often you run the detection
+  searches) to refresh the baselines. You can also provide additional filtering for
+  this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter`
+  macro.
+known_false_positives: When a legitimate new user logins for the first time, this
+  activity will be detected. Check how old the account is and verify that the user
+  activity is legitimate.
 references: []
 tags:
   analytic_story:
@@ -21,7 +47,8 @@ tags:
   asset_type: AWS Instance
   confidence: 60
   impact: 60
-  message: User $user$ is logging into the AWS console from Region $Region$ for the first time
+  message: User $user$ is logging into the AWS console from Region $Region$ for the
+    first time
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -35,16 +62,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.signature
-  - Authentication.user
-  - Authentication.src
   security_domain: threat
-  manual_test: This search needs the baseline to be run first to create a lookup. It also requires that the timestamps in the dataset be updated.
+  manual_test: This search needs the baseline to be run first to create a lookup.
+    It also requires that the timestamps in the dataset be updated.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml b/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml
index 5fb56a5a12..faa5323732 100644
--- a/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml
+++ b/detections/cloud/detect_gcp_storage_access_from_a_new_ip.yml
@@ -5,11 +5,40 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies access to GCP Storage buckets from new or previously unseen remote IP addresses. It leverages GCP Storage bucket-access logs ingested via Cloud Pub/Sub and compares current access events against a lookup table of previously seen IP addresses. This activity is significant as it may indicate unauthorized access or potential reconnaissance by an attacker. If confirmed malicious, this could lead to data exfiltration, unauthorized data manipulation, or further compromise of the GCP environment.
+description: The following analytic identifies access to GCP Storage buckets from
+  new or previously unseen remote IP addresses. It leverages GCP Storage bucket-access
+  logs ingested via Cloud Pub/Sub and compares current access events against a lookup
+  table of previously seen IP addresses. This activity is significant as it may indicate
+  unauthorized access or potential reconnaissance by an attacker. If confirmed malicious,
+  this could lead to data exfiltration, unauthorized data manipulation, or further
+  compromise of the GCP environment.
 data_source: []
-search: '`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri | outputlookup previously_seen_gcp_storage_access_from_remote_ip | eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table  first_time last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter`'
-how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour.  It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets.
-known_false_positives: GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours.
+search: '`google_gcp_pubsub_message` | multikv | rename sc_status_ as status | rename
+  cs_object_ as bucket_name | rename c_ip_ as remote_ip | rename cs_uri_ as request_uri
+  | rename cs_method_ as operation | search status="\"200\"" | stats earliest(_time)
+  as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri
+  | table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri | inputlookup
+  append=t previously_seen_gcp_storage_access_from_remote_ip | stats min(firstTime)
+  as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri
+  | outputlookup previously_seen_gcp_storage_access_from_remote_ip | eval newIP=if(firstTime
+  >= relative_time(now(),"-70m@m"), 1, 0) | where newIP=1 | eval first_time=strftime(firstTime,"%m/%d/%y
+  %H:%M:%S") | eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") | table  first_time
+  last_time bucket_name remote_ip operation request_uri | `detect_gcp_storage_access_from_a_new_ip_filter`'
+how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform,
+  setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and
+  logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
+  In order to capture public GCP Storage Bucket access logs, you must also enable
+  storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These
+  logs are deposited into the nominated Storage Bucket on an hourly basis and typically
+  show up by 15 minutes past the hour.  It is recommended to configure any saved searches
+  or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes
+  past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv)
+  stores the previously seen access requests, and is used by this search to determine
+  any newly seen IP addresses accessing the Storage Buckets.
+known_false_positives: GCP Storage buckets can be accessed from any IP (if the ACLs
+  are open to allow it), as long as it can make a successful connection. This will
+  be a false postive, since the search is looking for a new IP within the past two
+  hours.
 references: []
 tags:
   analytic_story:
@@ -29,11 +58,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - sc_status_
-  - cs_object_
-  - c_ip_
-  - cs_uri_
-  - cs_method_
   security_domain: network
diff --git a/detections/cloud/detect_new_open_gcp_storage_buckets.yml b/detections/cloud/detect_new_open_gcp_storage_buckets.yml
index 5f7356274a..48a66b7a61 100644
--- a/detections/cloud/detect_new_open_gcp_storage_buckets.yml
+++ b/detections/cloud/detect_new_open_gcp_storage_buckets.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the creation of new open/public GCP Storage buckets. It leverages GCP PubSub events, specifically monitoring for the `storage.setIamPermissions` method and checks if the `allUsers` member is added. This activity is significant because open storage buckets can expose sensitive data to the public, posing a severe security risk. If confirmed malicious, an attacker could access, modify, or delete data within the bucket, leading to data breaches and potential compliance violations.
+description: The following analytic identifies the creation of new open/public GCP
+  Storage buckets. It leverages GCP PubSub events, specifically monitoring for the
+  `storage.setIamPermissions` method and checks if the `allUsers` member is added.
+  This activity is significant because open storage buckets can expose sensitive data
+  to the public, posing a severe security risk. If confirmed malicious, an attacker
+  could access, modify, or delete data within the bucket, leading to data breaches
+  and potential compliance violations.
 data_source: []
-search: '`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member | search (member=allUsers AND action=ADD) | table  _time, bucketName, src, user, location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter`'
-how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
-known_false_positives: While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group.
+search: '`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions
+  | spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action
+  | spath output=user path=data.protoPayload.authenticationInfo.principalEmail | spath
+  output=location path=data.protoPayload.resourceLocation.currentLocations{} | spath
+  output=src path=data.protoPayload.requestMetadata.callerIp | spath output=bucketName
+  path=data.protoPayload.resourceName | spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role
+  | spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member
+  | search (member=allUsers AND action=ADD) | table  _time, bucketName, src, user,
+  location, action, role, member | search `detect_new_open_gcp_storage_buckets_filter`'
+how_to_implement: This search relies on the Splunk Add-on for Google Cloud Platform,
+  setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and
+  logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
+known_false_positives: While this search has no known false positives, it is possible
+  that a GCP admin has legitimately created a public bucket for a specific purpose.
+  That said, GCP strongly advises against granting full control to the "allUsers"
+  group.
 references: []
 tags:
   analytic_story:
@@ -29,15 +48,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - data.resource.type
-  - data.protoPayload.methodName
-  - data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action
-  - data.protoPayload.authenticationInfo.principalEmail
-  - data.protoPayload.resourceLocation.currentLocations{}
-  - data.protoPayload.requestMetadata.callerIp
-  - data.protoPayload.resourceName
-  - data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role
-  - data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member
   security_domain: network
diff --git a/detections/cloud/detect_new_open_s3_buckets.yml b/detections/cloud/detect_new_open_s3_buckets.yml
index e849ac060a..12e35b9b66 100644
--- a/detections/cloud/detect_new_open_s3_buckets.yml
+++ b/detections/cloud/detect_new_open_s3_buckets.yml
@@ -5,20 +5,43 @@ date: '2024-09-30'
 author: Bhavin Patel, Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of open/public S3 buckets in AWS. It detects this activity by analyzing AWS CloudTrail events for `PutBucketAcl` actions where the access control list (ACL) grants permissions to all users or authenticated users. This activity is significant because open S3 buckets can expose sensitive data to unauthorized access, leading to data breaches. If confirmed malicious, an attacker could read, write, or fully control the contents of the bucket, potentially leading to data exfiltration or tampering.
+description: The following analytic identifies the creation of open/public S3 buckets
+  in AWS. It detects this activity by analyzing AWS CloudTrail events for `PutBucketAcl`
+  actions where the access control list (ACL) grants permissions to all users or authenticated
+  users. This activity is significant because open S3 buckets can expose sensitive
+  data to unauthorized access, leading to data breaches. If confirmed malicious, an
+  attacker could read, write, or fully control the contents of the bucket, potentially
+  leading to data exfiltration or tampering.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw "(?<json_field>{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") | rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_filter`'
+search: '`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl | rex field=_raw
+  "(?<json_field>{.+})" | spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{}
+  | search grantees=* | mvexpand grantees | spath input=grantees output=uri path=Grantee.URI
+  | spath input=grantees output=permission path=Permission | search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers")
+  | search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") |
+  rename requestParameters.bucketName AS bucketName | stats count min(_time) as firstTime
+  max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission
+  bucketName | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `detect_new_open_s3_buckets_filter`'
 how_to_implement: You must install the AWS App for Splunk.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created a public bucket for a specific purpose.
+  That said, AWS strongly advises against granting full control to the "All Users"
+  group.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user_arn$" and "$bucketName$"
-  search: '%original_detection_search% | search  user_arn = "$user_arn$" bucketName = "$bucketName$"'
+  search: '%original_detection_search% | search  user_arn = "$user_arn$" bucketName
+    = "$bucketName$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_arn$" and "$bucketName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$", "$bucketName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_arn$",
+    "$bucketName$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +50,8 @@ tags:
   asset_type: S3 Bucket
   confidence: 80
   impact: 60
-  message: User $user_arn$ has created an open/public bucket $bucketName$ with the following permissions $permission$
+  message: User $user_arn$ has created an open/public bucket $bucketName$ with the
+    following permissions $permission$
   mitre_attack_id:
   - T1530
   observable:
@@ -43,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventSource
-  - eventName
-  - requestParameters.bucketName
-  - user_arn
-  - userIdentity.principalId
-  - userAgent
-  - uri
-  - permission
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml b/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml
index ce6823b56a..1583c21c56 100644
--- a/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml
+++ b/detections/cloud/detect_new_open_s3_buckets_over_aws_cli.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of open/public S3 buckets via the AWS CLI. It leverages AWS CloudTrail logs to identify events where a user has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers." This activity is significant because open S3 buckets can expose sensitive data to unauthorized users, leading to data breaches. If confirmed malicious, an attacker could gain unauthorized access to potentially sensitive information stored in the S3 bucket, posing a significant security risk.
+description: The following analytic detects the creation of open/public S3 buckets
+  via the AWS CLI. It leverages AWS CloudTrail logs to identify events where a user
+  has set bucket permissions to allow access to "AuthenticatedUsers" or "AllUsers."
+  This activity is significant because open S3 buckets can expose sensitive data to
+  unauthorized users, leading to data breaches. If confirmed malicious, an attacker
+  could gain unauthorized access to potentially sensitive information stored in the
+  S3 bucket, posing a significant security risk.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter`'
-how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
-known_false_positives: While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.
+search: '`cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli*
+  ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp
+  IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write
+  IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp
+  IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control
+  IN ("*AuthenticatedUsers","*AllUsers") | rename requestParameters.bucketName AS
+  bucketName | fillnull | stats count min(_time) as firstTime max(_time) as lastTime
+  by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read
+  requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write
+  requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_new_open_s3_buckets_over_aws_cli_filter`'
+how_to_implement: The Splunk AWS Add-on and Splunk App for AWS is required to utilize
+  this data. The search requires AWS Cloudtrail logs.
+known_false_positives: While this search has no known false positives, it is possible
+  that an AWS admin has legitimately created a public bucket for a specific purpose.
+  That said, AWS strongly advises against granting full control to the "All Users"
+  group.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$userIdentity.userName$"
@@ -18,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$userIdentity.userName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userIdentity.userName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userIdentity.userName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +52,10 @@ tags:
   asset_type: S3 Bucket
   confidence: 80
   impact: 60
-  message: User $userIdentity.userName$ has created an open/public bucket $bucketName$ using AWS CLI with the following permissions - $requestParameters.accessControlList.x-amz-grant-read$ $requestParameters.accessControlList.x-amz-grant-read-acp$ $requestParameters.accessControlList.x-amz-grant-write$ $requestParameters.accessControlList.x-amz-grant-write-acp$ $requestParameters.accessControlList.x-amz-grant-full-control$
+  message: User $userIdentity.userName$ has created an open/public bucket $bucketName$
+    using AWS CLI with the following permissions - $requestParameters.accessControlList.x-amz-grant-read$
+    $requestParameters.accessControlList.x-amz-grant-read-acp$ $requestParameters.accessControlList.x-amz-grant-write$
+    $requestParameters.accessControlList.x-amz-grant-write-acp$ $requestParameters.accessControlList.x-amz-grant-full-control$
   mitre_attack_id:
   - T1530
   observable:
@@ -39,23 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventSource
-  - eventName
-  - requestParameters.accessControlList.x-amz-grant-read-acp
-  - requestParameters.accessControlList.x-amz-grant-write
-  - requestParameters.accessControlList.x-amz-grant-write-acp
-  - requestParameters.accessControlList.x-amz-grant-full-control
-  - requestParameters.bucketName
-  - userIdentity.userName
-  - userIdentity.principalId
-  - userAgent
-  - bucketName
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json
     sourcetype: aws:cloudtrail
     source: aws_cloudtrail
diff --git a/detections/cloud/detect_s3_access_from_a_new_ip.yml b/detections/cloud/detect_s3_access_from_a_new_ip.yml
index dfeb465493..8621cec431 100644
--- a/detections/cloud/detect_s3_access_from_a_new_ip.yml
+++ b/detections/cloud/detect_s3_access_from_a_new_ip.yml
@@ -5,11 +5,31 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies access to an S3 bucket from a new or previously unseen remote IP address. It leverages S3 bucket-access logs, specifically focusing on successful access events (http_status=200). This activity is significant because access from unfamiliar IP addresses could indicate unauthorized access or potential data exfiltration attempts. If confirmed malicious, this activity could lead to unauthorized data access, data theft, or further exploitation of the compromised S3 bucket, posing a significant risk to sensitive information stored within the bucket.
+description: The following analytic identifies access to an S3 bucket from a new or
+  previously unseen remote IP address. It leverages S3 bucket-access logs, specifically
+  focusing on successful access events (http_status=200). This activity is significant
+  because access from unfamiliar IP addresses could indicate unauthorized access or
+  potential data exfiltration attempts. If confirmed malicious, this activity could
+  lead to unauthorized data access, data theft, or further exploitation of the compromised
+  S3 bucket, posing a significant risk to sensitive information stored within the
+  bucket.
 data_source: []
-search: '`aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names.
-known_false_positives: S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour
+search: '`aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_status=200
+  | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip
+  | inputlookup append=t previously_seen_S3_access_from_remote_ip | stats min(firstTime)
+  as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup
+  previously_seen_S3_access_from_remote_ip| eval newIP=if(firstTime >= relative_time(now(),
+  "-70m@m"), 1, 0) | where newIP=1 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip
+  | table _time bucket_name src_ip City Country operation request_uri | `detect_s3_access_from_a_new_ip_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access
+  logs' inputs. This search works best when you run the "Previously Seen S3 Bucket
+  Access by Remote IP" support search once to create a history of previously seen
+  remote IPs and bucket names.
+known_false_positives: S3 buckets can be accessed from any IP, as long as it can make
+  a successful connection. This will be a false postive, since the search is looking
+  for a new IP within the past hour
 references: []
 tags:
   analytic_story:
@@ -33,9 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_status
-  - bucket_name
-  - remote_ip
   security_domain: network
diff --git a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml
index 8510bb6d60..c7ce6157e5 100644
--- a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml
+++ b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_ec2_instance.yml
@@ -5,11 +5,27 @@ date: '2024-10-09'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a spike in the number of AWS Security Hub alerts for an EC2 instance within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect anomalies. This activity is significant for a SOC as a sudden increase in alerts may indicate potential security incidents or misconfigurations requiring immediate attention. If confirmed malicious, this could signify an ongoing attack, leading to unauthorized access, data exfiltration, or disruption of services on the affected EC2 instance.
+description: The following analytic identifies a spike in the number of AWS Security
+  Hub alerts for an EC2 instance within a 4-hour interval. It leverages AWS Security
+  Hub findings data, calculating the average and standard deviation of alerts to detect
+  anomalies. This activity is significant for a SOC as a sudden increase in alerts
+  may indicate potential security incidents or misconfigurations requiring immediate
+  attention. If confirmed malicious, this could signify an ongoing attack, leading
+  to unauthorized access, data exfiltration, or disruption of services on the affected
+  EC2 instance.
 data_source:
 - AWS Security Hub
-search: '`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
+search: '`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance | bucket span=4h
+  _time | stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account)
+  as vendor_account values(vendor_region) as vendor_region values(severity) as severity
+  by _time dest | eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev
+  | eval threshold_value = 3 | eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev
+  * threshold_value), 1, 0) | search isOutlier=1 | table _time dest alerts Title Types
+  vendor_account vendor_region severity isOutlier total_alerts_avg | `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security
+  Hub inputs. The threshold_value should be tuned to your environment and schedule
+  these searches according to the bucket span interval.
 known_false_positives: None
 references: []
 drilldown_searches:
@@ -18,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,19 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Resources{}.Type
-  - Title
-  - Types{}
-  - vendor_account
-  - vendor_region
-  - severity
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json
     sourcetype: aws:securityhub:finding
     source: aws_securityhub_finding
diff --git a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml
index c1eeaf8491..d5f499f0d3 100644
--- a/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml
+++ b/detections/cloud/detect_spike_in_aws_security_hub_alerts_for_user.yml
@@ -5,11 +5,24 @@ date: '2024-10-09'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies a spike in the number of AWS Security Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect significant deviations. This activity is significant as a sudden increase in alerts for a specific user may indicate suspicious behavior or a potential security incident. If confirmed malicious, this could signify an ongoing attack, unauthorized access, or misuse of IAM credentials, potentially leading to data breaches or further exploitation.
+description: The following analytic identifies a spike in the number of AWS Security
+  Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security
+  Hub findings data, calculating the average and standard deviation of alerts to detect
+  significant deviations. This activity is significant as a sudden increase in alerts
+  for a specific user may indicate suspicious behavior or a potential security incident.
+  If confirmed malicious, this could signify an ongoing attack, unauthorized access,
+  or misuse of IAM credentials, potentially leading to data breaches or further exploitation.
 data_source:
 - AWS Security Hub
-search: '`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
+search: '`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser | rename
+  findings{}.Resources{}.Id as user | bucket span=4h _time | stats count AS alerts
+  by _time user | eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev
+  | eval threshold_value = 2 | eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev
+  * threshold_value), 1, 0) | search isOutlier=1 | table _time user alerts |`detect_spike_in_aws_security_hub_alerts_for_user_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security
+  Hub inputs. The threshold_value should be tuned to your environment and schedule
+  these searches according to the bucket span interval.
 known_false_positives: None
 references: []
 tags:
@@ -29,9 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - findings{}.Resources{}.Type
-  - indings{}.Resources{}.Id
-  - user
   security_domain: network
diff --git a/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml b/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml
index 7e92cd2a4b..ba55bcc112 100644
--- a/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml
+++ b/detections/cloud/detect_spike_in_blocked_outbound_traffic_from_your_aws.yml
@@ -5,11 +5,44 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies spikes in blocked outbound network connections originating from within your AWS environment. It leverages VPC Flow Logs data from CloudWatch, focusing on blocked actions from internal IP ranges to external destinations. This detection is significant as it can indicate potential exfiltration attempts or misconfigurations leading to data leakage. If confirmed malicious, such activity could allow attackers to bypass network defenses, leading to unauthorized data transfer or communication with malicious external entities.
+description: The following analytic identifies spikes in blocked outbound network
+  connections originating from within your AWS environment. It leverages VPC Flow
+  Logs data from CloudWatch, focusing on blocked actions from internal IP ranges to
+  external destinations. This detection is significant as it can indicate potential
+  exfiltration attempts or misconfigurations leading to data leakage. If confirmed
+  malicious, such activity could allow attackers to bypass network defenses, leading
+  to unauthorized data transfer or communication with malicious external entities.
 data_source: []
-search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as dest_ip, values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections.
-known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections.
+search: '`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12
+  OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow`
+  action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16)
+  ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  |
+  stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections
+  append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections
+  as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720
+  | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720))
+  | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections),
+  stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections),
+  numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip,
+  latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup
+  baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold
+  = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections)
+  AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip]
+  | stats values(dest_ip) as dest_ip, values(interface_id) as "resourceId" count as
+  numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip | `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow
+  logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
+  your environment. The `dataPointThreshold` variable is the number of data points
+  required to meet the definition of "spike." The `deviationThreshold` variable is
+  the number of standard deviations away from the mean that the value must be to be
+  considered a spike. This search works best when you run the "Baseline of Blocked
+  Outbound Connection" support search once to create a history of previously seen
+  blocked outbound connections.
+known_false_positives: The false-positive rate may vary based on the values of`dataPointThreshold`
+  and `deviationThreshold`. Additionally, false positives may result when AWS administrators
+  roll out policies enforcing network blocks, causing sudden increases in the number
+  of blocked outbound connections.
 references: []
 tags:
   analytic_story:
@@ -29,9 +62,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - action
-  - src_ip
-  - dest_ip
   security_domain: network
diff --git a/detections/cloud/detect_spike_in_s3_bucket_deletion.yml b/detections/cloud/detect_spike_in_s3_bucket_deletion.yml
index 10d0692f6f..693edb2f68 100644
--- a/detections/cloud/detect_spike_in_s3_bucket_deletion.yml
+++ b/detections/cloud/detect_spike_in_s3_bucket_deletion.yml
@@ -5,12 +5,41 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies a spike in API activity related to the deletion of S3 buckets in your AWS environment. It leverages AWS CloudTrail logs to detect anomalies by comparing current deletion activity against a historical baseline. This activity is significant as unusual spikes in S3 bucket deletions could indicate malicious actions such as data exfiltration or unauthorized data destruction. If confirmed malicious, this could lead to significant data loss, disruption of services, and potential exposure of sensitive information. Immediate investigation is required to determine the legitimacy of the activity.
+description: The following analytic identifies a spike in API activity related to
+  the deletion of S3 buckets in your AWS environment. It leverages AWS CloudTrail
+  logs to detect anomalies by comparing current deletion activity against a historical
+  baseline. This activity is significant as unusual spikes in S3 bucket deletions
+  could indicate malicious actions such as data exfiltration or unauthorized data
+  destruction. If confirmed malicious, this could lead to significant data loss, disruption
+  of services, and potential exposure of sensitive information. Immediate investigation
+  is required to determine the legitimacy of the activity.
 data_source:
 - AWS CloudTrail
-search: '`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter`'
-how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity.
-known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment.
+search: '`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket
+  | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup
+  s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn
+  | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720
+  | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720))
+  | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls,
+  stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1)
+  | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup
+  s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval
+  isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints
+  > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn |
+  table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName
+  path=requestParameters.bucketName | stats values(bucketName) as bucketName, count
+  as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user | `detect_spike_in_s3_bucket_deletion_filter`'
+how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail
+  inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
+  your environment. The `dataPointThreshold` variable is the minimum number of data
+  points required to have a statistically significant amount of data to determine.
+  The `deviationThreshold` variable is the number of standard deviations away from
+  the mean that the value must be to be considered a spike. This search works best
+  when you run the "Baseline of S3 Bucket deletion activity by ARN" support search
+  once to create a baseline of previously seen S3 bucket-deletion activity.
+known_false_positives: Based on the values of`dataPointThreshold` and `deviationThreshold`,
+  the false positive rate may vary. Please modify this according the your environment.
 references: []
 tags:
   analytic_story:
@@ -30,8 +59,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.arn
   security_domain: network
diff --git a/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml b/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml
index 45271bff9f..ea50b025e8 100644
--- a/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml
+++ b/detections/cloud/gcp_authentication_failed_during_mfa_challenge.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects failed authentication attempts during the Multi-Factor Authentication (MFA) challenge on a Google Cloud Platform (GCP) tenant. It uses Google Workspace login failure events to identify instances where MFA methods were challenged but not successfully completed. This activity is significant as it may indicate an adversary attempting to access an account with compromised credentials despite MFA protection. If confirmed malicious, this could lead to unauthorized access attempts, potentially compromising sensitive data and resources within the GCP environment.
+description: The following analytic detects failed authentication attempts during
+  the Multi-Factor Authentication (MFA) challenge on a Google Cloud Platform (GCP)
+  tenant. It uses Google Workspace login failure events to identify instances where
+  MFA methods were challenged but not successfully completed. This activity is significant
+  as it may indicate an adversary attempting to access an account with compromised
+  credentials despite MFA protection. If confirmed malicious, this could lead to unauthorized
+  access attempts, potentially compromising sensitive data and resources within the
+  GCP environment.
 data_source:
 - Google Workspace login_failure
-search: '`gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | stats count min(_time) as firstTime max(_time) as lastTime by user, src_ip, login_challenge_method | `gcp_authentication_failed_during_mfa_challenge_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events.
-known_false_positives: Legitimate users may miss to reply the MFA challenge within the time window or deny it by mistake.
+search: '`gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | stats
+  count min(_time) as firstTime max(_time) as lastTime by user, src_ip, login_challenge_method
+  | `gcp_authentication_failed_during_mfa_challenge_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Google
+  Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows
+  Splunk administrators to collect Google Workspace event data in Splunk using Google
+  Workspace APIs. Specifically, this analytic leverages the User log events.
+known_false_positives: Legitimate users may miss to reply the MFA challenge within
+  the time window or deny it by mistake.
 references:
 - https://attack.mitre.org/techniques/T1621/
 - https://attack.mitre.org/techniques/T1078/004/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - src_ip
-  - login_challenge_method
-  - event.parameters{}.multiValue{}
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/gcp_failed_mfa/gws_login.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/gcp_failed_mfa/gws_login.log
     source: gws:reports:login
     sourcetype: gws:reports:login
diff --git a/detections/cloud/gcp_detect_gcploit_framework.yml b/detections/cloud/gcp_detect_gcploit_framework.yml
index 84b20f6802..5c9eff7abe 100644
--- a/detections/cloud/gcp_detect_gcploit_framework.yml
+++ b/detections/cloud/gcp_detect_gcploit_framework.yml
@@ -5,11 +5,24 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the use of the GCPloit exploitation framework within Google Cloud Platform (GCP). It detects specific GCP Pub/Sub messages with a function timeout of 539 seconds, which is indicative of GCPloit activity. This detection is significant as GCPloit can be used to escalate privileges and facilitate lateral movement from compromised high-privilege accounts. If confirmed malicious, this activity could allow attackers to gain unauthorized access, escalate their privileges, and move laterally within the GCP environment, potentially compromising sensitive data and critical resources.
+description: The following analytic identifies the use of the GCPloit exploitation
+  framework within Google Cloud Platform (GCP). It detects specific GCP Pub/Sub messages
+  with a function timeout of 539 seconds, which is indicative of GCPloit activity.
+  This detection is significant as GCPloit can be used to escalate privileges and
+  facilitate lateral movement from compromised high-privilege accounts. If confirmed
+  malicious, this activity could allow attackers to gain unauthorized access, escalate
+  their privileges, and move laterally within the GCP environment, potentially compromising
+  sensitive data and critical resources.
 data_source: []
-search: '`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent | `gcp_detect_gcploit_framework_filter`'
-how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
-known_false_positives: Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects
+search: '`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s
+  | table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail
+  data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location
+  http_user_agent | `gcp_detect_gcploit_framework_filter`'
+how_to_implement: You must install splunk GCP add-on. This search works with gcp:pubsub:message
+  logs
+known_false_positives: Payload.request.function.timeout value can possibly be match
+  with other functions or requests however the source user and target request account
+  may indicate an attempt to move laterally accross acounts or projects
 references:
 - https://github.com/dxa4481/gcploit
 - https://www.youtube.com/watch?v=Ml09R38jpok
@@ -31,14 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - data.protoPayload.request.function.timeout
-  - src
-  - src_user
-  - data.resource.labels.project_id
-  - data.protoPayload.request.function.serviceAccountEmail
-  - data.protoPayload.authorizationInfo{}.permission
-  - data.protoPayload.request.location
-  - http_user_agent
   security_domain: threat
diff --git a/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml b/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml
index 0149a23e30..51affff4ae 100644
--- a/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml
+++ b/detections/cloud/gcp_kubernetes_cluster_pod_scan_detection.yml
@@ -5,11 +5,22 @@ date: '2024-10-17'
 author: Rod Soto, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies unauthenticated requests to Kubernetes cluster pods. It detects this activity by analyzing GCP Pub/Sub messages for audit logs where the response status code is 401, indicating unauthorized access attempts. This activity is significant for a SOC because it may indicate reconnaissance or scanning attempts by an attacker trying to identify vulnerable pods. If confirmed malicious, this activity could lead to unauthorized access, allowing the attacker to exploit vulnerabilities within the cluster, potentially compromising sensitive data or gaining control over the Kubernetes environment.
+description: The following analytic identifies unauthenticated requests to Kubernetes
+  cluster pods. It detects this activity by analyzing GCP Pub/Sub messages for audit
+  logs where the response status code is 401, indicating unauthorized access attempts.
+  This activity is significant for a SOC because it may indicate reconnaissance or
+  scanning attempts by an attacker trying to identify vulnerable pods. If confirmed
+  malicious, this activity could lead to unauthorized access, allowing the attacker
+  to exploit vulnerabilities within the cluster, potentially compromising sensitive
+  data or gaining control over the Kubernetes environment.
 data_source: []
-search: '`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter`'
-how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
-known_false_positives: Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods  will provide context.
+search: '`google_gcp_pubsub_message` category=kube-audit |spath input=properties.log
+  |search responseStatus.code=401 |table sourceIPs{} userAgent verb requestURI responseStatus.reason
+  properties.pod | `gcp_kubernetes_cluster_pod_scan_detection_filter`'
+how_to_implement: You must install the GCP App for Splunk (version 2.0.0 or later),
+  then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
+known_false_positives: Not all unauthenticated requests are malicious, but frequency,
+  User Agent, source IPs and pods  will provide context.
 references: []
 tags:
   analytic_story:
@@ -29,14 +40,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - category
-  - responseStatus.code
-  - sourceIPs{}
-  - userAgent
-  - verb
-  - requestURI
-  - responseStatus.reason
-  - properties.pod
   security_domain: threat
diff --git a/detections/cloud/gcp_multi_factor_authentication_disabled.yml b/detections/cloud/gcp_multi_factor_authentication_disabled.yml
index 0c81fdc1da..b9e4f56335 100644
--- a/detections/cloud/gcp_multi_factor_authentication_disabled.yml
+++ b/detections/cloud/gcp_multi_factor_authentication_disabled.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Bhavin Patel, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects an attempt to disable multi-factor authentication (MFA) for a Google Cloud Platform (GCP) user. It leverages Google Workspace Admin log events, specifically the `UNENROLL_USER_FROM_STRONG_AUTH` command. This activity is significant because disabling MFA can allow an adversary to maintain persistence within the environment using a compromised account without raising suspicion. If confirmed malicious, this action could enable attackers to bypass additional security layers, potentially leading to unauthorized access, data exfiltration, or further exploitation of the compromised account.
+description: The following analytic detects an attempt to disable multi-factor authentication
+  (MFA) for a Google Cloud Platform (GCP) user. It leverages Google Workspace Admin
+  log events, specifically the `UNENROLL_USER_FROM_STRONG_AUTH` command. This activity
+  is significant because disabling MFA can allow an adversary to maintain persistence
+  within the environment using a compromised account without raising suspicion. If
+  confirmed malicious, this action could enable attackers to bypass additional security
+  layers, potentially leading to unauthorized access, data exfiltration, or further
+  exploitation of the compromised account.
 data_source:
 - Google Workspace
-search: '`gws_reports_admin` command=UNENROLL_USER_FROM_STRONG_AUTH | stats count min(_time) as firstTime max(_time) as lastTime by user, command, actor.email, status, id.applicationName, event.name, vendor_account, action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `gcp_multi_factor_authentication_disabled_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the Admin log events.
-known_false_positives: Legitimate use case may require for users to disable MFA. Filter as needed.
+search: '`gws_reports_admin` command=UNENROLL_USER_FROM_STRONG_AUTH | stats count
+  min(_time) as firstTime max(_time) as lastTime by user, command, actor.email, status,
+  id.applicationName, event.name, vendor_account, action | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `gcp_multi_factor_authentication_disabled_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Google
+  Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows
+  Splunk administrators to collect Google Workspace event data in Splunk using Google
+  Workspace APIs. Specifically, this analytic leverages the Admin log events.
+known_false_positives: Legitimate use case may require for users to disable MFA. Filter
+  as needed.
 references:
 - https://support.google.com/cloudidentity/answer/2537800?hl=en
 - https://attack.mitre.org/tactics/TA0005/
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,16 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - actor.email
-  - user
-  - command
-  - status
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/gcp_disable_mfa/gws_admin.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/gcp_disable_mfa/gws_admin.log
     source: gws:reports:admin
     sourcetype: gws:reports:admin
diff --git a/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml
index 723d00e574..113902a152 100644
--- a/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml
+++ b/detections/cloud/gcp_multiple_failed_mfa_requests_for_user.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects multiple failed multi-factor authentication (MFA) requests for a single user within a Google Cloud Platform (GCP) tenant. It triggers when 10 or more MFA prompts fail within a 5-minute window, using Google Workspace login failure events. This behavior is significant as it may indicate an adversary attempting to bypass MFA by bombarding the user with repeated authentication requests. If confirmed malicious, this activity could lead to unauthorized access, allowing attackers to compromise accounts and potentially escalate privileges within the GCP environment.
+description: The following analytic detects multiple failed multi-factor authentication
+  (MFA) requests for a single user within a Google Cloud Platform (GCP) tenant. It
+  triggers when 10 or more MFA prompts fail within a 5-minute window, using Google
+  Workspace login failure events. This behavior is significant as it may indicate
+  an adversary attempting to bypass MFA by bombarding the user with repeated authentication
+  requests. If confirmed malicious, this activity could lead to unauthorized access,
+  allowing attackers to compromise accounts and potentially escalate privileges within
+  the GCP environment.
 data_source:
 - Google Workspace login_failure
-search: '`gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | bucket span=5m _time | stats dc(_raw) AS mfa_prompts values(user) AS user by src_ip, login_challenge_method,  _time | where mfa_prompts >= 10 | `gcp_multiple_failed_mfa_requests_for_user_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `mfa_prompts` threshold values according to your environment. Specifically, this analytic leverages the User log events.
-known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
+search: '`gws_reports_login` event.name=login_failure `gws_login_mfa_methods` | bucket
+  span=5m _time | stats dc(_raw) AS mfa_prompts values(user) AS user by src_ip, login_challenge_method,  _time
+  | where mfa_prompts >= 10 | `gcp_multiple_failed_mfa_requests_for_user_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Google
+  Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows
+  Splunk administrators to collect Google Workspace event data in Splunk using Google
+  Workspace APIs. We would also recommend tuning the detection by adjusting the window
+  `span` and `mfa_prompts` threshold values according to your environment. Specifically,
+  this analytic leverages the User log events.
+known_false_positives: Multiple Failed MFA requests may also be a sign of authentication
+  or application issues. Filter as needed.
 references:
 - https://www.mandiant.com/resources/blog/russian-targeting-gov-business
 - https://arstechnica.com/information-technology/2022/03/lapsus-and-solar-winds-hackers-both-use-the-same-old-trick-to-bypass-mfa/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,12 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_gws/gws_login.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/multiple_failed_mfa_gws/gws_login.log
     source: gws:reports:login
     sourcetype: gws:reports:login
diff --git a/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml
index cdb717b8bc..20802f5009 100644
--- a/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml
+++ b/detections/cloud/gcp_multiple_users_failing_to_authenticate_from_ip.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a single source IP address failing to authenticate into more than 20 unique Google Workspace user accounts within a 5-minute window. It leverages Google Workspace login failure events to identify potential password spraying attacks. This activity is significant as it may indicate an adversary attempting to gain unauthorized access or elevate privileges within the Google Cloud Platform. If confirmed malicious, this behavior could lead to unauthorized access to sensitive resources, data breaches, or further exploitation within the environment.
+description: The following analytic detects a single source IP address failing to
+  authenticate into more than 20 unique Google Workspace user accounts within a 5-minute
+  window. It leverages Google Workspace login failure events to identify potential
+  password spraying attacks. This activity is significant as it may indicate an adversary
+  attempting to gain unauthorized access or elevate privileges within the Google Cloud
+  Platform. If confirmed malicious, this behavior could lead to unauthorized access
+  to sensitive resources, data breaches, or further exploitation within the environment.
 data_source:
 - Google Workspace login_failure
-search: '`gws_reports_login` event.type = login event.name = login_failure | bucket span=5m _time | stats count dc(user) AS unique_accounts values(user) as tried_accounts values(authentication_method) AS authentication_method earliest(_time) as firstTime latest(_time) as lastTime by _time event.name src app id.applicationName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |  where unique_accounts > 20 | `gcp_multiple_users_failing_to_authenticate_from_ip_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `unique_accounts` threshold values according to your environment. Specifically, this analytic leverages the User log events.
-known_false_positives: No known false postives for this detection. Please review this alert.
+search: '`gws_reports_login` event.type = login event.name = login_failure | bucket
+  span=5m _time | stats count dc(user) AS unique_accounts values(user) as tried_accounts
+  values(authentication_method) AS authentication_method earliest(_time) as firstTime
+  latest(_time) as lastTime by _time event.name src app id.applicationName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` |  where unique_accounts > 20 | `gcp_multiple_users_failing_to_authenticate_from_ip_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Google
+  Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows
+  Splunk administrators to collect Google Workspace event data in Splunk using Google
+  Workspace APIs. We would also recommend tuning the detection by adjusting the window
+  `span` and `unique_accounts` threshold values according to your environment. Specifically,
+  this analytic leverages the User log events.
+known_false_positives: No known false postives for this detection. Please review this
+  alert.
 references:
 - https://cloud.google.com/blog/products/identity-security/how-google-cloud-can-help-stop-credential-stuffing-attacks
 - https://www.slideshare.net/dafthack/ok-google-how-do-i-red-team-gsuite
@@ -22,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$tried_accounts$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: Google Cloud Platform tenant
   confidence: 90
   impact: 60
-  message: 'Multiple failed login attempts (Count: $unique_accounts$) against users seen from $src$'
+  message: 'Multiple failed login attempts (Count: $unique_accounts$) against users
+    seen from $src$'
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -51,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event.name
-  - event.type
-  - authentication_method
-  - app
-  - id.applicationName
-  - src
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json
     source: gws_login
     sourcetype: gws:reports:login
diff --git a/detections/cloud/gcp_successful_single_factor_authentication.yml b/detections/cloud/gcp_successful_single_factor_authentication.yml
index f256b15e96..0be72c6207 100644
--- a/detections/cloud/gcp_successful_single_factor_authentication.yml
+++ b/detections/cloud/gcp_successful_single_factor_authentication.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Bhavin Patel, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a successful single-factor authentication event against Google Cloud Platform (GCP) for an account without Multi-Factor Authentication (MFA) enabled. It uses Google Workspace login event data to detect instances where MFA is not utilized. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to GCP resources, potentially leading to data breaches, service disruptions, or further exploitation within the cloud environment.
+description: The following analytic identifies a successful single-factor authentication
+  event against Google Cloud Platform (GCP) for an account without Multi-Factor Authentication
+  (MFA) enabled. It uses Google Workspace login event data to detect instances where
+  MFA is not utilized. This activity is significant as it may indicate a misconfiguration,
+  policy violation, or potential account takeover attempt. If confirmed malicious,
+  an attacker could gain unauthorized access to GCP resources, potentially leading
+  to data breaches, service disruptions, or further exploitation within the cloud
+  environment.
 data_source:
 - Google Workspace login_success
-search: '`gws_reports_login` event.name=login_success NOT `gws_login_mfa_methods` | stats count min(_time) as firstTime max(_time) as lastTime by user, src_ip,  login_challenge_method, app, event.name, vendor_account, action |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `gcp_successful_single_factor_authentication_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events.
-known_false_positives: Although not recommended, certain users may be required without multi-factor authentication. Filter as needed
+search: '`gws_reports_login` event.name=login_success NOT `gws_login_mfa_methods`
+  | stats count min(_time) as firstTime max(_time) as lastTime by user, src_ip,  login_challenge_method,
+  app, event.name, vendor_account, action |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `gcp_successful_single_factor_authentication_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Google
+  Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows
+  Splunk administrators to collect Google Workspace event data in Splunk using Google
+  Workspace APIs. Specifically, this analytic leverages the User log events.
+known_false_positives: Although not recommended, certain users may be required without
+  multi-factor authentication. Filter as needed
 references:
 - https://attack.mitre.org/techniques/T1078/004/
 - https://support.google.com/a/answer/175197?hl=en
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event.name
-  - event.parameters{}.multiValue{}
-  - user
-  - src_ip
-  - login_challenge_method
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/gcp_single_factor_auth/gws_login.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/gcp_single_factor_auth/gws_login.log
     source: gws:reports:login
     sourcetype: gws:reports:login
diff --git a/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml b/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml
index d5c3bb9e00..b0b49b6c58 100644
--- a/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml
+++ b/detections/cloud/gcp_unusual_number_of_failed_authentications_from_ip.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a single source IP failing to authenticate into Google Workspace with multiple valid users, potentially indicating a Password Spraying attack. It uses Google Workspace login failure events and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, data breaches, or further exploitation within the environment.
+description: The following analytic identifies a single source IP failing to authenticate
+  into Google Workspace with multiple valid users, potentially indicating a Password
+  Spraying attack. It uses Google Workspace login failure events and calculates the
+  standard deviation for source IPs, applying the 3-sigma rule to detect unusual failed
+  authentication attempts. This activity is significant as it may signal an adversary
+  attempting to gain initial access or elevate privileges. If confirmed malicious,
+  this could lead to unauthorized access, data breaches, or further exploitation within
+  the environment.
 data_source:
 - Google Workspace login_failure
-search: '`gws_reports_login` event.type = login event.name = login_failure| bucket span=5m _time | stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts values(authentication_method) AS authentication_method by _time, src | eventstats  avg(unique_accounts) as ip_avg , stdev(unique_accounts) as ip_std by _time | eval  upperBound=(ip_avg+ip_std*3) | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | where isOutlier =1| `gcp_unusual_number_of_failed_authentications_from_ip_filter`'
-how_to_implement: You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. We would also recommend tuning the detection by adjusting the window `span` and `unique_accounts` threshold values according to your environment. Specifically, this analytic leverages the User log events.
-known_false_positives: No known false positives for this detection. Please review this alert
+search: '`gws_reports_login` event.type = login event.name = login_failure| bucket
+  span=5m _time | stats  dc(user_name) AS unique_accounts values(user_name) as tried_accounts
+  values(authentication_method) AS authentication_method by _time, src | eventstats  avg(unique_accounts)
+  as ip_avg , stdev(unique_accounts) as ip_std by _time | eval  upperBound=(ip_avg+ip_std*3)
+  | eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,
+  0) | where isOutlier =1| `gcp_unusual_number_of_failed_authentications_from_ip_filter`'
+how_to_implement: You must install the latest version of Splunk Add-on for Google
+  Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows
+  Splunk administrators to collect Google Workspace event data in Splunk using Google
+  Workspace APIs. We would also recommend tuning the detection by adjusting the window
+  `span` and `unique_accounts` threshold values according to your environment. Specifically,
+  this analytic leverages the User log events.
+known_false_positives: No known false positives for this detection. Please review
+  this alert
 references:
 - https://cloud.google.com/blog/products/identity-security/how-google-cloud-can-help-stop-credential-stuffing-attacks
 - https://www.slideshare.net/dafthack/ok-google-how-do-i-red-team-gsuite
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$tried_accounts$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$tried_accounts$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +54,8 @@ tags:
   asset_type: Google Cloud Platform tenant
   confidence: 90
   impact: 60
-  message: 'Unusual number of failed console login attempts (Count: $unique_accounts$) against users from IP Address - $src$'
+  message: 'Unusual number of failed console login attempts (Count: $unique_accounts$)
+    against users from IP Address - $src$'
   mitre_attack_id:
   - T1586
   - T1586.003
@@ -51,16 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event.name
-  - src
-  - event.type
-  - user_name
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/gcp_gws_multiple_login_failure/gws_login.json
     source: gws_login
     sourcetype: gws:reports:login
diff --git a/detections/cloud/gdrive_suspicious_file_sharing.yml b/detections/cloud/gdrive_suspicious_file_sharing.yml
index c338860bc5..3b88284d83 100644
--- a/detections/cloud/gdrive_suspicious_file_sharing.yml
+++ b/detections/cloud/gdrive_suspicious_file_sharing.yml
@@ -5,11 +5,27 @@ date: '2024-10-17'
 author: Rod Soto, Teoderick Contreras
 status: experimental
 type: Hunting
-description: The following analytic identifies suspicious file-sharing activity on Google Drive, where internal users share documents with more than 50 external recipients. It leverages GSuite Drive logs, focusing on changes in user access and filtering for emails outside the organization's domain. This activity is significant as it may indicate compromised accounts or intentional data exfiltration. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, data leaks, and potential compliance violations.
+description: The following analytic identifies suspicious file-sharing activity on
+  Google Drive, where internal users share documents with more than 50 external recipients.
+  It leverages GSuite Drive logs, focusing on changes in user access and filtering
+  for emails outside the organization's domain. This activity is significant as it
+  may indicate compromised accounts or intentional data exfiltration. If confirmed
+  malicious, this behavior could lead to unauthorized access to sensitive information,
+  data leaks, and potential compliance violations.
 data_source: []
-search: '`gsuite_drive` name=change_user_access | rename parameters.* as * | search email = "*@yourdomain.com" target_user != "*@yourdomain.com" | stats count values(owner) as owner values(target_user) as target values(doc_type) as doc_type values(doc_title) as doc_title dc(target_user) as distinct_target by src_ip email | where distinct_target > 50 | `gdrive_suspicious_file_sharing_filter`'
-how_to_implement: Need to implement Gsuite logging targeting Google suite drive activity. In order for the search to work for your environment please update `yourdomain.com` value in the query with the domain relavant for your organization.
-known_false_positives: This is an anomaly search, you must specify your domain in the parameters so it either filters outside domains or focus on internal domains. This search may also help investigate compromise of accounts. By looking at for example source ip addresses, document titles and abnormal number of shares and shared target users.
+search: '`gsuite_drive` name=change_user_access | rename parameters.* as * | search
+  email = "*@yourdomain.com" target_user != "*@yourdomain.com" | stats count values(owner)
+  as owner values(target_user) as target values(doc_type) as doc_type values(doc_title)
+  as doc_title dc(target_user) as distinct_target by src_ip email | where distinct_target
+  > 50 | `gdrive_suspicious_file_sharing_filter`'
+how_to_implement: Need to implement Gsuite logging targeting Google suite drive activity.
+  In order for the search to work for your environment please update `yourdomain.com`
+  value in the query with the domain relavant for your organization.
+known_false_positives: This is an anomaly search, you must specify your domain in
+  the parameters so it either filters outside domains or focus on internal domains.
+  This search may also help investigate compromise of accounts. By looking at for
+  example source ip addresses, document titles and abnormal number of shares and shared
+  target users.
 references:
 - https://www.splunk.com/en_us/blog/security/investigating-gsuite-phishing-attacks-with-splunk.html
 tags:
@@ -31,11 +47,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - src_ip
-  - parameters.owner
-  - parameters.target_user
-  - parameters.doc_title
-  - parameters.doc_type
   security_domain: threat
diff --git a/detections/cloud/github_actions_disable_security_workflow.yml b/detections/cloud/github_actions_disable_security_workflow.yml
index 1844ccadcc..dd9ce8bad7 100644
--- a/detections/cloud/github_actions_disable_security_workflow.yml
+++ b/detections/cloud/github_actions_disable_security_workflow.yml
@@ -5,11 +5,28 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the disabling of a security workflow in GitHub Actions. It leverages GitHub logs to identify when a workflow, excluding those named *security-testing*, is disabled following a push or pull request event. This activity is significant as it may indicate an attempt by an attacker to conceal malicious code by disabling security checks. If confirmed malicious, this could allow the attacker to introduce and persist undetected malicious code within the repository, potentially compromising the integrity and security of the codebase.
+description: The following analytic detects the disabling of a security workflow in
+  GitHub Actions. It leverages GitHub logs to identify when a workflow, excluding
+  those named *security-testing*, is disabled following a push or pull request event.
+  This activity is significant as it may indicate an attempt by an attacker to conceal
+  malicious code by disabling security checks. If confirmed malicious, this could
+  allow the attacker to introduce and persist undetected malicious code within the
+  repository, potentially compromising the integrity and security of the codebase.
 data_source:
 - GitHub
-search: '`github` workflow_run.event=push OR workflow_run.event=pull_request | stats values(workflow_run.name) as workflow_run.name by workflow_run.head_commit.id workflow_run.event workflow_run.head_branch workflow_run.head_commit.author.email workflow_run.head_commit.author.name workflow_run.head_commit.message workflow_run.head_commit.timestamp workflow_run.head_repository.full_name workflow_run.head_repository.owner.id workflow_run.head_repository.owner.login workflow_run.head_repository.owner.type | rename workflow_run.head_commit.author.name as user, workflow_run.head_commit.author.email as user_email, workflow_run.head_repository.full_name as repository, workflow_run.head_branch as branch | search NOT workflow_run.name=*security-testing* | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_actions_disable_security_workflow_filter`'
-how_to_implement: You must index GitHub logs. You can follow the url in reference to onboard GitHub logs. Sometimes GitHub logs are truncated, make sure to disable it in props.conf. Replace *security-testing* with the name of your security testing workflow in GitHub Actions.
+search: '`github` workflow_run.event=push OR workflow_run.event=pull_request | stats
+  values(workflow_run.name) as workflow_run.name by workflow_run.head_commit.id workflow_run.event
+  workflow_run.head_branch workflow_run.head_commit.author.email workflow_run.head_commit.author.name
+  workflow_run.head_commit.message workflow_run.head_commit.timestamp workflow_run.head_repository.full_name
+  workflow_run.head_repository.owner.id workflow_run.head_repository.owner.login workflow_run.head_repository.owner.type
+  | rename workflow_run.head_commit.author.name as user, workflow_run.head_commit.author.email
+  as user_email, workflow_run.head_repository.full_name as repository, workflow_run.head_branch
+  as branch | search NOT workflow_run.name=*security-testing* | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `github_actions_disable_security_workflow_filter`'
+how_to_implement: You must index GitHub logs. You can follow the url in reference
+  to onboard GitHub logs. Sometimes GitHub logs are truncated, make sure to disable
+  it in props.conf. Replace *security-testing* with the name of your security testing
+  workflow in GitHub Actions.
 known_false_positives: unknown
 references:
 - https://www.splunk.com/en_us/blog/tips-and-tricks/getting-github-data-with-webhooks.html
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$repository$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,23 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - workflow_run.event
-  - workflow_run.name
-  - workflow_run.head_commit.id
-  - workflow_run.event workflow_run.head_branch
-  - workflow_run.head_commit.author.email
-  - workflow_run.head_commit.author.name
-  - workflow_run.head_commit.message
-  - workflow_run.head_commit.timestamp
-  - workflow_run.head_repository.full_name
-  - workflow_run.head_repository.owner.id
-  - workflow_run.head_repository.owner.login
-  - workflow_run.head_repository.owner.type
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/github_actions_disable_security_workflow/github_actions_disable_security_workflow.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/github_actions_disable_security_workflow/github_actions_disable_security_workflow.log
     source: github
     sourcetype: aws:firehose:json
diff --git a/detections/cloud/github_commit_changes_in_master.yml b/detections/cloud/github_commit_changes_in_master.yml
index f717346c05..860e6f48d4 100644
--- a/detections/cloud/github_commit_changes_in_master.yml
+++ b/detections/cloud/github_commit_changes_in_master.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects direct commits or pushes to the master or main branch in a GitHub repository. It leverages GitHub logs to identify events where changes are made directly to these critical branches. This activity is significant because direct modifications to the master or main branch bypass the standard review process, potentially introducing unreviewed and harmful changes. If confirmed malicious, this could lead to unauthorized code execution, security vulnerabilities, or compromised project integrity.
+description: The following analytic detects direct commits or pushes to the master
+  or main branch in a GitHub repository. It leverages GitHub logs to identify events
+  where changes are made directly to these critical branches. This activity is significant
+  because direct modifications to the master or main branch bypass the standard review
+  process, potentially introducing unreviewed and harmful changes. If confirmed malicious,
+  this could lead to unauthorized code execution, security vulnerabilities, or compromised
+  project integrity.
 data_source:
 - GitHub
-search: '`github` branches{}.name = main OR branches{}.name = master |  stats count min(_time) as firstTime max(_time) as lastTime by commit.commit.author.email commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date repository.full_name | rename commit.author.login as user, repository.full_name as repository | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_commit_changes_in_master_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project.
+search: '`github` branches{}.name = main OR branches{}.name = master |  stats count
+  min(_time) as firstTime max(_time) as lastTime by commit.commit.author.email commit.author.login
+  commit.commit.message repository.pushed_at commit.commit.committer.date repository.full_name
+  | rename commit.author.login as user, repository.full_name as repository | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `github_commit_changes_in_master_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to github logs having the fork, commit, push metadata that can be use
+  to monitor the changes in a github project.
 known_false_positives: Admin can do changes directly to master branch
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$commit.commit.author.email$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$commit.commit.author.email$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$commit.commit.author.email$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,12 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1199/github_push_master/github_push_master.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1199/github_push_master/github_push_master.log
     source: github
     sourcetype: aws:firehose:json
diff --git a/detections/cloud/github_commit_in_develop.yml b/detections/cloud/github_commit_in_develop.yml
index 5a76f2a49e..c122802b36 100644
--- a/detections/cloud/github_commit_in_develop.yml
+++ b/detections/cloud/github_commit_in_develop.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects commits pushed directly to the 'develop' or 'main' branches in a GitHub repository. It leverages GitHub logs, focusing on commit metadata such as author details, commit messages, and timestamps. This activity is significant as direct commits to these branches can bypass the review process, potentially introducing unvetted changes. If confirmed malicious, this could lead to unauthorized code modifications, introducing vulnerabilities or backdoors into the codebase, and compromising the integrity of the development lifecycle.
+description: The following analytic detects commits pushed directly to the 'develop'
+  or 'main' branches in a GitHub repository. It leverages GitHub logs, focusing on
+  commit metadata such as author details, commit messages, and timestamps. This activity
+  is significant as direct commits to these branches can bypass the review process,
+  potentially introducing unvetted changes. If confirmed malicious, this could lead
+  to unauthorized code modifications, introducing vulnerabilities or backdoors into
+  the codebase, and compromising the integrity of the development lifecycle.
 data_source:
 - GitHub
-search: '`github` branches{}.name = main OR branches{}.name = develop |  stats count min(_time) as firstTime max(_time) as lastTime  by commit.author.html_url commit.commit.author.email commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_commit_in_develop_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to github logs having the fork, commit, push metadata that can be use to monitor the changes in a github project.
+search: '`github` branches{}.name = main OR branches{}.name = develop |  stats count
+  min(_time) as firstTime max(_time) as lastTime  by commit.author.html_url commit.commit.author.email
+  commit.author.login commit.commit.message repository.pushed_at commit.commit.committer.date
+  | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `github_commit_in_develop_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to github logs having the fork, commit, push metadata that can be use
+  to monitor the changes in a github project.
 known_false_positives: admin can do changes directly to develop branch
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$commit.commit.author.email$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$commit.commit.author.email$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$commit.commit.author.email$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,12 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1199/github_push_master/github_push_develop.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1199/github_push_master/github_push_develop.json
     source: github
     sourcetype: aws:firehose:json
diff --git a/detections/cloud/github_dependabot_alert.yml b/detections/cloud/github_dependabot_alert.yml
index 91e136c9e6..edb7e09236 100644
--- a/detections/cloud/github_dependabot_alert.yml
+++ b/detections/cloud/github_dependabot_alert.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the creation of GitHub Dependabot alerts, which indicate potential vulnerabilities in the codebase. It detects this activity by searching for logs with the "create" action and analyzing fields such as affected package, severity, and fixed version. This detection is significant for a SOC because it helps identify and address security risks in the codebase proactively. If confirmed malicious, these vulnerabilities could be exploited by attackers to gain unauthorized access or cause breaches, leading to potential data loss or system compromise.
+description: The following analytic identifies the creation of GitHub Dependabot alerts,
+  which indicate potential vulnerabilities in the codebase. It detects this activity
+  by searching for logs with the "create" action and analyzing fields such as affected
+  package, severity, and fixed version. This detection is significant for a SOC because
+  it helps identify and address security risks in the codebase proactively. If confirmed
+  malicious, these vulnerabilities could be exploited by attackers to gain unauthorized
+  access or cause breaches, leading to potential data loss or system compromise.
 data_source:
 - GitHub
-search: '`github` alert.id=* action=create | rename repository.full_name as repository, repository.html_url as repository_url sender.login as user | stats min(_time) as firstTime max(_time) as lastTime by action alert.affected_package_name alert.affected_range alert.created_at alert.external_identifier alert.external_reference alert.fixed_in alert.severity repository repository_url user | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_dependabot_alert_filter`'
-how_to_implement: You must index GitHub logs. You can follow the url in reference to onboard GitHub logs.
+search: '`github` alert.id=* action=create | rename repository.full_name as repository,
+  repository.html_url as repository_url sender.login as user | stats min(_time) as
+  firstTime max(_time) as lastTime by action alert.affected_package_name alert.affected_range
+  alert.created_at alert.external_identifier alert.external_reference alert.fixed_in
+  alert.severity repository repository_url user | eval phase="code" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `github_dependabot_alert_filter`'
+how_to_implement: You must index GitHub logs. You can follow the url in reference
+  to onboard GitHub logs.
 known_false_positives: unknown
 references:
 - https://www.splunk.com/en_us/blog/tips-and-tricks/getting-github-data-with-webhooks.html
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$repository$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,23 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - alert.id
-  - repository.full_name
-  - repository.html_url
-  - action
-  - alert.affected_package_name
-  - alert.affected_range
-  - alert.created_at
-  - alert.external_identifier
-  - alert.external_reference
-  - alert.fixed_in
-  - alert.severity
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/github_security_advisor_alert/github_security_advisor_alert.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/github_security_advisor_alert/github_security_advisor_alert.json
     sourcetype: aws:firehose:json
     source: github
diff --git a/detections/cloud/github_pull_request_from_unknown_user.yml b/detections/cloud/github_pull_request_from_unknown_user.yml
index 28e5805e3f..00774adeb2 100644
--- a/detections/cloud/github_pull_request_from_unknown_user.yml
+++ b/detections/cloud/github_pull_request_from_unknown_user.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects pull requests from unknown users on GitHub. It uses a Splunk query to identify pull requests where the user ID is not specified and cross-references these with a known users lookup table. This activity is significant because pull requests from unknown users can introduce malicious code or unauthorized changes to repositories. If confirmed malicious, this could lead to unauthorized code changes, data breaches, or other security incidents. Immediate steps include reviewing the author's name, repository, head reference, and commit message, and investigating any related artifacts and processes.
+description: The following analytic detects pull requests from unknown users on GitHub.
+  It uses a Splunk query to identify pull requests where the user ID is not specified
+  and cross-references these with a known users lookup table. This activity is significant
+  because pull requests from unknown users can introduce malicious code or unauthorized
+  changes to repositories. If confirmed malicious, this could lead to unauthorized
+  code changes, data breaches, or other security incidents. Immediate steps include
+  reviewing the author's name, repository, head reference, and commit message, and
+  investigating any related artifacts and processes.
 data_source:
 - GitHub
-search: '`github` check_suite.pull_requests{}.id=* | stats count by check_suite.head_commit.author.name repository.full_name check_suite.pull_requests{}.head.ref check_suite.head_commit.message | rename check_suite.head_commit.author.name as user repository.full_name as repository check_suite.pull_requests{}.head.ref as ref_head check_suite.head_commit.message as commit_message | search NOT `github_known_users` | eval phase="code" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `github_pull_request_from_unknown_user_filter`'
-how_to_implement: You must index GitHub logs. You can follow the url in reference to onboard GitHub logs.
+search: '`github` check_suite.pull_requests{}.id=* | stats count by check_suite.head_commit.author.name
+  repository.full_name check_suite.pull_requests{}.head.ref check_suite.head_commit.message
+  | rename check_suite.head_commit.author.name as user repository.full_name as repository
+  check_suite.pull_requests{}.head.ref as ref_head check_suite.head_commit.message
+  as commit_message | search NOT `github_known_users` | eval phase="code" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `github_pull_request_from_unknown_user_filter`'
+how_to_implement: You must index GitHub logs. You can follow the url in reference
+  to onboard GitHub logs.
 known_false_positives: unknown
 references:
 - https://www.splunk.com/en_us/blog/tips-and-tricks/getting-github-data-with-webhooks.html
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$repository$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$repository$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,23 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - alert.id
-  - repository.full_name
-  - repository.html_url
-  - action
-  - alert.affected_package_name
-  - alert.affected_range
-  - alert.created_at
-  - alert.external_identifier
-  - alert.external_reference
-  - alert.fixed_in
-  - alert.severity
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/github_pull_request/github_pull_request.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.001/github_pull_request/github_pull_request.json
     sourcetype: aws:firehose:json
     source: github
diff --git a/detections/cloud/gsuite_drive_share_in_external_email.yml b/detections/cloud/gsuite_drive_share_in_external_email.yml
index ddc0e7cc28..58fb0e5f0c 100644
--- a/detections/cloud/gsuite_drive_share_in_external_email.yml
+++ b/detections/cloud/gsuite_drive_share_in_external_email.yml
@@ -5,12 +5,31 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects Google Drive or Google Docs files shared externally from an internal domain. It leverages GSuite Drive logs, extracting and comparing the source and destination email domains to identify external sharing. This activity is significant as it may indicate potential data exfiltration by an attacker or insider. If confirmed malicious, this could lead to unauthorized access to sensitive information, data leakage, and potential compliance violations. Monitoring this behavior helps in early detection and mitigation of data breaches.
+description: The following analytic detects Google Drive or Google Docs files shared
+  externally from an internal domain. It leverages GSuite Drive logs, extracting and
+  comparing the source and destination email domains to identify external sharing.
+  This activity is significant as it may indicate potential data exfiltration by an
+  attacker or insider. If confirmed malicious, this could lead to unauthorized access
+  to sensitive information, data leakage, and potential compliance violations. Monitoring
+  this behavior helps in early detection and mitigation of data breaches.
 data_source:
 - G Suite Drive
-search: '`gsuite_drive` NOT (email IN("", "null")) | rex field=parameters.owner "[^@]+@(?<src_domain>[^@]+)" | rex field=email "[^@]+@(?<dest_domain>[^@]+)" | where src_domain = "internal_test_email.com" and not dest_domain = "internal_test_email.com" | eval phase="plan" | eval severity="low" | stats values(parameters.doc_title) as doc_title, values(parameters.doc_type) as doc_types, values(email) as dst_email_list, values(parameters.visibility) as visibility, values(parameters.doc_id) as doc_id, count min(_time) as firstTime max(_time) as lastTime by parameters.owner ip_address phase severity  | rename parameters.owner as user ip_address as src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_drive_share_in_external_email_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. In order for the search to work for your environment, please edit the query to use your company specific email domain instead of `internal_test_email.com`.
-known_false_positives: network admin or normal user may share files to customer and external team.
+search: '`gsuite_drive` NOT (email IN("", "null")) | rex field=parameters.owner "[^@]+@(?<src_domain>[^@]+)"
+  | rex field=email "[^@]+@(?<dest_domain>[^@]+)" | where src_domain = "internal_test_email.com"
+  and not dest_domain = "internal_test_email.com" | eval phase="plan" | eval severity="low"
+  | stats values(parameters.doc_title) as doc_title, values(parameters.doc_type) as
+  doc_types, values(email) as dst_email_list, values(parameters.visibility) as visibility,
+  values(parameters.doc_id) as doc_id, count min(_time) as firstTime max(_time) as
+  lastTime by parameters.owner ip_address phase severity  | rename parameters.owner
+  as user ip_address as src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `gsuite_drive_share_in_external_email_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to gsuite having the file attachment metadata like file type, file
+  extension, source email, destination email, num of attachment and etc. In order
+  for the search to work for your environment, please edit the query to use your company
+  specific email domain instead of `internal_test_email.com`.
+known_false_positives: network admin or normal user may share files to customer and
+  external team.
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
 tags:
@@ -37,19 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - parameters.doc_title
-  - src_domain
-  - dest_domain
-  - email
-  - parameters.visibility
-  - parameters.owner
-  - parameters.doc_type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567.002/gsuite_share_drive/gdrive_share_external.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567.002/gsuite_share_drive/gdrive_share_external.log
     source: http:gsuite
     sourcetype: gsuite:drive:json
diff --git a/detections/cloud/gsuite_email_suspicious_attachment.yml b/detections/cloud/gsuite_email_suspicious_attachment.yml
index 1e6b1ceca4..491baf2b62 100644
--- a/detections/cloud/gsuite_email_suspicious_attachment.yml
+++ b/detections/cloud/gsuite_email_suspicious_attachment.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious attachment file extensions in GSuite emails, potentially indicating a spear-phishing attack. It leverages GSuite Gmail logs to identify emails with attachments having file extensions commonly associated with malware, such as .exe, .bat, and .js. This activity is significant as these file types are often used to deliver malicious payloads, posing a risk of compromising targeted machines. If confirmed malicious, this could lead to unauthorized code execution, data breaches, or further network infiltration.
+description: The following analytic detects suspicious attachment file extensions
+  in GSuite emails, potentially indicating a spear-phishing attack. It leverages GSuite
+  Gmail logs to identify emails with attachments having file extensions commonly associated
+  with malware, such as .exe, .bat, and .js. This activity is significant as these
+  file types are often used to deliver malicious payloads, posing a risk of compromising
+  targeted machines. If confirmed malicious, this could lead to unauthorized code
+  execution, data breaches, or further network infiltration.
 data_source:
 - G Suite Gmail
-search: '`gsuite_gmail` "attachment{}.file_extension_type" IN ("pl", "py", "rb", "sh", "bat", "exe", "dll", "cpl", "com", "js", "vbs", "ps1", "reg","swf", "cmd", "go") | eval phase="plan" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size by destination{}.service num_message_attachments  subject destination{}.address source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_suspicious_attachment_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
-known_false_positives: network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack.
+search: '`gsuite_gmail` "attachment{}.file_extension_type" IN ("pl", "py", "rb", "sh",
+  "bat", "exe", "dll", "cpl", "com", "js", "vbs", "ps1", "reg","swf", "cmd", "go")
+  | eval phase="plan" | eval severity="medium" | stats count min(_time) as firstTime
+  max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments,
+  values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size
+  by destination{}.service num_message_attachments  subject destination{}.address
+  source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `gsuite_email_suspicious_attachment_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to gsuite having the file attachment metadata like file type, file
+  extension, source email, destination email, num of attachment and etc.
+known_false_positives: network admin and normal user may send this file attachment
+  as part of their day to day work. having a good protocol in attaching this file
+  type to an e-mail may reduce the risk of having a spear phishing attack.
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
 drilldown_searches:
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$destination{}.address$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - attachment{}.file_extension_type
-  - attachment{}.sha256
-  - destination{}.service
-  - num_message_attachments
-  - payload_size
-  - subject
-  - destination{}.address
-  - source.address
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_attachment_ext/gsuite_gmail_file_ext.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_attachment_ext/gsuite_gmail_file_ext.log
     source: http:gsuite
     sourcetype: gsuite:gmail:bigquery
diff --git a/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml b/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml
index f690cdc4bc..230fb281a5 100644
--- a/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml
+++ b/detections/cloud/gsuite_email_suspicious_subject_with_attachment.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies Gsuite emails with suspicious subjects and attachments commonly used in spear phishing attacks. It leverages Gsuite email logs, focusing on specific keywords in the subject line and known malicious file types in attachments. This activity is significant for a SOC as spear phishing is a prevalent method for initial compromise, often leading to further malicious actions. If confirmed malicious, this activity could result in unauthorized access, data exfiltration, or further malware deployment, posing a significant risk to the organization's security.
+description: The following analytic identifies Gsuite emails with suspicious subjects
+  and attachments commonly used in spear phishing attacks. It leverages Gsuite email
+  logs, focusing on specific keywords in the subject line and known malicious file
+  types in attachments. This activity is significant for a SOC as spear phishing is
+  a prevalent method for initial compromise, often leading to further malicious actions.
+  If confirmed malicious, this activity could result in unauthorized access, data
+  exfiltration, or further malware deployment, posing a significant risk to the organization's
+  security.
 data_source:
 - G Suite Gmail
-search: '`gsuite_gmail` num_message_attachments > 0 subject IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "* fedex *", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") attachment{}.file_extension_type IN ("doc", "docx", "xls", "xlsx", "ppt", "pptx", "pdf", "zip", "rar", "html","htm","hta") | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type) as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size) as payload_size by destination{}.service num_message_attachments  subject destination{}.address source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_suspicious_subject_with_attachment_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
-known_false_positives: normal user or normal transaction may contain the subject and file type attachment that this detection try to search.
+search: '`gsuite_gmail` num_message_attachments > 0 subject IN ("*dhl*", "* ups *",
+  "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "* fedex *", "* usps
+  *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") attachment{}.file_extension_type
+  IN ("doc", "docx", "xls", "xlsx", "ppt", "pptx", "pdf", "zip", "rar", "html","htm","hta")
+  | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address
+  "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com"
+  and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="medium"
+  | stats count min(_time) as firstTime max(_time) as lastTime values(attachment{}.file_extension_type)
+  as email_attachments, values(attachment{}.sha256) as attachment_sha256, values(payload_size)
+  as payload_size by destination{}.service num_message_attachments  subject destination{}.address
+  source.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `gsuite_email_suspicious_subject_with_attachment_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to gsuite having the file attachment metadata like file type, file
+  extension, source email, destination email, num of attachment and etc.
+known_false_positives: normal user or normal transaction may contain the subject and
+  file type attachment that this detection try to search.
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
 - https://www.mandiant.com/resources/top-words-used-in-spear-phishing-attacks
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$destination{}.address$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,12 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_subj/gsuite_susp_subj_attach.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_subj/gsuite_susp_subj_attach.log
     source: http:gsuite
     sourcetype: gsuite:gmail:bigquery
diff --git a/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml b/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml
index 8e838e8530..2211248c2d 100644
--- a/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml
+++ b/detections/cloud/gsuite_email_with_known_abuse_web_service_link.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects emails in Gsuite containing links to known abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite Gmail logs to identify emails with these specific domains in their links. This activity is significant because these services are commonly used by attackers to deliver malicious payloads. If confirmed malicious, this could lead to the delivery of malware, phishing attacks, or other harmful activities, potentially compromising sensitive information or systems within the organization.
+description: The following analytic detects emails in Gsuite containing links to known
+  abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite
+  Gmail logs to identify emails with these specific domains in their links. This activity
+  is significant because these services are commonly used by attackers to deliver
+  malicious payloads. If confirmed malicious, this could lead to the delivery of malware,
+  phishing attacks, or other harmful activities, potentially compromising sensitive
+  information or systems within the organization.
 data_source:
 - G Suite Gmail
-search: '`gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me") | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" |stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count by is_spam source.address source.from_header_address subject destination{}.address phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_email_with_known_abuse_web_service_link_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
-known_false_positives: normal email contains this link that are known application within the organization or network can be catched by this detection.
+search: '`gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me")
+  | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address
+  "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com"
+  and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low"
+  |stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time)
+  as lastTime count by is_spam source.address source.from_header_address subject destination{}.address
+  phase severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `gsuite_email_with_known_abuse_web_service_link_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to gsuite having the file attachment metadata like file type, file
+  extension, source email, destination email, num of attachment and etc.
+known_false_positives: normal email contains this link that are known application
+  within the organization or network can be catched by this detection.
 references:
 - https://news.sophos.com/en-us/2021/07/22/malware-increasingly-targets-discord-for-abuse/
 drilldown_searches:
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$destination{}.address$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,12 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_url/gsuite_susp_url.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_url/gsuite_susp_url.log
     source: http:gsuite
     sourcetype: gsuite:gmail:bigquery
diff --git a/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml b/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml
index 24da353a0a..a1b38a4ec2 100644
--- a/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml
+++ b/detections/cloud/gsuite_outbound_email_with_attachment_to_external_domain.yml
@@ -5,12 +5,29 @@ date: '2024-10-17'
 author: Teoderick Contreras, Stanislav Miskovic, Splunk
 status: production
 type: Hunting
-description: The following analytic detects outbound emails with attachments sent from an internal email domain to an external domain. It leverages Gsuite Gmail logs, parsing the source and destination email domains, and flags emails with fewer than 20 outbound instances. This activity is significant as it may indicate potential data exfiltration or insider threats. If confirmed malicious, an attacker could use this method to exfiltrate sensitive information, leading to data breaches and compliance violations.
+description: The following analytic detects outbound emails with attachments sent
+  from an internal email domain to an external domain. It leverages Gsuite Gmail logs,
+  parsing the source and destination email domains, and flags emails with fewer than
+  20 outbound instances. This activity is significant as it may indicate potential
+  data exfiltration or insider threats. If confirmed malicious, an attacker could
+  use this method to exfiltrate sensitive information, leading to data breaches and
+  compliance violations.
 data_source:
 - G Suite Gmail
-search: '`gsuite_gmail` num_message_attachments > 0 | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" | where source_domain="internal_test_email.com" and not dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" | stats values(subject) as subject, values(source.from_header_address) as src_domain_list, count as numEvents, dc(source.from_header_address) as numSrcAddresses, min(_time) as firstTime max(_time) as lastTime by dest_domain phase severity | where numSrcAddresses < 20 |sort - numSrcAddresses | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_outbound_email_with_attachment_to_external_domain_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
-known_false_positives: network admin and normal user may send this file attachment as part of their day to day work. having a good protocol in attaching this file type to an e-mail may reduce the risk of having a spear phishing attack.
+search: '`gsuite_gmail` num_message_attachments > 0 | rex field=source.from_header_address
+  "[^@]+@(?<source_domain>[^@]+)" | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)"
+  | where source_domain="internal_test_email.com" and not dest_domain="internal_test_email.com"
+  | eval phase="plan" | eval severity="low" | stats values(subject) as subject, values(source.from_header_address)
+  as src_domain_list, count as numEvents, dc(source.from_header_address) as numSrcAddresses,
+  min(_time) as firstTime max(_time) as lastTime by dest_domain phase severity | where
+  numSrcAddresses < 20 |sort - numSrcAddresses | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `gsuite_outbound_email_with_attachment_to_external_domain_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to gsuite having the file attachment metadata like file type, file
+  extension, source email, destination email, num of attachment and etc.
+known_false_positives: network admin and normal user may send this file attachment
+  as part of their day to day work. having a good protocol in attaching this file
+  type to an e-mail may reduce the risk of having a spear phishing attack.
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
 tags:
@@ -37,18 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - source.from_header_address
-  - destination.address
-  - num_message_attachments
-  - dest_domain
-  - phase
-  - severity
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_outbound_email_to_external/gsuite_external_domain.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_outbound_email_to_external/gsuite_external_domain.log
     source: http:gsuite
     sourcetype: gsuite:gmail:bigquery
diff --git a/detections/cloud/gsuite_suspicious_calendar_invite.yml b/detections/cloud/gsuite_suspicious_calendar_invite.yml
index a20878c80e..696e95b1da 100644
--- a/detections/cloud/gsuite_suspicious_calendar_invite.yml
+++ b/detections/cloud/gsuite_suspicious_calendar_invite.yml
@@ -5,11 +5,28 @@ date: '2024-10-17'
 author: Rod Soto, Teoderick Contreras
 status: experimental
 type: Hunting
-description: The following analytic detects suspicious calendar invites sent via GSuite, potentially indicating compromised accounts or malicious internal activity. It leverages GSuite calendar logs, focusing on events where a high volume of invites (over 100) is sent within a 5-minute window. This behavior is significant as it may involve the distribution of malicious links or attachments, posing a security risk. If confirmed malicious, this activity could lead to widespread phishing attacks, unauthorized access, or malware distribution within the organization.
+description: The following analytic detects suspicious calendar invites sent via GSuite,
+  potentially indicating compromised accounts or malicious internal activity. It leverages
+  GSuite calendar logs, focusing on events where a high volume of invites (over 100)
+  is sent within a 5-minute window. This behavior is significant as it may involve
+  the distribution of malicious links or attachments, posing a security risk. If confirmed
+  malicious, this activity could lead to widespread phishing attacks, unauthorized
+  access, or malware distribution within the organization.
 data_source: []
-search: '`gsuite_calendar` |bin span=5m _time |rename parameters.* as * |search target_calendar_id!=null email="*yourdomain.com"| stats  count values(target_calendar_id) values(event_title) values(event_guest) by email _time | where count >100| `gsuite_suspicious_calendar_invite_filter`'
-how_to_implement: In order to successfully implement this search, you need to be ingesting logs related to gsuite (gsuite:calendar:json) having the file sharing metadata like file type, source owner, destination target user, description, etc. This search can also be made more specific by selecting specific emails, subdomains timeframe, organizational units, targeted user, etc. In order for the search to work for your environment please update `yourdomain.com` value in the query with the domain relavant for your organization.
-known_false_positives: This search will also produce normal activity statistics. Fields such as email, ip address, name, parameters.organizer_calendar_id, parameters.target_calendar_id and parameters.event_title may give away phishing intent.For more specific results use email parameter.
+search: '`gsuite_calendar` |bin span=5m _time |rename parameters.* as * |search target_calendar_id!=null
+  email="*yourdomain.com"| stats  count values(target_calendar_id) values(event_title)
+  values(event_guest) by email _time | where count >100| `gsuite_suspicious_calendar_invite_filter`'
+how_to_implement: In order to successfully implement this search, you need to be ingesting
+  logs related to gsuite (gsuite:calendar:json) having the file sharing metadata like
+  file type, source owner, destination target user, description, etc. This search
+  can also be made more specific by selecting specific emails, subdomains timeframe,
+  organizational units, targeted user, etc. In order for the search to work for your
+  environment please update `yourdomain.com` value in the query with the domain relavant
+  for your organization.
+known_false_positives: This search will also produce normal activity statistics. Fields
+  such as email, ip address, name, parameters.organizer_calendar_id, parameters.target_calendar_id
+  and parameters.event_title may give away phishing intent.For more specific results
+  use email parameter.
 references:
 - https://www.techrepublic.com/article/how-to-avoid-the-dreaded-google-calendar-malicious-invite-issue/
 - https://gcn.com/cybersecurity/2012/09/the-20-most-common-words-in-phishing-attacks/280956/
@@ -31,10 +48,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - email
-  - parameters.event_title
-  - parameters.target_calendar_id
-  - parameters.event_title
   security_domain: threat
diff --git a/detections/cloud/gsuite_suspicious_shared_file_name.yml b/detections/cloud/gsuite_suspicious_shared_file_name.yml
index 2e9e9932c0..711c20433b 100644
--- a/detections/cloud/gsuite_suspicious_shared_file_name.yml
+++ b/detections/cloud/gsuite_suspicious_shared_file_name.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects shared files in Google Drive with suspicious filenames commonly used in spear phishing campaigns. It leverages GSuite Drive logs to identify documents with titles that include keywords like "dhl," "ups," "invoice," and "shipment." This activity is significant because such filenames are often used to lure users into opening malicious documents or clicking harmful links. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further compromise of the user's system.
+description: The following analytic detects shared files in Google Drive with suspicious
+  filenames commonly used in spear phishing campaigns. It leverages GSuite Drive logs
+  to identify documents with titles that include keywords like "dhl," "ups," "invoice,"
+  and "shipment." This activity is significant because such filenames are often used
+  to lure users into opening malicious documents or clicking harmful links. If confirmed
+  malicious, this activity could lead to unauthorized access, data theft, or further
+  compromise of the user's system.
 data_source:
 - G Suite Drive
-search: '`gsuite_drive` parameters.owner_is_team_drive=false "parameters.doc_title" IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*", "*fedex*", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*", "*new order*") parameters.doc_type IN ("document","pdf", "msexcel", "msword", "spreadsheet", "presentation") | rex field=parameters.owner "[^@]+@(?<source_domain>[^@]+)" | rex field=parameters.target_user "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low" | stats count min(_time) as firstTime max(_time) as lastTime by email parameters.owner parameters.target_user parameters.doc_title parameters.doc_type phase severity | rename parameters.target_user AS user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `gsuite_suspicious_shared_file_name_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc. In order for the search to work for your environment, please edit the query to use your company specific email domain instead of `internal_test_email.com`.
-known_false_positives: normal user or normal transaction may contain the subject and file type attachment that this detection try to search
+search: '`gsuite_drive` parameters.owner_is_team_drive=false "parameters.doc_title"
+  IN ("*dhl*", "* ups *", "*delivery*", "*parcel*", "*label*", "*invoice*", "*postal*",
+  "*fedex*", "* usps *", "* express *", "*shipment*", "*Banking/Tax*","*shipment*",
+  "*new order*") parameters.doc_type IN ("document","pdf", "msexcel", "msword", "spreadsheet",
+  "presentation") | rex field=parameters.owner "[^@]+@(?<source_domain>[^@]+)" | rex
+  field=parameters.target_user "[^@]+@(?<dest_domain>[^@]+)" | where not source_domain="internal_test_email.com"
+  and dest_domain="internal_test_email.com" | eval phase="plan" | eval severity="low"
+  | stats count min(_time) as firstTime max(_time) as lastTime by email parameters.owner
+  parameters.target_user parameters.doc_title parameters.doc_type phase severity |
+  rename parameters.target_user AS user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `gsuite_suspicious_shared_file_name_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs related to gsuite having the file attachment metadata like file type, file
+  extension, source email, destination email, num of attachment and etc. In order
+  for the search to work for your environment, please edit the query to use your company
+  specific email domain instead of `internal_test_email.com`.
+known_false_positives: normal user or normal transaction may contain the subject and
+  file type attachment that this detection try to search
 references:
 - https://www.redhat.com/en/topics/devops/what-is-devsecops
 - https://www.mandiant.com/resources/top-words-used-in-spear-phishing-attacks
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$email$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$email$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$email$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - parameters.doc_title
-  - src_domain
-  - dest_domain
-  - email
-  - parameters.visibility
-  - parameters.owner
-  - parameters.doc_type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gdrive_susp_file_share/gdrive_susp_attach.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gdrive_susp_file_share/gdrive_susp_attach.log
     source: http:gsuite
     sourcetype: gsuite:drive:json
diff --git a/detections/cloud/high_number_of_login_failures_from_a_single_source.yml b/detections/cloud/high_number_of_login_failures_from_a_single_source.yml
index 1aef3d3232..8edbedcaa0 100644
--- a/detections/cloud/high_number_of_login_failures_from_a_single_source.yml
+++ b/detections/cloud/high_number_of_login_failures_from_a_single_source.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Bhavin Patel, Mauricio Velazco, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects multiple failed login attempts in Office365 Azure Active Directory from a single source IP address. It leverages Office365 management activity logs, specifically AzureActiveDirectoryStsLogon records, aggregating these logs in 5-minute intervals to count failed login attempts. This activity is significant as it may indicate brute-force attacks or password spraying, which are critical to monitor. If confirmed malicious, an attacker could gain unauthorized access to Office365 accounts, leading to potential data breaches, lateral movement within the organization, or further malicious activities using the compromised account.
+description: The following analytic detects multiple failed login attempts in Office365
+  Azure Active Directory from a single source IP address. It leverages Office365 management
+  activity logs, specifically AzureActiveDirectoryStsLogon records, aggregating these
+  logs in 5-minute intervals to count failed login attempts. This activity is significant
+  as it may indicate brute-force attacks or password spraying, which are critical
+  to monitor. If confirmed malicious, an attacker could gain unauthorized access to
+  Office365 accounts, leading to potential data breaches, lateral movement within
+  the organization, or further malicious activities using the compromised account.
 data_source:
 - O365 UserLoginFailed
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed record_type=AzureActiveDirectoryStsLogon | bucket span=5m _time | stats dc(_raw) AS failed_attempts values(user) as user values(LogonError) as LogonError values(signature) as signature values(UserAgent) as UserAgent by _time, src_ip | where failed_attempts > 10 | `high_number_of_login_failures_from_a_single_source_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. Adjust the threshold value to suit the specific environment, as environments with naturally higher login failures might generate false positives at a lower threshold.
-known_false_positives: An Ip address with more than 10 failed authentication attempts in the span of 5 minutes may also be triggered by a broken application.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed
+  record_type=AzureActiveDirectoryStsLogon | bucket span=5m _time | stats dc(_raw)
+  AS failed_attempts values(user) as user values(LogonError) as LogonError values(signature)
+  as signature values(UserAgent) as UserAgent by _time, src_ip | where failed_attempts
+  > 10 | `high_number_of_login_failures_from_a_single_source_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. Adjust the threshold value to suit the specific
+  environment, as environments with naturally higher login failures might generate
+  false positives at a lower threshold.
+known_false_positives: An Ip address with more than 10 failed authentication attempts
+  in the span of 5 minutes may also be triggered by a broken application.
 references:
 - https://attack.mitre.org/techniques/T1110/001/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,22 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - record_type
-  - app
-  - user
-  - LogonError
-  - authentication_method
-  - signature
-  - UserAgent
-  - src_ip
-  - record_type
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml
index 268dac178c..c39cbd85d0 100644
--- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml
+++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_location.yml
@@ -5,11 +5,33 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets from unusual locations. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests by country. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this behavior could indicate an attacker attempting to exfiltrate or misuse these secrets, potentially leading to unauthorized access to sensitive systems or data.
+description: The following analytic detects unauthorized access or misuse of Kubernetes
+  Secrets from unusual locations. It leverages Kubernetes Audit logs to identify anomalies
+  in access patterns by analyzing the source of requests by country. This activity
+  is significant for a SOC as Kubernetes Secrets store sensitive information like
+  passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed
+  malicious, this behavior could indicate an attacker attempting to exfiltrate or
+  misuse these secrets, potentially leading to unauthorized access to sensitive systems
+  or data.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=secrets verb=get | iplocation sourceIPs{} | fillnull | search NOT `kube_allowed_locations` | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb City Country | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_abuse_of_secret_by_unusual_location_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=secrets verb=get | iplocation sourceIPs{}
+  | fillnull | search NOT `kube_allowed_locations` | stats count by objectRef.name
+  objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code
+  sourceIPs{} stage user.groups{} user.uid user.username userAgent verb City Country
+  | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_abuse_of_secret_by_unusual_location_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +55,8 @@ tags:
   asset_type: Kubernetes
   confidence: 70
   impact: 70
-  message: Access of Kubernetes secret $objectRef.name$ from unusual location $Country$ by $user$
+  message: Access of Kubernetes secret $objectRef.name$ from unusual location $Country$
+    by $user$
   mitre_attack_id:
   - T1552.007
   observable:
@@ -44,25 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - objectRef.resource
-  - verb
-  - objectRef.name
-  - objectRef.namespace
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.groups{}
-  - user.uid
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml
index f4c92505eb..3dad0b1f75 100644
--- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml
+++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_agent.yml
@@ -5,11 +5,33 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user agents. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests based on user agents. This activity is significant for a SOC because Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, potentially resulting in significant security breaches and exfiltration of critical information.
+description: The following analytic detects unauthorized access or misuse of Kubernetes
+  Secrets by unusual user agents. It leverages Kubernetes Audit logs to identify anomalies
+  in access patterns by analyzing the source of requests based on user agents. This
+  activity is significant for a SOC because Kubernetes Secrets store sensitive information
+  like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed
+  malicious, this activity could lead to unauthorized access to sensitive systems
+  or data, potentially resulting in significant security breaches and exfiltration
+  of critical information.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_agents` | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_abuse_of_secret_by_unusual_user_agent_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_agents`
+  | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource
+  requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{}
+  user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username
+  as user | `kubernetes_abuse_of_secret_by_unusual_user_agent_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +55,8 @@ tags:
   asset_type: Kubernetes
   confidence: 70
   impact: 70
-  message: Access of Kubernetes secret $objectRef.name$ from unusual user agent $userAgent$ by $user$
+  message: Access of Kubernetes secret $objectRef.name$ from unusual user agent $userAgent$
+    by $user$
   mitre_attack_id:
   - T1552.007
   observable:
@@ -44,25 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - objectRef.resource
-  - verb
-  - objectRef.name
-  - objectRef.namespace
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.groups{}
-  - user.uid
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml
index 781b1ccd5e..c41ce1c595 100644
--- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml
+++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_group.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user groups. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests and user groups. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys. If confirmed malicious, this behavior could indicate an attacker attempting to exfiltrate or misuse these secrets, potentially leading to unauthorized access to sensitive systems or data.
+description: The following analytic detects unauthorized access or misuse of Kubernetes
+  Secrets by unusual user groups. It leverages Kubernetes Audit logs to identify anomalies
+  in access patterns by analyzing the source of requests and user groups. This activity
+  is significant for a SOC as Kubernetes Secrets store sensitive information like
+  passwords, OAuth tokens, and SSH keys. If confirmed malicious, this behavior could
+  indicate an attacker attempting to exfiltrate or misuse these secrets, potentially
+  leading to unauthorized access to sensitive systems or data.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_groups` | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_abuse_of_secret_by_unusual_user_group_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_groups`
+  | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource
+  requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{}
+  user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username
+  as user | `kubernetes_abuse_of_secret_by_unusual_user_group_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +54,8 @@ tags:
   asset_type: Kubernetes
   confidence: 70
   impact: 70
-  message: Access of Kubernetes secret $objectRef.name$ from unusual user group $user.groups{}$ by user name $user$
+  message: Access of Kubernetes secret $objectRef.name$ from unusual user group $user.groups{}$
+    by user name $user$
   mitre_attack_id:
   - T1552.007
   observable:
@@ -44,25 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - objectRef.resource
-  - verb
-  - objectRef.name
-  - objectRef.namespace
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.groups{}
-  - user.uid
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml
index ec1b0ba542..96495908f9 100644
--- a/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml
+++ b/detections/cloud/kubernetes_abuse_of_secret_by_unusual_user_name.yml
@@ -5,11 +5,33 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user names. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests based on user names. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, potentially resulting in significant security breaches and exfiltration of sensitive information.
+description: The following analytic detects unauthorized access or misuse of Kubernetes
+  Secrets by unusual user names. It leverages Kubernetes Audit logs to identify anomalies
+  in access patterns by analyzing the source of requests based on user names. This
+  activity is significant for a SOC as Kubernetes Secrets store sensitive information
+  like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed
+  malicious, this activity could lead to unauthorized access to sensitive systems
+  or data, potentially resulting in significant security breaches and exfiltration
+  of sensitive information.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_names` | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_abuse_of_secret_by_unusual_user_name_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=secrets verb=get | search NOT `kube_allowed_user_names`
+  | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource
+  requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{}
+  user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username
+  as user | `kubernetes_abuse_of_secret_by_unusual_user_name_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,25 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - objectRef.resource
-  - verb
-  - objectRef.name
-  - objectRef.namespace
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.groups{}
-  - user.uid
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.007/kube_audit_get_secret/kube_audit_get_secret.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_access_scanning.yml b/detections/cloud/kubernetes_access_scanning.yml
index 04646cf2d5..1e3bd24c55 100644
--- a/detections/cloud/kubernetes_access_scanning.yml
+++ b/detections/cloud/kubernetes_access_scanning.yml
@@ -5,11 +5,35 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects potential scanning activities within a Kubernetes environment. It identifies unauthorized access attempts, probing of public APIs, or attempts to exploit known vulnerabilities by monitoring Kubernetes audit logs for repeated failed access attempts or unusual API requests. This activity is significant for a SOC as it may indicate an attacker's preliminary reconnaissance to gather information about the system. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, posing a severe security risk.
+description: The following analytic detects potential scanning activities within a
+  Kubernetes environment. It identifies unauthorized access attempts, probing of public
+  APIs, or attempts to exploit known vulnerabilities by monitoring Kubernetes audit
+  logs for repeated failed access attempts or unusual API requests. This activity
+  is significant for a SOC as it may indicate an attacker's preliminary reconnaissance
+  to gather information about the system. If confirmed malicious, this activity could
+  lead to unauthorized access to sensitive systems or data, posing a severe security
+  risk.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 | iplocation sourceIPs{} | stats count values(userAgent) as userAgent values(user.username) as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) as responseStatus.message values(responseStatus.reason) as responseStatus.reason values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City | where count > 5 | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_access_scanning_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403
+  | iplocation sourceIPs{} | stats count values(userAgent) as userAgent values(user.username)
+  as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI)
+  as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message)
+  as responseStatus.message values(responseStatus.reason) as responseStatus.reason
+  values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City
+  | where count > 5 | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_access_scanning_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,22 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - verb
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - user.groups{}
-  - user.username
-  - userAgent
-  - verb
-  - responseStatus.reason
-  - responseStatus.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml b/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml
index dffb768cc7..51eb2dc2a8 100644
--- a/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml
+++ b/detections/cloud/kubernetes_anomalous_inbound_network_activity_from_process.yml
@@ -5,14 +5,40 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies anomalous inbound network traffic volumes from processes within containerized workloads. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days. This activity is significant as it may indicate unauthorized data reception, potential breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, it could lead to command and control installation, data integrity damage, container escape, and further environment compromise.
+description: The following analytic identifies anomalous inbound network traffic volumes
+  from processes within containerized workloads. It leverages Network Performance
+  Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability
+  Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets,
+  udp.bytes, udp.packets) over the last hour with the average over the past 30 days.
+  This activity is significant as it may indicate unauthorized data reception, potential
+  breaches, vulnerability exploitation, or malware propagation. If confirmed malicious,
+  it could lead to command and control installation, data integrity damage, container
+  escape, and further environment compromise.
 data_source: []
-search: '| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name dest.workload.name dest.process.name  span=10s | eval key=''dest.workload.name'' + ":" + ''dest.process.name'' | join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by dest.workload.name dest.process.name | eval key=''dest.workload.name'' + ":" + ''dest.process.name'' ] | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 3 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") | where anomalies!="" | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name dest.workload.name dest.process.name | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_inbound_network_activity_from_process_filter`'
-how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:
-
-  * Name sim_npm_metrics_to_metrics_index
-
-  * Metric Resolution 10000'
+search: "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics`
+  AND earliest=-1h by k8s.cluster.name dest.workload.name dest.process.name  span=10s
+  | eval key='dest.workload.name' + \":\" + 'dest.process.name' | join type=left key
+  [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.*
+  avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h
+  by dest.workload.name dest.process.name | eval key='dest.workload.name' + \":\"
+  + 'dest.process.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies
+  =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 3 * 'stdev_<<MATCHSTR>>'), anomalies
+  + \"<<MATCHSTR>> higher than average by \" + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>'
+  ,2)) + \" Standard Deviations. <<MATCHSTR>>=\" + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\"\
+  \ + tostring('avg_<<MATCHSTR>>') + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>')
+  + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \"\
+  ,\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count
+  values(anomalies) as anomalies by k8s.cluster.name dest.workload.name dest.process.name
+  | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_inbound_network_activity_from_process_filter`"
+how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster
+  and enable Network Performance Monitoring according to instructions found in Splunk
+  Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup
+  In order to access those metrics from within Splunk Enterprise and ES, the Splunk
+  Infrastructure Monitoring add-on must be installed and configured on a Splunk Search
+  Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and
+  Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the
+  following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n
+  * Metric Resolution 10000"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -22,7 +48,8 @@ tags:
   asset_type: Kubernetes
   confidence: 50
   impact: 50
-  message: Kubernetes Anomalous Inbound Network Activity from Process in kubernetes cluster $host$
+  message: Kubernetes Anomalous Inbound Network Activity from Process in kubernetes
+    cluster $host$
   mitre_attack_id:
   - T1204
   observable:
@@ -34,10 +61,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - tcp.*
-  - udp.*
-  - k8s.cluster.name
-  - dest.process.name
-  - dest.workload.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml b/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml
index ab2edbcb8a..fc1e9208be 100644
--- a/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml
+++ b/detections/cloud/kubernetes_anomalous_inbound_outbound_network_io.yml
@@ -5,32 +5,47 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies high inbound or outbound network I/O anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. A lookup table with average and standard deviation values for network I/O is used to detect anomalies persisting over a 1-hour period. This activity is significant as it may indicate data exfiltration, command and control communication, or unauthorized data transfers. If confirmed malicious, it could lead to data breaches, service outages, financial losses, and reputational damage.
+description: The following analytic identifies high inbound or outbound network I/O
+  anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector
+  and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. A lookup
+  table with average and standard deviation values for network I/O is used to detect
+  anomalies persisting over a 1-hour period. This activity is significant as it may
+  indicate data exfiltration, command and control communication, or unauthorized data
+  transfers. If confirmed malicious, it could lead to data breaches, service outages,
+  financial losses, and reputational damage.
 data_source: []
-search: '| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s | eval service = replace(''k8s.pod.name'', "-\w{5}$$|-[abcdef0-9]{8,10}-\w{5}$$", "") | stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by k8s.cluster.name k8s.node.name k8s.pod.name service _time | eval key = ''k8s.cluster.name'' + ":" + ''service'' | lookup k8s_container_network_io_baseline key | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 4 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | eval anomalies = replace(anomalies, ",\s$$", "") | where anomalies!="" | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service | rename service as k8s.service | where count > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_outbound_network_io_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name
+  k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name',
+  \"-\\w{5}$$|-[abcdef0-9]{8,10}-\\w{5}$$\", \"\") | stats avg(eval(if(direction=\"\
+  transmit\", io,null()))) as outbound_network_io avg(eval(if(direction=\"receive\"\
+  , io,null()))) as inbound_network_io by k8s.cluster.name k8s.node.name k8s.pod.name
+  service _time | eval key = 'k8s.cluster.name' + \":\" + 'service' | lookup k8s_container_network_io_baseline
+  key | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>'
+  > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + \"<<MATCHSTR>> higher
+  than average by \" + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>'
+  ,2)) + \" Standard Deviations. <<MATCHSTR>>=\" + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\"\
+  \ + tostring('avg_<<MATCHSTR>>') + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>')
+  + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$$\", \"\")
+  | where anomalies!=\"\" | stats count values(anomalies) as anomalies by k8s.cluster.name
+  k8s.node.name k8s.pod.name service | rename service as k8s.service | where count
+  > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_outbound_network_io_filter`"
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -40,7 +55,8 @@ tags:
   asset_type: Kubernetes
   confidence: 50
   impact: 50
-  message: Kubernetes Anomalous Inbound Outbound Network IO from container on host $host$
+  message: Kubernetes Anomalous Inbound Outbound Network IO from container on host
+    $host$
   mitre_attack_id:
   - T1204
   observable:
@@ -52,10 +68,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.pod.network.io
-  - direction
-  - k8s.cluster.name
-  - k8s.node.name
-  - k8s.pod.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml b/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml
index 50d679b840..537886992e 100644
--- a/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml
+++ b/detections/cloud/kubernetes_anomalous_inbound_to_outbound_network_io_ratio.yml
@@ -5,32 +5,50 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. Anomalies are detected using a lookup table containing average and standard deviation values for network IO, triggering an event if the anomaly persists for over an hour. This activity is significant as it may indicate data exfiltration, command and control communication, or compromised container behavior. If confirmed malicious, it could lead to data breaches, service outages, and unauthorized access within the Kubernetes cluster.
+description: The following analytic identifies significant changes in network communication
+  behavior within Kubernetes containers by examining the inbound to outbound network
+  IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats
+  Receiver, along with data from Splunk Observability Cloud. Anomalies are detected
+  using a lookup table containing average and standard deviation values for network
+  IO, triggering an event if the anomaly persists for over an hour. This activity
+  is significant as it may indicate data exfiltration, command and control communication,
+  or compromised container behavior. If confirmed malicious, it could lead to data
+  breaches, service outages, and unauthorized access within the Kubernetes cluster.
 data_source: []
-search: '| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s | eval service = replace(''k8s.pod.name'', "-\w{5}$|-[abcdef0-9]{8,10}-\w{5}$", "") | eval key = ''k8s.cluster.name'' + ":" + ''service'' | stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by key service k8s.cluster.name k8s.pod.name k8s.node.name _time | eval inbound:outbound = inbound_network_io/outbound_network_io | eval outbound:inbound = outbound_network_io/inbound_network_io | fields - *network_io | lookup k8s_container_network_io_ratio_baseline key | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 4 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> ratio higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | eval anomalies = replace(anomalies, ",\s$", "") | where anomalies!="" | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service | rename service as k8s.service | where count > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_to_outbound_network_io_ratio_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: "| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name
+  k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name',
+  \"-\\w{5}$|-[abcdef0-9]{8,10}-\\w{5}$\", \"\") | eval key = 'k8s.cluster.name' +
+  \":\" + 'service' | stats avg(eval(if(direction=\"transmit\", io,null()))) as outbound_network_io
+  avg(eval(if(direction=\"receive\", io,null()))) as inbound_network_io by key service
+  k8s.cluster.name k8s.pod.name k8s.node.name _time | eval inbound:outbound = inbound_network_io/outbound_network_io
+  | eval outbound:inbound = outbound_network_io/inbound_network_io | fields - *network_io
+  | lookup k8s_container_network_io_ratio_baseline key | eval anomalies = \"\" | foreach
+  stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'),
+  anomalies + \"<<MATCHSTR>> ratio higher than average by \" + tostring(round(('<<MATCHSTR>>'
+  - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>' ,2)) + \" Standard Deviations. <<MATCHSTR>>=\"\
+  \ + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\" + tostring('avg_<<MATCHSTR>>')
+  + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>') + \", \" , anomalies)
+  ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") | where anomalies!=\"\"\
+  \ | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name
+  k8s.pod.name service | rename service as k8s.service | where count > 5 | rename
+  k8s.node.name as host | `kubernetes_anomalous_inbound_to_outbound_network_io_ratio_filter`"
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -40,7 +58,8 @@ tags:
   asset_type: Kubernetes
   confidence: 50
   impact: 50
-  message: Kubernetes Anomalous Inbound to Outbound Network IO Ratio from Container on host $host$
+  message: Kubernetes Anomalous Inbound to Outbound Network IO Ratio from Container
+    on host $host$
   mitre_attack_id:
   - T1204
   observable:
@@ -52,10 +71,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.pod.network.io
-  - direction
-  - k8s.cluster.name
-  - k8s.node.name
-  - k8s.pod.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml b/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml
index 53f7abf9f6..aad86b699e 100644
--- a/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml
+++ b/detections/cloud/kubernetes_anomalous_outbound_network_activity_from_process.yml
@@ -5,14 +5,41 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies anomalously high outbound network activity from processes running within containerized workloads in a Kubernetes environment. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average metrics over the past 30 days. This activity is significant as it may indicate data exfiltration, process modification, or container compromise. If confirmed malicious, it could lead to unauthorized data exfiltration, communication with malicious entities, or further attacks within the containerized environment.
+description: The following analytic identifies anomalously high outbound network activity
+  from processes running within containerized workloads in a Kubernetes environment.
+  It leverages Network Performance Monitoring metrics collected via an OTEL collector
+  and pulled from Splunk Observability Cloud. The detection compares recent network
+  metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the
+  last hour with the average metrics over the past 30 days. This activity is significant
+  as it may indicate data exfiltration, process modification, or container compromise.
+  If confirmed malicious, it could lead to unauthorized data exfiltration, communication
+  with malicious entities, or further attacks within the containerized environment.
 data_source: []
-search: '| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name source.process.name  span=10s | eval key=''source.workload.name'' + ":" + ''source.process.name'' | join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name source.process.name | eval key=''source.workload.name'' + ":" + ''source.process.name'' ] | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 3 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") | where anomalies!="" | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name source.process.name | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_outbound_network_activity_from_process_filter`'
-how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:
-
-  * Name sim_npm_metrics_to_metrics_index
-
-  * Metric Resolution 10000'
+search: "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics`
+  AND earliest=-1h by k8s.cluster.name source.workload.name source.process.name  span=10s
+  | eval key='source.workload.name' + \":\" + 'source.process.name' | join type=left
+  key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.*
+  avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h
+  by source.workload.name source.process.name | eval key='source.workload.name' +
+  \":\" + 'source.process.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval
+  anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 3 * 'stdev_<<MATCHSTR>>'),
+  anomalies + \"<<MATCHSTR>> higher than average by \" + tostring(round(('<<MATCHSTR>>'
+  - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>' ,2)) + \" Standard Deviations. <<MATCHSTR>>=\"\
+  \ + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\" + tostring('avg_<<MATCHSTR>>')
+  + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>') + \", \" , anomalies)
+  ] | fillnull | eval anomalies = split(replace(anomalies, \",\\s$$$$\", \"\") ,\"\
+  , \") | where anomalies!=\"\" | stats count(anomalies) as count values(anomalies)
+  as anomalies by k8s.cluster.name source.workload.name source.process.name | where
+  count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_outbound_network_activity_from_process_filter`"
+how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster
+  and enable Network Performance Monitoring according to instructions found in Splunk
+  Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup
+  In order to access those metrics from within Splunk Enterprise and ES, the Splunk
+  Infrastructure Monitoring add-on must be installed and configured on a Splunk Search
+  Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and
+  Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the
+  following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n
+  * Metric Resolution 10000"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -22,7 +49,8 @@ tags:
   asset_type: Kubernetes
   confidence: 50
   impact: 50
-  message: Kubernetes Anomalous Outbound Network Activity from Process in kubernetes cluster $host$
+  message: Kubernetes Anomalous Outbound Network Activity from Process in kubernetes
+    cluster $host$
   mitre_attack_id:
   - T1204
   observable:
@@ -34,11 +62,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - tcp.*
-  - udp.*
-  - k8s.cluster.name
-  - source.workload.name
-  - dest.workload.name
-  - udp.packets
   security_domain: network
diff --git a/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml b/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml
index 41d3294198..47b1139b05 100644
--- a/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml
+++ b/detections/cloud/kubernetes_anomalous_traffic_on_network_edge.yml
@@ -5,14 +5,41 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.
+description: The following analytic identifies anomalous network traffic volumes between
+  Kubernetes workloads or between a workload and external sources. It leverages Network
+  Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk
+  Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets,
+  tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the
+  past 30 days to identify significant deviations. This activity is significant as
+  unexpected spikes may indicate unauthorized data transfers or lateral movement.
+  If confirmed malicious, it could lead to data exfiltration or compromise of additional
+  services, potentially resulting in data breaches.
 data_source: []
-search: '| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name span=10s | eval key=''source.workload.name'' + ":" + ''dest.workload.name'' | join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval key=''source.workload.name'' + ":" + ''dest.workload.name'' ] | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 3 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") | where anomalies!="" | stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name dest.workload.name | rename service as k8s.service | where count > 5 | rename k8s.cluster.name as host | `kubernetes_anomalous_traffic_on_network_edge_filter`'
-how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:
-
-  * Name sim_npm_metrics_to_metrics_index
-
-  * Metric Resolution 10000'
+search: "| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics`
+  AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name span=10s
+  | eval key='source.workload.name' + \":\" + 'dest.workload.name' | join type=left
+  key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.*
+  avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h
+  by source.workload.name dest.workload.name | eval key='source.workload.name' + \"\
+  :\" + 'dest.workload.name' ] | eval anomalies = \"\" | foreach stdev_* [ eval anomalies
+  =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 3 * 'stdev_<<MATCHSTR>>'), anomalies
+  + \"<<MATCHSTR>> higher than average by \" + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>'
+  ,2)) + \" Standard Deviations. <<MATCHSTR>>=\" + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\"\
+  \ + tostring('avg_<<MATCHSTR>>') + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>')
+  + \", \" , anomalies) ] | fillnull | eval anomalies = split(replace(anomalies, \"\
+  ,\\s$$$$\", \"\") ,\", \") | where anomalies!=\"\" | stats count(anomalies) as count
+  values(anomalies) as anomalies by k8s.cluster.name source.workload.name dest.workload.name
+  | rename service as k8s.service | where count > 5 | rename k8s.cluster.name as host
+  | `kubernetes_anomalous_traffic_on_network_edge_filter`"
+how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster
+  and enable Network Performance Monitoring according to instructions found in Splunk
+  Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup
+  In order to access those metrics from within Splunk Enterprise and ES, the Splunk
+  Infrastructure Monitoring add-on must be installed and configured on a Splunk Search
+  Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and
+  Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the
+  following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n
+  * Metric Resolution 10000"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -34,11 +61,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - tcp.*
-  - udp.*
-  - k8s.cluster.name
-  - source.workload.name
-  - dest.workload.name
-  - udp.packets
   security_domain: network
diff --git a/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml b/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml
index e81b698d32..531819b83f 100644
--- a/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml
+++ b/detections/cloud/kubernetes_aws_detect_suspicious_kubectl_calls.yml
@@ -5,12 +5,35 @@ date: '2024-10-17'
 author: Rod Soto, Patrick Bareiss, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects anonymous and unauthenticated requests to a Kubernetes cluster. It identifies this behavior by monitoring API calls from users who have not provided any token or password in their request, using data from `kube_audit` logs. This activity is significant for a SOC as it indicates a severe misconfiguration, allowing unfettered access to the cluster with no traceability. If confirmed malicious, an attacker could gain access to sensitive data or control over the cluster, posing a substantial security risk.
+description: The following analytic detects anonymous and unauthenticated requests
+  to a Kubernetes cluster. It identifies this behavior by monitoring API calls from
+  users who have not provided any token or password in their request, using data from
+  `kube_audit` logs. This activity is significant for a SOC as it indicates a severe
+  misconfiguration, allowing unfettered access to the cluster with no traceability.
+  If confirmed malicious, an attacker could gain access to sensitive data or control
+  over the cluster, posing a substantial security risk.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` user.username="system:anonymous" user.groups{} IN ("system:unauthenticated") | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user |`kubernetes_aws_detect_suspicious_kubectl_calls_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
-known_false_positives: Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
+search: '`kube_audit` user.username="system:anonymous" user.groups{} IN ("system:unauthenticated")
+  | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource
+  requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{}
+  user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username
+  as user |`kubernetes_aws_detect_suspicious_kubectl_calls_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+known_false_positives: Kubectl calls are not malicious by nature. However source IP,
+  verb and Object can reveal potential malicious activity, specially anonymous suspicious
+  IPs and sensitive objects such as configmaps or secrets
 references: []
 tags:
   analytic_story:
@@ -28,19 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - objectRef.resource
-  - verb
-  - objectRef.name
-  - objectRef.namespace
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.groups{}
-  - user.uid
-  - user.username
-  - userAgent
-  - verb
   security_domain: threat
diff --git a/detections/cloud/kubernetes_create_or_update_privileged_pod.yml b/detections/cloud/kubernetes_create_or_update_privileged_pod.yml
index 6f6136afd6..4738fb5d03 100644
--- a/detections/cloud/kubernetes_create_or_update_privileged_pod.yml
+++ b/detections/cloud/kubernetes_create_or_update_privileged_pod.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation or update of privileged pods in Kubernetes. It identifies this activity by monitoring Kubernetes Audit logs for pod configurations that include root privileges. This behavior is significant for a SOC as it could indicate an attempt to escalate privileges, exploit the kernel, and gain full access to the host's namespace and devices. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, data breaches, and service disruptions, posing a severe threat to the environment.
+description: The following analytic detects the creation or update of privileged pods
+  in Kubernetes. It identifies this activity by monitoring Kubernetes Audit logs for
+  pod configurations that include root privileges. This behavior is significant for
+  a SOC as it could indicate an attempt to escalate privileges, exploit the kernel,
+  and gain full access to the host's namespace and devices. If confirmed malicious,
+  this activity could lead to unauthorized access to sensitive information, data breaches,
+  and service disruptions, posing a severe threat to the environment.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"privileged\":true* | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{} stage user.username userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_create_or_update_privileged_pod_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"privileged\":true*
+  | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name
+  objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{}
+  stage user.username userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration
+  | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_create_or_update_privileged_pod_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,25 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - user.groups{}
-  - kind
-  - objectRef.name
-  - objectRef.namespace
-  - objectRef.resource
-  - requestObject.kind
-  - requestObject.spec.type
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.username
-  - userAgent
-  - verb
-  - requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_cron_job_creation.yml b/detections/cloud/kubernetes_cron_job_creation.yml
index c74feeb6c2..e55e1dfe54 100644
--- a/detections/cloud/kubernetes_cron_job_creation.yml
+++ b/detections/cloud/kubernetes_cron_job_creation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of a Kubernetes cron job, which is a task scheduled to run automatically at specified intervals. It identifies this activity by monitoring Kubernetes Audit logs for the creation events of cron jobs. This behavior is significant for a SOC as it could allow an attacker to execute malicious tasks repeatedly and automatically, posing a threat to the Kubernetes infrastructure. If confirmed malicious, this activity could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.
+description: The following analytic detects the creation of a Kubernetes cron job,
+  which is a task scheduled to run automatically at specified intervals. It identifies
+  this activity by monitoring Kubernetes Audit logs for the creation events of cron
+  jobs. This behavior is significant for a SOC as it could allow an attacker to execute
+  malicious tasks repeatedly and automatically, posing a threat to the Kubernetes
+  infrastructure. If confirmed malicious, this activity could lead to persistent attacks,
+  service disruptions, or unauthorized access to sensitive information.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` verb=create "objectRef.resource"=cronjobs | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace objectRef.resource requestObject.kind requestObject.spec.schedule requestObject.spec.jobTemplate.spec.template.spec.containers{}.image responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_cron_job_creation_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` verb=create "objectRef.resource"=cronjobs | fillnull | stats
+  count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace
+  objectRef.resource requestObject.kind requestObject.spec.schedule requestObject.spec.jobTemplate.spec.template.spec.containers{}.image
+  responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{}
+  as src_ip, user.username as user | `kubernetes_cron_job_creation_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,25 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - user.groups{}
-  - kind
-  - objectRef.name
-  - objectRef.namespace
-  - objectRef.resource
-  - requestObject.kind
-  - requestObject.spec.schedule
-  - requestObject.spec.jobTemplate.spec.template.spec.containers{}.image
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.007/kubernetes_audit_cron_job_creation/kubernetes_audit_cron_job_creation.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.007/kubernetes_audit_cron_job_creation/kubernetes_audit_cron_job_creation.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_daemonset_deployed.yml b/detections/cloud/kubernetes_daemonset_deployed.yml
index 1755f03692..6a39d9305c 100644
--- a/detections/cloud/kubernetes_daemonset_deployed.yml
+++ b/detections/cloud/kubernetes_daemonset_deployed.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of a DaemonSet in a Kubernetes cluster. This behavior is identified by monitoring Kubernetes Audit logs for the creation event of a DaemonSet. DaemonSets ensure a specific pod runs on every node, making them a potential vector for persistent access. This activity is significant for a SOC as it could indicate an attempt to maintain persistent access to the Kubernetes infrastructure. If confirmed malicious, it could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.
+description: The following analytic detects the creation of a DaemonSet in a Kubernetes
+  cluster. This behavior is identified by monitoring Kubernetes Audit logs for the
+  creation event of a DaemonSet. DaemonSets ensure a specific pod runs on every node,
+  making them a potential vector for persistent access. This activity is significant
+  for a SOC as it could indicate an attempt to maintain persistent access to the Kubernetes
+  infrastructure. If confirmed malicious, it could lead to persistent attacks, service
+  disruptions, or unauthorized access to sensitive information.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` "objectRef.resource"=daemonsets verb=create | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_daemonset_deployed_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` "objectRef.resource"=daemonsets verb=create | fillnull | stats
+  count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace
+  objectRef.resource requestObject.kind responseStatus.code sourceIPs{} stage user.username
+  userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_daemonset_deployed_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - user.groups{}
-  - kind
-  - objectRef.name
-  - objectRef.namespace
-  - objectRef.resource
-  - requestObject.kind
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_audit_daemonset_created/kubernetes_audit_daemonset_created.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_audit_daemonset_created/kubernetes_audit_daemonset_created.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_falco_shell_spawned.yml b/detections/cloud/kubernetes_falco_shell_spawned.yml
index cc9605a6ae..6185ae4b22 100644
--- a/detections/cloud/kubernetes_falco_shell_spawned.yml
+++ b/detections/cloud/kubernetes_falco_shell_spawned.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects instances where a shell is spawned within a Kubernetes container. Leveraging Falco, a cloud-native runtime security tool, this analytic monitors system calls within the Kubernetes environment and flags when a shell is spawned. This activity is significant for a SOC as it may indicate unauthorized access, allowing an attacker to execute arbitrary commands, manipulate container processes, or escalate privileges. If confirmed malicious, this could lead to data breaches, service disruptions, or unauthorized access to sensitive information, severely impacting the Kubernetes infrastructure's integrity and security.
+description: The following analytic detects instances where a shell is spawned within
+  a Kubernetes container. Leveraging Falco, a cloud-native runtime security tool,
+  this analytic monitors system calls within the Kubernetes environment and flags
+  when a shell is spawned. This activity is significant for a SOC as it may indicate
+  unauthorized access, allowing an attacker to execute arbitrary commands, manipulate
+  container processes, or escalate privileges. If confirmed malicious, this could
+  lead to data breaches, service disruptions, or unauthorized access to sensitive
+  information, severely impacting the Kubernetes infrastructure's integrity and security.
 data_source:
 - Kubernetes Falco
-search: '`kube_container_falco` "A shell was spawned in a container" |  fillnull | stats count by container_image container_image_tag container_name parent proc_exepath process user | `kubernetes_falco_shell_spawned_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_container_falco` "A shell was spawned in a container" |  fillnull |
+  stats count by container_image container_image_tag container_name parent proc_exepath
+  process user | `kubernetes_falco_shell_spawned_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - container_image
-  - container_image_tag
-  - container_name
-  - parent
-  - proc_exepath
-  - process
-  - user
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_falco_shell_spawned/kubernetes_falco_shell_spawned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_falco_shell_spawned/kubernetes_falco_shell_spawned.log
     sourcetype: kube:container:falco
     source: kubernetes
diff --git a/detections/cloud/kubernetes_newly_seen_tcp_edge.yml b/detections/cloud/kubernetes_newly_seen_tcp_edge.yml
index 8e5c1cc92f..5c1a415ae9 100644
--- a/detections/cloud/kubernetes_newly_seen_tcp_edge.yml
+++ b/detections/cloud/kubernetes_newly_seen_tcp_edge.yml
@@ -5,14 +5,31 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies newly seen TCP communication between source and destination workload pairs within a Kubernetes cluster. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to spot new inter-workload communications. This is significant as new connections can indicate changes in application behavior or potential security threats. If malicious, unauthorized connections could lead to data breaches, privilege escalation, lateral movement, or disruption of critical services, compromising the application's integrity, availability, and confidentiality.
+description: The following analytic identifies newly seen TCP communication between
+  source and destination workload pairs within a Kubernetes cluster. It leverages
+  Network Performance Monitoring metrics collected via an OTEL collector and pulled
+  from Splunk Observability Cloud. The detection compares network activity over the
+  last hour with the past 30 days to spot new inter-workload communications. This
+  is significant as new connections can indicate changes in application behavior or
+  potential security threats. If malicious, unauthorized connections could lead to
+  data breaches, privilege escalation, lateral movement, or disruption of critical
+  services, compromising the application's integrity, availability, and confidentiality.
 data_source: []
-search: '| mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name | eval current="True" | append [ mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval current="false" ] | eventstats values(current) as current by source.workload.name dest.workload.name | search current="true" current!="false" | rename k8s.cluster.name as host | `kubernetes_newly_seen_tcp_edge_filter`'
-how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:
-
-  * Name sim_npm_metrics_to_metrics_index
-
-  * Metric Resolution 10000'
+search: '| mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics`
+  AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name | eval
+  current="True" | append [ mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics`
+  AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval current="false"
+  ] | eventstats values(current) as current by source.workload.name dest.workload.name
+  | search current="true" current!="false" | rename k8s.cluster.name as host | `kubernetes_newly_seen_tcp_edge_filter`'
+how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster
+  and enable Network Performance Monitoring according to instructions found in Splunk
+  Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup
+  In order to access those metrics from within Splunk Enterprise and ES, the Splunk
+  Infrastructure Monitoring add-on must be installed and configured on a Splunk Search
+  Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and
+  Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the
+  following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n
+  * Metric Resolution 10000"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -34,9 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.cluster.name
-  - source.workload.name
-  - dest.workload.name
-  - tcp.packets
   security_domain: network
diff --git a/detections/cloud/kubernetes_newly_seen_udp_edge.yml b/detections/cloud/kubernetes_newly_seen_udp_edge.yml
index 7e7a7eec5d..e1d780a91e 100644
--- a/detections/cloud/kubernetes_newly_seen_udp_edge.yml
+++ b/detections/cloud/kubernetes_newly_seen_udp_edge.yml
@@ -5,14 +5,31 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects UDP communication between a newly seen source and destination workload pair within a Kubernetes cluster. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. This detection compares network activity over the last hour with the past 30 days to identify new inter-workload communication. Such changes in network behavior can indicate potential security threats or anomalies. If confirmed malicious, unauthorized connections may enable attackers to infiltrate the application ecosystem, leading to data breaches, privilege escalation, lateral movement, or disruption of critical services.
+description: The following analytic detects UDP communication between a newly seen
+  source and destination workload pair within a Kubernetes cluster. It leverages Network
+  Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk
+  Observability Cloud. This detection compares network activity over the last hour
+  with the past 30 days to identify new inter-workload communication. Such changes
+  in network behavior can indicate potential security threats or anomalies. If confirmed
+  malicious, unauthorized connections may enable attackers to infiltrate the application
+  ecosystem, leading to data breaches, privilege escalation, lateral movement, or
+  disruption of critical services.
 data_source: []
-search: '| mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name | eval current="True" | append [ mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval current="false" ] | eventstats values(current) as current by source.workload.name dest.workload.name | search current="true" current!="false" | rename k8s.cluster.name as host | `kubernetes_newly_seen_udp_edge_filter`'
-how_to_implement: 'To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:
-
-  * Name sim_npm_metrics_to_metrics_index
-
-  * Metric Resolution 10000'
+search: '| mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics`
+  AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name | eval
+  current="True" | append [ mstats count(udp.packets) as udp.packets_count where `kubernetes_metrics`
+  AND earliest=-30d latest=-1h by source.workload.name dest.workload.name | eval current="false"
+  ] | eventstats values(current) as current by source.workload.name dest.workload.name
+  | search current="true" current!="false" | rename k8s.cluster.name as host | `kubernetes_newly_seen_udp_edge_filter`'
+how_to_implement: "To gather NPM metrics the Open Telemetry to the Kubernetes Cluster
+  and enable Network Performance Monitoring according to instructions found in Splunk
+  Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup
+  In order to access those metrics from within Splunk Enterprise and ES, the Splunk
+  Infrastructure Monitoring add-on must be installed and configured on a Splunk Search
+  Head.  Once installed, first configure the add-on with your O11y Cloud Org ID and
+  Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the
+  following settings, and any other settings left at default:\n* Name sim_npm_metrics_to_metrics_index\n
+  * Metric Resolution 10000"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -34,9 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.cluster.name
-  - source.workload.name
-  - dest.workload.name
-  - udp.packets
   security_domain: network
diff --git a/detections/cloud/kubernetes_nginx_ingress_lfi.yml b/detections/cloud/kubernetes_nginx_ingress_lfi.yml
index 6d5faf3966..d2749d8a64 100644
--- a/detections/cloud/kubernetes_nginx_ingress_lfi.yml
+++ b/detections/cloud/kubernetes_nginx_ingress_lfi.yml
@@ -5,9 +5,22 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects local file inclusion (LFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields such as `request` and `status` to identify suspicious patterns indicative of LFI attempts. This activity is significant because LFI attacks can allow attackers to read sensitive files from the server, potentially exposing critical information. If confirmed malicious, this could lead to unauthorized access to sensitive data, further exploitation, and potential compromise of the Kubernetes environment.
+description: The following analytic detects local file inclusion (LFI) attacks targeting
+  Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields
+  such as `request` and `status` to identify suspicious patterns indicative of LFI
+  attempts. This activity is significant because LFI attacks can allow attackers to
+  read sensitive files from the server, potentially exposing critical information.
+  If confirmed malicious, this could lead to unauthorized access to sensitive data,
+  further exploitation, and potential compromise of the Kubernetes environment.
 data_source: []
-search: '`kubernetes_container_controller` | rex field=_raw "^(?<remote_addr>\S+)\s+-\s+-\s+\[(?<time_local>[^\]]*)\]\s\"(?<request>[^\"]*)\"\s(?<status>\S*)\s(?<body_bytes_sent>\S*)\s\"(?<http_referer>[^\"]*)\"\s\"(?<http_user_agent>[^\"]*)\"\s(?<request_length>\S*)\s(?<request_time>\S*)\s\[(?<proxy_upstream_name>[^\]]*)\]\s\[(?<proxy_alternative_upstream_name>[^\]]*)\]\s(?<upstream_addr>\S*)\s(?<upstream_response_length>\S*)\s(?<upstream_response_time>\S*)\s(?<upstream_status>\S*)\s(?<req_id>\S*)" | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | rex field=request "^(?<http_method>\S+)\s(?<url>\S+)\s" | eval phase="operate" | eval severity="high" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, status, url, http_method, host, http_user_agent, proxy, phase, severity, request | lookup local_file_inclusion_paths local_file_inclusion_paths AS request OUTPUT lfi_path | search lfi_path=yes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_lfi_filter`'
+search: '`kubernetes_container_controller` | rex field=_raw "^(?<remote_addr>\S+)\s+-\s+-\s+\[(?<time_local>[^\]]*)\]\s\"(?<request>[^\"]*)\"\s(?<status>\S*)\s(?<body_bytes_sent>\S*)\s\"(?<http_referer>[^\"]*)\"\s\"(?<http_user_agent>[^\"]*)\"\s(?<request_length>\S*)\s(?<request_time>\S*)\s\[(?<proxy_upstream_name>[^\]]*)\]\s\[(?<proxy_alternative_upstream_name>[^\]]*)\]\s(?<upstream_addr>\S*)\s(?<upstream_response_length>\S*)\s(?<upstream_response_time>\S*)\s(?<upstream_status>\S*)\s(?<req_id>\S*)"
+  | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as
+  proxy | rex field=request "^(?<http_method>\S+)\s(?<url>\S+)\s" | eval phase="operate"
+  | eval severity="high" | stats count min(_time) as firstTime max(_time) as lastTime
+  by src_ip, status, url, http_method, host, http_user_agent, proxy, phase, severity,
+  request | lookup local_file_inclusion_paths local_file_inclusion_paths AS request
+  OUTPUT lfi_path | search lfi_path=yes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `kubernetes_nginx_ingress_lfi_filter`'
 how_to_implement: You must ingest Kubernetes logs through Splunk Connect for Kubernetes.
 known_false_positives: unknown
 references:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,12 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - raw
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kubernetes_nginx_lfi_attack/kubernetes_nginx_lfi_attack.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kubernetes_nginx_lfi_attack/kubernetes_nginx_lfi_attack.log
     sourcetype: kube:container:controller
     source: kubernetes
diff --git a/detections/cloud/kubernetes_nginx_ingress_rfi.yml b/detections/cloud/kubernetes_nginx_ingress_rfi.yml
index 52aee47562..27b8253f3f 100644
--- a/detections/cloud/kubernetes_nginx_ingress_rfi.yml
+++ b/detections/cloud/kubernetes_nginx_ingress_rfi.yml
@@ -5,9 +5,21 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects remote file inclusion (RFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs from the Nginx ingress controller, parsing fields such as `remote_addr`, `request`, and `url` to identify suspicious activity. This activity is significant because RFI attacks can allow attackers to execute arbitrary code or access sensitive files on the server. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the Kubernetes environment.
+description: The following analytic detects remote file inclusion (RFI) attacks targeting
+  Kubernetes Nginx ingress controllers. It leverages Kubernetes logs from the Nginx
+  ingress controller, parsing fields such as `remote_addr`, `request`, and `url` to
+  identify suspicious activity. This activity is significant because RFI attacks can
+  allow attackers to execute arbitrary code or access sensitive files on the server.
+  If confirmed malicious, this could lead to unauthorized access, data exfiltration,
+  or further compromise of the Kubernetes environment.
 data_source: []
-search: '`kubernetes_container_controller` | rex field=_raw "^(?<remote_addr>\S+)\s+-\s+-\s+\[(?<time_local>[^\]]*)\]\s\"(?<request>[^\"]*)\"\s(?<status>\S*)\s(?<body_bytes_sent>\S*)\s\"(?<http_referer>[^\"]*)\"\s\"(?<http_user_agent>[^\"]*)\"\s(?<request_length>\S*)\s(?<request_time>\S*)\s\[(?<proxy_upstream_name>[^\]]*)\]\s\[(?<proxy_alternative_upstream_name>[^\]]*)\]\s(?<upstream_addr>\S*)\s(?<upstream_response_length>\S*)\s(?<upstream_response_time>\S*)\s(?<upstream_status>\S*)\s(?<req_id>\S*)" | rex field=request "^(?<http_method>\S+)?\s(?<url>\S+)\s" | rex field=url "(?<dest_ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" | search dest_ip=* | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name as proxy | eval phase="operate" | eval severity="medium" | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, dest_ip status, url, http_method, host, http_user_agent, proxy, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_rfi_filter`'
+search: '`kubernetes_container_controller` | rex field=_raw "^(?<remote_addr>\S+)\s+-\s+-\s+\[(?<time_local>[^\]]*)\]\s\"(?<request>[^\"]*)\"\s(?<status>\S*)\s(?<body_bytes_sent>\S*)\s\"(?<http_referer>[^\"]*)\"\s\"(?<http_user_agent>[^\"]*)\"\s(?<request_length>\S*)\s(?<request_time>\S*)\s\[(?<proxy_upstream_name>[^\]]*)\]\s\[(?<proxy_alternative_upstream_name>[^\]]*)\]\s(?<upstream_addr>\S*)\s(?<upstream_response_length>\S*)\s(?<upstream_response_time>\S*)\s(?<upstream_status>\S*)\s(?<req_id>\S*)"
+  | rex field=request "^(?<http_method>\S+)?\s(?<url>\S+)\s" | rex field=url "(?<dest_ip>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"
+  | search dest_ip=* | rename remote_addr AS src_ip, upstream_status as status, proxy_upstream_name
+  as proxy | eval phase="operate" | eval severity="medium" | stats count min(_time)
+  as firstTime max(_time) as lastTime by src_ip, dest_ip status, url, http_method,
+  host, http_user_agent, proxy, phase, severity | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `kubernetes_nginx_ingress_rfi_filter`'
 how_to_implement: You must ingest Kubernetes logs through Splunk Connect for Kubernetes.
 known_false_positives: unknown
 references:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,12 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - raw
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kuberntest_nginx_rfi_attack/kubernetes_nginx_rfi_attack.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1212/kuberntest_nginx_rfi_attack/kubernetes_nginx_rfi_attack.log
     sourcetype: kube:container:controller
     source: kubernetes
diff --git a/detections/cloud/kubernetes_node_port_creation.yml b/detections/cloud/kubernetes_node_port_creation.yml
index df23bbc02e..297f0cb0fa 100644
--- a/detections/cloud/kubernetes_node_port_creation.yml
+++ b/detections/cloud/kubernetes_node_port_creation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of a Kubernetes NodePort service, which exposes a service to the external network. It identifies this activity by monitoring Kubernetes Audit logs for the creation of NodePort services. This behavior is significant for a SOC as it could allow an attacker to access internal services, posing a threat to the Kubernetes infrastructure's integrity and security. If confirmed malicious, this activity could lead to data breaches, service disruptions, or unauthorized access to sensitive information.
+description: The following analytic detects the creation of a Kubernetes NodePort
+  service, which exposes a service to the external network. It identifies this activity
+  by monitoring Kubernetes Audit logs for the creation of NodePort services. This
+  behavior is significant for a SOC as it could allow an attacker to access internal
+  services, posing a threat to the Kubernetes infrastructure's integrity and security.
+  If confirmed malicious, this activity could lead to data breaches, service disruptions,
+  or unauthorized access to sensitive information.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` "objectRef.resource"=services verb=create requestObject.spec.type=NodePort | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace objectRef.resource requestObject.kind requestObject.spec.type responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_node_port_creation_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` "objectRef.resource"=services verb=create requestObject.spec.type=NodePort
+  | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name
+  objectRef.namespace objectRef.resource requestObject.kind requestObject.spec.type
+  responseStatus.code sourceIPs{} stage user.username userAgent verb | rename sourceIPs{}
+  as src_ip, user.username as user | `kubernetes_node_port_creation_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,24 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - user.groups{}
-  - kind
-  - objectRef.name
-  - objectRef.namespace
-  - objectRef.resource
-  - requestObject.kind
-  - requestObject.spec.type
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kube_audit_create_node_port_service/kube_audit_create_node_port_service.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kube_audit_create_node_port_service/kube_audit_create_node_port_service.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_pod_created_in_default_namespace.yml b/detections/cloud/kubernetes_pod_created_in_default_namespace.yml
index ee30e901cf..5f18991d93 100644
--- a/detections/cloud/kubernetes_pod_created_in_default_namespace.yml
+++ b/detections/cloud/kubernetes_pod_created_in_default_namespace.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of Kubernetes pods in the default, kube-system, or kube-public namespaces. It leverages Kubernetes audit logs to identify pod creation events within these specific namespaces. This activity is significant for a SOC as it may indicate an attacker attempting to hide their presence or evade defenses. Unauthorized pod creation in these namespaces can suggest a successful cluster breach, potentially leading to privilege escalation, persistent access, or further malicious activities within the cluster.
+description: The following analytic detects the creation of Kubernetes pods in the
+  default, kube-system, or kube-public namespaces. It leverages Kubernetes audit logs
+  to identify pod creation events within these specific namespaces. This activity
+  is significant for a SOC as it may indicate an attacker attempting to hide their
+  presence or evade defenses. Unauthorized pod creation in these namespaces can suggest
+  a successful cluster breach, potentially leading to privilege escalation, persistent
+  access, or further malicious activities within the cluster.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=pods verb=create objectRef.namespace IN ("default", "kube-system", "kube-public") | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_pod_created_in_default_namespace_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=pods verb=create objectRef.namespace IN ("default",
+  "kube-system", "kube-public") | fillnull | stats count by objectRef.name objectRef.namespace
+  objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{}
+  stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as
+  src_ip, user.username as user | `kubernetes_pod_created_in_default_namespace_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,25 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - objectRef.resource
-  - verb
-  - objectRef.name
-  - objectRef.namespace
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.groups{}
-  - user.uid
-  - user.username
-  - userAgent
-  - verb
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_pod_with_host_network_attachment.yml b/detections/cloud/kubernetes_pod_with_host_network_attachment.yml
index f0cbf3cf4d..3104dee5a4 100644
--- a/detections/cloud/kubernetes_pod_with_host_network_attachment.yml
+++ b/detections/cloud/kubernetes_pod_with_host_network_attachment.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation or update of a Kubernetes pod with host network attachment. It leverages Kubernetes Audit logs to identify pods configured with host network settings. This activity is significant for a SOC as it could allow an attacker to monitor all network traffic on the node, potentially capturing sensitive information and escalating privileges. If confirmed malicious, this could lead to unauthorized access, data breaches, and service disruptions, severely impacting the security and integrity of the Kubernetes environment.
+description: The following analytic detects the creation or update of a Kubernetes
+  pod with host network attachment. It leverages Kubernetes Audit logs to identify
+  pods configured with host network settings. This activity is significant for a SOC
+  as it could allow an attacker to monitor all network traffic on the node, potentially
+  capturing sensitive information and escalating privileges. If confirmed malicious,
+  this could lead to unauthorized access, data breaches, and service disruptions,
+  severely impacting the security and integrity of the Kubernetes environment.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"hostNetwork\":true* | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{} stage user.username userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_pod_with_host_network_attachment_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"hostNetwork\":true*
+  | fillnull | stats count values(user.groups{}) as user_groups by kind objectRef.name
+  objectRef.namespace objectRef.resource requestObject.kind responseStatus.code sourceIPs{}
+  stage user.username userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration
+  | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_pod_with_host_network_attachment_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,25 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - user.groups{}
-  - kind
-  - objectRef.name
-  - objectRef.namespace
-  - objectRef.resource
-  - requestObject.kind
-  - requestObject.spec.type
-  - responseStatus.code
-  - sourceIPs{}
-  - stage
-  - user.username
-  - userAgent
-  - verb
-  - requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_privileged_pod/kubernetes_privileged_pod.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_previously_unseen_container_image_name.yml b/detections/cloud/kubernetes_previously_unseen_container_image_name.yml
index 776f509e19..03ab1d7272 100644
--- a/detections/cloud/kubernetes_previously_unseen_container_image_name.yml
+++ b/detections/cloud/kubernetes_previously_unseen_container_image_name.yml
@@ -5,32 +5,40 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies the creation of containerized workloads using previously unseen images in a Kubernetes cluster. It leverages process metrics from an OTEL collector and Kubernetes cluster receiver, pulled from Splunk Observability Cloud. The detection compares container image names seen in the last hour with those from the previous 30 days. This activity is significant as unfamiliar container images may introduce vulnerabilities, malware, or misconfigurations, posing threats to the cluster's integrity. If confirmed malicious, compromised images can lead to data breaches, service disruptions, unauthorized access, and potential lateral movement within the cluster.
+description: The following analytic identifies the creation of containerized workloads
+  using previously unseen images in a Kubernetes cluster. It leverages process metrics
+  from an OTEL collector and Kubernetes cluster receiver, pulled from Splunk Observability
+  Cloud. The detection compares container image names seen in the last hour with those
+  from the previous 30 days. This activity is significant as unfamiliar container
+  images may introduce vulnerabilities, malware, or misconfigurations, posing threats
+  to the cluster's integrity. If confirmed malicious, compromised images can lead
+  to data breaches, service disruptions, unauthorized access, and potential lateral
+  movement within the cluster.
 data_source: []
-search: '| mstats  count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` AND earliest=-24h by host.name k8s.cluster.name k8s.node.name container.image.name | eval current="True" | append [mstats  count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` AND earliest=-30d latest=-1h  by host.name k8s.cluster.name k8s.node.name container.image.name | eval current="false" ] | stats values(current) as current by host.name k8s.cluster.name k8s.node.name container.image.name | search current="true" AND current!="false" | rename host.name as host | `kubernetes_previously_unseen_container_image_name_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: '| mstats  count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics`
+  AND earliest=-24h by host.name k8s.cluster.name k8s.node.name container.image.name
+  | eval current="True" | append [mstats  count(k8s.container.ready) as k8s.container.ready_count
+  where `kubernetes_metrics` AND earliest=-30d latest=-1h  by host.name k8s.cluster.name
+  k8s.node.name container.image.name | eval current="false" ] | stats values(current)
+  as current by host.name k8s.cluster.name k8s.node.name container.image.name | search
+  current="true" AND current!="false" | rename host.name as host | `kubernetes_previously_unseen_container_image_name_filter`'
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -52,9 +60,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - k8s.container.ready_count
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_previously_unseen_process.yml b/detections/cloud/kubernetes_previously_unseen_process.yml
index f817e5c7d3..98b615ad9b 100644
--- a/detections/cloud/kubernetes_previously_unseen_process.yml
+++ b/detections/cloud/kubernetes_previously_unseen_process.yml
@@ -5,32 +5,41 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects previously unseen processes within the Kubernetes environment on master or worker nodes. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud. This detection compares processes observed in the last hour against those seen in the previous 30 days. Identifying new processes is crucial as they may indicate unauthorized activity or attempts to compromise the node. If confirmed malicious, these processes could lead to data exfiltration, privilege escalation, denial-of-service attacks, or the introduction of malware, posing significant risks to the Kubernetes cluster.
+description: The following analytic detects previously unseen processes within the
+  Kubernetes environment on master or worker nodes. It leverages process metrics collected
+  via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability
+  Cloud. This detection compares processes observed in the last hour against those
+  seen in the previous 30 days. Identifying new processes is crucial as they may indicate
+  unauthorized activity or attempts to compromise the node. If confirmed malicious,
+  these processes could lead to data exfiltration, privilege escalation, denial-of-service
+  attacks, or the introduction of malware, posing significant risks to the Kubernetes
+  cluster.
 data_source: []
-search: '| mstats  count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name process.executable.name | eval current="True" | append [mstats  count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name process.executable.name ] | stats count values(current) as current by host.name k8s.cluster.name k8s.node.name process.executable.name | where count=1 and current="True" | rename host.name as host | `kubernetes_previously_unseen_process_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: '| mstats  count(process.memory.utilization) as process.memory.utilization_count
+  where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name
+  process.executable.name | eval current="True" | append [mstats  count(process.memory.utilization)
+  as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d
+  latest=-1h by host.name k8s.cluster.name k8s.node.name process.executable.name ]
+  | stats count values(current) as current by host.name k8s.cluster.name k8s.node.name
+  process.executable.name | where count=1 and current="True" | rename host.name as
+  host | `kubernetes_previously_unseen_process_filter`'
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -52,10 +61,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.memory.utilization
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
-  - process.executable.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_process_running_from_new_path.yml b/detections/cloud/kubernetes_process_running_from_new_path.yml
index ffc4da7b50..1c4157f55e 100644
--- a/detections/cloud/kubernetes_process_running_from_new_path.yml
+++ b/detections/cloud/kubernetes_process_running_from_new_path.yml
@@ -5,32 +5,42 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies processes running from newly seen paths within a Kubernetes environment. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud using the Splunk Infrastructure Monitoring Add-on. This detection compares processes observed in the last hour with those seen over the previous 30 days. This activity is significant as it may indicate unauthorized changes, compromised nodes, or the introduction of malicious software. If confirmed malicious, it could lead to unauthorized process execution, control over critical resources, data exfiltration, privilege escalation, or malware introduction within the Kubernetes cluster.
+description: The following analytic identifies processes running from newly seen paths
+  within a Kubernetes environment. It leverages process metrics collected via an OTEL
+  collector and hostmetrics receiver, and data is pulled from Splunk Observability
+  Cloud using the Splunk Infrastructure Monitoring Add-on. This detection compares
+  processes observed in the last hour with those seen over the previous 30 days. This
+  activity is significant as it may indicate unauthorized changes, compromised nodes,
+  or the introduction of malicious software. If confirmed malicious, it could lead
+  to unauthorized process execution, control over critical resources, data exfiltration,
+  privilege escalation, or malware introduction within the Kubernetes cluster.
 data_source: []
-search: '| mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name process.pid process.executable.path process.executable.name | eval current="True" | append [ mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name process.pid process.executable.path process.executable.name ] | stats count values(current) as current by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name process.executable.path | where count=1 and current="True" | rename host.name as host | `kubernetes_process_running_from_new_path_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: '| mstats count(process.memory.utilization) as process.memory.utilization_count
+  where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name
+  process.pid process.executable.path process.executable.name | eval current="True"
+  | append [ mstats count(process.memory.utilization) as process.memory.utilization_count
+  where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name
+  k8s.node.name process.pid process.executable.path process.executable.name ] | stats
+  count values(current) as current by host.name k8s.cluster.name k8s.node.name process.pid
+  process.executable.name process.executable.path | where count=1 and current="True"
+  | rename host.name as host | `kubernetes_process_running_from_new_path_filter`'
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -52,10 +62,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.memory.utilization
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
-  - process.executable.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml b/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml
index 300ef634d1..34f13d5c05 100644
--- a/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml
+++ b/detections/cloud/kubernetes_process_with_anomalous_resource_utilisation.yml
@@ -5,32 +5,44 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies high resource utilization anomalies in Kubernetes processes. It leverages process metrics from an OTEL collector and hostmetrics receiver, fetched via the Splunk Infrastructure Monitoring Add-on. The detection uses a lookup table with average and standard deviation values to spot anomalies. This activity is significant as high resource utilization can indicate security threats like cryptojacking, unauthorized data exfiltration, or compromised containers. If confirmed malicious, such anomalies can disrupt services, exhaust resources, increase costs, and allow attackers to evade detection or maintain access.
+description: The following analytic identifies high resource utilization anomalies
+  in Kubernetes processes. It leverages process metrics from an OTEL collector and
+  hostmetrics receiver, fetched via the Splunk Infrastructure Monitoring Add-on. The
+  detection uses a lookup table with average and standard deviation values to spot
+  anomalies. This activity is significant as high resource utilization can indicate
+  security threats like cryptojacking, unauthorized data exfiltration, or compromised
+  containers. If confirmed malicious, such anomalies can disrupt services, exhaust
+  resources, increase costs, and allow attackers to evade detection or maintain access.
 data_source: []
-search: '| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s | eval key = ''k8s.cluster.name'' + ":" + ''host.name'' + ":" + ''process.executable.name'' | lookup k8s_process_resource_baseline key | fillnull | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 4 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | eval anomalies = replace(anomalies, ",\s$", "") | where anomalies!="" | stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name | sort - count | where count > 5 | rename host.name as host | `kubernetes_process_with_anomalous_resource_utilisation_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name
+  k8s.cluster.name k8s.node.name process.executable.name span=10s | eval key = 'k8s.cluster.name'
+  + \":\" + 'host.name' + \":\" + 'process.executable.name' | lookup k8s_process_resource_baseline
+  key | fillnull | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>'
+  > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + \"<<MATCHSTR>> higher
+  than average by \" + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>'
+  ,2)) + \" Standard Deviations. <<MATCHSTR>>=\" + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\"\
+  \ + tostring('avg_<<MATCHSTR>>') + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>')
+  + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") |
+  where anomalies!=\"\" | stats count values(anomalies) as anomalies by host.name
+  k8s.cluster.name k8s.node.name process.executable.name | sort - count | where count
+  > 5 | rename host.name as host | `kubernetes_process_with_anomalous_resource_utilisation_filter`"
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -52,10 +64,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.*
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
-  - process.executable.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml b/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml
index e19222a8c0..0eb3e9d724 100644
--- a/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml
+++ b/detections/cloud/kubernetes_process_with_resource_ratio_anomalies.yml
@@ -5,32 +5,49 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects anomalous changes in resource utilization ratios for processes running on a Kubernetes node. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, analyzed through Splunk Observability Cloud. The detection uses a lookup table containing average and standard deviation values for various resource ratios (e.g., CPU:memory, CPU:disk operations). Significant deviations from these baselines may indicate compromised processes, malicious activity, or misconfigurations. If confirmed malicious, this could signify a security breach, allowing attackers to manipulate workloads, potentially leading to data exfiltration or service disruption.
+description: The following analytic detects anomalous changes in resource utilization
+  ratios for processes running on a Kubernetes node. It leverages process metrics
+  collected via an OTEL collector and hostmetrics receiver, analyzed through Splunk
+  Observability Cloud. The detection uses a lookup table containing average and standard
+  deviation values for various resource ratios (e.g., CPU:memory, CPU:disk operations).
+  Significant deviations from these baselines may indicate compromised processes,
+  malicious activity, or misconfigurations. If confirmed malicious, this could signify
+  a security breach, allowing attackers to manipulate workloads, potentially leading
+  to data exfiltration or service disruption.
 data_source: []
-search: '| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s | eval cpu:mem = ''process.cpu.utilization''/''process.memory.utilization'' | eval cpu:disk = ''process.cpu.utilization''/''process.disk.operations'' | eval mem:disk = ''process.memory.utilization''/''process.disk.operations'' | eval cpu:threads = ''process.cpu.utilization''/''process.threads'' | eval disk:threads = ''process.disk.operations''/''process.threads'' | eval key = ''k8s.cluster.name'' + ":" + ''host.name'' + ":" + ''process.executable.name'' | lookup k8s_process_resource_ratio_baseline key | fillnull | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( ''<<MATCHSTR>>'' > (''avg_<<MATCHSTR>>'' + 4 * ''stdev_<<MATCHSTR>>''), anomalies + "<<MATCHSTR>> ratio higher than average by " + tostring(round((''<<MATCHSTR>>'' - ''avg_<<MATCHSTR>>'')/''stdev_<<MATCHSTR>>'' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring(''<<MATCHSTR>>'') + " avg_<<MATCHSTR>>=" + tostring(''avg_<<MATCHSTR>>'') + " ''stdev_<<MATCHSTR>>''=" + tostring(''stdev_<<MATCHSTR>>'') + ", " , anomalies) ] | eval anomalies = replace(anomalies, ",\s$", "") | where anomalies!="" | stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name | where count > 5 | rename host.name as host | `kubernetes_process_with_resource_ratio_anomalies_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: "| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name
+  k8s.cluster.name k8s.node.name process.executable.name span=10s | eval cpu:mem =
+  'process.cpu.utilization'/'process.memory.utilization' | eval cpu:disk = 'process.cpu.utilization'/'process.disk.operations'
+  | eval mem:disk = 'process.memory.utilization'/'process.disk.operations' | eval
+  cpu:threads = 'process.cpu.utilization'/'process.threads' | eval disk:threads =
+  'process.disk.operations'/'process.threads' | eval key = 'k8s.cluster.name' + \"\
+  :\" + 'host.name' + \":\" + 'process.executable.name' | lookup k8s_process_resource_ratio_baseline
+  key | fillnull | eval anomalies = \"\" | foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>'
+  > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + \"<<MATCHSTR>> ratio
+  higher than average by \" + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>'
+  ,2)) + \" Standard Deviations. <<MATCHSTR>>=\" + tostring('<<MATCHSTR>>') + \" avg_<<MATCHSTR>>=\"\
+  \ + tostring('avg_<<MATCHSTR>>') + \" 'stdev_<<MATCHSTR>>'=\" + tostring('stdev_<<MATCHSTR>>')
+  + \", \" , anomalies) ] | eval anomalies = replace(anomalies, \",\\s$\", \"\") |
+  where anomalies!=\"\" | stats count values(anomalies) as anomalies by host.name
+  k8s.cluster.name k8s.node.name process.executable.name | where count > 5 | rename
+  host.name as host | `kubernetes_process_with_resource_ratio_anomalies_filter`"
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart
@@ -52,10 +69,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.*
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
-  - process.executable.name
   security_domain: network
diff --git a/detections/cloud/kubernetes_scanner_image_pulling.yml b/detections/cloud/kubernetes_scanner_image_pulling.yml
index a0b86d1f18..6538d3e553 100644
--- a/detections/cloud/kubernetes_scanner_image_pulling.yml
+++ b/detections/cloud/kubernetes_scanner_image_pulling.yml
@@ -5,9 +5,21 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the pulling of known Kubernetes security scanner images such as kube-hunter, kube-bench, and kube-recon. It leverages Kubernetes logs ingested through Splunk Connect for Kubernetes, specifically monitoring for messages indicating the pulling of these images. This activity is significant because the use of security scanners can indicate an attempt to identify vulnerabilities within the Kubernetes environment. If confirmed malicious, this could lead to the discovery and exploitation of security weaknesses, potentially compromising the entire Kubernetes cluster.
+description: The following analytic detects the pulling of known Kubernetes security
+  scanner images such as kube-hunter, kube-bench, and kube-recon. It leverages Kubernetes
+  logs ingested through Splunk Connect for Kubernetes, specifically monitoring for
+  messages indicating the pulling of these images. This activity is significant because
+  the use of security scanners can indicate an attempt to identify vulnerabilities
+  within the Kubernetes environment. If confirmed malicious, this could lead to the
+  discovery and exploitation of security weaknesses, potentially compromising the
+  entire Kubernetes cluster.
 data_source: []
-search: '`kube_objects_events` object.message IN ("Pulling image *kube-hunter*", "Pulling image *kube-bench*", "Pulling image *kube-recon*", "Pulling image *kube-recon*") | rename object.* AS * | rename involvedObject.* AS * | rename source.host AS host | eval phase="operate" | eval severity="high" | stats min(_time) as firstTime max(_time) as lastTime count by host, name, namespace, kind, reason, message, phase, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_scanner_image_pulling_filter`'
+search: '`kube_objects_events` object.message IN ("Pulling image *kube-hunter*", "Pulling
+  image *kube-bench*", "Pulling image *kube-recon*", "Pulling image *kube-recon*")
+  | rename object.* AS * | rename involvedObject.* AS * | rename source.host AS host
+  | eval phase="operate" | eval severity="high" | stats min(_time) as firstTime max(_time)
+  as lastTime count by host, name, namespace, kind, reason, message, phase, severity
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kubernetes_scanner_image_pulling_filter`'
 how_to_implement: You must ingest Kubernetes logs through Splunk Connect for Kubernetes.
 known_false_positives: unknown
 references:
@@ -18,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - object.message
-  - source.host
-  - object.involvedObject.name
-  - object.involvedObject.namespace
-  - object.involvedObject.kind
-  - object.message
-  - object.reason
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_kube_hunter/kubernetes_kube_hunter.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_kube_hunter/kubernetes_kube_hunter.json
     sourcetype: kube:objects:events
     source: kubernetes
diff --git a/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml b/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml
index dea13b3cc2..0afc937374 100644
--- a/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml
+++ b/detections/cloud/kubernetes_scanning_by_unauthenticated_ip_address.yml
@@ -5,11 +5,35 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies potential scanning activities within a Kubernetes environment by unauthenticated IP addresses. It leverages Kubernetes audit logs to detect multiple unauthorized access attempts (HTTP 403 responses) from the same source IP. This activity is significant as it may indicate an attacker probing for vulnerabilities or attempting to exploit known issues. If confirmed malicious, such scanning could lead to unauthorized access, data breaches, or further exploitation of the Kubernetes infrastructure, compromising the security and integrity of the environment.
+description: The following analytic identifies potential scanning activities within
+  a Kubernetes environment by unauthenticated IP addresses. It leverages Kubernetes
+  audit logs to detect multiple unauthorized access attempts (HTTP 403 responses)
+  from the same source IP. This activity is significant as it may indicate an attacker
+  probing for vulnerabilities or attempting to exploit known issues. If confirmed
+  malicious, such scanning could lead to unauthorized access, data breaches, or further
+  exploitation of the Kubernetes infrastructure, compromising the security and integrity
+  of the environment.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 | iplocation sourceIPs{} | stats count values(userAgent) as userAgent values(user.username) as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) as responseStatus.message values(responseStatus.reason) as responseStatus.reason values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City | where count > 5 | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_scanning_by_unauthenticated_ip_address_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403
+  | iplocation sourceIPs{} | stats count values(userAgent) as userAgent values(user.username)
+  as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI)
+  as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message)
+  as responseStatus.message values(responseStatus.reason) as responseStatus.reason
+  values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City
+  | where count > 5 | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_scanning_by_unauthenticated_ip_address_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,22 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - verb
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - user.groups{}
-  - user.username
-  - userAgent
-  - verb
-  - responseStatus.reason
-  - responseStatus.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/kubernetes_scanning/kubernetes_scanning.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_shell_running_on_worker_node.yml b/detections/cloud/kubernetes_shell_running_on_worker_node.yml
index e8f1f9b96b..0fc3123860 100644
--- a/detections/cloud/kubernetes_shell_running_on_worker_node.yml
+++ b/detections/cloud/kubernetes_shell_running_on_worker_node.yml
@@ -5,32 +5,40 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies shell activity within the Kubernetes privilege scope on a worker node. It leverages process metrics from an OTEL collector hostmetrics receiver, specifically process.cpu.utilization and process.memory.utilization, pulled from Splunk Observability Cloud. This activity is significant as unauthorized shell processes can indicate potential security threats, providing attackers an entry point to compromise the node and the entire Kubernetes cluster. If confirmed malicious, this activity could lead to data theft, service disruption, privilege escalation, lateral movement, and further attacks, severely compromising the cluster's security and integrity.
+description: The following analytic identifies shell activity within the Kubernetes
+  privilege scope on a worker node. It leverages process metrics from an OTEL collector
+  hostmetrics receiver, specifically process.cpu.utilization and process.memory.utilization,
+  pulled from Splunk Observability Cloud. This activity is significant as unauthorized
+  shell processes can indicate potential security threats, providing attackers an
+  entry point to compromise the node and the entire Kubernetes cluster. If confirmed
+  malicious, this activity could lead to data theft, service disruption, privilege
+  escalation, lateral movement, and further attacks, severely compromising the cluster's
+  security and integrity.
 data_source: []
-search: '| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization where `kubernetes_metrics` AND process.executable.name IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name span=10s | search process.cpu.utilization>0 OR process.memory.utilization>0 | stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name | rename host.name as host | `kubernetes_shell_running_on_worker_node_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: '| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization)
+  as process.memory.utilization where `kubernetes_metrics` AND process.executable.name
+  IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid
+  process.executable.name span=10s | search process.cpu.utilization>0 OR process.memory.utilization>0
+  | stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization)
+  as process.memory.utilization by host.name k8s.cluster.name k8s.node.name process.pid
+  process.executable.name | rename host.name as host | `kubernetes_shell_running_on_worker_node_filter`'
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart/tree/main
@@ -52,12 +60,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.cpu.utilization
-  - process.memory.utilization
-  - process.executable.name
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
-  - process.pid
   security_domain: network
diff --git a/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml b/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml
index 8d2063b099..7bb4661bfd 100644
--- a/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml
+++ b/detections/cloud/kubernetes_shell_running_on_worker_node_with_cpu_activity.yml
@@ -5,32 +5,41 @@ date: '2024-10-17'
 author: Matthew Moore, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies shell activity within the Kubernetes privilege scope on a worker node, specifically when shell processes are consuming CPU resources. It leverages process metrics from an OTEL collector hostmetrics receiver, pulled from Splunk Observability Cloud via the Splunk Infrastructure Monitoring Add-on, focusing on process.cpu.utilization and process.memory.utilization. This activity is significant as unauthorized shell processes can indicate a security threat, potentially compromising the node and the entire Kubernetes cluster. If confirmed malicious, attackers could gain full control over the host's resources, leading to data theft, service disruption, privilege escalation, and further attacks within the cluster.
+description: The following analytic identifies shell activity within the Kubernetes
+  privilege scope on a worker node, specifically when shell processes are consuming
+  CPU resources. It leverages process metrics from an OTEL collector hostmetrics receiver,
+  pulled from Splunk Observability Cloud via the Splunk Infrastructure Monitoring
+  Add-on, focusing on process.cpu.utilization and process.memory.utilization. This
+  activity is significant as unauthorized shell processes can indicate a security
+  threat, potentially compromising the node and the entire Kubernetes cluster. If
+  confirmed malicious, attackers could gain full control over the host's resources,
+  leading to data theft, service disruption, privilege escalation, and further attacks
+  within the cluster.
 data_source: []
-search: '| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization where `kubernetes_metrics` AND process.executable.name IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name span=10s | search process.cpu.utilization>0 | stats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name | rename host.name as host | `kubernetes_shell_running_on_worker_node_with_cpu_activity_filter`'
-how_to_implement: 'To implement this detection, follow these steps:
-
-  * Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
-
-  * Enable the hostmetrics/process receiver in the OTEL configuration.
-
-  * Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
-
-  * Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
-
-  * Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
-
-  * Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
-
-  * In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
-
-  * Set the Signal Flow Program to the following: data(''process.threads'').publish(label=''A''); data(''process.cpu.utilization'').publish(label=''B''); data(''process.cpu.time'').publish(label=''C''); data(''process.disk.io'').publish(label=''D''); data(''process.memory.usage'').publish(label=''E''); data(''process.memory.virtual'').publish(label=''F''); data(''process.memory.utilization'').publish(label=''G''); data(''process.cpu.utilization'').publish(label=''H''); data(''process.disk.operations'').publish(label=''I''); data(''process.handles'').publish(label=''J''); data(''process.threads'').publish(label=''K'')
-
-  * Set the Metric Resolution to 10000.
-
-  * Leave all other settings at their default values.
-
-  * Run the Search Baseline Of Kubernetes Container Network IO Ratio'
+search: '| mstats avg(process.cpu.utilization) as process.cpu.utilization avg(process.memory.utilization)
+  as process.memory.utilization where `kubernetes_metrics` AND process.executable.name
+  IN ("sh","bash","csh", "tcsh") by host.name k8s.cluster.name k8s.node.name process.pid
+  process.executable.name span=10s | search process.cpu.utilization>0 | stats avg(process.cpu.utilization)
+  as process.cpu.utilization avg(process.memory.utilization) as process.memory.utilization
+  by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name
+  | rename host.name as host | `kubernetes_shell_running_on_worker_node_with_cpu_activity_filter`'
+how_to_implement: "To implement this detection, follow these steps:\n* Deploy the
+  OpenTelemetry Collector (OTEL) to your Kubernetes cluster.\n* Enable the hostmetrics/process
+  receiver in the OTEL configuration.\n* Ensure that the process metrics, specifically
+  Process.cpu.utilization and process.memory.utilization, are enabled.\n* Install
+  the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)\n
+  * Configure the SIM add-on with your Observability Cloud Organization ID and Access
+  Token.\n* Set up the SIM modular input to ingest Process Metrics. Name this input
+  \"sim_process_metrics_to_metrics_index\".\n* In the SIM configuration, set the Organization
+  ID to your Observability Cloud Organization ID.\n* Set the Signal Flow Program to
+  the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B');
+  data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D');
+  data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F');
+  data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H');
+  data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J');
+  data('process.threads').publish(label='K')\n* Set the Metric Resolution to 10000.\n
+  * Leave all other settings at their default values.\n* Run the Search Baseline Of
+  Kubernetes Container Network IO Ratio"
 known_false_positives: unknown
 references:
 - https://github.com/signalfx/splunk-otel-collector-chart/tree/main
@@ -52,12 +61,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - process.cpu.utilization
-  - process.memory.utilization
-  - process.executable.name
-  - host.name
-  - k8s.cluster.name
-  - k8s.node.name
-  - process.pid
   security_domain: network
diff --git a/detections/cloud/kubernetes_suspicious_image_pulling.yml b/detections/cloud/kubernetes_suspicious_image_pulling.yml
index faddaab806..ef299c16b4 100644
--- a/detections/cloud/kubernetes_suspicious_image_pulling.yml
+++ b/detections/cloud/kubernetes_suspicious_image_pulling.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious image pulling in Kubernetes environments. It identifies this activity by monitoring Kubernetes audit logs for image pull requests that do not match a predefined list of allowed images. This behavior is significant for a SOC as it may indicate an attacker attempting to deploy malicious software or infiltrate the system. If confirmed malicious, the impact could be severe, potentially leading to unauthorized access to sensitive systems or data, and enabling further malicious activities within the cluster.
+description: The following analytic detects suspicious image pulling in Kubernetes
+  environments. It identifies this activity by monitoring Kubernetes audit logs for
+  image pull requests that do not match a predefined list of allowed images. This
+  behavior is significant for a SOC as it may indicate an attacker attempting to deploy
+  malicious software or infiltrate the system. If confirmed malicious, the impact
+  could be severe, potentially leading to unauthorized access to sensitive systems
+  or data, and enabling further malicious activities within the cluster.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` requestObject.message="Pulling image*" | search NOT `kube_allowed_images` | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_suspicious_image_pulling_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` requestObject.message="Pulling image*" | search NOT `kube_allowed_images`
+  | fillnull | stats count by objectRef.name objectRef.namespace objectRef.resource
+  requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{}
+  user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username
+  as user | `kubernetes_suspicious_image_pulling_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +54,8 @@ tags:
   asset_type: Kubernetes
   confidence: 70
   impact: 70
-  message: Suspicious image $objectRef.name$ pulled in Kubernetes from ip $src_ip$ by user $user$
+  message: Suspicious image $objectRef.name$ pulled in Kubernetes from ip $src_ip$
+    by user $user$
   mitre_attack_id:
   - T1526
   observable:
@@ -44,22 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - verb
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - user.groups{}
-  - user.username
-  - userAgent
-  - verb
-  - responseStatus.reason
-  - responseStatus.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_audit_pull_image/kubernetes_audit_pull_image.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/kubernetes_audit_pull_image/kubernetes_audit_pull_image.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/kubernetes_unauthorized_access.yml b/detections/cloud/kubernetes_unauthorized_access.yml
index 3cfc20d5d1..675ba5dd35 100644
--- a/detections/cloud/kubernetes_unauthorized_access.yml
+++ b/detections/cloud/kubernetes_unauthorized_access.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized access attempts to Kubernetes by analyzing Kubernetes audit logs. It identifies anomalies in access patterns by examining the source of requests and their response statuses. This activity is significant for a SOC as it may indicate an attacker attempting to infiltrate the Kubernetes environment. If confirmed malicious, such access could lead to unauthorized control over Kubernetes resources, potentially compromising sensitive systems or data within the cluster.
+description: The following analytic detects unauthorized access attempts to Kubernetes
+  by analyzing Kubernetes audit logs. It identifies anomalies in access patterns by
+  examining the source of requests and their response statuses. This activity is significant
+  for a SOC as it may indicate an attacker attempting to infiltrate the Kubernetes
+  environment. If confirmed malicious, such access could lead to unauthorized control
+  over Kubernetes resources, potentially compromising sensitive systems or data within
+  the cluster.
 data_source:
 - Kubernetes Audit
-search: '`kube_audit` verb=create responseStatus.reason=Forbidden | fillnull | stats count by objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code responseStatus.message sourceIPs{} stage user.groups{} user.uid user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user | `kubernetes_unauthorized_access_filter`'
-how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
+search: '`kube_audit` verb=create responseStatus.reason=Forbidden | fillnull | stats
+  count by objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI
+  responseStatus.code responseStatus.message sourceIPs{} stage user.groups{} user.uid
+  user.username userAgent verb | rename sourceIPs{} as src_ip, user.username as user
+  | `kubernetes_unauthorized_access_filter`'
+how_to_implement: The detection is based on data that originates from Kubernetes Audit
+  logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes
+  audit logs provide a record of the requests made to the Kubernetes API server, which
+  is crucial for monitoring and detecting suspicious activities. Configure the audit
+  policy in Kubernetes to determine what kind of activities are logged. This is done
+  by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry
+  Collector for Kubernetes to collect the logs. This doc will describe how to collect
+  the audit log file 
+  https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.
+  When you want to use this detection with AWS EKS, you need to enable EKS control
+  plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html.
+  Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
 known_false_positives: unknown
 references:
 - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,22 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - verb
-  - requestReceivedTimestamp
-  - requestURI
-  - responseStatus.code
-  - sourceIPs{}
-  - user.groups{}
-  - user.username
-  - userAgent
-  - verb
-  - responseStatus.reason
-  - responseStatus.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_unauthorized_access/kubernetes_unauthorized_access.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kubernetes_unauthorized_access/kubernetes_unauthorized_access.json
     sourcetype: _json
     source: kubernetes
diff --git a/detections/cloud/o365_add_app_role_assignment_grant_user.yml b/detections/cloud/o365_add_app_role_assignment_grant_user.yml
index 442831f4a3..12c3b6f913 100644
--- a/detections/cloud/o365_add_app_role_assignment_grant_user.yml
+++ b/detections/cloud/o365_add_app_role_assignment_grant_user.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of an application role assignment grant to a user in Office 365. It leverages data from the `o365_management_activity` dataset, specifically monitoring the "Add app role assignment grant to user" operation. This activity is significant as it can indicate unauthorized privilege escalation or the assignment of sensitive roles to users. If confirmed malicious, this could allow an attacker to gain elevated permissions, potentially leading to unauthorized access to critical resources and data within the Office 365 environment.
+description: The following analytic detects the addition of an application role assignment
+  grant to a user in Office 365. It leverages data from the `o365_management_activity`
+  dataset, specifically monitoring the "Add app role assignment grant to user" operation.
+  This activity is significant as it can indicate unauthorized privilege escalation
+  or the assignment of sensitive roles to users. If confirmed malicious, this could
+  allow an attacker to gain elevated permissions, potentially leading to unauthorized
+  access to critical resources and data within the Office 365 environment.
 data_source:
 - O365 Add app role assignment grant to user.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." | stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type values(ModifiedProperties{}.Name) as modified_properties_name by user dest ResultStatus Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app
+  role assignment grant to user." | stats count min(_time) as firstTime max(_time)
+  as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type values(ModifiedProperties{}.Name)
+  as modified_properties_name by user dest ResultStatus Operation | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_add_app_role_assignment_grant_user_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: The creation of a new Federation is not necessarily malicious,
+  however this events need to be followed closely, as it may indicate federated credential
+  abuse or backdoor via federated identities at a different cloud provider.
 references:
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
 - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 30
-  message: User $user$ has created a new federation setting $modified_properties_name$ on $dest$
+  message: User $user$ has created a new federation setting $modified_properties_name$
+    on $dest$
   mitre_attack_id:
   - T1136.003
   - T1136
@@ -47,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Actor{}.ID
-  - Actor{}.Type
-  - UserId
-  - dest
-  - ResultStatus
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_added_service_principal.yml b/detections/cloud/o365_added_service_principal.yml
index 09b03aa9ef..ca7635ad9e 100644
--- a/detections/cloud/o365_added_service_principal.yml
+++ b/detections/cloud/o365_added_service_principal.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of new service principal accounts in O365 tenants. It leverages data from the `o365_management_activity` dataset, specifically monitoring for operations related to adding or creating service principals. This activity is significant because attackers can exploit service principals to gain unauthorized access and perform malicious actions within an organization's environment. If confirmed malicious, this could allow attackers to interact with APIs, access resources, and execute operations on behalf of the organization, potentially leading to data breaches or further compromise.
+description: The following analytic detects the addition of new service principal
+  accounts in O365 tenants. It leverages data from the `o365_management_activity`
+  dataset, specifically monitoring for operations related to adding or creating service
+  principals. This activity is significant because attackers can exploit service principals
+  to gain unauthorized access and perform malicious actions within an organization's
+  environment. If confirmed malicious, this could allow attackers to interact with
+  APIs, access resources, and execute operations on behalf of the organization, potentially
+  leading to data breaches or further compromise.
 data_source:
 - O365
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="*Add service principal*" OR (Operation = "*principal*" AND action = "created") | stats count values(ModifiedProperties{}.NewValue) as new_value by src_user src_user_type action Operation authentication_service Workload | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_added_service_principal_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="*Add
+  service principal*" OR (Operation = "*principal*" AND action = "created") | stats
+  count values(ModifiedProperties{}.NewValue) as new_value by src_user src_user_type
+  action Operation authentication_service Workload | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_added_service_principal_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: The creation of a new Federation is not necessarily malicious,
+  however these events need to be followed closely, as it may indicate federated credential
+  abuse or backdoor via federated identities at a different cloud provider.
 references:
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
 - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - signature
-  - src_user
-  - src_user_type
-  - action
-  - Operation
-  - authentication_service
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_added_service_principal/o365_add_service_principal.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_added_service_principal/o365_add_service_principal.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml b/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml
index 211e7a2a37..374b2931a6 100644
--- a/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml
+++ b/detections/cloud/o365_admin_consent_bypassed_by_service_principal.yml
@@ -7,10 +7,27 @@ data_source:
 - O365 Add app role assignment to service principal.
 type: TTP
 status: production
-description: The following analytic identifies instances where a service principal in Office 365 Azure Active Directory assigns app roles without standard admin consent. It leverages `o365_management_activity` logs, specifically focusing on the 'Add app role assignment to service principal' operation. This activity is significant for SOCs as it may indicate a bypass of critical administrative controls, potentially leading to unauthorized access or privilege escalation. If confirmed malicious, this could allow an attacker to misuse automated processes to assign sensitive permissions, compromising the security of the environment.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment to service principal." | eval len=mvcount(''Actor{}.ID'') | eval userType = mvindex(''Actor{}.ID'',len-1) | eval roleId = mvindex(''ModifiedProperties{}.NewValue'', 0) | eval roleValue = mvindex(''ModifiedProperties{}.NewValue'', 1) | eval roleDescription = mvindex(''ModifiedProperties{}.NewValue'', 2) | eval dest_user = mvindex(''Target{}.ID'', 0) | search userType = "ServicePrincipal" | eval src_user = user | stats count earliest(_time) as firstTime latest(_time) as lastTime by src_user dest_user roleId roleValue roleDescription | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  | `o365_admin_consent_bypassed_by_service_principal_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Service Principals are sometimes configured to legitimately bypass the consent process for purposes of automation. Filter as needed.
+description: The following analytic identifies instances where a service principal
+  in Office 365 Azure Active Directory assigns app roles without standard admin consent.
+  It leverages `o365_management_activity` logs, specifically focusing on the 'Add
+  app role assignment to service principal' operation. This activity is significant
+  for SOCs as it may indicate a bypass of critical administrative controls, potentially
+  leading to unauthorized access or privilege escalation. If confirmed malicious,
+  this could allow an attacker to misuse automated processes to assign sensitive permissions,
+  compromising the security of the environment.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add
+  app role assignment to service principal.\" | eval len=mvcount('Actor{}.ID') | eval
+  userType = mvindex('Actor{}.ID',len-1) | eval roleId = mvindex('ModifiedProperties{}.NewValue',
+  0) | eval roleValue = mvindex('ModifiedProperties{}.NewValue', 1) | eval roleDescription
+  = mvindex('ModifiedProperties{}.NewValue', 2) | eval dest_user = mvindex('Target{}.ID',
+  0) | search userType = \"ServicePrincipal\" | eval src_user = user | stats count
+  earliest(_time) as firstTime latest(_time) as lastTime by src_user dest_user roleId
+  roleValue roleDescription | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`\
+  \  | `o365_admin_consent_bypassed_by_service_principal_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Service Principals are sometimes configured to legitimately
+  bypass the consent process for purposes of automation. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/
@@ -24,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +55,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 90
-  message: Service principal $src_user$ bypassed the admin consent process and granted permissions to $dest_user$
+  message: Service principal $src_user$ bypassed the admin consent process and granted
+    permissions to $dest_user$
   mitre_attack_id:
   - T1098.003
   observable:
@@ -45,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Actor{}.ID
-  - ModifiedProperties{}.NewValue
-  - src_user
-  - dest_user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_bypass_admin_consent/o365_bypass_admin_consent.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_bypass_admin_consent/o365_bypass_admin_consent.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_advanced_audit_disabled.yml b/detections/cloud/o365_advanced_audit_disabled.yml
index 944ff0f14d..b130c66843 100644
--- a/detections/cloud/o365_advanced_audit_disabled.yml
+++ b/detections/cloud/o365_advanced_audit_disabled.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - O365 Change user license.
-description: The following analytic detects instances where the O365 advanced audit is disabled for a specific user within the Office 365 tenant. It uses O365 audit logs, focusing on events related to audit license changes in AzureActiveDirectory workloads. This activity is significant because the O365 advanced audit provides critical logging and insights into user and administrator activities. Disabling it can blind security teams to potential malicious actions. If confirmed malicious, attackers could operate within the user's mailbox or account with reduced risk of detection, leading to unauthorized data access, data exfiltration, or account compromise.
-search: '`o365_management_activity` Operation="Change user license."  | eval property_name = mvindex (''ExtendedProperties{}.Name'', 1) | search property_name = "extendedAuditEventCategory" | eval additionalDetails = mvindex(''ExtendedProperties{}.Value'',0) | eval split_value=split(additionalDetails, "NewValue") | eval possible_plan=mvindex(split_value, 1)  | rex field="possible_plan" "DisabledPlans=\[(?P<DisabledPlans>[^\]]+)\]" | search DisabledPlans IN ("*M365_ADVANCED_AUDITING*") | stats min(_time) as firstTime max(_time) as lastTime by Operation user object DisabledPlans | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_advanced_audit_disabled_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Administrators might temporarily disable the advanced audit for troubleshooting, performance reasons, or other administrative tasks. Filter as needed.
+description: The following analytic detects instances where the O365 advanced audit
+  is disabled for a specific user within the Office 365 tenant. It uses O365 audit
+  logs, focusing on events related to audit license changes in AzureActiveDirectory
+  workloads. This activity is significant because the O365 advanced audit provides
+  critical logging and insights into user and administrator activities. Disabling
+  it can blind security teams to potential malicious actions. If confirmed malicious,
+  attackers could operate within the user's mailbox or account with reduced risk of
+  detection, leading to unauthorized data access, data exfiltration, or account compromise.
+search: "`o365_management_activity` Operation=\"Change user license.\"  | eval property_name
+  = mvindex ('ExtendedProperties{}.Name', 1) | search property_name = \"extendedAuditEventCategory\"\
+  \ | eval additionalDetails = mvindex('ExtendedProperties{}.Value',0) | eval split_value=split(additionalDetails,
+  \"NewValue\") | eval possible_plan=mvindex(split_value, 1)  | rex field=\"possible_plan\"\
+  \ \"DisabledPlans=\\[(?P<DisabledPlans>[^\\]]+)\\]\" | search DisabledPlans IN (\"\
+  *M365_ADVANCED_AUDITING*\") | stats min(_time) as firstTime max(_time) as lastTime
+  by Operation user object DisabledPlans | `security_content_ctime(firstTime)`  |
+  `security_content_ctime(lastTime)` | `o365_advanced_audit_disabled_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Administrators might temporarily disable the advanced audit
+  for troubleshooting, performance reasons, or other administrative tasks. Filter
+  as needed.
 references:
 - https://attack.mitre.org/techniques/T1562/008/
 - https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ExtendedProperties{}.Name
-  - ExtendedProperties{}.Value
-  - user
-  - object
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/o365_advanced_audit_disabled/o365_advanced_audit_disabled.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.008/o365_advanced_audit_disabled/o365_advanced_audit_disabled.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_application_available_to_other_tenants.yml b/detections/cloud/o365_application_available_to_other_tenants.yml
index 7cc0b28111..66676fa9dd 100644
--- a/detections/cloud/o365_application_available_to_other_tenants.yml
+++ b/detections/cloud/o365_application_available_to_other_tenants.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the configuration of Azure Active Directory Applications in a manner that allows authentication from external tenants or personal accounts. This configuration can lead to inappropriate or malicious access of any data or capabilities the application is allowed to access. This detection leverages the O365 Universal Audit Log data source.
+description: The following analytic identifies the configuration of Azure Active Directory
+  Applications in a manner that allows authentication from external tenants or personal
+  accounts. This configuration can lead to inappropriate or malicious access of any
+  data or capabilities the application is allowed to access. This detection leverages
+  the O365 Universal Audit Log data source.
 data_source:
 - Office 365 Universal Audit Log
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation IN ("Add application.","Update application.") ModifiedProperties{}.Name=AvailableToOtherTenants | eval result = case(match(mvindex(''ModifiedProperties{}.NewValue'',mvfind(''ModifiedProperties{}.Name'',"AvailableToOtherTenants")),"false"),"removed",true(),"added"), object_name=mvindex(''Target{}.ID'', 3), signature=Operation, object_attrs = "AvailableToOtherTenants", user = case(match(mvindex(''Actor{}.ID'',-1),"User"),mvindex(''Actor{}.ID'',0),match(mvindex(''Actor{}.ID'',-1),"ServicePrincipal"),mvindex(''Actor{}.ID'',3),true(),mvindex(''Actor{}.ID'',0)) | search result = "added" | stats values(ActorIpAddress) as src, count, min(_time) as firstTime, max(_time) as lastTime by signature, user, object, object_name, object_attrs, result | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_application_available_to_other_tenants_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"\
+  Add application.\",\"Update application.\") ModifiedProperties{}.Name=AvailableToOtherTenants
+  | eval result = case(match(mvindex('ModifiedProperties{}.NewValue',mvfind('ModifiedProperties{}.Name',\"\
+  AvailableToOtherTenants\")),\"false\"),\"removed\",true(),\"added\"), object_name=mvindex('Target{}.ID',
+  3), signature=Operation, object_attrs = \"AvailableToOtherTenants\", user = case(match(mvindex('Actor{}.ID',-1),\"\
+  User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"ServicePrincipal\"\
+  ),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | search result = \"added\"\
+  \ | stats values(ActorIpAddress) as src, count, min(_time) as firstTime, max(_time)
+  as lastTime by signature, user, object, object_name, object_attrs, result | `security_content_ctime(firstTime)`\
+  \  | `security_content_ctime(lastTime)` | `o365_application_available_to_other_tenants_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
 known_false_positives: Business approved changes by known administrators.
 references:
 - https://attack.mitre.org/techniques/T1098/
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 50
-  message: An Azure Application [$object_name$] was configured by [$user$] as accessible to external tenants.
+  message: An Azure Application [$object_name$] was configured by [$user$] as accessible
+    to external tenants.
   mitre_attack_id:
   - T1098.003
   - T1098
@@ -49,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.Name
-  - UserId
-  - Workload
-  - Target{}.ID
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_application_registration_owner_added.yml b/detections/cloud/o365_application_registration_owner_added.yml
index 6efb9881ba..0576075ce1 100644
--- a/detections/cloud/o365_application_registration_owner_added.yml
+++ b/detections/cloud/o365_application_registration_owner_added.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - O365 Add owner to application.
-description: The following analytic identifies instances where a new owner is assigned to an application registration within an Azure AD and Office 365 tenant. It leverages O365 audit logs, specifically events related to changes in owner assignments within the AzureActiveDirectory workload. This activity is significant because assigning a new owner to an application registration can grant significant control over the application's configuration, permissions, and behavior. If confirmed malicious, an attacker could modify the application's settings, permissions, and behavior, leading to unauthorized data access, privilege escalation, or the introduction of malicious behavior within the application's operations.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add owner to application." | eval app_id=mvindex(''ModifiedProperties{}.NewValue'', 0) | eval app_displayName=mvindex(''ModifiedProperties{}.NewValue'', 1) | stats max(_time) as lastTime values(ModifiedProperties{}.NewValue) by Operation, user, app_displayName, object | `security_content_ctime(lastTime)` | `o365_application_registration_owner_added_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Application owners may be added for legitimate reasons, filter as needed.
+description: The following analytic identifies instances where a new owner is assigned
+  to an application registration within an Azure AD and Office 365 tenant. It leverages
+  O365 audit logs, specifically events related to changes in owner assignments within
+  the AzureActiveDirectory workload. This activity is significant because assigning
+  a new owner to an application registration can grant significant control over the
+  application's configuration, permissions, and behavior. If confirmed malicious,
+  an attacker could modify the application's settings, permissions, and behavior,
+  leading to unauthorized data access, privilege escalation, or the introduction of
+  malicious behavior within the application's operations.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add
+  owner to application.\" | eval app_id=mvindex('ModifiedProperties{}.NewValue', 0)
+  | eval app_displayName=mvindex('ModifiedProperties{}.NewValue', 1) | stats max(_time)
+  as lastTime values(ModifiedProperties{}.NewValue) by Operation, user, app_displayName,
+  object | `security_content_ctime(lastTime)` | `o365_application_registration_owner_added_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Application owners may be added for legitimate reasons, filter
+  as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 - https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/overview-assign-app-owners
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_add_app_registration_owner/o365_add_app_registration_owner.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_add_app_registration_owner/o365_add_app_registration_owner.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_applicationimpersonation_role_assigned.yml b/detections/cloud/o365_applicationimpersonation_role_assigned.yml
index d6a70ee918..e11b69b993 100644
--- a/detections/cloud/o365_applicationimpersonation_role_assigned.yml
+++ b/detections/cloud/o365_applicationimpersonation_role_assigned.yml
@@ -7,21 +7,38 @@ status: production
 type: TTP
 data_source:
 - O365
-description: The following analytic detects the assignment of the ApplicationImpersonation role in Office 365 to a user or application. It uses the Office 365 Management Activity API to monitor Azure Active Directory audit logs for role assignment events. This activity is significant because the ApplicationImpersonation role allows impersonation of any user, enabling access to and modification of their mailbox. If confirmed malicious, an attacker could gain unauthorized access to sensitive information, manipulate mailbox data, and perform actions as a legitimate user, posing a severe security risk to the organization.
-search: '`o365_management_activity` Workload=Exchange Operation="New-ManagementRoleAssignment"  Role=ApplicationImpersonation |  rename User as target_user | stats  max(_time) as lastTime by Operation, user, object, ObjectId, Role, target_user | `security_content_ctime(lastTime)` | `o365_applicationimpersonation_role_assigned_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: While infrequent, the ApplicationImpersonation role may be granted for leigimate reasons, filter as needed.
+description: The following analytic detects the assignment of the ApplicationImpersonation
+  role in Office 365 to a user or application. It uses the Office 365 Management Activity
+  API to monitor Azure Active Directory audit logs for role assignment events. This
+  activity is significant because the ApplicationImpersonation role allows impersonation
+  of any user, enabling access to and modification of their mailbox. If confirmed
+  malicious, an attacker could gain unauthorized access to sensitive information,
+  manipulate mailbox data, and perform actions as a legitimate user, posing a severe
+  security risk to the organization.
+search: '`o365_management_activity` Workload=Exchange Operation="New-ManagementRoleAssignment"  Role=ApplicationImpersonation
+  |  rename User as target_user | stats  max(_time) as lastTime by Operation, user,
+  object, ObjectId, Role, target_user | `security_content_ctime(lastTime)` | `o365_applicationimpersonation_role_assigned_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: While infrequent, the ApplicationImpersonation role may be
+  granted for leigimate reasons, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/002/
 - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452
 - https://www.mandiant.com/media/17656
 drilldown_searches:
 - name: View the detection results for - "$target_user$" and "$user$"
-  search: '%original_detection_search% | search  target_user = "$target_user$" user = "$user$"'
+  search: '%original_detection_search% | search  target_user = "$target_user$" user
+    = "$user$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$target_user$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$target_user$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$target_user$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Role
-  - user
-  - User
-  - object
-  - ObjectId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/application_impersonation_role_assigned/application_impersonation_role_assigned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/application_impersonation_role_assigned/application_impersonation_role_assigned.log
     source: O365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml b/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml
index b6db618be5..989e93d258 100644
--- a/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml
+++ b/detections/cloud/o365_block_user_consent_for_risky_apps_disabled.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - O365 Update authorization policy.
-description: The following analytic detects when the "risk-based step-up consent" security setting in Microsoft 365 is disabled. It monitors Azure Active Directory logs for the "Update authorization policy" operation, specifically changes to the "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling this feature can expose the organization to OAuth phishing threats, allowing users to grant consent to malicious applications. If confirmed malicious, attackers could gain unauthorized access to user data and sensitive information, leading to data breaches and further compromise within the organization.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update authorization policy." | eval index_number = if(mvfind(''ModifiedProperties{}.Name'', "AllowUserConsentForRiskyApps") >= 0, mvfind(''ModifiedProperties{}.Name'', "AllowUserConsentForRiskyApps"), -1) | search index_number >= 0  | eval AllowUserConsentForRiskyApps = mvindex(''ModifiedProperties{}.NewValue'',index_number) | where AllowUserConsentForRiskyApps like "%true%" | stats count min(_time) as firstTime max(_time) as lastTime by user, Operation, AllowUserConsentForRiskyApps, user_agent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_block_user_consent_for_risky_apps_disabled_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting by administrators, perhaps as part of a policy update or security assessment, may trigger this alert, necessitating verification of the change's intent and authorization.
+description: The following analytic detects when the "risk-based step-up consent"
+  security setting in Microsoft 365 is disabled. It monitors Azure Active Directory
+  logs for the "Update authorization policy" operation, specifically changes to the
+  "AllowUserConsentForRiskyApps" setting. This activity is significant because disabling
+  this feature can expose the organization to OAuth phishing threats, allowing users
+  to grant consent to malicious applications. If confirmed malicious, attackers could
+  gain unauthorized access to user data and sensitive information, leading to data
+  breaches and further compromise within the organization.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update
+  authorization policy.\" | eval index_number = if(mvfind('ModifiedProperties{}.Name',
+  \"AllowUserConsentForRiskyApps\") >= 0, mvfind('ModifiedProperties{}.Name', \"AllowUserConsentForRiskyApps\"\
+  ), -1) | search index_number >= 0  | eval AllowUserConsentForRiskyApps = mvindex('ModifiedProperties{}.NewValue',index_number)
+  | where AllowUserConsentForRiskyApps like \"%true%\" | stats count min(_time) as
+  firstTime max(_time) as lastTime by user, Operation, AllowUserConsentForRiskyApps,
+  user_agent | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_block_user_consent_for_risky_apps_disabled_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Legitimate changes to the 'risk-based step-up consent' setting
+  by administrators, perhaps as part of a policy update or security assessment, may
+  trigger this alert, necessitating verification of the change's intent and authorization.
 references:
 - https://attack.mitre.org/techniques/T1562/
 - https://goodworkaround.com/2020/10/19/a-look-behind-the-azure-ad-permission-classifications-preview/
@@ -22,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.Name
-  - ModifiedProperties{}.NewValue
-  - user
-  - user_agent
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/o365_disable_blockconsent_for_riskapps/o365_disable_blockconsent_for_riskapps.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/o365_disable_blockconsent_for_riskapps/o365_disable_blockconsent_for_riskapps.log
     source: O365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml b/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml
index 64aece0669..be8630b5c7 100644
--- a/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml
+++ b/detections/cloud/o365_bypass_mfa_via_trusted_ip.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Bhavin Patel, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where new IP addresses are added to the trusted IPs list in Office 365, potentially allowing users from these IPs to bypass Multi-Factor Authentication (MFA) during login. It leverages O365 audit logs, specifically focusing on events related to the modification of trusted IP settings. This activity is significant because adding trusted IPs can weaken the security posture by bypassing MFA, which is a critical security control. If confirmed malicious, this could lead to unauthorized access, compromising sensitive information and systems. Immediate investigation is required to validate the legitimacy of the IP addition.
+description: The following analytic identifies instances where new IP addresses are
+  added to the trusted IPs list in Office 365, potentially allowing users from these
+  IPs to bypass Multi-Factor Authentication (MFA) during login. It leverages O365
+  audit logs, specifically focusing on events related to the modification of trusted
+  IP settings. This activity is significant because adding trusted IPs can weaken
+  the security posture by bypassing MFA, which is a critical security control. If
+  confirmed malicious, this could lead to unauthorized access, compromising sensitive
+  information and systems. Immediate investigation is required to validate the legitimacy
+  of the IP addition.
 data_source:
 - O365 Set Company Information.
-search: '`o365_management_activity` Operation="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy | rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added Operation Workload vendor_account status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `o365_bypass_mfa_via_trusted_ip_filter`'
-how_to_implement: You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration.
+search: '`o365_management_activity` Operation="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy
+  | rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})"
+  | rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})"
+  | eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") | mvexpand
+  ip_addresses_new_added | where isnull(mvfind(ip_addresses_old,ip_addresses_new_added))
+  |stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old)
+  as ip_addresses_old by user ip_addresses_new_added Operation Workload vendor_account
+  status user_id action | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `o365_bypass_mfa_via_trusted_ip_filter`'
+how_to_implement: You must install Splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: Unless it is a special case, it is uncommon to continually
+  update Trusted IPs to MFA configuration.
 references:
 - https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf
 - https://attack.mitre.org/techniques/T1562/007/
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +53,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: User $user_id$ has added new IP addresses $ip_addresses_new_added$ to a list of trusted IPs to bypass MFA
+  message: User $user_id$ has added new IP addresses $ip_addresses_new_added$ to a
+    list of trusted IPs to bypass MFA
   mitre_attack_id:
   - T1562.007
   - T1562
@@ -47,21 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - signature
-  - ModifiedProperties{}.Name
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.OldValue
-  - user
-  - vendor_account
-  - status
-  - user_id
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_compliance_content_search_exported.yml b/detections/cloud/o365_compliance_content_search_exported.yml
index 2b386f9034..0f67da403b 100644
--- a/detections/cloud/o365_compliance_content_search_exported.yml
+++ b/detections/cloud/o365_compliance_content_search_exported.yml
@@ -6,10 +6,22 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies when the results of a content search within the Office 365 Security and Compliance Center are exported. It uses the SearchExported operation from the SecurityComplianceCenter workload in the o365_management_activity data source. This activity is significant because exporting search results can involve sensitive or critical organizational data, potentially leading to data exfiltration. If confirmed malicious, an attacker could gain access to and exfiltrate sensitive information, posing a severe risk to the organization's data security and compliance posture.
-search: '`o365_management_activity`  Workload=SecurityComplianceCenter Operation="SearchExported" | rename user_id as user | stats count earliest(_time) as firstTime latest(_time) as lastTime by Operation, ObjectId, ExchangeLocations, user, Query  |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_compliance_content_search_exported_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Compliance content searche exports may be executed for legitimate purposes, filter as needed.
+description: The following analytic identifies when the results of a content search
+  within the Office 365 Security and Compliance Center are exported. It uses the SearchExported
+  operation from the SecurityComplianceCenter workload in the o365_management_activity
+  data source. This activity is significant because exporting search results can involve
+  sensitive or critical organizational data, potentially leading to data exfiltration.
+  If confirmed malicious, an attacker could gain access to and exfiltrate sensitive
+  information, posing a severe risk to the organization's data security and compliance
+  posture.
+search: '`o365_management_activity`  Workload=SecurityComplianceCenter Operation="SearchExported"
+  | rename user_id as user | stats count earliest(_time) as firstTime latest(_time)
+  as lastTime by Operation, ObjectId, ExchangeLocations, user, Query  |`security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `o365_compliance_content_search_exported_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Compliance content searche exports may be executed for legitimate
+  purposes, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/002/
 - https://learn.microsoft.com/en-us/purview/ediscovery-content-search-overview
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ObjectId
-  - ExchangeLocations
-  - Query
-  - user_id
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_exported/o365_compliance_content_search_exported.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_exported/o365_compliance_content_search_exported.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_compliance_content_search_started.yml b/detections/cloud/o365_compliance_content_search_started.yml
index 4ca9f99fcb..14f98f64f1 100644
--- a/detections/cloud/o365_compliance_content_search_started.yml
+++ b/detections/cloud/o365_compliance_content_search_started.yml
@@ -6,10 +6,22 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic detects when a content search is initiated within the Office 365 Security and Compliance Center. It leverages the SearchCreated operation from the o365_management_activity logs under the SecurityComplianceCenter workload. This activity is significant as it may indicate an attempt to access sensitive organizational data, including emails and documents. If confirmed malicious, this could lead to unauthorized data access, potential data exfiltration, and compliance violations. Monitoring this behavior helps ensure the integrity and security of organizational data.
-search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=SearchCreated | rename user_id as user | stats count earliest(_time) as firstTime latest(_time) as lastTime by Operation, ObjectId, ExchangeLocations, user, Query  |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_compliance_content_search_started_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Compliance content searches may be executed for legitimate purposes, filter as needed.
+description: The following analytic detects when a content search is initiated within
+  the Office 365 Security and Compliance Center. It leverages the SearchCreated operation
+  from the o365_management_activity logs under the SecurityComplianceCenter workload.
+  This activity is significant as it may indicate an attempt to access sensitive organizational
+  data, including emails and documents. If confirmed malicious, this could lead to
+  unauthorized data access, potential data exfiltration, and compliance violations.
+  Monitoring this behavior helps ensure the integrity and security of organizational
+  data.
+search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=SearchCreated
+  | rename user_id as user | stats count earliest(_time) as firstTime latest(_time)
+  as lastTime by Operation, ObjectId, ExchangeLocations, user, Query  |`security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `o365_compliance_content_search_started_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Compliance content searches may be executed for legitimate
+  purposes, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/002/
 - https://learn.microsoft.com/en-us/purview/ediscovery-content-search-overview
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ObjectId
-  - ExchangeLocations
-  - Query
-  - user_id
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_started/o365_compliance_content_search_started.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_compliance_content_search_started/o365_compliance_content_search_started.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_concurrent_sessions_from_different_ips.yml b/detections/cloud/o365_concurrent_sessions_from_different_ips.yml
index ea90b4b9ae..4a3467279f 100644
--- a/detections/cloud/o365_concurrent_sessions_from_different_ips.yml
+++ b/detections/cloud/o365_concurrent_sessions_from_different_ips.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies user sessions in Office 365 accessed from multiple IP addresses, indicating potential adversary-in-the-middle (AiTM) phishing attacks. It detects this activity by analyzing Azure Active Directory logs for 'UserLoggedIn' operations and flags sessions with more than one associated IP address. This behavior is significant as it suggests unauthorized concurrent access, which is uncommon in normal usage. If confirmed malicious, the impact could include data theft, account takeover, and the launching of internal phishing campaigns, posing severe risks to organizational security.
+description: The following analytic identifies user sessions in Office 365 accessed
+  from multiple IP addresses, indicating potential adversary-in-the-middle (AiTM)
+  phishing attacks. It detects this activity by analyzing Azure Active Directory logs
+  for 'UserLoggedIn' operations and flags sessions with more than one associated IP
+  address. This behavior is significant as it suggests unauthorized concurrent access,
+  which is uncommon in normal usage. If confirmed malicious, the impact could include
+  data theft, account takeover, and the launching of internal phishing campaigns,
+  posing severe risks to organizational security.
 data_source:
 - O365 UserLoggedIn
-search: '`o365_management_activity` Workload=AzureActiveDirectory  Operation=UserLoggedIn | stats min(_time) as firstTime max(_time) as lastTime values(src_ip) as ips values(user_agent) as user_agents by Operation, user, SessionId | where mvcount(ips) > 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_concurrent_sessions_from_different_ips_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
+search: '`o365_management_activity` Workload=AzureActiveDirectory  Operation=UserLoggedIn
+  | stats min(_time) as firstTime max(_time) as lastTime values(src_ip) as ips values(user_agent)
+  as user_agents by Operation, user, SessionId | where mvcount(ips) > 1 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_concurrent_sessions_from_different_ips_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
 known_false_positives: Unknown
 references:
 - https://attack.mitre.org/techniques/T1185/
@@ -21,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +46,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: User $user$ has logged in with the same session id from more than one unique IP address
+  message: User $user$ has logged in with the same session id from more than one unique
+    IP address
   mitre_attack_id:
   - T1185
   observable:
@@ -46,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Workload
-  - src_ip
-  - user
-  - user_agent
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/o365_concurrent_sessions_from_different_ips/o365_concurrent_sessions_from_different_ips.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/o365_concurrent_sessions_from_different_ips/o365_concurrent_sessions_from_different_ips.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_cross_tenant_access_change.yml b/detections/cloud/o365_cross_tenant_access_change.yml
index 22495a13c0..ea5bfd62a3 100644
--- a/detections/cloud/o365_cross_tenant_access_change.yml
+++ b/detections/cloud/o365_cross_tenant_access_change.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when cross-tenant access/synchronization policies are changed in an Azure tenant. Adversaries have been observed altering victim cross-tenant policies as a method of lateral movement or maintaining persistent access to compromised environments. These policies should be considered sensitive and monitored for changes and/or loose configuration.
+description: The following analytic identifies when cross-tenant access/synchronization
+  policies are changed in an Azure tenant. Adversaries have been observed altering
+  victim cross-tenant policies as a method of lateral movement or maintaining persistent
+  access to compromised environments. These policies should be considered sensitive
+  and monitored for changes and/or loose configuration.
 data_source:
 - Office 365 Universal Audit Log
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation IN ("Add a partner to cross-tenant access setting.","Delete partner specific cross-tenant access setting.") | eval user = case(match(mvindex(''Actor{}.ID'',-1),"User"),mvindex(''Actor{}.ID'',0),match(mvindex(''Actor{}.ID'',-1),"ServicePrincipal"),mvindex(''Actor{}.ID'',3),true(),mvindex(''Actor{}.ID'',0)) | stats values(Workload) as category, values(ClientIP) as src, values(ModifiedProperties{}.Name) as object_name, values(ModifiedProperties{}.NewValue) as object_attrs, count, min(_time) as firstTime, max(_time) as lastTime by Id,user,Operation | rename Operation as signature, Id as signature_id | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_cross_tenant_access_change_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"\
+  Add a partner to cross-tenant access setting.\",\"Delete partner specific cross-tenant
+  access setting.\") | eval user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"\
+  ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | stats
+  values(Workload) as category, values(ClientIP) as src, values(ModifiedProperties{}.Name)
+  as object_name, values(ModifiedProperties{}.NewValue) as object_attrs, count, min(_time)
+  as firstTime, max(_time) as lastTime by Id,user,Operation | rename Operation as
+  signature, Id as signature_id | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `o365_cross_tenant_access_change_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
 known_false_positives: Business approved changes by known administrators.
 references:
 - https://attack.mitre.org/techniques/T1484/002/
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 75
   impact: 75
-  message: The user [$user$] changed the Azure cross-tenant access settings for $object_name$ $object_attrs$ [$signature$]
+  message: The user [$user$] changed the Azure cross-tenant access settings for $object_name$
+    $object_attrs$ [$signature$]
   mitre_attack_id:
   - T1484.002
   observable:
@@ -47,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.Name
-  - UserId
-  - Workload
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_disable_mfa.yml b/detections/cloud/o365_disable_mfa.yml
index 2194249603..2064d6600b 100644
--- a/detections/cloud/o365_disable_mfa.yml
+++ b/detections/cloud/o365_disable_mfa.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where Multi-Factor Authentication (MFA) is disabled for a user within the Office 365 environment. It leverages O365 audit logs, specifically focusing on events related to MFA settings. Disabling MFA removes a critical security layer, making accounts more vulnerable to unauthorized access. If confirmed malicious, this activity could indicate an attacker attempting to maintain persistence or an insider threat, significantly increasing the risk of unauthorized access. Immediate investigation is required to validate the reason for disabling MFA, potentially re-enable it, and assess any other suspicious activities related to the affected account.
+description: The following analytic identifies instances where Multi-Factor Authentication
+  (MFA) is disabled for a user within the Office 365 environment. It leverages O365
+  audit logs, specifically focusing on events related to MFA settings. Disabling MFA
+  removes a critical security layer, making accounts more vulnerable to unauthorized
+  access. If confirmed malicious, this activity could indicate an attacker attempting
+  to maintain persistence or an insider threat, significantly increasing the risk
+  of unauthorized access. Immediate investigation is required to validate the reason
+  for disabling MFA, potentially re-enable it, and assess any other suspicious activities
+  related to the affected account.
 data_source:
 - O365 Disable Strong Authentication.
-search: '`o365_management_activity` Operation="Disable Strong Authentication." | stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation UserId ResultStatus object | rename UserType AS user_type, Operation AS action, UserId AS src_user, object AS user, ResultStatus AS result | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_disable_mfa_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: Unless it is a special case, it is uncommon to disable MFA or Strong Authentication
+search: '`o365_management_activity` Operation="Disable Strong Authentication." | stats
+  count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation
+  UserId ResultStatus object | rename UserType AS user_type, Operation AS action,
+  UserId AS src_user, object AS user, ResultStatus AS result | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_disable_mfa_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: Unless it is a special case, it is uncommon to disable MFA
+  or Strong Authentication
 references:
 - https://attack.mitre.org/techniques/T1556/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,19 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - UserType
-  - user
-  - status
-  - signature
-  - dest
-  - ResultStatus
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_dlp_rule_triggered.yml b/detections/cloud/o365_dlp_rule_triggered.yml
index 4232c87ddf..f4a6cecaf3 100644
--- a/detections/cloud/o365_dlp_rule_triggered.yml
+++ b/detections/cloud/o365_dlp_rule_triggered.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects when Microsoft Office 365 Data Loss Prevention (DLP) rules have been triggered. DLP rules can be configured for any number of security, regulatory, or business compliance reasons, as such this analytic will only be as accurate as the upstream DLP configuration. Detections from this analytic should be evaluated thoroughly to determine what, if any, security relevance the underlying DLP events contain.
+description: The following analytic detects when Microsoft Office 365 Data Loss Prevention
+  (DLP) rules have been triggered. DLP rules can be configured for any number of security,
+  regulatory, or business compliance reasons, as such this analytic will only be as
+  accurate as the upstream DLP configuration. Detections from this analytic should
+  be evaluated thoroughly to determine what, if any, security relevance the underlying
+  DLP events contain.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Operation=DLPRuleMatch | eval recipient = ''ExchangeMetaData.To{}'', signature_id = ''ExchangeMetaData.UniqueID'', signature = ''PolicyDetails{}.Rules{}.RuleName'' , src_user = UserId, reason =''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.SensitiveInformationTypeName'', result=''PolicyDetails{}.Rules{}.Actions{}'', file_name=case(NOT match(''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location'',"Message Body"),''PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location'') | stats min(_time) as firstTime max(_time) as lastTime values(signature) as signature values(file_name) as file_name values(ExchangeMetaData.Subject) AS subject values(Workload) as app values(result) as result by src_user,recipient,signature_id,reason | `o365_dlp_rule_triggered_filter` | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(*) AS * by src_user,signature_id | eval action = CASE(match(result,"Halt"),"blocked",isnotnull(result),"alert",true(),"allow") |`security_content_ctime(firstTime)`  |`security_content_ctime(lastTime)`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. You must deploy DLP rules through O365 security and compliance functions.
-known_false_positives: WIll depending on accuracy of DLP rules, these can be noisy so tune appropriately.
+search: "`o365_management_activity` Operation=DLPRuleMatch | eval recipient = 'ExchangeMetaData.To{}',
+  signature_id = 'ExchangeMetaData.UniqueID', signature = 'PolicyDetails{}.Rules{}.RuleName'
+  , src_user = UserId, reason ='PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.SensitiveInformationTypeName',
+  result='PolicyDetails{}.Rules{}.Actions{}', file_name=case(NOT match('PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location',\"\
+  Message Body\"),'PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location')
+  | stats min(_time) as firstTime max(_time) as lastTime values(signature) as signature
+  values(file_name) as file_name values(ExchangeMetaData.Subject) AS subject values(Workload)
+  as app values(result) as result by src_user,recipient,signature_id,reason | `o365_dlp_rule_triggered_filter`
+  | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(*) AS
+  * by src_user,signature_id | eval action = CASE(match(result,\"Halt\"),\"blocked\"\
+  ,isnotnull(result),\"alert\",true(),\"allow\") |`security_content_ctime(firstTime)`\
+  \  |`security_content_ctime(lastTime)`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. You must deploy DLP rules through O365 security
+  and compliance functions.
+known_false_positives: WIll depending on accuracy of DLP rules, these can be noisy
+  so tune appropriately.
 references:
 - https://learn.microsoft.com/en-us/purview/dlp-learn-about-dlp
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ExchangeMetaData.To{}
-  - ExchangeMetaData.UniqueID
-  - PolicyDetails{}.Rules{}.RuleName
-  - UserId
-  - PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.SensitiveInformationTypeName
-  - PolicyDetails{}.Rules{}.Actions{}
-  - PolicyDetails{}.Rules{}.ConditionsMatched.SensitiveInformation{}.Location
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_elevated_mailbox_permission_assigned.yml b/detections/cloud/o365_elevated_mailbox_permission_assigned.yml
index 6b4086b0fb..c1cbb93366 100644
--- a/detections/cloud/o365_elevated_mailbox_permission_assigned.yml
+++ b/detections/cloud/o365_elevated_mailbox_permission_assigned.yml
@@ -6,10 +6,23 @@ author: Patrick Bareiss, Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies the assignment of elevated mailbox permissions in an Office 365 environment via the Add-MailboxPermission operation. It leverages logs from the Exchange workload in the o365_management_activity data source, focusing on permissions such as FullAccess, ChangePermission, or ChangeOwner. This activity is significant as it indicates potential unauthorized access or control over mailboxes, which could lead to data exfiltration or privilege escalation. If confirmed malicious, attackers could gain extensive access to sensitive email data and potentially manipulate mailbox settings, posing a severe security risk.
-search: '`o365_management_activity` Workload=Exchange Operation=Add-MailboxPermission | search (AccessRights=FullAccess OR AccessRights=ChangePermission OR AccessRights=ChangeOwner) | rename Identity AS dest_user | stats count earliest(_time) as firstTime latest(_time) as lastTime by user dest_user Operation AccessRights |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_elevated_mailbox_permission_assigned_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: FullAccess mailbox delegation may be assigned for legitimate purposes, filter as needed.
+description: The following analytic identifies the assignment of elevated mailbox
+  permissions in an Office 365 environment via the Add-MailboxPermission operation.
+  It leverages logs from the Exchange workload in the o365_management_activity data
+  source, focusing on permissions such as FullAccess, ChangePermission, or ChangeOwner.
+  This activity is significant as it indicates potential unauthorized access or control
+  over mailboxes, which could lead to data exfiltration or privilege escalation. If
+  confirmed malicious, attackers could gain extensive access to sensitive email data
+  and potentially manipulate mailbox settings, posing a severe security risk.
+search: '`o365_management_activity` Workload=Exchange Operation=Add-MailboxPermission
+  | search (AccessRights=FullAccess OR AccessRights=ChangePermission OR AccessRights=ChangeOwner)
+  | rename Identity AS dest_user | stats count earliest(_time) as firstTime latest(_time)
+  as lastTime by user dest_user Operation AccessRights |`security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `o365_elevated_mailbox_permission_assigned_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: FullAccess mailbox delegation may be assigned for legitimate
+  purposes, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/002/
 - https://learn.microsoft.com/en-us/powershell/module/exchange/add-mailboxpermission
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - AccessRights
-  - user
-  - src_user
-  - dest_user
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json
     source: o365:management:activity
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_email_access_by_security_administrator.yml b/detections/cloud/o365_email_access_by_security_administrator.yml
index 671ce8f689..cf257e30a3 100644
--- a/detections/cloud/o365_email_access_by_security_administrator.yml
+++ b/detections/cloud/o365_email_access_by_security_administrator.yml
@@ -5,12 +5,22 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when a user with sufficient access to O365 Security & Compliance portal uses premium investigation features (Threat Explorer) to directly view email. Adversaries may exploit privileged access with this premium feature to enumerate or exfiltrate sensitive data.
+description: The following analytic identifies when a user with sufficient access
+  to O365 Security & Compliance portal uses premium investigation features (Threat
+  Explorer) to directly view email. Adversaries may exploit privileged access with
+  this premium feature to enumerate or exfiltrate sensitive data.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminMailAccess | stats values(Workload) as category, values(MailboxId) as user, values(Operation) as signature, count, min(_time) as firstTime, max(_time) as lastTime by InternetMessageId, UserId | rename InternetMessageId as signature_id, UserId as src_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_email_access_by_security_administrator_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. Threat Explorer is a premium feature with o365, logging may not be available with proper license.
-known_false_positives: Legitamate access by security administators for incident response measures.
+search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminMailAccess
+  | stats values(Workload) as category, values(MailboxId) as user, values(Operation)
+  as signature, count, min(_time) as firstTime, max(_time) as lastTime by InternetMessageId,
+  UserId | rename InternetMessageId as signature_id, UserId as src_user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_email_access_by_security_administrator_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. Threat Explorer is a premium feature with
+  o365, logging may not be available with proper license.
+known_false_positives: Legitamate access by security administators for incident response
+  measures.
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/threat-explorer-investigate-delivered-malicious-email?view=o365-worldwide
 drilldown_searches:
@@ -19,7 +29,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - MailboxId
-  - InternetMessageId
-  - UserId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_email_reported_by_admin_found_malicious.yml b/detections/cloud/o365_email_reported_by_admin_found_malicious.yml
index bf8b6cdeb7..1a8c6508cc 100644
--- a/detections/cloud/o365_email_reported_by_admin_found_malicious.yml
+++ b/detections/cloud/o365_email_reported_by_admin_found_malicious.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when an email manually submitted to Microsoft through the Security & Compliance portal is found to be malicious. This capability is an enhanced protection feature that can be used within o365 tenants by administrative users to report potentially malicious emails. This correlation looks for any submission that returns a Phish or Malware verdict upon submission.
+description: The following analytic detects when an email manually submitted to Microsoft
+  through the Security & Compliance portal is found to be malicious. This capability
+  is an enhanced protection feature that can be used within o365 tenants by administrative
+  users to report potentially malicious emails. This correlation looks for any submission
+  that returns a Phish or Malware verdict upon submission.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminSubmission | search RescanVerdict IN (Phish,Malware) | stats values(Subject) as subject, values(RescanVerdict) as result, values(SenderIP) as src, values(P2Sender) as sender, values(P1Sender) as src_user, values(Recipients{}) as user, count min(_time) as firstTime, max(_time) as lastTime, by Id,Operation,UserId | rename Name as signature, Id as signature_id, UserId as o365_adminuser | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_email_reported_by_admin_found_malicious_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
+search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AdminSubmission
+  | search RescanVerdict IN (Phish,Malware) | stats values(Subject) as subject, values(RescanVerdict)
+  as result, values(SenderIP) as src, values(P2Sender) as sender, values(P1Sender)
+  as src_user, values(Recipients{}) as user, count min(_time) as firstTime, max(_time)
+  as lastTime, by Id,Operation,UserId | rename Name as signature, Id as signature_id,
+  UserId as o365_adminuser | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_email_reported_by_admin_found_malicious_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
 known_false_positives: Administrators that submit known phishing training exercises.
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/submissions-outlook-report-messages?view=o365-worldwide
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +45,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 50
-  message: O365 security admin $o365_adminuser$ manually reported a suspicious email from $src_user$
+  message: O365 security admin $o365_adminuser$ manually reported a suspicious email
+    from $src_user$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -51,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Id
-  - Operation
-  - UserId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_email_reported_by_user_found_malicious.yml b/detections/cloud/o365_email_reported_by_user_found_malicious.yml
index 181e947d7c..f0d69ac197 100644
--- a/detections/cloud/o365_email_reported_by_user_found_malicious.yml
+++ b/detections/cloud/o365_email_reported_by_user_found_malicious.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when an email submitted to Microsoft using the built-in report button in Outlook is found to be malicious. This capability is an enhanced protection feature that can be used within o365 tenants by users to report potentially malicious emails. This correlation looks for any submission that returns a Phish or Malware verdict upon submission.
+description: The following analytic detects when an email submitted to Microsoft using
+  the built-in report button in Outlook is found to be malicious. This capability
+  is an enhanced protection feature that can be used within o365 tenants by users
+  to report potentially malicious emails. This correlation looks for any submission
+  that returns a Phish or Malware verdict upon submission.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=SecurityComplianceCenter  Operation=AlertEntityGenerated Name="Email reported by user as*" | fromjson Data | rename _raw AS temp etps AS _raw | extract pairdelim=";" kvdelim=":" | rename _raw AS etps temp AS _raw | search RescanVerdict IN (Phish,Malware) | rex field=tsd "\<(?<src_user>.+)\>" | eval src_user = case(isnull(src_user),tsd,true(),src_user) | stats count min(_time) as firstTime max(_time) as lastTime values(ms) as subject values(RescanVerdict) as result values(tsd) as sender values(src_user) as src_user by AlertId,AlertEntityId,Operation,Name | rename Name as signature, AlertId as signature_id, AlertEntityId as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_email_reported_by_user_found_malicious_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. You must deploy/allow the usage of the Microsoft Office Report A Message function.
+search: '`o365_management_activity` Workload=SecurityComplianceCenter  Operation=AlertEntityGenerated
+  Name="Email reported by user as*" | fromjson Data | rename _raw AS temp etps AS
+  _raw | extract pairdelim=";" kvdelim=":" | rename _raw AS etps temp AS _raw | search
+  RescanVerdict IN (Phish,Malware) | rex field=tsd "\<(?<src_user>.+)\>" | eval src_user
+  = case(isnull(src_user),tsd,true(),src_user) | stats count min(_time) as firstTime
+  max(_time) as lastTime values(ms) as subject values(RescanVerdict) as result values(tsd)
+  as sender values(src_user) as src_user by AlertId,AlertEntityId,Operation,Name |
+  rename Name as signature, AlertId as signature_id, AlertEntityId as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_email_reported_by_user_found_malicious_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. You must deploy/allow the usage of the Microsoft
+  Office Report A Message function.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/submissions-outlook-report-messages?view=o365-worldwide
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,20 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Name
-  - Data
-  - AlertId
-  - AlertEntityId
-  - tsd
-  - etps
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_email_security_feature_changed.yml b/detections/cloud/o365_email_security_feature_changed.yml
index 7e5dcfe3b9..3d847a4b9b 100644
--- a/detections/cloud/o365_email_security_feature_changed.yml
+++ b/detections/cloud/o365_email_security_feature_changed.yml
@@ -5,12 +5,22 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when specific O365 advanced security settings are altered within the Office 365 tenant. If an attacker successfully disables O365 security settings, they can operate within the tenant with reduced risk of detection. This can lead to unauthorized data access, data exfiltration, account compromise, or other malicious activities without leaving a detailed audit trail.
+description: The following analytic identifies when specific O365 advanced security
+  settings are altered within the Office 365 tenant. If an attacker successfully disables
+  O365 security settings, they can operate within the tenant with reduced risk of
+  detection. This can lead to unauthorized data access, data exfiltration, account
+  compromise, or other malicious activities without leaving a detailed audit trail.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*") Operation IN ("*AntiPhish*","*SafeLink*","*SafeAttachment*","*Malware*") | stats values(ObjectId) as object, min(_time) as firstTime, max(_time) as lastTime, count  by Id, UserId, Operation | rename Id as object_id, UserId as user, Operation as signature | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_email_security_feature_changed_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Administrators might alter features for troubleshooting, performance reasons, or other administrative tasks. Filter as needed.
+search: '`o365_management_activity` Workload=Exchange AND Operation IN ("Set-*","Disable-*","New-*","Remove-*")
+  Operation IN ("*AntiPhish*","*SafeLink*","*SafeAttachment*","*Malware*") | stats
+  values(ObjectId) as object, min(_time) as firstTime, max(_time) as lastTime, count  by
+  Id, UserId, Operation | rename Id as object_id, UserId as user, Operation as signature
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_email_security_feature_changed_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Administrators might alter features for troubleshooting, performance
+  reasons, or other administrative tasks. Filter as needed.
 references:
 - https://learn.microsoft.com/en-us/entra/fundamentals/security-defaults
 - https://attack.mitre.org/techniques/T1562/008/
@@ -20,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,16 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Id
-  - UserId
-  - Operation
-  - Workload
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_email_suspicious_behavior_alert.yml b/detections/cloud/o365_email_suspicious_behavior_alert.yml
index 877fe71e8a..e4d2509378 100644
--- a/detections/cloud/o365_email_suspicious_behavior_alert.yml
+++ b/detections/cloud/o365_email_suspicious_behavior_alert.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when one of O365 the built-in security detections for suspicious email behaviors are triggered.  These alerts often indicate that an attacker may have compromised a mailbox within the environment. Any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities further enhance these detection and response functions.
+description: The following analytic identifies when one of O365 the built-in security
+  detections for suspicious email behaviors are triggered.  These alerts often indicate
+  that an attacker may have compromised a mailbox within the environment. Any detections
+  from built-in Office 365 capabilities should be monitored and responded to appropriately.
+  Certain premium Office 365 capabilities further enhance these detection and response
+  functions.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated Name IN ("Suspicious email sending patterns detected","User restricted from sending email","Suspicious Email Forwarding Activity","Email sending limit exceeded") | fromjson Data | stats count min(_time) as firstTime max(_time) as lastTime by AlertId,ObjectId,Operation,Name | rename Name as signature, AlertId as signature_id, ObjectId as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_email_suspicious_behavior_alert_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The alerts must be enabled in the o365 security portal.
-known_false_positives: Users emailing for legitimate business purposes that appear suspicious.
+search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated
+  Name IN ("Suspicious email sending patterns detected","User restricted from sending
+  email","Suspicious Email Forwarding Activity","Email sending limit exceeded") |
+  fromjson Data | stats count min(_time) as firstTime max(_time) as lastTime by AlertId,ObjectId,Operation,Name
+  | rename Name as signature, AlertId as signature_id, ObjectId as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_email_suspicious_behavior_alert_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. The alerts must be enabled in the o365 security
+  portal.
+known_false_positives: Users emailing for legitimate business purposes that appear
+  suspicious.
 references:
 - https://learn.microsoft.com/en-us/purview/alert-policies
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - AlertId
-  - ObjectId
-  - Operation
-  - Name
-  - Workload
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_excessive_authentication_failures_alert.yml b/detections/cloud/o365_excessive_authentication_failures_alert.yml
index 0328df1cef..1516b2291a 100644
--- a/detections/cloud/o365_excessive_authentication_failures_alert.yml
+++ b/detections/cloud/o365_excessive_authentication_failures_alert.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies an excessive number of authentication failures, including failed attempts against MFA prompt codes. It uses data from the `o365_management_activity` dataset, focusing on events where the authentication status is marked as failure. This behavior is significant as it may indicate a brute force attack or an attempt to compromise user accounts. If confirmed malicious, this activity could lead to unauthorized access, data breaches, or further exploitation within the environment.
+description: The following analytic identifies an excessive number of authentication
+  failures, including failed attempts against MFA prompt codes. It uses data from
+  the `o365_management_activity` dataset, focusing on events where the authentication
+  status is marked as failure. This behavior is significant as it may indicate a brute
+  force attack or an attempt to compromise user accounts. If confirmed malicious,
+  this activity could lead to unauthorized access, data breaches, or further exploitation
+  within the environment.
 data_source: []
-search: '`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=failure | stats count earliest(_time) AS firstTime latest(_time) AS lastTime values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_excessive_authentication_failures_alert_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: The threshold for alert is above 10 attempts and this should reduce the number of false positives.
+search: '`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=*
+  status=failure | stats count earliest(_time) AS firstTime latest(_time) AS lastTime
+  values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS
+  UserAgent values(status) AS status values(src_ip) AS src_ip by user | where count
+  > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_excessive_authentication_failures_alert_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: The threshold for alert is above 10 attempts and this should
+  reduce the number of false positives.
 references:
 - https://attack.mitre.org/techniques/T1110/
 drilldown_searches:
@@ -18,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +45,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 80
   impact: 80
-  message: User $user$ has caused excessive number of authentication failures from $src_ip$ using UserAgent $UserAgent$.
+  message: User $user$ has caused excessive number of authentication failures from
+    $src_ip$ using UserAgent $UserAgent$.
   mitre_attack_id:
   - T1110
   observable:
@@ -43,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - UserAuthenticationMethod
-  - status
-  - UserAgent
-  - src_ip
-  - user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_excessive_sso_logon_errors.yml b/detections/cloud/o365_excessive_sso_logon_errors.yml
index cfd32039e9..71468b4d0f 100644
--- a/detections/cloud/o365_excessive_sso_logon_errors.yml
+++ b/detections/cloud/o365_excessive_sso_logon_errors.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects accounts experiencing a high number of Single Sign-On (SSO) logon errors. It leverages data from the `o365_management_activity` dataset, focusing on failed user login attempts with SSO errors. This activity is significant as it may indicate brute-force attempts or the hijacking/reuse of SSO tokens. If confirmed malicious, attackers could potentially gain unauthorized access to user accounts, leading to data breaches, privilege escalation, or further lateral movement within the organization.
+description: The following analytic detects accounts experiencing a high number of
+  Single Sign-On (SSO) logon errors. It leverages data from the `o365_management_activity`
+  dataset, focusing on failed user login attempts with SSO errors. This activity is
+  significant as it may indicate brute-force attempts or the hijacking/reuse of SSO
+  tokens. If confirmed malicious, attackers could potentially gain unauthorized access
+  to user accounts, leading to data breaches, privilege escalation, or further lateral
+  movement within the organization.
 data_source:
 - O365 UserLoginFailed
-search: '`o365_management_activity` Workload=AzureActiveDirectory LogonError=*Sso* Operation=UserLoginFailed | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by  src_ip signature user_agent authentication_service action| where count >= 5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_excessive_sso_logon_errors_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack.
+search: '`o365_management_activity` Workload=AzureActiveDirectory LogonError=*Sso*
+  Operation=UserLoginFailed | stats count min(_time) as firstTime max(_time) as lastTime
+  values(user) as user by  src_ip signature user_agent authentication_service action|
+  where count >= 5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_excessive_sso_logon_errors_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: Logon errors may not be malicious in nature however it may
+  indicate attempts to reuse a token or password obtained via credential access attack.
 references:
 - https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,22 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - src_ip
-  - Workload
-  - LogonError
-  - ActorIpAddress
-  - UserAgent
-  - UserId
-  - authentication_service
-  - authentication_method
-  - Operation
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors2.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors2.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_external_guest_user_invited.yml b/detections/cloud/o365_external_guest_user_invited.yml
index 474ae293af..19c181fbd7 100644
--- a/detections/cloud/o365_external_guest_user_invited.yml
+++ b/detections/cloud/o365_external_guest_user_invited.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the invitation of an external guest user within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`. This detection leverages the Universal Audit Log (UAL)/o365:management:activity sourcetype as a detection data source.
+description: The following analytic identifies the invitation of an external guest
+  user within Azure AD. With Azure AD B2B collaboration, users and administrators
+  can invite external users to collaborate with internal users. External guest account
+  invitations should be monitored by security teams as they could potentially lead
+  to unauthorized access. An example of this attack vector was described at BlackHat
+  2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking
+  Azure AD Accounts by Abusing External Identities`. This detection leverages the
+  Universal Audit Log (UAL)/o365:management:activity sourcetype as a detection data
+  source.
 data_source:
 - Office 365 Universal Audit Log
-search: '`o365_management_activity` Workload=AzureActiveDirectory AND Operation="Add user*" AND ModifiedProperties{}.NewValue="[*Guest*]" AND ModifiedProperties{}.NewValue="[*Invitation*]" | eval user = (mvindex(''ModifiedProperties{}.NewValue'',5)), src_user = case(match(mvindex(''Actor{}.ID'',-1),"User"),mvindex(''Actor{}.ID'',0),match(mvindex(''Actor{}.ID'',-1),"ServicePrincipal"),mvindex(''Actor{}.ID'',3),true(),mvindex(''Actor{}.ID'',0)) | rex field=user "(?<user>[\\w\\.-]+@[\\w-]+\\.[\\w-]{2,4})" | stats values(user) as user, min(_time) as firstTime, max(_time) as lastTime, count by Operation,Id,src_user | rename Operation as signature, Id as signature_id | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_external_guest_user_invited_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Administrator may legitimately invite external guest users. Filter as needed.
+search: "`o365_management_activity` Workload=AzureActiveDirectory AND Operation=\"\
+  Add user*\" AND ModifiedProperties{}.NewValue=\"[*Guest*]\" AND ModifiedProperties{}.NewValue=\"\
+  [*Invitation*]\" | eval user = (mvindex('ModifiedProperties{}.NewValue',5)), src_user
+  = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"\
+  ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | rex
+  field=user \"(?<user>[\\\\w\\\\.-]+@[\\\\w-]+\\\\.[\\\\w-]{2,4})\" | stats values(user)
+  as user, min(_time) as firstTime, max(_time) as lastTime, count by Operation,Id,src_user
+  | rename Operation as signature, Id as signature_id | `security_content_ctime(firstTime)`\
+  \  | `security_content_ctime(lastTime)` | `o365_external_guest_user_invited_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Administrator may legitimately invite external guest users.
+  Filter as needed.
 references:
 - https://dirkjanm.io/assets/raw/US-22-Mollema-Backdooring-and-hijacking-Azure-AD-accounts_final.pdf
 - https://www.blackhat.com/us-22/briefings/schedule/#backdooring-and-hijacking-azure-ad-accounts-by-abusing-external-identities-26999
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.Name
-  - UserId
-  - Id
-  - Workload
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_external_identity_policy_changed.yml b/detections/cloud/o365_external_identity_policy_changed.yml
index 24abbbdb83..dbb7c9a4c3 100644
--- a/detections/cloud/o365_external_identity_policy_changed.yml
+++ b/detections/cloud/o365_external_identity_policy_changed.yml
@@ -5,11 +5,36 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when changes are made to the external guest policies within Azure AD. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. This detection also attempts to highlight what may have changed. External guest account invitations should be monitored by security teams as they could potentially lead to unauthorized access. An example of this attack vector was described at BlackHat 2022 by security researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts by Abusing External Identities`.
+description: The following analytic identifies when changes are made to the external
+  guest policies within Azure AD. With Azure AD B2B collaboration, users and administrators
+  can invite external users to collaborate with internal users. This detection also
+  attempts to highlight what may have changed. External guest account invitations
+  should be monitored by security teams as they could potentially lead to unauthorized
+  access. An example of this attack vector was described at BlackHat 2022 by security
+  researcher Dirk-Jan during his tall `Backdooring and Hijacking Azure AD Accounts
+  by Abusing External Identities`.
 data_source:
 - Office 365 Universal Audit Log
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update policy." Target{}.ID="B2BManagementPolicy" | eval object_attrs = mvindex(''ModifiedProperties{}.NewValue'',0), object_attrs_old = mvindex(''ModifiedProperties{}.OldValue'',0), object_name = mvindex(''Target{}.ID'',3), signature=Operation, user = case(match(mvindex(''Actor{}.ID'',-1),"User"),mvindex(''Actor{}.ID'',0),match(mvindex(''Actor{}.ID'',-1),"ServicePrincipal"),mvindex(''Actor{}.ID'',3),true(),mvindex(''Actor{}.ID'',0)) | spath input=object_attrs_old output=B2BOld path={} | spath input=B2BOld | rename B2BManagementPolicy.* as B2BManagementPolicyOld.* | spath input=object_attrs output=B2BNew path={} | spath input=B2BNew | eval object_attrs = ''B2BManagementPolicy.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}'' , object_attrs_old = ''B2BManagementPolicyOld.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}'' | eval diff_add=mvmap(object_attrs,if(isnull(mvfind(object_attrs_old,object_attrs)),object_attrs,null)) | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs,object_attrs_old)),object_attrs_old,null)) | eval result = case(isnotnull(diff_add),"Added ".mvjoin(diff_add,","),isnotnull(diff_remove),"Removed ".mvjoin(diff_remove,",")), action = case(isnotnull(diff_add),"created",isnotnull(diff_remove),"deleted") | stats values(object_attrs) as object_attrs, values(action) as action, values(result) as result, values(B2BManagementPolicy*) as B2BManagementPolicy*, count, min(_time) as firstTime, max(_time) as lastTime by user,signature,object_name | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_external_identity_policy_changed_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update
+  policy.\" Target{}.ID=\"B2BManagementPolicy\" | eval object_attrs = mvindex('ModifiedProperties{}.NewValue',0),
+  object_attrs_old = mvindex('ModifiedProperties{}.OldValue',0), object_name = mvindex('Target{}.ID',3),
+  signature=Operation, user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"\
+  ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)) | spath
+  input=object_attrs_old output=B2BOld path={} | spath input=B2BOld | rename B2BManagementPolicy.*
+  as B2BManagementPolicyOld.* | spath input=object_attrs output=B2BNew path={} | spath
+  input=B2BNew | eval object_attrs = 'B2BManagementPolicy.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}'
+  , object_attrs_old = 'B2BManagementPolicyOld.InvitationsAllowedAndBlockedDomainsPolicy.AllowedDomains{}'
+  | eval diff_add=mvmap(object_attrs,if(isnull(mvfind(object_attrs_old,object_attrs)),object_attrs,null))
+  | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs,object_attrs_old)),object_attrs_old,null))
+  | eval result = case(isnotnull(diff_add),\"Added \".mvjoin(diff_add,\",\"),isnotnull(diff_remove),\"\
+  Removed \".mvjoin(diff_remove,\",\")), action = case(isnotnull(diff_add),\"created\"\
+  ,isnotnull(diff_remove),\"deleted\") | stats values(object_attrs) as object_attrs,
+  values(action) as action, values(result) as result, values(B2BManagementPolicy*)
+  as B2BManagementPolicy*, count, min(_time) as firstTime, max(_time) as lastTime
+  by user,signature,object_name | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `o365_external_identity_policy_changed_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
 known_false_positives: Business approved changes by known administrators.
 references:
 - https://medium.com/tenable-techblog/roles-allowing-to-abuse-entra-id-federation-for-persistence-and-privilege-escalation-df9ca6e58360
@@ -20,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,17 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.Name
-  - UserId
-  - Workload
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml b/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml
index 28253bbd14..d976adebbf 100644
--- a/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml
+++ b/detections/cloud/o365_file_permissioned_application_consent_granted_by_user.yml
@@ -7,10 +7,25 @@ status: production
 type: TTP
 data_source:
 - O365 Consent to application.
-description: The following analytic identifies instances where a user in the Office 365 environment grants consent to an application requesting file permissions for OneDrive or SharePoint. It leverages O365 audit logs, focusing on OAuth application consent events. This activity is significant because granting such permissions can allow applications to access, modify, or delete files, posing a risk if the application is malicious or overly permissive. If confirmed malicious, this could lead to data breaches, data loss, or unauthorized data manipulation, necessitating immediate investigation to validate the application's legitimacy and assess potential risks.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Consent to application." ResultStatus=Success | eval admin_consent =mvindex(''ModifiedProperties{}.NewValue'', 0) | search admin_consent=False | eval permissions =mvindex(''ModifiedProperties{}.NewValue'', 4) | rex field=permissions "Scope: (?<Scope>[^,]+)" | makemv delim=" " Scope | search Scope IN ("Files.Read", "Files.Read.All", "Files.ReadWrite", "Files.ReadWrite.All", "Files.ReadWrite.AppFolder") | stats max(_time) as lastTime values(Scope) by Operation, user, object, ObjectId | `security_content_ctime(lastTime)` | `o365_file_permissioned_application_consent_granted_by_user_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: OAuth applications that require file permissions may be legitimate, investigate and filter as needed.
+description: The following analytic identifies instances where a user in the Office
+  365 environment grants consent to an application requesting file permissions for
+  OneDrive or SharePoint. It leverages O365 audit logs, focusing on OAuth application
+  consent events. This activity is significant because granting such permissions can
+  allow applications to access, modify, or delete files, posing a risk if the application
+  is malicious or overly permissive. If confirmed malicious, this could lead to data
+  breaches, data loss, or unauthorized data manipulation, necessitating immediate
+  investigation to validate the application's legitimacy and assess potential risks.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent
+  to application.\" ResultStatus=Success | eval admin_consent =mvindex('ModifiedProperties{}.NewValue',
+  0) | search admin_consent=False | eval permissions =mvindex('ModifiedProperties{}.NewValue',
+  4) | rex field=permissions \"Scope: (?<Scope>[^,]+)\" | makemv delim=\" \" Scope
+  | search Scope IN (\"Files.Read\", \"Files.Read.All\", \"Files.ReadWrite\", \"Files.ReadWrite.All\"\
+  , \"Files.ReadWrite.AppFolder\") | stats max(_time) as lastTime values(Scope) by
+  Operation, user, object, ObjectId | `security_content_ctime(lastTime)` | `o365_file_permissioned_application_consent_granted_by_user_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: OAuth applications that require file permissions may be legitimate,
+  investigate and filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1528/
 - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ResultStatus
-  - ModifiedProperties{}.NewValue
-  - object
-  - ObjectId
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_file_permissions/o365_user_consent_file_permissions.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_file_permissions/o365_user_consent_file_permissions.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_fullaccessasapp_permission_assigned.yml b/detections/cloud/o365_fullaccessasapp_permission_assigned.yml
index ea38de0dad..4e85eaa773 100644
--- a/detections/cloud/o365_fullaccessasapp_permission_assigned.yml
+++ b/detections/cloud/o365_fullaccessasapp_permission_assigned.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - O365 Update application.
-description: The following analytic detects the assignment of the 'full_access_as_app' permission to an application registration in Office 365 Exchange Online. This detection leverages Office 365 management activity logs and filters Azure Active Directory workload events to identify when the specific permission, identified by GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40', is granted. This activity is significant because it provides extensive control over Office 365 operations, including access to all mailboxes and the ability to send mail as any user. If confirmed malicious, this could lead to unauthorized data access, exfiltration, or account compromise. Immediate investigation is required.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update application." | eval newvalue = mvindex(''ModifiedProperties{}.NewValue'',0) | spath input=newvalue  | search "{}.ResourceAppId"="00000002-0000-0ff1-ce00-000000000000"  "{}.RequiredAppPermissions{}.EntitlementId"="dc890d15-9560-4a4c-9b7f-a736ec74ec40" | eval Permissions = ''{}.RequiredAppPermissions{}.EntitlementId'' | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user, object, user_agent, Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_fullaccessasapp_permission_assigned_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: The full_access_as_app API permission may be assigned to legitimate applications. Filter as needed.
+description: The following analytic detects the assignment of the 'full_access_as_app'
+  permission to an application registration in Office 365 Exchange Online. This detection
+  leverages Office 365 management activity logs and filters Azure Active Directory
+  workload events to identify when the specific permission, identified by GUID 'dc890d15-9560-4a4c-9b7f-a736ec74ec40',
+  is granted. This activity is significant because it provides extensive control over
+  Office 365 operations, including access to all mailboxes and the ability to send
+  mail as any user. If confirmed malicious, this could lead to unauthorized data access,
+  exfiltration, or account compromise. Immediate investigation is required.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update
+  application.\" | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) | spath
+  input=newvalue  | search \"{}.ResourceAppId\"=\"00000002-0000-0ff1-ce00-000000000000\"\
+  \  \"{}.RequiredAppPermissions{}.EntitlementId\"=\"dc890d15-9560-4a4c-9b7f-a736ec74ec40\"\
+  \ | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | stats count
+  earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user,
+  object, user_agent, Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_fullaccessasapp_permission_assigned_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: The full_access_as_app API permission may be assigned to legitimate
+  applications. Filter as needed.
 references:
 - https://msrc.microsoft.com/blog/2024/01/microsoft-actions-following-attack-by-nation-state-actor-midnight-blizzard/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 80
-  message: User $user$ assigned the full_access_as_app permission to the app registration $object$
+  message: User $user$ assigned the full_access_as_app permission to the app registration
+    $object$
   mitre_attack_id:
   - T1098.002
   - T1098.003
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - object
-  - user
-  - user_agent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_full_access_as_app_permission_assigned/o365_full_access_as_app_permission_assigned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_full_access_as_app_permission_assigned/o365_full_access_as_app_permission_assigned.log
     source: o365:management:activity
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml b/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml
index 734d3f1d42..f3c6c93cea 100644
--- a/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml
+++ b/detections/cloud/o365_high_number_of_failed_authentications_for_user.yml
@@ -7,10 +7,21 @@ status: production
 type: TTP
 data_source:
 - O365 UserLoginFailed
-description: The following analytic identifies an O365 account experiencing more than 20 failed authentication attempts within 5 minutes. It uses O365 Unified Audit Logs, specifically "UserLoginFailed" events, to monitor and flag accounts exceeding this threshold. This activity is significant as it may indicate a brute force attack or password guessing attempt. If confirmed malicious, an attacker could gain unauthorized access to the O365 environment, potentially compromising sensitive emails, documents, and other data. Prompt investigation and action are crucial to prevent unauthorized access and data breaches.
-search: '`o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon Workload=AzureActiveDirectory | bucket span=5m _time | stats  dc(_raw) AS failed_attempts  values(src_ip) as src_ip by user, _time | where failed_attempts > 10 | `o365_high_number_of_failed_authentications_for_user_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Although unusual, users who have lost their passwords may trigger this detection. Filter as needed.
+description: The following analytic identifies an O365 account experiencing more than
+  20 failed authentication attempts within 5 minutes. It uses O365 Unified Audit Logs,
+  specifically "UserLoginFailed" events, to monitor and flag accounts exceeding this
+  threshold. This activity is significant as it may indicate a brute force attack
+  or password guessing attempt. If confirmed malicious, an attacker could gain unauthorized
+  access to the O365 environment, potentially compromising sensitive emails, documents,
+  and other data. Prompt investigation and action are crucial to prevent unauthorized
+  access and data breaches.
+search: '`o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon
+  Workload=AzureActiveDirectory | bucket span=5m _time | stats  dc(_raw) AS failed_attempts  values(src_ip)
+  as src_ip by user, _time | where failed_attempts > 10 | `o365_high_number_of_failed_authentications_for_user_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Although unusual, users who have lost their passwords may trigger
+  this detection. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1110/
 - https://attack.mitre.org/techniques/T1110/001/
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +45,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 70
   impact: 50
-  message: User $user$ failed to authenticate more than 10 times in the span of 5 minutes.
+  message: User $user$ failed to authenticate more than 10 times in the span of 5
+    minutes.
   mitre_attack_id:
   - T1110
   - T1110.001
@@ -46,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - src_ip
-  - user
-  - Operation
-  - record_type
-  - Workload
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.001/o365_high_number_authentications_for_user/o365_high_number_authentications_for_user.log
     source: o365:management:activity
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_high_privilege_role_granted.yml b/detections/cloud/o365_high_privilege_role_granted.yml
index 36f639ef26..14530c21ad 100644
--- a/detections/cloud/o365_high_privilege_role_granted.yml
+++ b/detections/cloud/o365_high_privilege_role_granted.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - O365 Add member to role.
-description: The following analytic detects when high-privilege roles such as "Exchange Administrator," "SharePoint Administrator," or "Global Administrator" are granted within Office 365. It leverages O365 audit logs to identify events where these roles are assigned to any user or service account. This activity is significant for SOCs as these roles provide extensive permissions, allowing broad access and control over critical resources and data. If confirmed malicious, this could enable attackers to gain significant control over O365 resources, access, modify, or delete critical data, and compromise the overall security and functionality of the O365 environment.
-search: '`o365_management_activity` Operation="Add member to role." Workload=AzureActiveDirectory | eval role_id = mvindex(''ModifiedProperties{}.NewValue'',2) | eval role_name = mvindex(''ModifiedProperties{}.NewValue'',1) | where role_id IN ("29232cdf-9323-42fd-ade2-1d097af3e4de", "f28a1f50-f6e7-4571-818b-6a12f2af6b6c", "62e90394-69f5-4237-9190-012177145e10")  | stats earliest(_time) as firstTime latest(_time) as lastTime by user Operation ObjectId role_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_high_privilege_role_granted_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Privilege roles may be assigned for legitimate purposes, filter as needed.
+description: The following analytic detects when high-privilege roles such as "Exchange
+  Administrator," "SharePoint Administrator," or "Global Administrator" are granted
+  within Office 365. It leverages O365 audit logs to identify events where these roles
+  are assigned to any user or service account. This activity is significant for SOCs
+  as these roles provide extensive permissions, allowing broad access and control
+  over critical resources and data. If confirmed malicious, this could enable attackers
+  to gain significant control over O365 resources, access, modify, or delete critical
+  data, and compromise the overall security and functionality of the O365 environment.
+search: "`o365_management_activity` Operation=\"Add member to role.\" Workload=AzureActiveDirectory
+  | eval role_id = mvindex('ModifiedProperties{}.NewValue',2) | eval role_name = mvindex('ModifiedProperties{}.NewValue',1)
+  | where role_id IN (\"29232cdf-9323-42fd-ade2-1d097af3e4de\", \"f28a1f50-f6e7-4571-818b-6a12f2af6b6c\"\
+  , \"62e90394-69f5-4237-9190-012177145e10\")  | stats earliest(_time) as firstTime
+  latest(_time) as lastTime by user Operation ObjectId role_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_high_privilege_role_granted_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Privilege roles may be assigned for legitimate purposes, filter
+  as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Workload
-  - ModifiedProperties{}.NewValue
-  - user
-  - ObjectId
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_high_priv_role_assigned/o365_high_priv_role_assigned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_high_priv_role_assigned/o365_high_priv_role_assigned.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml b/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml
index cf6e96a7b7..5ec50163c5 100644
--- a/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml
+++ b/detections/cloud/o365_mail_permissioned_application_consent_granted_by_user.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - O365 Consent to application.
-description: The following analytic identifies instances where a user grants consent to an application requesting mail-related permissions within the Office 365 environment. It leverages O365 audit logs, specifically focusing on events related to application permissions and user consent actions. This activity is significant as it can indicate potential security risks, such as data exfiltration or spear phishing, if malicious applications gain access. If confirmed malicious, this could lead to unauthorized data access, email forwarding, or sending malicious emails from the compromised account. Validating the legitimacy of the application and consent context is crucial to prevent data breaches.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Consent to application." ResultStatus=Success | eval admin_consent =mvindex(''ModifiedProperties{}.NewValue'', 0) | search admin_consent=False | eval permissions =mvindex(''ModifiedProperties{}.NewValue'', 4) | rex field=permissions "Scope: (?<Scope>[^,]+)" | makemv delim=" " Scope | search Scope IN ("Mail.Read", "Mail.ReadBasic", "Mail.ReadWrite", "Mail.Read.Shared", "Mail.ReadWrite.Shared", "Mail.Send", "Mail.Send.Shared") | stats max(_time) as lastTime values(Scope) by Operation, user, object, ObjectId | `security_content_ctime(lastTime)` | `o365_mail_permissioned_application_consent_granted_by_user_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: OAuth applications that require mail permissions may be legitimate, investigate and filter as needed.
+description: The following analytic identifies instances where a user grants consent
+  to an application requesting mail-related permissions within the Office 365 environment.
+  It leverages O365 audit logs, specifically focusing on events related to application
+  permissions and user consent actions. This activity is significant as it can indicate
+  potential security risks, such as data exfiltration or spear phishing, if malicious
+  applications gain access. If confirmed malicious, this could lead to unauthorized
+  data access, email forwarding, or sending malicious emails from the compromised
+  account. Validating the legitimacy of the application and consent context is crucial
+  to prevent data breaches.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent
+  to application.\" ResultStatus=Success | eval admin_consent =mvindex('ModifiedProperties{}.NewValue',
+  0) | search admin_consent=False | eval permissions =mvindex('ModifiedProperties{}.NewValue',
+  4) | rex field=permissions \"Scope: (?<Scope>[^,]+)\" | makemv delim=\" \" Scope
+  | search Scope IN (\"Mail.Read\", \"Mail.ReadBasic\", \"Mail.ReadWrite\", \"Mail.Read.Shared\"\
+  , \"Mail.ReadWrite.Shared\", \"Mail.Send\", \"Mail.Send.Shared\") | stats max(_time)
+  as lastTime values(Scope) by Operation, user, object, ObjectId | `security_content_ctime(lastTime)`
+  | `o365_mail_permissioned_application_consent_granted_by_user_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: OAuth applications that require mail permissions may be legitimate,
+  investigate and filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1528/
 - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
@@ -24,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ResultStatus
-  - ModifiedProperties{}.NewValue
-  - object
-  - ObjectId
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_mail_permissions/o365_user_consent_mail_permissions.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_mail_permissions/o365_user_consent_mail_permissions.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_mailbox_email_forwarding_enabled.yml b/detections/cloud/o365_mailbox_email_forwarding_enabled.yml
index af00a7465b..752423ae2a 100644
--- a/detections/cloud/o365_mailbox_email_forwarding_enabled.yml
+++ b/detections/cloud/o365_mailbox_email_forwarding_enabled.yml
@@ -6,10 +6,25 @@ author: Patrick Bareiss, Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies instances where email forwarding has been enabled on mailboxes within an Office 365 environment. It detects this activity by monitoring the Set-Mailbox operation within the o365_management_activity logs, specifically looking for changes to the ForwardingAddress or ForwardingSmtpAddress parameters. This activity is significant as unauthorized email forwarding can lead to data exfiltration and unauthorized access to sensitive information. If confirmed malicious, attackers could intercept and redirect emails, potentially compromising confidential communications and leading to data breaches.
-search: '`o365_management_activity` Operation=Set-Mailbox  | eval match1=mvfind(''Parameters{}.Name'', "ForwardingAddress") | eval match2=mvfind(''Parameters{}.Name'', "ForwardingSmtpAddress") | where match1>= 0 OR match2>= 0 | eval ForwardTo=coalesce(ForwardingAddress, ForwardingSmtpAddress) | search ForwardTo!=""  | rename user_id as user | stats count earliest(_time) as firstTime latest(_time) as lastTime values(ForwardTo) as ForwardTo by user ObjectId |`security_content_ctime(firstTime)`  |`security_content_ctime(lastTime)` | `o365_mailbox_email_forwarding_enabled_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Email forwarding may be configured for legitimate purposes, filter as needed.
+description: The following analytic identifies instances where email forwarding has
+  been enabled on mailboxes within an Office 365 environment. It detects this activity
+  by monitoring the Set-Mailbox operation within the o365_management_activity logs,
+  specifically looking for changes to the ForwardingAddress or ForwardingSmtpAddress
+  parameters. This activity is significant as unauthorized email forwarding can lead
+  to data exfiltration and unauthorized access to sensitive information. If confirmed
+  malicious, attackers could intercept and redirect emails, potentially compromising
+  confidential communications and leading to data breaches.
+search: "`o365_management_activity` Operation=Set-Mailbox  | eval match1=mvfind('Parameters{}.Name',
+  \"ForwardingAddress\") | eval match2=mvfind('Parameters{}.Name', \"ForwardingSmtpAddress\"\
+  ) | where match1>= 0 OR match2>= 0 | eval ForwardTo=coalesce(ForwardingAddress,
+  ForwardingSmtpAddress) | search ForwardTo!=\"\"  | rename user_id as user | stats
+  count earliest(_time) as firstTime latest(_time) as lastTime values(ForwardTo) as
+  ForwardTo by user ObjectId |`security_content_ctime(firstTime)`  |`security_content_ctime(lastTime)`
+  | `o365_mailbox_email_forwarding_enabled_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Email forwarding may be configured for legitimate purposes,
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/003/
 - https://learn.microsoft.com/en-us/exchange/recipients/user-mailboxes/email-forwarding?view=exchserver-2019
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Parameters{}.Name
-  - src_user
-  - DeliverToMailboxAndForward
-  - ObjectId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml b/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml
index 7ce36b1a66..c6b786c9b5 100644
--- a/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml
+++ b/detections/cloud/o365_mailbox_folder_read_permission_assigned.yml
@@ -6,10 +6,25 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies instances where read permissions are assigned to mailbox folders within an Office 365 environment. It leverages the `o365_management_activity` data source, specifically monitoring the `ModifyFolderPermissions` and `AddFolderPermissions` operations, while excluding Calendar, Contacts, and PersonMetadata objects. This activity is significant as unauthorized read permissions can lead to data exposure and potential information leakage. If confirmed malicious, an attacker could gain unauthorized access to sensitive emails, leading to data breaches and compromising the confidentiality of organizational communications.
-search: '`o365_management_activity` Workload=Exchange (Operation=ModifyFolderPermissions OR Operation=AddFolderPermissions) Workload=Exchange object!=Calendar object!=Contacts object!=PersonMetadata | eval isReadRole=if(match(''Item.ParentFolder.MemberRights'', "(ReadAny)"), "true", "false")  | rename UserId as user  | stats count earliest(_time) as firstTime latest(_time) as lastTime by Operation, user, object, Item.ParentFolder.MemberUpn, Item.ParentFolder.MemberRights | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_folder_read_permission_assigned_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Mailbox folder permissions may be configured for legitimate purposes, filter as needed.
+description: The following analytic identifies instances where read permissions are
+  assigned to mailbox folders within an Office 365 environment. It leverages the `o365_management_activity`
+  data source, specifically monitoring the `ModifyFolderPermissions` and `AddFolderPermissions`
+  operations, while excluding Calendar, Contacts, and PersonMetadata objects. This
+  activity is significant as unauthorized read permissions can lead to data exposure
+  and potential information leakage. If confirmed malicious, an attacker could gain
+  unauthorized access to sensitive emails, leading to data breaches and compromising
+  the confidentiality of organizational communications.
+search: "`o365_management_activity` Workload=Exchange (Operation=ModifyFolderPermissions
+  OR Operation=AddFolderPermissions) Workload=Exchange object!=Calendar object!=Contacts
+  object!=PersonMetadata | eval isReadRole=if(match('Item.ParentFolder.MemberRights',
+  \"(ReadAny)\"), \"true\", \"false\")  | rename UserId as user  | stats count earliest(_time)
+  as firstTime latest(_time) as lastTime by Operation, user, object, Item.ParentFolder.MemberUpn,
+  Item.ParentFolder.MemberRights | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_mailbox_folder_read_permission_assigned_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Mailbox folder permissions may be configured for legitimate
+  purposes, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/002/
 - https://learn.microsoft.com/en-us/openspecs/exchange_server_protocols/ms-oxodlgt/5610c6e6-3268-44e3-adff-8804f5315946
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Workload
-  - UserId
-  - object
-  - Item.ParentFolder.MemberRights
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_mailbox_folder_read_permission_granted.yml b/detections/cloud/o365_mailbox_folder_read_permission_granted.yml
index c74e3c2d81..16b8fca194 100644
--- a/detections/cloud/o365_mailbox_folder_read_permission_granted.yml
+++ b/detections/cloud/o365_mailbox_folder_read_permission_granted.yml
@@ -6,10 +6,24 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies instances where read permissions are granted to mailbox folders within an Office 365 environment. It detects this activity by monitoring the `o365_management_activity` data source for the `Set-MailboxFolderPermission` and `Add-MailboxFolderPermission` operations. This behavior is significant as it may indicate unauthorized access or changes to mailbox folder permissions, potentially exposing sensitive email content. If confirmed malicious, an attacker could gain unauthorized access to read email communications, leading to data breaches or information leakage.
-search: '`o365_management_activity` Workload=Exchange (Operation="Set-MailboxFolderPermission" OR Operation="Add-MailboxFolderPermission" ) | eval isReadRole=if(match(AccessRights, "^(ReadItems|Author|NonEditingAuthor|Owner|PublishingAuthor|Reviewer)$"), "true", "false") | search isReadRole="true" | rename UserId as user | stats count earliest(_time) as firstTime latest(_time) as lastTime by Operation, user, Identity, AccessRights | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_folder_read_permission_granted_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Mailbox folder permissions may be configured for legitimate purposes, filter as needed.
+description: The following analytic identifies instances where read permissions are
+  granted to mailbox folders within an Office 365 environment. It detects this activity
+  by monitoring the `o365_management_activity` data source for the `Set-MailboxFolderPermission`
+  and `Add-MailboxFolderPermission` operations. This behavior is significant as it
+  may indicate unauthorized access or changes to mailbox folder permissions, potentially
+  exposing sensitive email content. If confirmed malicious, an attacker could gain
+  unauthorized access to read email communications, leading to data breaches or information
+  leakage.
+search: '`o365_management_activity` Workload=Exchange (Operation="Set-MailboxFolderPermission"
+  OR Operation="Add-MailboxFolderPermission" ) | eval isReadRole=if(match(AccessRights,
+  "^(ReadItems|Author|NonEditingAuthor|Owner|PublishingAuthor|Reviewer)$"), "true",
+  "false") | search isReadRole="true" | rename UserId as user | stats count earliest(_time)
+  as firstTime latest(_time) as lastTime by Operation, user, Identity, AccessRights
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_folder_read_permission_granted_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Mailbox folder permissions may be configured for legitimate
+  purposes, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/002/
 - https://learn.microsoft.com/en-us/powershell/module/exchange/add-mailboxfolderpermission?view=exchange-ps
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - AccessRights
-  - UserId
-  - Identity
-  - User
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.002/o365_mailbox_folder_read_granted/o365_mailbox_folder_read_granted.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml b/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml
index e300bfd1a7..d59f9588e7 100644
--- a/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml
+++ b/detections/cloud/o365_mailbox_inbox_folder_shared_with_all_users.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - O365 ModifyFolderPermissions
-description: The following analytic detects instances where the inbox folder of an Office 365 mailbox is shared with all users within the tenant. It leverages Office 365 management activity events to identify when the 'Inbox' folder permissions are modified to include 'Everyone' with read rights. This activity is significant as it represents a potential security risk, allowing unauthorized access to sensitive emails. If confirmed malicious, this could lead to data breaches, exfiltration of confidential information, and further compromise through spear-phishing or other malicious activities based on the accessed email content.
-search: '`o365_management_activity` Operation=ModifyFolderPermissions Workload=Exchange object=Inbox Item.ParentFolder.MemberUpn=Everyone | eval isReadRole=if(match(''Item.ParentFolder.MemberRights'', "(ReadAny)"), "true", "false")  | search isReadRole = "true" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Operation, UserId, object, MailboxOwnerUPN, Item.ParentFolder.MemberUpn, Item.ParentFolder.MemberRights | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_mailbox_inbox_folder_shared_with_all_users_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Administrators might temporarily share a mailbox with all users for legitimate reasons, such as troubleshooting, migrations, or other administrative tasks. Some organizations use shared mailboxes for teams or departments where multiple users need access to the same mailbox. Filter as needed.
+description: The following analytic detects instances where the inbox folder of an
+  Office 365 mailbox is shared with all users within the tenant. It leverages Office
+  365 management activity events to identify when the 'Inbox' folder permissions are
+  modified to include 'Everyone' with read rights. This activity is significant as
+  it represents a potential security risk, allowing unauthorized access to sensitive
+  emails. If confirmed malicious, this could lead to data breaches, exfiltration of
+  confidential information, and further compromise through spear-phishing or other
+  malicious activities based on the accessed email content.
+search: "`o365_management_activity` Operation=ModifyFolderPermissions Workload=Exchange
+  object=Inbox Item.ParentFolder.MemberUpn=Everyone | eval isReadRole=if(match('Item.ParentFolder.MemberRights',
+  \"(ReadAny)\"), \"true\", \"false\")  | search isReadRole = \"true\" | stats count
+  earliest(_time) as firstTime latest(_time) as lastTime by Operation, UserId, object,
+  MailboxOwnerUPN, Item.ParentFolder.MemberUpn, Item.ParentFolder.MemberRights | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_mailbox_inbox_folder_shared_with_all_users_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Administrators might temporarily share a mailbox with all users
+  for legitimate reasons, such as troubleshooting, migrations, or other administrative
+  tasks. Some organizations use shared mailboxes for teams or departments where multiple
+  users need access to the same mailbox. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/002/
 - https://www.mandiant.com/sites/default/files/2022-08/remediation-hardening-strategies-for-m365-defend-against-apt29-white-paper.pdf
@@ -23,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$MailboxOwnerUPN$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MailboxOwnerUPN$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$MailboxOwnerUPN$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Workload
-  - object
-  - Item.ParentFolder.MemberUpn
-  - Item.ParentFolder.MemberRights
-  - UserId
-  - MailboxOwnerUPN
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_inbox_shared_with_all_users/o365_inbox_shared_with_all_users.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_inbox_shared_with_all_users/o365_inbox_shared_with_all_users.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_mailbox_read_access_granted_to_application.yml b/detections/cloud/o365_mailbox_read_access_granted_to_application.yml
index 5d4da5e9be..94aa988088 100644
--- a/detections/cloud/o365_mailbox_read_access_granted_to_application.yml
+++ b/detections/cloud/o365_mailbox_read_access_granted_to_application.yml
@@ -7,10 +7,25 @@ status: production
 type: TTP
 data_source:
 - O365 Update application.
-description: The following analytic identifies instances where the Mail.Read Graph API permissions are granted to an application registration within an Office 365 tenant. It leverages O365 audit logs, specifically events related to changes in application permissions within the AzureActiveDirectory workload. This activity is significant because the Mail.Read permission allows applications to access and read all emails within a user's mailbox, which often contain sensitive or confidential information. If confirmed malicious, this could lead to data exfiltration, spear-phishing attacks, or further compromise based on the information gathered from the emails.
-search: '`o365_management_activity` Operation="Update application." | eval json_data=mvindex(''ModifiedProperties{}.NewValue'', 0) | eval json_data=replace(json_data, "^\[\s*", "") | eval json_data=replace(json_data, "\s*\]$", "") | spath input=json_data path=RequiredAppPermissions{}.EntitlementId output=EntitlementIds | eval match_found=mvfind(EntitlementIds, "810c84a8-4a9e-49e6-bf7d-12d183f40d01") | where isnotnull(match_found) | stats max(_time) as lastTime values(EntitlementIds) as EntitlementIds by Operation, user, object | `security_content_ctime(lastTime)` | `o365_mailbox_read_access_granted_to_application_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: There are legitimate scenarios in wich an Application registrations requires Mailbox read access. Filter as needed.
+description: The following analytic identifies instances where the Mail.Read Graph
+  API permissions are granted to an application registration within an Office 365
+  tenant. It leverages O365 audit logs, specifically events related to changes in
+  application permissions within the AzureActiveDirectory workload. This activity
+  is significant because the Mail.Read permission allows applications to access and
+  read all emails within a user's mailbox, which often contain sensitive or confidential
+  information. If confirmed malicious, this could lead to data exfiltration, spear-phishing
+  attacks, or further compromise based on the information gathered from the emails.
+search: "`o365_management_activity` Operation=\"Update application.\" | eval json_data=mvindex('ModifiedProperties{}.NewValue',
+  0) | eval json_data=replace(json_data, \"^\\[\\s*\", \"\") | eval json_data=replace(json_data,
+  \"\\s*\\]$\", \"\") | spath input=json_data path=RequiredAppPermissions{}.EntitlementId
+  output=EntitlementIds | eval match_found=mvfind(EntitlementIds, \"810c84a8-4a9e-49e6-bf7d-12d183f40d01\"\
+  ) | where isnotnull(match_found) | stats max(_time) as lastTime values(EntitlementIds)
+  as EntitlementIds by Operation, user, object | `security_content_ctime(lastTime)`
+  | `o365_mailbox_read_access_granted_to_application_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: There are legitimate scenarios in wich an Application registrations
+  requires Mailbox read access. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://attack.mitre.org/techniques/T1114/002/
@@ -24,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,14 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Operation
-  - _time
-  - ModifiedProperties{}.NewValue
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_grant_mail_read/o365_grant_mail_read.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_grant_mail_read/o365_grant_mail_read.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multi_source_failed_authentications_spike.yml b/detections/cloud/o365_multi_source_failed_authentications_spike.yml
index badbfe8d09..d4d934c376 100644
--- a/detections/cloud/o365_multi_source_failed_authentications_spike.yml
+++ b/detections/cloud/o365_multi_source_failed_authentications_spike.yml
@@ -7,10 +7,32 @@ status: production
 type: Hunting
 data_source:
 - O365 UserLoginFailed
-description: The following analytic identifies a spike in failed authentication attempts within an Office 365 environment, indicative of a potential distributed password spraying attack. It leverages UserLoginFailed events from O365 Management Activity logs, focusing on ErrorNumber 50126. This detection is significant as it highlights attempts to bypass security controls using multiple IP addresses and user agents. If confirmed malicious, this activity could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization. Early detection is crucial to prevent account takeovers and mitigate subsequent threats.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126 | bucket span=5m _time | eval uniqueIPUserCombo = src_ip . "-" . user | stats dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, values(user) as user, values(src_ip) as ips, values(user_agent) as user_agents by _time | where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 | `o365_multi_source_failed_authentications_spike_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The thresholds set within the analytic (such as unique IPs, unique users, etc.) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment.
-known_false_positives: This detection may yield false positives in scenarios where legitimate bulk sign-in activities occur, such as during company-wide system updates or when users are accessing resources from varying locations in a short time frame, such as in the case of VPNs or cloud services that rotate IP addresses. Filter as needed.
+description: The following analytic identifies a spike in failed authentication attempts
+  within an Office 365 environment, indicative of a potential distributed password
+  spraying attack. It leverages UserLoginFailed events from O365 Management Activity
+  logs, focusing on ErrorNumber 50126. This detection is significant as it highlights
+  attempts to bypass security controls using multiple IP addresses and user agents.
+  If confirmed malicious, this activity could lead to unauthorized access, data breaches,
+  privilege escalation, and lateral movement within the organization. Early detection
+  is crucial to prevent account takeovers and mitigate subsequent threats.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed
+  ErrorNumber=50126 | bucket span=5m _time | eval uniqueIPUserCombo = src_ip . "-"
+  . user | stats dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers,
+  dc(src_ip) as uniqueIPs, values(user) as user, values(src_ip) as ips, values(user_agent)
+  as user_agents by _time | where uniqueIpUserCombinations > 20 AND uniqueUsers >
+  20 AND uniqueIPs > 20 | `o365_multi_source_failed_authentications_spike_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. The thresholds set within the analytic (such
+  as unique IPs, unique users, etc.) are initial guidelines and should be customized
+  based on the organization's user behavior and risk profile. Security teams are encouraged
+  to adjust these thresholds to optimize the balance between detecting genuine threats
+  and minimizing false positives, ensuring the detection is tailored to their specific
+  environment.
+known_false_positives: This detection may yield false positives in scenarios where
+  legitimate bulk sign-in activities occur, such as during company-wide system updates
+  or when users are accessing resources from varying locations in a short time frame,
+  such as in the case of VPNs or cloud services that rotate IP addresses. Filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -40,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ErrorNumber
-  - user
-  - src_ip
-  - user_agent
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_distributed_spray/o365_distributed_spray.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_distributed_spray/o365_distributed_spray.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml b/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml
index 2177097e3d..78a59c041b 100644
--- a/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml
+++ b/detections/cloud/o365_multiple_appids_and_useragents_authentication_spike.yml
@@ -8,10 +8,25 @@ type: Anomaly
 data_source:
 - O365 UserLoggedIn
 - O365 UserLoginFailed
-description: The following analytic identifies unusual authentication activity in an O365 environment, where a single user account experiences more than 8 authentication attempts using 3 or more unique application IDs and over 5 unique user agents within a short timeframe. It leverages O365 audit logs, focusing on authentication events and applying statistical thresholds. This behavior is significant as it may indicate an adversary probing for multi-factor authentication weaknesses. If confirmed malicious, it suggests a compromised account, potentially leading to unauthorized access, privilege escalation, and data exfiltration. Early detection is crucial to prevent further exploitation.
-search: '`o365_management_activity` Workload=AzureActiveDirectory (Operation=UserLoggedIn OR Operation=UserLoginFailed) | bucket span=5m _time | stats  dc(_raw) as failed_attempts dc(ApplicationId) as unique_app_ids dc(UserAgent) as unique_user_agents values(ApplicationId) values(OS) by _time user src_ip | where failed_attempts > 5 and unique_user_agents > 5 and unique_app_ids > 2 | `o365_multiple_appids_and_useragents_authentication_spike_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Rapid authentication from the same user using more than 5 different user agents and 3 application IDs is highly unlikely under normal circumstances. However, there are potential scenarios that could lead to false positives.
+description: The following analytic identifies unusual authentication activity in
+  an O365 environment, where a single user account experiences more than 8 authentication
+  attempts using 3 or more unique application IDs and over 5 unique user agents within
+  a short timeframe. It leverages O365 audit logs, focusing on authentication events
+  and applying statistical thresholds. This behavior is significant as it may indicate
+  an adversary probing for multi-factor authentication weaknesses. If confirmed malicious,
+  it suggests a compromised account, potentially leading to unauthorized access, privilege
+  escalation, and data exfiltration. Early detection is crucial to prevent further
+  exploitation.
+search: '`o365_management_activity` Workload=AzureActiveDirectory (Operation=UserLoggedIn
+  OR Operation=UserLoginFailed) | bucket span=5m _time | stats  dc(_raw) as failed_attempts
+  dc(ApplicationId) as unique_app_ids dc(UserAgent) as unique_user_agents values(ApplicationId)
+  values(OS) by _time user src_ip | where failed_attempts > 5 and unique_user_agents
+  > 5 and unique_app_ids > 2 | `o365_multiple_appids_and_useragents_authentication_spike_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Rapid authentication from the same user using more than 5 different
+  user agents and 3 application IDs is highly unlikely under normal circumstances.
+  However, there are potential scenarios that could lead to false positives.
 references:
 - https://attack.mitre.org/techniques/T1078/
 - https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +52,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 80
   impact: 60
-  message: $user$ authenticated in a short period of time with more than 5 different user agents across 3 or more unique application ids.
+  message: $user$ authenticated in a short period of time with more than 5 different
+    user agents across 3 or more unique application ids.
   mitre_attack_id:
   - T1078
   observable:
@@ -48,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ApplicationId
-  - UserAgent
-  - OS
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/o365_multiple_appids_and_useragents_auth/o365_multiple_appids_and_useragents_auth.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/o365_multiple_appids_and_useragents_auth/o365_multiple_appids_and_useragents_auth.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml b/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml
index ca01dd2bad..e13bd906c5 100644
--- a/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml
+++ b/detections/cloud/o365_multiple_failed_mfa_requests_for_user.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - O365 UserLoginFailed
-description: The following analytic identifies potential "MFA fatigue" attacks targeting Office 365 users by detecting more than nine Multi-Factor Authentication (MFA) prompts within a 10-minute timeframe. It leverages O365 management activity logs, focusing on Azure Active Directory events with the UserLoginFailed operation, a Success ResultStatus, and an ErrorNumber of 500121. This activity is significant as attackers may exploit MFA fatigue to gain unauthorized access by overwhelming users with repeated MFA requests. If confirmed malicious, this could lead to data breaches, unauthorized data access, or further compromise within the O365 environment. Immediate investigation is crucial.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ResultStatus=Success  ErrorNumber=500121 | bucket span=10m _time | stats  dc(_raw) as mfa_prompts values(LogonError) as LogonError values(signature) as signature by user, _time | where  mfa_prompts  > 9 | `o365_multiple_failed_mfa_requests_for_user_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Multiple Failed MFA requests may also be a sign of authentication or application issues. Filter as needed.
+description: The following analytic identifies potential "MFA fatigue" attacks targeting
+  Office 365 users by detecting more than nine Multi-Factor Authentication (MFA) prompts
+  within a 10-minute timeframe. It leverages O365 management activity logs, focusing
+  on Azure Active Directory events with the UserLoginFailed operation, a Success ResultStatus,
+  and an ErrorNumber of 500121. This activity is significant as attackers may exploit
+  MFA fatigue to gain unauthorized access by overwhelming users with repeated MFA
+  requests. If confirmed malicious, this could lead to data breaches, unauthorized
+  data access, or further compromise within the O365 environment. Immediate investigation
+  is crucial.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed
+  ResultStatus=Success  ErrorNumber=500121 | bucket span=10m _time | stats  dc(_raw)
+  as mfa_prompts values(LogonError) as LogonError values(signature) as signature by
+  user, _time | where  mfa_prompts  > 9 | `o365_multiple_failed_mfa_requests_for_user_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Multiple Failed MFA requests may also be a sign of authentication
+  or application issues. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1621/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,19 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ResultStatus
-  - ErrorNumber
-  - user
-  - LogonError
-  - signature
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/o365_multiple_failed_mfa_requests/o365_multiple_failed_mfa_requests.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/o365_multiple_failed_mfa_requests/o365_multiple_failed_mfa_requests.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml b/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml
index fa8430370f..b90fc5a6ec 100644
--- a/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml
+++ b/detections/cloud/o365_multiple_mailboxes_accessed_via_api.yml
@@ -7,10 +7,25 @@ data_source:
 - O365 MailItemsAccessed
 type: TTP
 status: production
-description: The following analytic detects when a high number of Office 365 Exchange mailboxes are accessed via API (Microsoft Graph API or Exchange Web Services) within a short timeframe. It leverages 'MailItemsAccessed' operations in Exchange, using AppId and regex to identify API interactions. This activity is significant as it may indicate unauthorized mass email access, potentially signaling data exfiltration or account compromise. If confirmed malicious, attackers could gain access to sensitive information, leading to data breaches and further exploitation of compromised accounts. The threshold is set to flag over five unique mailboxes accessed within 10 minutes, but should be tailored to your environment.
-search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* ClientAppId=* | bucket span=10m _time | eval matchRegex=if(match(ClientInfoString, "^Client=WebServices;ExchangeWebServices"), 1, 0) | search (AppId="00000003-0000-0000-c000-000000000000" OR matchRegex=1) | stats values(ClientIPAddress) as src_ip dc(user) as unique_mailboxes values(user) as user by _time ClientAppId ClientInfoString | where unique_mailboxes > 5 | `o365_multiple_mailboxes_accessed_via_api_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Legitimate applications may access multiple mailboxes via an API. You can filter by the ClientAppId or the CLientIpAddress fields.
+description: The following analytic detects when a high number of Office 365 Exchange
+  mailboxes are accessed via API (Microsoft Graph API or Exchange Web Services) within
+  a short timeframe. It leverages 'MailItemsAccessed' operations in Exchange, using
+  AppId and regex to identify API interactions. This activity is significant as it
+  may indicate unauthorized mass email access, potentially signaling data exfiltration
+  or account compromise. If confirmed malicious, attackers could gain access to sensitive
+  information, leading to data breaches and further exploitation of compromised accounts.
+  The threshold is set to flag over five unique mailboxes accessed within 10 minutes,
+  but should be tailored to your environment.
+search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed
+  AppId=* ClientAppId=* | bucket span=10m _time | eval matchRegex=if(match(ClientInfoString,
+  "^Client=WebServices;ExchangeWebServices"), 1, 0) | search (AppId="00000003-0000-0000-c000-000000000000"
+  OR matchRegex=1) | stats values(ClientIPAddress) as src_ip dc(user) as unique_mailboxes
+  values(user) as user by _time ClientAppId ClientInfoString | where unique_mailboxes
+  > 5 | `o365_multiple_mailboxes_accessed_via_api_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Legitimate applications may access multiple mailboxes via an
+  API. You can filter by the ClientAppId or the CLientIpAddress fields.
 references:
 - https://attack.mitre.org/techniques/T1114/002/
 - https://learn.microsoft.com/en-us/troubleshoot/azure/active-directory/verify-first-party-apps-sign-in
@@ -24,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +54,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: An Oauth application identified with id $ClientAppId$ accessed multiple mailboxes in a short period of time via an API.
+  message: An Oauth application identified with id $ClientAppId$ accessed multiple
+    mailboxes in a short period of time via an API.
   mitre_attack_id:
   - T1114.002
   observable:
@@ -46,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - AppId
-  - ClientAppId
-  - ClientInfoString
-  - ClientIPAddress
-  - user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_multiple_mailboxes_accessed_via_api/o365_multiple_mailboxes_accessed_via_api.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_multiple_mailboxes_accessed_via_api/o365_multiple_mailboxes_accessed_via_api.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multiple_service_principals_created_by_sp.yml b/detections/cloud/o365_multiple_service_principals_created_by_sp.yml
index e0d42c069f..e65c4e7596 100644
--- a/detections/cloud/o365_multiple_service_principals_created_by_sp.yml
+++ b/detections/cloud/o365_multiple_service_principals_created_by_sp.yml
@@ -7,10 +7,25 @@ data_source:
 - O365 Add service principal.
 type: Anomaly
 status: production
-description: The following analytic identifies instances where a single service principal creates more than three unique OAuth applications within a 10-minute timeframe. It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service principal' operation in the Office 365 Azure Active Directory environment. This activity is significant as it may indicate a compromised or malicious service principal attempting to expand control or access within the network. If confirmed malicious, this could lead to unauthorized access and potential lateral movement within the environment, posing a significant security risk.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Add service principal."  | bucket span=10m _time | eval len=mvcount(''Actor{}.ID'') | eval userType = mvindex(''Actor{}.ID'',len-1) | search userType = "ServicePrincipal" | eval displayName = object | stats count earliest(_time) as firstTime latest(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps by src_user | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_multiple_service_principals_created_by_sp_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed.
+description: The following analytic identifies instances where a single service principal
+  creates more than three unique OAuth applications within a 10-minute timeframe.
+  It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service
+  principal' operation in the Office 365 Azure Active Directory environment. This
+  activity is significant as it may indicate a compromised or malicious service principal
+  attempting to expand control or access within the network. If confirmed malicious,
+  this could lead to unauthorized access and potential lateral movement within the
+  environment, posing a significant security risk.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Add
+  service principal.\"  | bucket span=10m _time | eval len=mvcount('Actor{}.ID') |
+  eval userType = mvindex('Actor{}.ID',len-1) | search userType = \"ServicePrincipal\"\
+  \ | eval displayName = object | stats count earliest(_time) as firstTime latest(_time)
+  as lastTime values(displayName) as displayName dc(displayName) as unique_apps by
+  src_user | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_multiple_service_principals_created_by_sp_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Certain users or applications may create multiple service principals
+  in a short period of time for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1136/003/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +50,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: Multiple OAuth applications were created by $src_user$ in a short period of time
+  message: Multiple OAuth applications were created by $src_user$ in a short period
+    of time
   mitre_attack_id:
   - T1136.003
   observable:
@@ -42,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Actor{}.ID
-  - src_user
-  - object
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multiple_service_principals_created_by_user.yml b/detections/cloud/o365_multiple_service_principals_created_by_user.yml
index 567201b566..08009e6037 100644
--- a/detections/cloud/o365_multiple_service_principals_created_by_user.yml
+++ b/detections/cloud/o365_multiple_service_principals_created_by_user.yml
@@ -7,10 +7,25 @@ data_source:
 - O365 Add service principal.
 type: Anomaly
 status: production
-description: The following analytic identifies instances where a single user creates more than three unique OAuth applications within a 10-minute window in the Office 365 environment. It leverages O365 logs from the Unified Audit Log, focusing on the 'Add service principal' operation in Azure Active Directory. This activity is significant as it may indicate a compromised user account or unauthorized actions, potentially leading to broader network infiltration or privilege escalation. If confirmed malicious, this behavior could allow attackers to gain persistent access, escalate privileges, or exfiltrate sensitive information.
-search: '`o365_management_activity`  Workload=AzureActiveDirectory Operation="Add service principal."  | bucket span=10m _time | eval len=mvcount(''Actor{}.ID'') | eval userType = mvindex(''Actor{}.ID'',len-1) | search userType = "User" | eval displayName = object | stats count earliest(_time) as firstTime latest(_time) as lastTime values(displayName) as displayName dc(displayName) as unique_apps by src_user | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_multiple_service_principals_created_by_user_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Certain users or applications may create multiple service principals in a short period of time for legitimate purposes. Filter as needed.
+description: The following analytic identifies instances where a single user creates
+  more than three unique OAuth applications within a 10-minute window in the Office
+  365 environment. It leverages O365 logs from the Unified Audit Log, focusing on
+  the 'Add service principal' operation in Azure Active Directory. This activity is
+  significant as it may indicate a compromised user account or unauthorized actions,
+  potentially leading to broader network infiltration or privilege escalation. If
+  confirmed malicious, this behavior could allow attackers to gain persistent access,
+  escalate privileges, or exfiltrate sensitive information.
+search: "`o365_management_activity`  Workload=AzureActiveDirectory Operation=\"Add
+  service principal.\"  | bucket span=10m _time | eval len=mvcount('Actor{}.ID') |
+  eval userType = mvindex('Actor{}.ID',len-1) | search userType = \"User\" | eval
+  displayName = object | stats count earliest(_time) as firstTime latest(_time) as
+  lastTime values(displayName) as displayName dc(displayName) as unique_apps by src_user
+  | where unique_apps > 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_multiple_service_principals_created_by_user_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Certain users or applications may create multiple service principals
+  in a short period of time for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1136/003/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +50,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: Multiple OAuth applications were created by $src_user$ in a short period of time
+  message: Multiple OAuth applications were created by $src_user$ in a short period
+    of time
   mitre_attack_id:
   - T1136.003
   observable:
@@ -42,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Actor{}.ID
-  - src_user
-  - object
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_multiple_service_principals_created/o365_multiple_service_principals_created.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml b/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml
index d0889b11cc..64f3c121d9 100644
--- a/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml
+++ b/detections/cloud/o365_multiple_users_failing_to_authenticate_from_ip.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - O365 UserLoginFailed
-description: The following analytic identifies instances where more than 10 unique user accounts fail to authenticate from a single IP address within a 5-minute window. This detection leverages O365 audit logs, specifically Azure Active Directory login failures (AzureActiveDirectoryStsLogon). Such activity is significant as it may indicate brute-force attacks or password spraying attempts. If confirmed malicious, this behavior suggests an external entity is attempting to breach security by targeting multiple accounts, potentially leading to unauthorized access. Immediate action is required to block or monitor the suspicious IP and notify affected users to enhance their security measures.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126 |  bucket span=5m _time |  stats dc(user) as unique_accounts values(user) as user values(LogonError) as LogonError values(signature) as signature values(UserAgent) as UserAgent by _time, src_ip |  where unique_accounts > 10 | `o365_multiple_users_failing_to_authenticate_from_ip_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: A source Ip failing to authenticate with multiple users in a short period of time is not common legitimate behavior.
+description: The following analytic identifies instances where more than 10 unique
+  user accounts fail to authenticate from a single IP address within a 5-minute window.
+  This detection leverages O365 audit logs, specifically Azure Active Directory login
+  failures (AzureActiveDirectoryStsLogon). Such activity is significant as it may
+  indicate brute-force attacks or password spraying attempts. If confirmed malicious,
+  this behavior suggests an external entity is attempting to breach security by targeting
+  multiple accounts, potentially leading to unauthorized access. Immediate action
+  is required to block or monitor the suspicious IP and notify affected users to enhance
+  their security measures.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed
+  ErrorNumber=50126 |  bucket span=5m _time |  stats dc(user) as unique_accounts values(user)
+  as user values(LogonError) as LogonError values(signature) as signature values(UserAgent)
+  as UserAgent by _time, src_ip |  where unique_accounts > 10 | `o365_multiple_users_failing_to_authenticate_from_ip_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: A source Ip failing to authenticate with multiple users in
+  a short period of time is not common legitimate behavior.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,20 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - record_type
-  - user
-  - LogonError
-  - signature
-  - UserAgent
-  - ErrorNumber
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_multiple_users_from_ip/o365_multiple_users_from_ip.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/o365_multiple_users_from_ip/o365_multiple_users_from_ip.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_new_email_forwarding_rule_created.yml b/detections/cloud/o365_new_email_forwarding_rule_created.yml
index 83dac5ffc0..aaa8fec5bd 100644
--- a/detections/cloud/o365_new_email_forwarding_rule_created.yml
+++ b/detections/cloud/o365_new_email_forwarding_rule_created.yml
@@ -6,10 +6,25 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies the creation of new email forwarding rules in an Office 365 environment. It detects events logged under New-InboxRule and Set-InboxRule operations within the o365_management_activity data source, focusing on parameters like ForwardTo, ForwardAsAttachmentTo, and RedirectTo. This activity is significant as unauthorized email forwarding can lead to data exfiltration and unauthorized access to sensitive information. If confirmed malicious, attackers could intercept and redirect emails, potentially compromising confidential communications and leading to data breaches.
-search: '`o365_management_activity` (Operation=New-InboxRule OR Operation=set-InboxRule) | eval match1=mvfind(''Parameters{}.Name'', "ForwardTo") | eval match2=mvfind(''Parameters{}.Name'', "ForwardAsAttachmentTo") | eval match3=mvfind(''Parameters{}.Name'', "RedirectTo") | where match1>= 0 OR match2>= 0 OR match3>= 0 | eval ForwardTo=coalesce(ForwardTo, ForwardAsAttachmentTo, RedirectTo) | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name by user Operation ForwardTo | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_email_forwarding_rule_created_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Users may create email forwarding rules for legitimate purposes. Filter as needed.
+description: The following analytic identifies the creation of new email forwarding
+  rules in an Office 365 environment. It detects events logged under New-InboxRule
+  and Set-InboxRule operations within the o365_management_activity data source, focusing
+  on parameters like ForwardTo, ForwardAsAttachmentTo, and RedirectTo. This activity
+  is significant as unauthorized email forwarding can lead to data exfiltration and
+  unauthorized access to sensitive information. If confirmed malicious, attackers
+  could intercept and redirect emails, potentially compromising confidential communications
+  and leading to data breaches.
+search: "`o365_management_activity` (Operation=New-InboxRule OR Operation=set-InboxRule)
+  | eval match1=mvfind('Parameters{}.Name', \"ForwardTo\") | eval match2=mvfind('Parameters{}.Name',
+  \"ForwardAsAttachmentTo\") | eval match3=mvfind('Parameters{}.Name', \"RedirectTo\"\
+  ) | where match1>= 0 OR match2>= 0 OR match3>= 0 | eval ForwardTo=coalesce(ForwardTo,
+  ForwardAsAttachmentTo, RedirectTo) | stats count min(_time) as firstTime max(_time)
+  as lastTime values(Name) as Name by user Operation ForwardTo | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_new_email_forwarding_rule_created_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Users may create email forwarding rules for legitimate purposes.
+  Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/003/
 drilldown_searches:
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Parameters{}.Name
-  - Name
-  - user
-  - UserId
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_new_email_forwarding_rule_enabled.yml b/detections/cloud/o365_new_email_forwarding_rule_enabled.yml
index 8ec8ae84c6..33c9dab618 100644
--- a/detections/cloud/o365_new_email_forwarding_rule_enabled.yml
+++ b/detections/cloud/o365_new_email_forwarding_rule_enabled.yml
@@ -6,10 +6,29 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies the creation of new email forwarding rules in an Office 365 environment via the UpdateInboxRules operation. It leverages Office 365 management activity events to detect rules that forward emails to external recipients by examining the OperationProperties for specific forwarding actions. This activity is significant as it may indicate unauthorized email redirection, potentially leading to data exfiltration. If confirmed malicious, attackers could intercept sensitive communications, leading to data breaches and information leakage.
-search: '`o365_management_activity` Workload=Exchange Operation=UpdateInboxRules  | eval match1=mvfind(''OperationProperties{}.Value'', "ForwardToRecipientsAction") | eval match2=mvfind(''OperationProperties{}.Value'', "ForwardAsAttachmentToRecipientsAction") | eval match3=mvfind(''OperationProperties{}.Value'', "RedirectToRecipientsAction") | eval index = mvfind(''OperationProperties{}.Name'', "ServerRule") | where match1>= 0 OR match2>= 0 OR match3>= 0 | eval ServerRule = mvindex(''OperationProperties{}.Value'', index-1) | spath input=ServerRule path=Actions{}.Recipients{}.Values{}.Value output=valueExtracted | mvexpand valueExtracted | search valueExtracted="*@*.*" | eval ForwardTo=if(match(valueExtracted, "^[^@]+@[^@]+\\.[^@]+$"), valueExtracted, null) | dedup ForwardTo | where isnotnull(ForwardTo) | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name by user Operation ForwardTo | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_email_forwarding_rule_enabled_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Users may create email forwarding rules for legitimate purposes. Filter as needed.
+description: The following analytic identifies the creation of new email forwarding
+  rules in an Office 365 environment via the UpdateInboxRules operation. It leverages
+  Office 365 management activity events to detect rules that forward emails to external
+  recipients by examining the OperationProperties for specific forwarding actions.
+  This activity is significant as it may indicate unauthorized email redirection,
+  potentially leading to data exfiltration. If confirmed malicious, attackers could
+  intercept sensitive communications, leading to data breaches and information leakage.
+search: "`o365_management_activity` Workload=Exchange Operation=UpdateInboxRules \
+  \ | eval match1=mvfind('OperationProperties{}.Value', \"ForwardToRecipientsAction\"\
+  ) | eval match2=mvfind('OperationProperties{}.Value', \"ForwardAsAttachmentToRecipientsAction\"\
+  ) | eval match3=mvfind('OperationProperties{}.Value', \"RedirectToRecipientsAction\"\
+  ) | eval index = mvfind('OperationProperties{}.Name', \"ServerRule\") | where match1>=
+  0 OR match2>= 0 OR match3>= 0 | eval ServerRule = mvindex('OperationProperties{}.Value',
+  index-1) | spath input=ServerRule path=Actions{}.Recipients{}.Values{}.Value output=valueExtracted
+  | mvexpand valueExtracted | search valueExtracted=\"*@*.*\" | eval ForwardTo=if(match(valueExtracted,
+  \"^[^@]+@[^@]+\\\\.[^@]+$\"), valueExtracted, null) | dedup ForwardTo | where isnotnull(ForwardTo)
+  | stats count min(_time) as firstTime max(_time) as lastTime values(Name) as Name
+  by user Operation ForwardTo | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_new_email_forwarding_rule_enabled_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Users may create email forwarding rules for legitimate purposes.
+  Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/003/
 drilldown_searches:
@@ -18,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Actions
-  - Name
-  - user
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule_created/o365_email_forwarding_rule_created.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_new_federated_domain_added.yml b/detections/cloud/o365_new_federated_domain_added.yml
index faedd0ab6f..adfccb57ce 100644
--- a/detections/cloud/o365_new_federated_domain_added.yml
+++ b/detections/cloud/o365_new_federated_domain_added.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Rod Soto, Mauricio Velazco Splunk
 status: production
 type: TTP
-description: The following analytic identifies the addition of a new federated domain in an Office 365 environment. This behavior is detected by analyzing Office 365 management activity logs, specifically filtering for Workload=Exchange and Operation="Add-FederatedDomain". The addition of a new federated domain is significant as it may indicate unauthorized changes or potential compromises. If confirmed malicious, attackers could establish a backdoor, bypass security measures, or exfiltrate data, leading to data breaches and unauthorized access to sensitive information. Immediate investigation is required to review the details of the added domain and any concurrent suspicious activities.
+description: The following analytic identifies the addition of a new federated domain
+  in an Office 365 environment. This behavior is detected by analyzing Office 365
+  management activity logs, specifically filtering for Workload=Exchange and Operation="Add-FederatedDomain".
+  The addition of a new federated domain is significant as it may indicate unauthorized
+  changes or potential compromises. If confirmed malicious, attackers could establish
+  a backdoor, bypass security measures, or exfiltrate data, leading to data breaches
+  and unauthorized access to sensitive information. Immediate investigation is required
+  to review the details of the added domain and any concurrent suspicious activities.
 data_source:
 - O365
-search: '`o365_management_activity` Operation IN ("*add*", "*new*") AND Operation="*domain*" | stats count values(ModifiedProperties{}.NewValue) as new_value by  user user_agent authentication_service action Workload Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity.
-known_false_positives: The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider.
+search: '`o365_management_activity` Operation IN ("*add*", "*new*") AND Operation="*domain*"
+  | stats count values(ModifiedProperties{}.NewValue) as new_value by  user user_agent
+  authentication_service action Workload Operation | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_new_federated_domain_added_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity.
+known_false_positives: The creation of a new Federated domain is not necessarily malicious,
+  however these events need to be followed closely, as it may indicate federated credential
+  abuse or backdoor via federated identities at a similar or different cloud provider.
 references:
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
 - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - authentication_service
-  - user
-  - user_agent
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain_added/o365_add_federated_domain.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain_added/o365_add_federated_domain.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml b/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml
index f804e06525..1482798598 100644
--- a/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml
+++ b/detections/cloud/o365_new_forwarding_mailflow_rule_created.yml
@@ -6,10 +6,25 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic detects the creation of new mail flow rules in Office 365 that may redirect or copy emails to unauthorized or external addresses. It leverages Office 365 Management Activity logs, specifically querying for the "New-TransportRule" operation and parameters like "BlindCopyTo", "CopyTo", and "RedirectMessageTo". This activity is significant as it can indicate potential data exfiltration or unauthorized access to sensitive information. If confirmed malicious, attackers could intercept or redirect email communications, leading to data breaches or information leakage.
-search: '`o365_management_activity` Workload=Exchange Operation="New-TransportRule" | eval match1=mvfind(''Parameters{}.Name'', "BlindCopyTo") | eval match2=mvfind(''Parameters{}.Name'', "CopyTo") | eval match3=mvfind(''Parameters{}.Name'', "RedirectMessageTo") | where match1>= 0 OR match2>= 0 OR match3>=0 | eval ForwardTo=coalesce(BlindCopyTo, CopyTo, RedirectMessageTo) | search ForwardTo!=""  | rename UserId as user  | stats count earliest(_time) as firstTime latest(_time) as lastTime by Operation, user, Name, ForwardTo  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`   | `o365_new_forwarding_mailflow_rule_created_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Forwarding mail flow rules may be created for legitimate reasons, filter as needed.
+description: The following analytic detects the creation of new mail flow rules in
+  Office 365 that may redirect or copy emails to unauthorized or external addresses.
+  It leverages Office 365 Management Activity logs, specifically querying for the
+  "New-TransportRule" operation and parameters like "BlindCopyTo", "CopyTo", and "RedirectMessageTo".
+  This activity is significant as it can indicate potential data exfiltration or unauthorized
+  access to sensitive information. If confirmed malicious, attackers could intercept
+  or redirect email communications, leading to data breaches or information leakage.
+search: "`o365_management_activity` Workload=Exchange Operation=\"New-TransportRule\"\
+  \ | eval match1=mvfind('Parameters{}.Name', \"BlindCopyTo\") | eval match2=mvfind('Parameters{}.Name',
+  \"CopyTo\") | eval match3=mvfind('Parameters{}.Name', \"RedirectMessageTo\") | where
+  match1>= 0 OR match2>= 0 OR match3>=0 | eval ForwardTo=coalesce(BlindCopyTo, CopyTo,
+  RedirectMessageTo) | search ForwardTo!=\"\"  | rename UserId as user  | stats count
+  earliest(_time) as firstTime latest(_time) as lastTime by Operation, user, Name,
+  ForwardTo  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`\
+  \   | `o365_new_forwarding_mailflow_rule_created_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Forwarding mail flow rules may be created for legitimate reasons,
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1114/
 - https://learn.microsoft.com/en-us/exchange/security-and-compliance/mail-flow-rules/mail-flow-rules
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,16 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Parameters{}.Name
-  - user
-  - Name
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_new_forwarding_mailflow_rule_created/o365_new_forwarding_mailflow_rule_created.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_new_forwarding_mailflow_rule_created/o365_new_forwarding_mailflow_rule_created.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_new_mfa_method_registered.yml b/detections/cloud/o365_new_mfa_method_registered.yml
index 2d1943a1df..77eebebfb6 100644
--- a/detections/cloud/o365_new_mfa_method_registered.yml
+++ b/detections/cloud/o365_new_mfa_method_registered.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - O365 Update user.
-description: The following analytic detects the registration of a new Multi-Factor Authentication (MFA) method for a user account within Office 365. It leverages O365 audit logs to identify changes in MFA configurations. This activity is significant as it may indicate an attacker's attempt to maintain persistence on a compromised account. If confirmed malicious, the attacker could bypass existing security measures, solidify their access, and potentially escalate privileges or access sensitive data. Immediate verification and remediation are required to secure the affected account.
-search: '`o365_management_activity` Workload=AzureActiveDirectory  Operation="Update user."  | eval propertyName = mvindex(''ModifiedProperties{}.Name'', 0) | search propertyName = StrongAuthenticationMethod | eval oldvalue = mvindex(''ModifiedProperties{}.OldValue'',0) | eval newvalue = mvindex(''ModifiedProperties{}.NewValue'',0) | rex field=newvalue max_match=0 "(?i)(?<new_method_type>\"MethodType\")" | rex field=oldvalue max_match=0 "(?i)(?<old_method_type>\"MethodType\")" | eval count_new_method_type = coalesce(mvcount(new_method_type), 0) | eval count_old_method_type = coalesce(mvcount(old_method_type), 0) |  where count_new_method_type > count_old_method_type |  stats earliest(_time) as firstTime latest(_time) as lastTime values(propertyName) by user newvalue oldvalue | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_mfa_method_registered_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Users may register MFA methods legitimally, investigate and filter as needed.
+description: The following analytic detects the registration of a new Multi-Factor
+  Authentication (MFA) method for a user account within Office 365. It leverages O365
+  audit logs to identify changes in MFA configurations. This activity is significant
+  as it may indicate an attacker's attempt to maintain persistence on a compromised
+  account. If confirmed malicious, the attacker could bypass existing security measures,
+  solidify their access, and potentially escalate privileges or access sensitive data.
+  Immediate verification and remediation are required to secure the affected account.
+search: "`o365_management_activity` Workload=AzureActiveDirectory  Operation=\"Update
+  user.\"  | eval propertyName = mvindex('ModifiedProperties{}.Name', 0) | search
+  propertyName = StrongAuthenticationMethod | eval oldvalue = mvindex('ModifiedProperties{}.OldValue',0)
+  | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) | rex field=newvalue
+  max_match=0 \"(?i)(?<new_method_type>\\\"MethodType\\\")\" | rex field=oldvalue
+  max_match=0 \"(?i)(?<old_method_type>\\\"MethodType\\\")\" | eval count_new_method_type
+  = coalesce(mvcount(new_method_type), 0) | eval count_old_method_type = coalesce(mvcount(old_method_type),
+  0) |  where count_new_method_type > count_old_method_type |  stats earliest(_time)
+  as firstTime latest(_time) as lastTime values(propertyName) by user newvalue oldvalue
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_new_mfa_method_registered_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Users may register MFA methods legitimally, investigate and
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/005/
 - https://www.microsoft.com/en-us/security/blog/2023/06/08/detecting-and-mitigating-a-multi-stage-aitm-phishing-and-bec-campaign/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.Name
-  - ModifiedProperties{}.OldValue
-  - ModifiedProperties{}.NewValue
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/o365_register_new_mfa_method/o365_register_new_mfa_method.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.005/o365_register_new_mfa_method/o365_register_new_mfa_method.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml b/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml
index d42a394727..39f691eace 100644
--- a/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml
+++ b/detections/cloud/o365_oauth_app_mailbox_access_via_ews.yml
@@ -7,10 +7,23 @@ status: production
 data_source:
 - O365 MailItemsAccessed
 type: TTP
-description: The following analytic detects when emails are accessed in Office 365 Exchange via Exchange Web Services (EWS) using OAuth-authenticated applications. It leverages the ClientInfoString field to identify EWS interactions and aggregates metrics such as access counts, timing, and client IP addresses, categorized by user, ClientAppId, OperationCount, and AppId. Monitoring OAuth applications accessing emails through EWS is crucial for identifying potential abuse or unauthorized data access. If confirmed malicious, this activity could lead to unauthorized email access, data exfiltration, or further compromise of sensitive information.
-search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* ClientAppId=* | regex ClientInfoString="^Client=WebServices;ExchangeWebServices" |  stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress) as src_ip by user ClientAppId OperationCount AppId ClientInfoString | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_oauth_app_mailbox_access_via_ews_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: OAuth applications may access mailboxes for legitimate purposes, you can use the src_ip to add trusted sources to an allow list.
+description: The following analytic detects when emails are accessed in Office 365
+  Exchange via Exchange Web Services (EWS) using OAuth-authenticated applications.
+  It leverages the ClientInfoString field to identify EWS interactions and aggregates
+  metrics such as access counts, timing, and client IP addresses, categorized by user,
+  ClientAppId, OperationCount, and AppId. Monitoring OAuth applications accessing
+  emails through EWS is crucial for identifying potential abuse or unauthorized data
+  access. If confirmed malicious, this activity could lead to unauthorized email access,
+  data exfiltration, or further compromise of sensitive information.
+search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed
+  AppId=* ClientAppId=* | regex ClientInfoString="^Client=WebServices;ExchangeWebServices"
+  |  stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress)
+  as src_ip by user ClientAppId OperationCount AppId ClientInfoString | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_oauth_app_mailbox_access_via_ews_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: OAuth applications may access mailboxes for legitimate purposes,
+  you can use the src_ip to add trusted sources to an allow list.
 references:
 - https://attack.mitre.org/techniques/T1114/002/
 - https://www.microsoft.com/en-us/security/blog/2024/01/25/midnight-blizzard-guidance-for-responders-on-nation-state-attack/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: An OAuth application identified with id $ClientAppId$ accesed mailboxes through the Graph API.
+  message: An OAuth application identified with id $ClientAppId$ accesed mailboxes
+    through the Graph API.
   mitre_attack_id:
   - T1114.002
   observable:
@@ -43,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - AppId
-  - ClientAppId
-  - OperationCount
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_ews_mailbox_access/o365_oauth_app_ews_mailbox_access.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_ews_mailbox_access/o365_oauth_app_ews_mailbox_access.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml b/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml
index 4a8919ac8b..f5baf69d1e 100644
--- a/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml
+++ b/detections/cloud/o365_oauth_app_mailbox_access_via_graph_api.yml
@@ -7,10 +7,22 @@ status: production
 data_source:
 - O365 MailItemsAccessed
 type: TTP
-description: "The following analytic detects when emails are accessed in Office 365 Exchange via the Microsoft Graph API using the client ID '00000003-0000-0000-c000-000000000000'. It leverages the 'MailItemsAccessed' operation within the Exchange workload, focusing on OAuth-authenticated applications. This activity is significant as unauthorized access to emails can lead to data breaches and information theft. If confirmed malicious, attackers could exfiltrate sensitive information, compromise user accounts, and further infiltrate the organization\u2019s network."
-search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed AppId=* AppId=00000003-0000-0000-c000-000000000000 |  stats count earliest(_time) as firstTime latest(_time) as lastTime values(ClientIPAddress) by user ClientAppId OperationCount AppId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_oauth_app_mailbox_access_via_graph_api_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: OAuth applications may access mailboxes for legitimate purposes, you can use the ClientAppId to add trusted applications to an allow list.
+description: The following analytic detects when emails are accessed in Office 365
+  Exchange via the Microsoft Graph API using the client ID '00000003-0000-0000-c000-000000000000'.
+  It leverages the 'MailItemsAccessed' operation within the Exchange workload, focusing
+  on OAuth-authenticated applications. This activity is significant as unauthorized
+  access to emails can lead to data breaches and information theft. If confirmed malicious,
+  attackers could exfiltrate sensitive information, compromise user accounts, and
+  further infiltrate the organization’s network.
+search: '`o365_management_activity` Workload=Exchange Operation=MailItemsAccessed
+  AppId=* AppId=00000003-0000-0000-c000-000000000000 |  stats count earliest(_time)
+  as firstTime latest(_time) as lastTime values(ClientIPAddress) by user ClientAppId
+  OperationCount AppId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_oauth_app_mailbox_access_via_graph_api_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: OAuth applications may access mailboxes for legitimate purposes,
+  you can use the ClientAppId to add trusted applications to an allow list.
 references:
 - https://attack.mitre.org/techniques/T1114/002/
 - https://learn.microsoft.com/en-us/troubleshoot/azure/active-directory/verify-first-party-apps-sign-in
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 70
-  message: An OAuth application identified with id $ClientAppId$ accesed mailboxes through the Graph API.
+  message: An OAuth application identified with id $ClientAppId$ accesed mailboxes
+    through the Graph API.
   mitre_attack_id:
   - T1114.002
   observable:
@@ -43,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - AppId
-  - ClientAppId
-  - OperationCount
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_graph_mailbox_access/o365_oauth_app_graph_mailbox_access.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/o365_oauth_app_graph_mailbox_access/o365_oauth_app_graph_mailbox_access.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_privileged_graph_api_permission_assigned.yml b/detections/cloud/o365_privileged_graph_api_permission_assigned.yml
index ede1699fdc..58bf0d7994 100644
--- a/detections/cloud/o365_privileged_graph_api_permission_assigned.yml
+++ b/detections/cloud/o365_privileged_graph_api_permission_assigned.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - O365 Update application.
-description: The following analytic detects the assignment of critical Graph API permissions in Azure AD using the O365 Unified Audit Log. It focuses on permissions such as Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory. The detection method leverages Azure Active Directory workload events, specifically 'Update application' operations. This activity is significant as these permissions provide extensive control over Azure AD settings, posing a high risk if misused. If confirmed malicious, this could allow unauthorized modifications, leading to potential data breaches or privilege escalation. Immediate investigation is crucial.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update application." | eval newvalue = mvindex(''ModifiedProperties{}.NewValue'',0) | spath input=newvalue  | search "{}.RequiredAppPermissions{}.EntitlementId"="1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9" OR "{}.RequiredAppPermissions{}.EntitlementId"="06b708a9-e830-4db3-a914-8e69da51d44f" OR "{}.RequiredAppPermissions{}.EntitlementId"="9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8"  | eval Permissions = ''{}.RequiredAppPermissions{}.EntitlementId'' | stats count earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user, object, user_agent, Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_privileged_graph_api_permission_assigned_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Privileged Graph API permissions may be assigned for legitimate purposes. Filter as needed.
+description: The following analytic detects the assignment of critical Graph API permissions
+  in Azure AD using the O365 Unified Audit Log. It focuses on permissions such as
+  Application.ReadWrite.All, AppRoleAssignment.ReadWrite.All, and RoleManagement.ReadWrite.Directory.
+  The detection method leverages Azure Active Directory workload events, specifically
+  'Update application' operations. This activity is significant as these permissions
+  provide extensive control over Azure AD settings, posing a high risk if misused.
+  If confirmed malicious, this could allow unauthorized modifications, leading to
+  potential data breaches or privilege escalation. Immediate investigation is crucial.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Update
+  application.\" | eval newvalue = mvindex('ModifiedProperties{}.NewValue',0) | spath
+  input=newvalue  | search \"{}.RequiredAppPermissions{}.EntitlementId\"=\"1bfefb4e-e0b5-418b-a88f-73c46d2cc8e9\"\
+  \ OR \"{}.RequiredAppPermissions{}.EntitlementId\"=\"06b708a9-e830-4db3-a914-8e69da51d44f\"\
+  \ OR \"{}.RequiredAppPermissions{}.EntitlementId\"=\"9e3f62cf-ca93-4989-b6ce-bf83c28f9fe8\"\
+  \  | eval Permissions = '{}.RequiredAppPermissions{}.EntitlementId' | stats count
+  earliest(_time) as firstTime latest(_time) as lastTime values(Permissions) by user,
+  object, user_agent, Operation | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_privileged_graph_api_permission_assigned_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Privileged Graph API permissions may be assigned for legitimate
+  purposes. Filter as needed.
 references:
 - https://cloudbrothers.info/en/azure-attack-paths/
 - https://github.com/mandiant/Mandiant-Azure-AD-Investigator/blob/master/MandiantAzureADInvestigator.json
@@ -23,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - RequiredAppPermissions{}.EntitlementId
-  - user
-  - object
-  - user_agent
-  - Operation
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_privileged_graph_perm_assigned/o365_privileged_graph_perm_assigned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_privileged_graph_perm_assigned/o365_privileged_graph_perm_assigned.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_privileged_role_assigned.yml b/detections/cloud/o365_privileged_role_assigned.yml
index b83fcf3d93..4a0a23f221 100644
--- a/detections/cloud/o365_privileged_role_assigned.yml
+++ b/detections/cloud/o365_privileged_role_assigned.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the assignment of sensitive and privileged Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike may assign these roles to a compromised account to establish Persistence in an Azure AD environment. This detection leverages the O365 Universal Audit Log data source.
+description: The following analytic identifies the assignment of sensitive and privileged
+  Azure Active Directory roles to an Azure AD user. Adversaries and red teams alike
+  may assign these roles to a compromised account to establish Persistence in an Azure
+  AD environment. This detection leverages the O365 Universal Audit Log data source.
 data_source:
 - Office 365 Universal Audit Log
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation IN ("Add member to role.","Add eligible member to role.") | eval user = ObjectId, src_user = case(match(mvindex(''Actor{}.ID'',-1),"User"),mvindex(''Actor{}.ID'',0),match(mvindex(''Actor{}.ID'',-1),"ServicePrincipal"),mvindex(''Actor{}.ID'',3),true(),mvindex(''Actor{}.ID'',0)), object_name = mvindex(''ModifiedProperties{}.NewValue'', mvfind(''ModifiedProperties{}.Name'',"Role\.DisplayName")), object_id = mvindex(''ModifiedProperties{}.NewValue'', mvfind(''ModifiedProperties{}.Name'',"Role\.TemplateId")), signature = Operation, result = ResultStatus, category = mvindex(''Target{}.ID'',2) | stats count, min(_time) as firstTime, max(_time) as lastTime by src_user, user, category, result, object_name, object_id, signature | lookup privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole | search isprvilegedadrole="TRUE" category="User" | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_privileged_role_assigned_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Administrators will legitimately assign the privileged roles users as part of administrative tasks. Microsoft Privileged Identity Management (PIM) may cause false positives / less accurate alerting.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"\
+  Add member to role.\",\"Add eligible member to role.\") | eval user = ObjectId,
+  src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"\
+  ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)), object_name
+  = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"\
+  Role\\.DisplayName\")), object_id = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"\
+  Role\\.TemplateId\")), signature = Operation, result = ResultStatus, category =
+  mvindex('Target{}.ID',2) | stats count, min(_time) as firstTime, max(_time) as lastTime
+  by src_user, user, category, result, object_name, object_id, signature | lookup
+  privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole
+  | search isprvilegedadrole=\"TRUE\" category=\"User\" | `security_content_ctime(firstTime)`\
+  \  | `security_content_ctime(lastTime)` | `o365_privileged_role_assigned_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Administrators will legitimately assign the privileged roles
+  users as part of administrative tasks. Microsoft Privileged Identity Management
+  (PIM) may cause false positives / less accurate alerting.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +52,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 75
-  message: A privileged Azure AD role [$object_name$] was assigned to user $user$ by $src_user$
+  message: A privileged Azure AD role [$object_name$] was assigned to user $user$
+    by $src_user$
   mitre_attack_id:
   - T1098
   - T1098.003
@@ -47,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.Name
-  - UserId
-  - ObjectId
-  - Workload
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml b/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml
index 0b92036b0f..7c487a03ba 100644
--- a/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml
+++ b/detections/cloud/o365_privileged_role_assigned_to_service_principal.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects potential privilege escalation threats in Azure Active Directory (AD). This detection is important because it identifies instances where privileged roles that hold elevated permissions are assigned to service principals. This prevents unauthorized access or malicious activities, which occur when these non-human entities access Azure resources to exploit them. False positives might occur since administrators can legitimately assign privileged roles to service principals. This detection leverages the O365 Universal Audit Log data source.
+description: The following analytic detects potential privilege escalation threats
+  in Azure Active Directory (AD). This detection is important because it identifies
+  instances where privileged roles that hold elevated permissions are assigned to
+  service principals. This prevents unauthorized access or malicious activities, which
+  occur when these non-human entities access Azure resources to exploit them. False
+  positives might occur since administrators can legitimately assign privileged roles
+  to service principals. This detection leverages the O365 Universal Audit Log data
+  source.
 data_source:
 - Office 365 Universal Audit Log
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation IN ("Add member to role.","Add eligible member to role.") | eval user = ObjectId, src_user = case(match(mvindex(''Actor{}.ID'',-1),"User"),mvindex(''Actor{}.ID'',0),match(mvindex(''Actor{}.ID'',-1),"ServicePrincipal"),mvindex(''Actor{}.ID'',3),true(),mvindex(''Actor{}.ID'',0)), object_name = mvindex(''ModifiedProperties{}.NewValue'', mvfind(''ModifiedProperties{}.Name'',"Role\.DisplayName")), object_id = mvindex(''ModifiedProperties{}.NewValue'', mvfind(''ModifiedProperties{}.Name'',"Role\.TemplateId")), signature = Operation, result = ResultStatus, category = mvindex(''Target{}.ID'',2) | stats count, min(_time) as firstTime, max(_time) as lastTime by src_user, user, category, result, object_name, object_id, signature | lookup privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole | search isprvilegedadrole="TRUE" category!="User" | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_privileged_role_assigned_to_service_principal_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Administrators may legitimately assign the privileged roles to Service Principals as part of administrative tasks. Filter as needed.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation IN (\"\
+  Add member to role.\",\"Add eligible member to role.\") | eval user = ObjectId,
+  src_user = case(match(mvindex('Actor{}.ID',-1),\"User\"),mvindex('Actor{}.ID',0),match(mvindex('Actor{}.ID',-1),\"\
+  ServicePrincipal\"),mvindex('Actor{}.ID',3),true(),mvindex('Actor{}.ID',0)), object_name
+  = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"\
+  Role\\.DisplayName\")), object_id = mvindex('ModifiedProperties{}.NewValue', mvfind('ModifiedProperties{}.Name',\"\
+  Role\\.TemplateId\")), signature = Operation, result = ResultStatus, category =
+  mvindex('Target{}.ID',2) | stats count, min(_time) as firstTime, max(_time) as lastTime
+  by src_user, user, category, result, object_name, object_id, signature | lookup
+  privileged_azure_ad_roles azuretemplateid as object_id OUTPUT isprvilegedadrole
+  | search isprvilegedadrole=\"TRUE\" category!=\"User\" | `security_content_ctime(firstTime)`\
+  \  | `security_content_ctime(lastTime)` | `o365_privileged_role_assigned_to_service_principal_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Administrators may legitimately assign the privileged roles
+  to Service Principals as part of administrative tasks. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://learn.microsoft.com/en-us/azure/active-directory/roles/permissions-reference
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 75
-  message: A privileged Azure AD role [$object_name$] was assigned to the Service Principal $user$ initiated by $src_user$
+  message: A privileged Azure AD role [$object_name$] was assigned to the Service
+    Principal $user$ initiated by $src_user$
   mitre_attack_id:
   - T1098
   - T1098.003
@@ -48,18 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.Name
-  - UserId
-  - ObjectId
-  - Workload
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/o365_azure_workload_events/o365_azure_workload_events.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_pst_export_alert.yml b/detections/cloud/o365_pst_export_alert.yml
index 005138ae32..4b00aa146b 100644
--- a/detections/cloud/o365_pst_export_alert.yml
+++ b/detections/cloud/o365_pst_export_alert.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances where a user has initiated an eDiscovery search or exported a PST file in an Office 365 environment. It leverages Office 365 management activity logs, specifically filtering for events under ThreatManagement with the name "eDiscovery search started or exported." This activity is significant as it may indicate data exfiltration attempts or unauthorized access to sensitive information. If confirmed malicious, it suggests an attacker or insider threat is attempting to gather or exfiltrate data, potentially leading to data breaches, loss of intellectual property, or unauthorized access to confidential communications. Immediate investigation is required.
+description: The following analytic detects instances where a user has initiated an
+  eDiscovery search or exported a PST file in an Office 365 environment. It leverages
+  Office 365 management activity logs, specifically filtering for events under ThreatManagement
+  with the name "eDiscovery search started or exported." This activity is significant
+  as it may indicate data exfiltration attempts or unauthorized access to sensitive
+  information. If confirmed malicious, it suggests an attacker or insider threat is
+  attempting to gather or exfiltrate data, potentially leading to data breaches, loss
+  of intellectual property, or unauthorized access to confidential communications.
+  Immediate investigation is required.
 data_source:
 - O365
-search: '`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" | stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter`'
-how_to_implement: You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
-known_false_positives: PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored.
+search: '`o365_management_activity` Category=ThreatManagement Name="eDiscovery search
+  started or exported" | stats count earliest(_time) as firstTime latest(_time) as
+  lastTime by Source Severity AlertEntityId Operation Name |`security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `o365_pst_export_alert_filter`'
+how_to_implement: You must install splunk Microsoft Office 365 add-on. This search
+  works with o365:management:activity
+known_false_positives: PST export can be done for legitimate purposes but due to the
+  sensitive nature of its content it must be monitored.
 references:
 - https://attack.mitre.org/techniques/T1114/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Source$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Source$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Source$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 60
   impact: 80
-  message: User $Source$ has exported a PST file from the search using this operation- $Operation$ with a severity of $Severity$
+  message: User $Source$ has exported a PST file from the search using this operation-
+    $Operation$ with a severity of $Severity$
   mitre_attack_id:
   - T1114
   observable:
@@ -41,18 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Category
-  - Name
-  - Source
-  - Severity
-  - AlertEntityId
-  - Operation
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_safe_links_detection.yml b/detections/cloud/o365_safe_links_detection.yml
index 65b3a05779..223a0d7709 100644
--- a/detections/cloud/o365_safe_links_detection.yml
+++ b/detections/cloud/o365_safe_links_detection.yml
@@ -5,11 +5,20 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when any Microsoft Safe Links alerting is triggered. This behavior may indicate when user has interacted with a phishing or otherwise malicious link within the Microsoft Office ecosystem.
+description: The following analytic detects when any Microsoft Safe Links alerting
+  is triggered. This behavior may indicate when user has interacted with a phishing
+  or otherwise malicious link within the Microsoft Office ecosystem.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Name="*a potentially malicious URL*" Operation=AlertEntityGenerated | fromjson Data | stats count min(_time) as firstTime max(_time) as lastTime values(ObjectId) as url values(od) as desc by AlertId,trc,Operation,Name,ot | rename Name as signature, AlertId as signature_id, trc as user,ot as action | eval action = CASE(action == "Allowed", "allowed", action=="BlockPageOverride", "allowed", true(),"blocked") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_safe_links_detection_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The Safe Links capability must be configured and is typically only available to E3/E5 level customers.
+search: '`o365_management_activity` Name="*a potentially malicious URL*" Operation=AlertEntityGenerated
+  | fromjson Data | stats count min(_time) as firstTime max(_time) as lastTime values(ObjectId)
+  as url values(od) as desc by AlertId,trc,Operation,Name,ot | rename Name as signature,
+  AlertId as signature_id, trc as user,ot as action | eval action = CASE(action ==
+  "Allowed", "allowed", action=="BlockPageOverride", "allowed", true(),"blocked")
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_safe_links_detection_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. The Safe Links capability must be configured
+  and is typically only available to E3/E5 level customers.
 known_false_positives: Based on Safe Links policies, may vary.
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/safe-links-about?view=o365-worldwide
@@ -20,7 +29,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - AlertId
-  - Operation
-  - Name
-  - AlertId
-  - trc
-  - ot
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_security_and_compliance_alert_triggered.yml b/detections/cloud/o365_security_and_compliance_alert_triggered.yml
index 7efe3cb934..1239168045 100644
--- a/detections/cloud/o365_security_and_compliance_alert_triggered.yml
+++ b/detections/cloud/o365_security_and_compliance_alert_triggered.yml
@@ -6,10 +6,28 @@ author: Mauricio Velazco, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic identifies alerts triggered by the Office 365 Security and Compliance Center, indicating potential threats or policy violations. It leverages data from the `o365_management_activity` dataset, focusing on events where the workload is SecurityComplianceCenter and the operation is AlertTriggered. This activity is significant as it highlights security and compliance issues within the O365 environment, which are crucial for maintaining organizational security. If confirmed malicious, these alerts could indicate attempts to breach security policies, leading to unauthorized access, data exfiltration, or other malicious activities.
-search: '`o365_management_activity` Workload=SecurityComplianceCenter Category=ThreatManagement Operation=AlertTriggered | spath input=Data path=f3u output=user | spath input=Data path=op output=operation | spath input=_raw path=wl | spath input=Data path=rid output=rule_id | spath input=Data path=ad output=alert_description | spath input=Data path=lon output=operation_name | spath input=Data path=an output=alert_name | spath input=Data path=sev output=severity |  stats count earliest(_time) as firstTime latest(_time) as lastTime by user, Name, operation, rule_id, alert_description, alert_name, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_security_and_compliance_alert_triggered_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: O365 Security and Compliance may also generate false positives or trigger on legitimate behavior, filter as needed.
+description: The following analytic identifies alerts triggered by the Office 365
+  Security and Compliance Center, indicating potential threats or policy violations.
+  It leverages data from the `o365_management_activity` dataset, focusing on events
+  where the workload is SecurityComplianceCenter and the operation is AlertTriggered.
+  This activity is significant as it highlights security and compliance issues within
+  the O365 environment, which are crucial for maintaining organizational security.
+  If confirmed malicious, these alerts could indicate attempts to breach security
+  policies, leading to unauthorized access, data exfiltration, or other malicious
+  activities.
+search: '`o365_management_activity` Workload=SecurityComplianceCenter Category=ThreatManagement
+  Operation=AlertTriggered | spath input=Data path=f3u output=user | spath input=Data
+  path=op output=operation | spath input=_raw path=wl | spath input=Data path=rid
+  output=rule_id | spath input=Data path=ad output=alert_description | spath input=Data
+  path=lon output=operation_name | spath input=Data path=an output=alert_name | spath
+  input=Data path=sev output=severity |  stats count earliest(_time) as firstTime
+  latest(_time) as lastTime by user, Name, operation, rule_id, alert_description,
+  alert_name, severity | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_security_and_compliance_alert_triggered_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: O365 Security and Compliance may also generate false positives
+  or trigger on legitimate behavior, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1078/004/
 - https://learn.microsoft.com/en-us/purview/alert-policies?view=o365-worldwide
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Category
-  - Operation
-  - Name
-  - Data
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/o365_security_and_compliance_alert_triggered/o365_security_and_compliance_alert_triggered.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.004/o365_security_and_compliance_alert_triggered/o365_security_and_compliance_alert_triggered.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_service_principal_new_client_credentials.yml b/detections/cloud/o365_service_principal_new_client_credentials.yml
index 17e44877e2..3ff1237816 100644
--- a/detections/cloud/o365_service_principal_new_client_credentials.yml
+++ b/detections/cloud/o365_service_principal_new_client_credentials.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of new credentials for Service Principals within an Office 365 tenant. It uses O365 audit logs, focusing on events related to credential modifications or additions in the AzureActiveDirectory workload. This activity is significant because Service Principals represent application identities, and their credentials allow applications to authenticate and access resources. If an attacker successfully adds or modifies these credentials, they can impersonate the application, leading to unauthorized data access, data exfiltration, or malicious operations under the application's identity.
+description: The following analytic detects the addition of new credentials for Service
+  Principals within an Office 365 tenant. It uses O365 audit logs, focusing on events
+  related to credential modifications or additions in the AzureActiveDirectory workload.
+  This activity is significant because Service Principals represent application identities,
+  and their credentials allow applications to authenticate and access resources. If
+  an attacker successfully adds or modifies these credentials, they can impersonate
+  the application, leading to unauthorized data access, data exfiltration, or malicious
+  operations under the application's identity.
 data_source:
 - O365
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update application*Certificates and secrets management " |  stats earliest(_time) as firstTime latest(_time) as lastTime by user ModifiedProperties{}.NewValue object ObjectId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_service_principal_new_client_credentials_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Service Principal client credential modifications may be part of legitimate administrative operations. Filter as needed.
+search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Update
+  application*Certificates and secrets management " |  stats earliest(_time) as firstTime
+  latest(_time) as lastTime by user ModifiedProperties{}.NewValue object ObjectId
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_service_principal_new_client_credentials_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Service Principal client credential modifications may be part
+  of legitimate administrative operations. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/001/
 - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452
@@ -22,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - object
-  - user
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/o365_service_principal_credentials/o365_service_principal_credentials.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.001/o365_service_principal_credentials/o365_service_principal_credentials.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml b/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml
index 7e138a7fc4..f4bcb91195 100644
--- a/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml
+++ b/detections/cloud/o365_sharepoint_allowed_domains_policy_changed.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when the allowed domain settings for O365 SharePoint have been changed. With Azure AD B2B collaboration, users and administrators can invite external users to collaborate with internal users. External guest account invitations may also need access to OneDrive/SharePoint resources. These changed should be monitored by security teams as they could potentially lead to unauthorized access.
+description: The following analytic identifies when the allowed domain settings for
+  O365 SharePoint have been changed. With Azure AD B2B collaboration, users and administrators
+  can invite external users to collaborate with internal users. External guest account
+  invitations may also need access to OneDrive/SharePoint resources. These changed
+  should be monitored by security teams as they could potentially lead to unauthorized
+  access.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=SharePoint Operation=SharingPolicyChanged "ModifiedProperties{}.Name"=AllowDomainList | eval signature_id = CorrelationId, signature=Operation, src = ClientIP, user = UserId, object_name=''ModifiedProperties{}.Name'', object_attrs_new = split(replace(''ModifiedProperties{}.NewValue'',"\.\.\.",""),","), object_attrs_old = split(replace(''ModifiedProperties{}.OldValue'',"\.\.\.",""),",") | stats values(object_attrs_new) as object_attrs_new, values(object_attrs_old) as object_attrs_old, values(src) as src, count, min(_time) as firstTime, max(_time) as lastTime by user,signature,signature_id,object_name | eval diff_add=mvmap(object_attrs_new,if(isnull(mvfind(object_attrs_old,object_attrs_new)),object_attrs_new,null)) | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs_new,object_attrs_old)),object_attrs_old,null)) | eval result = case(isnotnull(diff_add),"Added ".mvjoin(diff_add,","),isnotnull(diff_remove),"Removed ".mvjoin(diff_remove,",")), action = case(isnotnull(diff_add),"created",isnotnull(diff_remove),"deleted") | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_sharepoint_allowed_domains_policy_changed_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
+search: "`o365_management_activity` Workload=SharePoint Operation=SharingPolicyChanged
+  \"ModifiedProperties{}.Name\"=AllowDomainList | eval signature_id = CorrelationId,
+  signature=Operation, src = ClientIP, user = UserId, object_name='ModifiedProperties{}.Name',
+  object_attrs_new = split(replace('ModifiedProperties{}.NewValue',\"\\.\\.\\.\",\"\
+  \"),\",\"), object_attrs_old = split(replace('ModifiedProperties{}.OldValue',\"\\\
+  .\\.\\.\",\"\"),\",\") | stats values(object_attrs_new) as object_attrs_new, values(object_attrs_old)
+  as object_attrs_old, values(src) as src, count, min(_time) as firstTime, max(_time)
+  as lastTime by user,signature,signature_id,object_name | eval diff_add=mvmap(object_attrs_new,if(isnull(mvfind(object_attrs_old,object_attrs_new)),object_attrs_new,null))
+  | eval diff_remove=mvmap(object_attrs_old,if(isnull(mvfind(object_attrs_new,object_attrs_old)),object_attrs_old,null))
+  | eval result = case(isnotnull(diff_add),\"Added \".mvjoin(diff_add,\",\"),isnotnull(diff_remove),\"\
+  Removed \".mvjoin(diff_remove,\",\")), action = case(isnotnull(diff_add),\"created\"\
+  ,isnotnull(diff_remove),\"deleted\") | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `o365_sharepoint_allowed_domains_policy_changed_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
 known_false_positives: Business approved changes by known administrators.
 references:
 - https://learn.microsoft.com/en-us/sharepoint/external-sharing-overview
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ModifiedProperties{}.Name
-  - CorrelationId
-  - ClientIP
-  - UserId
-  - ModifiedProperties{}.NewValue
-  - ModifiedProperties{}.OldValue
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_sharepoint_malware_detection.yml b/detections/cloud/o365_sharepoint_malware_detection.yml
index 00421a8623..ec89886594 100644
--- a/detections/cloud/o365_sharepoint_malware_detection.yml
+++ b/detections/cloud/o365_sharepoint_malware_detection.yml
@@ -5,11 +5,20 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when a malicious file is detected within the SharePoint Online ecosystem. Attackers may stage and execute malicious files from within the Microsoft Office 365 ecosystem. Any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities further enhance these detection and response functions.
+description: The following analytic identifies when a malicious file is detected within
+  the SharePoint Online ecosystem. Attackers may stage and execute malicious files
+  from within the Microsoft Office 365 ecosystem. Any detections from built-in Office
+  365 capabilities should be monitored and responded to appropriately. Certain premium
+  Office 365 capabilities further enhance these detection and response functions.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Operation=FileMalwareDetected | stats values(Workload) as category, values(SourceFileName) as file_name values(ObjectId) as file_path, values(VirusInfo) as signature, count, min(_time) as firstTime, max(_time) as lastTime by Id, UserId | rename Id as signature_id, UserId as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_sharepoint_malware_detection_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
+search: '`o365_management_activity` Operation=FileMalwareDetected | stats values(Workload)
+  as category, values(SourceFileName) as file_name values(ObjectId) as file_path,
+  values(VirusInfo) as signature, count, min(_time) as firstTime, max(_time) as lastTime
+  by Id, UserId | rename Id as signature_id, UserId as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_sharepoint_malware_detection_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide
@@ -19,7 +28,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Workload
-  - SourceFileName
-  - ObjectId
-  - VirusInfo
-  - Id
-  - UserId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_tenant_wide_admin_consent_granted.yml b/detections/cloud/o365_tenant_wide_admin_consent_granted.yml
index e73158a9d1..8cbfd5103c 100644
--- a/detections/cloud/o365_tenant_wide_admin_consent_granted.yml
+++ b/detections/cloud/o365_tenant_wide_admin_consent_granted.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - O365 Consent to application.
-description: The following analytic identifies instances where admin consent is granted to an application within an Azure AD and Office 365 tenant. It leverages O365 audit logs, specifically events related to the admin consent action within the AzureActiveDirectory workload. This activity is significant because admin consent allows applications to access data across the entire tenant, potentially exposing vast amounts of organizational data. If confirmed malicious, an attacker could gain extensive and persistent access to organizational data, leading to data exfiltration, espionage, further malicious activities, and potential compliance violations.
-search: '`o365_management_activity` Operation="Consent to application."  | eval new_field=mvindex(''ModifiedProperties{}.NewValue'', 4) | rex field=new_field "ConsentType: (?<ConsentType>[^\,]+)" | rex field=new_field "Scope: (?<Scope>[^\,]+)"  | search  ConsentType = "AllPrincipals"  | stats count min(_time) as firstTime max(_time) as lastTime by Operation, user, object, ObjectId, ConsentType, Scope | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `o365_tenant_wide_admin_consent_granted_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Legitimate applications may be granted tenant wide consent, filter as needed.
+description: The following analytic identifies instances where admin consent is granted
+  to an application within an Azure AD and Office 365 tenant. It leverages O365 audit
+  logs, specifically events related to the admin consent action within the AzureActiveDirectory
+  workload. This activity is significant because admin consent allows applications
+  to access data across the entire tenant, potentially exposing vast amounts of organizational
+  data. If confirmed malicious, an attacker could gain extensive and persistent access
+  to organizational data, leading to data exfiltration, espionage, further malicious
+  activities, and potential compliance violations.
+search: "`o365_management_activity` Operation=\"Consent to application.\"  | eval
+  new_field=mvindex('ModifiedProperties{}.NewValue', 4) | rex field=new_field \"ConsentType:
+  (?<ConsentType>[^\\,]+)\" | rex field=new_field \"Scope: (?<Scope>[^\\,]+)\"  |
+  search  ConsentType = \"AllPrincipals\"  | stats count min(_time) as firstTime max(_time)
+  as lastTime by Operation, user, object, ObjectId, ConsentType, Scope | `security_content_ctime(firstTime)`\
+  \  | `security_content_ctime(lastTime)` | `o365_tenant_wide_admin_consent_granted_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Legitimate applications may be granted tenant wide consent,
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/003/
 - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - user
-  - object
-  - ObjectId
-  - ModifiedProperties{}.NewValue
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_admin_consent/o365_admin_consent.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.003/o365_admin_consent/o365_admin_consent.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml b/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml
index db5cff89d6..db6eaff435 100644
--- a/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml
+++ b/detections/cloud/o365_threat_intelligence_suspicious_email_delivered.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic identifies when a suspicious email is detected within the Microsoft Office 365 ecosystem through the Advanced Threat Protection engine and delivered to an end user. Attackers may execute several attacks through email, any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities such as Safe Attachment and Safe Links further enhance these detection and response functions.
+description: The following analytic identifies when a suspicious email is detected
+  within the Microsoft Office 365 ecosystem through the Advanced Threat Protection
+  engine and delivered to an end user. Attackers may execute several attacks through
+  email, any detections from built-in Office 365 capabilities should be monitored
+  and responded to appropriately. Certain premium Office 365 capabilities such as
+  Safe Attachment and Safe Links further enhance these detection and response functions.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=ThreatIntelligence Operation=TIMailData DeliveryAction!=Blocked Directionality=InBound | rename P2Sender as src_user, P1Sender as sender, Recipients{} as user, DeliveryAction as action | stats values(SenderIp) as src, values(Subject) as subject, values(user) as user, values(action) as action, values(SystemOverrides{}.Details) as reason, values(LatestDeliveryLocation) as result, values(ThreatsAndDetectionTech{}) as category, values(AttachmentData{}.FileName) as file_name, values(AttachmentData{}.FileType) as file_type, values(AttachmentData{}.SHA256) as file_hash values(DetectionMethod) as signature, min(_time) as firstTime max(_time) as lastTime, count  by src_user,sender | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_threat_intelligence_suspicious_email_delivered_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The threat intelligence workload is typically only visible to E3/E5 level customers.
+search: '`o365_management_activity` Workload=ThreatIntelligence Operation=TIMailData
+  DeliveryAction!=Blocked Directionality=InBound | rename P2Sender as src_user, P1Sender
+  as sender, Recipients{} as user, DeliveryAction as action | stats values(SenderIp)
+  as src, values(Subject) as subject, values(user) as user, values(action) as action,
+  values(SystemOverrides{}.Details) as reason, values(LatestDeliveryLocation) as result,
+  values(ThreatsAndDetectionTech{}) as category, values(AttachmentData{}.FileName)
+  as file_name, values(AttachmentData{}.FileType) as file_type, values(AttachmentData{}.SHA256)
+  as file_hash values(DetectionMethod) as signature, min(_time) as firstTime max(_time)
+  as lastTime, count  by src_user,sender | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `o365_threat_intelligence_suspicious_email_delivered_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. The threat intelligence workload is typically
+  only visible to E3/E5 level customers.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/anti-malware-protection-for-spo-odfb-teams-about?view=o365-worldwide
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 20
-  message: A suspicious email was delivered to $user$ by $src_user$ matching the $signature$ signature
+  message: A suspicious email was delivered to $user$ by $src_user$ matching the $signature$
+    signature
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -52,18 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - P2Sender
-  - P1Sender
-  - Recipients
-  - DeliveryAction
-  - Operation
-  - Workload
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml b/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml
index 5fd564bc68..c8b2fdab44 100644
--- a/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml
+++ b/detections/cloud/o365_threat_intelligence_suspicious_file_detected.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies when a malicious file is detected within the Microsoft Office 365 ecosystem through the Advanced Threat Protection engine. Attackers may stage and execute malicious files from within the Microsoft Office 365 ecosystem. Any detections from built-in Office 365 capabilities should be monitored and responded to appropriately. Certain premium Office 365 capabilities such as Safe Attachment and Safe Links further enhance these detection and response functions.
+description: The following analytic identifies when a malicious file is detected within
+  the Microsoft Office 365 ecosystem through the Advanced Threat Protection engine.
+  Attackers may stage and execute malicious files from within the Microsoft Office
+  365 ecosystem. Any detections from built-in Office 365 capabilities should be monitored
+  and responded to appropriately. Certain premium Office 365 capabilities such as
+  Safe Attachment and Safe Links further enhance these detection and response functions.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=ThreatIntelligence Operation=AtpDetection | stats values(DetectionMethod) as category  values(FileData.FileName) as file_name values(FileData.FilePath) as file_path values(FileData.FileSize) as file_size values(FileData.MalwareFamily) as signature count, min(_time) as firstTime, max(_time) as lastTime by Id, UserId | rename Id as signature_id, UserId as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_threat_intelligence_suspicious_file_detected_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The threat intelligence workload is typically only visible to E3/E5 level customers.
+search: '`o365_management_activity` Workload=ThreatIntelligence Operation=AtpDetection
+  | stats values(DetectionMethod) as category  values(FileData.FileName) as file_name
+  values(FileData.FilePath) as file_path values(FileData.FileSize) as file_size values(FileData.MalwareFamily)
+  as signature count, min(_time) as firstTime, max(_time) as lastTime by Id, UserId
+  | rename Id as signature_id, UserId as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `o365_threat_intelligence_suspicious_file_detected_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. The threat intelligence workload is typically
+  only visible to E3/E5 level customers.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/threat-explorer-real-time-detections-about?view=o365-worldwide
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 50
-  message: Threat Intelligence workload detected a malicious file [$file_name$] from user $user$
+  message: Threat Intelligence workload detected a malicious file [$file_name$] from
+    user $user$
   mitre_attack_id:
   - T1204.002
   - T1204
@@ -52,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Id
-  - UserId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/o365_user_consent_blocked_for_risky_application.yml b/detections/cloud/o365_user_consent_blocked_for_risky_application.yml
index 1007ddfa8f..7a44d7626f 100644
--- a/detections/cloud/o365_user_consent_blocked_for_risky_application.yml
+++ b/detections/cloud/o365_user_consent_blocked_for_risky_application.yml
@@ -7,10 +7,25 @@ status: production
 type: TTP
 data_source:
 - O365 Consent to application.
-description: The following analytic identifies instances where Office 365 has blocked a user's attempt to grant consent to an application deemed risky or potentially malicious. This detection leverages O365 audit logs, specifically focusing on failed user consent actions due to system-driven blocks. Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization. If confirmed malicious, this activity suggests that O365's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation.
-search: '`o365_management_activity` Workload=AzureActiveDirectory Operation="Consent to application." ResultStatus=Failure | eval permissions =mvindex(''ModifiedProperties{}.NewValue'', 4) | eval reason =mvindex(''ModifiedProperties{}.NewValue'', 5) |  search reason = "Risky application detected" | rex field=permissions "Scope: (?<Scope>[^,]+)" |  stats max(_time) as lastTime by Operation, user, reason, object, Scope | `security_content_ctime(lastTime)` | `o365_user_consent_blocked_for_risky_application_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: Microsofts algorithm to identify risky applications is unknown and may flag legitimate applications.
+description: The following analytic identifies instances where Office 365 has blocked
+  a user's attempt to grant consent to an application deemed risky or potentially
+  malicious. This detection leverages O365 audit logs, specifically focusing on failed
+  user consent actions due to system-driven blocks. Monitoring these blocked consent
+  attempts is crucial as it highlights potential threats early on, indicating that
+  a user might be targeted or that malicious applications are attempting to infiltrate
+  the organization. If confirmed malicious, this activity suggests that O365's security
+  measures successfully prevented a harmful application from accessing organizational
+  data, warranting immediate investigation.
+search: "`o365_management_activity` Workload=AzureActiveDirectory Operation=\"Consent
+  to application.\" ResultStatus=Failure | eval permissions =mvindex('ModifiedProperties{}.NewValue',
+  4) | eval reason =mvindex('ModifiedProperties{}.NewValue', 5) |  search reason =
+  \"Risky application detected\" | rex field=permissions \"Scope: (?<Scope>[^,]+)\"\
+  \ |  stats max(_time) as lastTime by Operation, user, reason, object, Scope | `security_content_ctime(lastTime)`
+  | `o365_user_consent_blocked_for_risky_application_filter`"
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: Microsofts algorithm to identify risky applications is unknown
+  and may flag legitimate applications.
 references:
 - https://attack.mitre.org/techniques/T1528/
 - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
@@ -24,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +53,8 @@ tags:
   asset_type: O365 Tenant
   confidence: 100
   impact: 30
-  message: O365 has blocked $user$ attempt to grant to consent to an application deemed risky.
+  message: O365 has blocked $user$ attempt to grant to consent to an application deemed
+    risky.
   mitre_attack_id:
   - T1528
   observable:
@@ -45,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - ResultStatus
-  - ModifiedProperties{}.NewValue
-  - object
-  - ObjectId
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_blocked/o365_user_consent_blocked.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_blocked/o365_user_consent_blocked.log
     source: o365
     sourcetype: o365:management:activity
diff --git a/detections/cloud/o365_user_consent_denied_for_oauth_application.yml b/detections/cloud/o365_user_consent_denied_for_oauth_application.yml
index b9c6afe8d2..e5b19b1150 100644
--- a/detections/cloud/o365_user_consent_denied_for_oauth_application.yml
+++ b/detections/cloud/o365_user_consent_denied_for_oauth_application.yml
@@ -7,10 +7,22 @@ status: production
 type: TTP
 data_source:
 - O365
-description: The following analytic identifies instances where a user has denied consent to an OAuth application seeking permissions within the Office 365 environment. This detection leverages O365 audit logs, focusing on events related to user consent actions. By filtering for denied consent actions associated with OAuth applications, it captures instances where users have actively rejected permission requests. This activity is significant as it may indicate users spotting potentially suspicious or unfamiliar applications. If confirmed malicious, it suggests an attempt by a potentially harmful application to gain unauthorized access, which was proactively blocked by the user.
-search: '`o365_graph` status.errorCode=65004 | rename userPrincipalName as user | rename ipAddress as src_ip | stats max(_time) as lastTime by user src_ip appDisplayName status.failureReason | `security_content_ctime(lastTime)` | `o365_user_consent_denied_for_oauth_application_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 events.
-known_false_positives: OAuth applications that require mail permissions may be legitimate, investigate and filter as needed.
+description: The following analytic identifies instances where a user has denied consent
+  to an OAuth application seeking permissions within the Office 365 environment. This
+  detection leverages O365 audit logs, focusing on events related to user consent
+  actions. By filtering for denied consent actions associated with OAuth applications,
+  it captures instances where users have actively rejected permission requests. This
+  activity is significant as it may indicate users spotting potentially suspicious
+  or unfamiliar applications. If confirmed malicious, it suggests an attempt by a
+  potentially harmful application to gain unauthorized access, which was proactively
+  blocked by the user.
+search: '`o365_graph` status.errorCode=65004 | rename userPrincipalName as user |
+  rename ipAddress as src_ip | stats max(_time) as lastTime by user src_ip appDisplayName
+  status.failureReason | `security_content_ctime(lastTime)` | `o365_user_consent_denied_for_oauth_application_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 events.
+known_false_positives: OAuth applications that require mail permissions may be legitimate,
+  investigate and filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1528/
 - https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
@@ -24,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - status.errorCode
-  - userPrincipalName
-  - ipAddress
-  - status.failureReason
   security_domain: identity
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_declined/o365_user_consent_declined.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1528/o365_user_consent_declined/o365_user_consent_declined.log
     source: o365
     sourcetype: o365:graph:api
diff --git a/detections/cloud/o365_zap_activity_detection.yml b/detections/cloud/o365_zap_activity_detection.yml
index 219f631d02..022b9f6980 100644
--- a/detections/cloud/o365_zap_activity_detection.yml
+++ b/detections/cloud/o365_zap_activity_detection.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects when the Microsoft Zero-hour Automatic Purge (ZAP) capability takes action against a user's mailbox. This capability is an enhanced protection feature that retro-actively removes email with known malicious content for user inboxes. Since this is a retroactive capability, there is still a window in which the user may fall victim to the malicious content.
+description: The following analytic detects when the Microsoft Zero-hour Automatic
+  Purge (ZAP) capability takes action against a user's mailbox. This capability is
+  an enhanced protection feature that retro-actively removes email with known malicious
+  content for user inboxes. Since this is a retroactive capability, there is still
+  a window in which the user may fall victim to the malicious content.
 data_source:
 - O365 Universal Audit Log
-search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated Name="*messages containing malicious*" | fromjson Data | stats count min(_time) as firstTime max(_time) as lastTime values(zu) as url values(zfn) as file_name values(ms) as subject values(ttr) as result values(tsd) as src_user by AlertId,trc,Operation,Name | rename Name as signature, AlertId as signature_id, trc as user | eval action = CASE(match(result,"Success"), "blocked", true(),"allowed"), url = split(url,";") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_zap_activity_detection_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. Some features of Zero-hour purge are only offered within E3/E5 license level tenants, events may not be available otherwise.
+search: '`o365_management_activity` Workload=SecurityComplianceCenter Operation=AlertEntityGenerated
+  Name="*messages containing malicious*" | fromjson Data | stats count min(_time)
+  as firstTime max(_time) as lastTime values(zu) as url values(zfn) as file_name values(ms)
+  as subject values(ttr) as result values(tsd) as src_user by AlertId,trc,Operation,Name
+  | rename Name as signature, AlertId as signature_id, trc as user | eval action =
+  CASE(match(result,"Success"), "blocked", true(),"allowed"), url = split(url,";")
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `o365_zap_activity_detection_filter`'
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events. Some features of Zero-hour purge are only
+  offered within E3/E5 license level tenants, events may not be available otherwise.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/microsoft-365/security/office-365-security/zero-hour-auto-purge?view=o365-worldwide
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Workload
-  - Operation
-  - Name
-  - Data
-  - AlertId
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/o365_various_alerts/o365_various_alerts.log
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml b/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml
index 3363b36cb8..413f2630e5 100644
--- a/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml
+++ b/detections/cloud/risk_rule_for_dev_sec_ops_by_repository.yml
@@ -5,10 +5,26 @@ date: '2024-10-22'
 author: Bhavin Patel
 status: production
 type: Correlation
-description: The following analytic identifies high-risk activities within repositories by correlating repository data with risk scores. It leverages risk events from the Dev Sec Ops analytic stories, summing risk scores and capturing source and user information. The detection focuses on high-risk scores above 100 and sources with more than three occurrences. This activity is significant as it highlights repositories frequently targeted by threats, providing insights into potential vulnerabilities. If confirmed malicious, attackers could exploit these repositories, leading to data breaches or infrastructure compromise.
+description: The following analytic identifies high-risk activities within repositories
+  by correlating repository data with risk scores. It leverages risk events from the
+  Dev Sec Ops analytic stories, summing risk scores and capturing source and user
+  information. The detection focuses on high-risk scores above 100 and sources with
+  more than three occurrences. This activity is significant as it highlights repositories
+  frequently targeted by threats, providing insights into potential vulnerabilities.
+  If confirmed malicious, attackers could exploit these repositories, leading to data
+  breaches or infrastructure compromise.
 data_source: []
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as sum_risk_score, values(All_Risk.annotations.mitre_attack.mitre_tactic) as annotations.mitre_attack.mitre_tactic, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Dev Sec Ops" All_Risk.risk_object_type = "other" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count > 3 and sum_risk_score > 100 | `risk_rule_for_dev_sec_ops_by_repository_filter`'
-how_to_implement: Ensure that all relevant detections in the Dev Sec Ops analytic stories are enabled and are configured to create risk events in Enterprise Security.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as sum_risk_score, values(All_Risk.annotations.mitre_attack.mitre_tactic)
+  as annotations.mitre_attack.mitre_tactic, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count values(source) as source, dc(source) as source_count
+  from datamodel=Risk.All_Risk where All_Risk.analyticstories="Dev Sec Ops" All_Risk.risk_object_type
+  = "other" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic
+  | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | where source_count > 3 and sum_risk_score > 100 | `risk_rule_for_dev_sec_ops_by_repository_filter`'
+how_to_implement: Ensure that all relevant detections in the Dev Sec Ops analytic
+  stories are enabled and are configured to create risk events in Enterprise Security.
 known_false_positives: Unknown
 references: []
 drilldown_searches:
@@ -17,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,12 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/attack_techniques/T1204.003/risk_dataset/aws_ecr_risk_dataset.log
+  - data: 
+      https://raw.githubusercontent.com/splunk/attack_data/master/datasets/attack_techniques/T1204.003/risk_dataset/aws_ecr_risk_dataset.log
     source: aws_ecr_risk_dataset.log
     sourcetype: stash
diff --git a/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml b/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml
index 815450ddba..76ee7bfe3a 100644
--- a/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_launched_by_user.yml
@@ -44,9 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - userName
   security_domain: network
diff --git a/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml b/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml
index b678ade538..fae7e4dd3e 100644
--- a/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_launched_by_user___mltk.yml
@@ -40,9 +40,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - src_user
   security_domain: network
diff --git a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml
index 4dfb7c49fa..a85f248b35 100644
--- a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user.yml
@@ -44,9 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - userName
   security_domain: network
diff --git a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml
index 49466d303c..fbedfc2b05 100644
--- a/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml
+++ b/detections/deprecated/abnormally_high_aws_instances_terminated_by_user___mltk.yml
@@ -39,9 +39,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - src_user
   security_domain: network
diff --git a/detections/deprecated/asl_aws_createaccesskey.yml b/detections/deprecated/asl_aws_createaccesskey.yml
index 6bb07e1bbb..f55a580332 100644
--- a/detections/deprecated/asl_aws_createaccesskey.yml
+++ b/detections/deprecated/asl_aws_createaccesskey.yml
@@ -5,35 +5,39 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: deprecated
 type: Hunting
-description: This detection rule monitors for the creation of AWS Identity and Access Management (IAM) access keys. 
-  An IAM access key consists of an access key ID and secret access key, which are used to sign programmatic requests to AWS services. 
-  While IAM access keys can be legitimately used by developers and administrators for API access, their creation can also be indicative 
-  of malicious activity. Attackers who have gained unauthorized access to an AWS environment might create access keys as a means to 
-  establish persistence or to exfiltrate data through the APIs. Moreover, because access keys can be used to authenticate with AWS 
-  services without the need for further interaction, they can be particularly appealing for bad actors looking to operate under the radar. 
-  Consequently, it's important to vigilantly monitor and scrutinize access key creation events, especially if they are associated with 
-  unusual activity or are created by users who don't typically perform these actions. This hunting query identifies when a potentially compromised user 
-  creates a IAM access key for another user who may have higher privilleges, which can be a sign for privilege escalation. Hunting queries are designed to be executed 
-  manual during threat hunting.
+description: This detection rule monitors for the creation of AWS Identity and Access
+  Management (IAM) access keys. An IAM access key consists of an access key ID and
+  secret access key, which are used to sign programmatic requests to AWS services.
+  While IAM access keys can be legitimately used by developers and administrators
+  for API access, their creation can also be indicative of malicious activity. Attackers
+  who have gained unauthorized access to an AWS environment might create access keys
+  as a means to establish persistence or to exfiltrate data through the APIs. Moreover,
+  because access keys can be used to authenticate with AWS services without the need
+  for further interaction, they can be particularly appealing for bad actors looking
+  to operate under the radar. Consequently, it's important to vigilantly monitor and
+  scrutinize access key creation events, especially if they are associated with unusual
+  activity or are created by users who don't typically perform these actions. This
+  hunting query identifies when a potentially compromised user creates a IAM access
+  key for another user who may have higher privilleges, which can be a sign for privilege
+  escalation. Hunting queries are designed to be executed manual during threat hunting.
 data_source: []
-search: '`amazon_security_lake` api.operation=CreateAccessKey http_request.user_agent!=console.amazonaws.com api.response.error=null
-  | rename unmapped{}.key as unmapped_key , unmapped{}.value as unmapped_value
-  | eval keyjoin=mvzip(unmapped_key,unmapped_value)
-  | mvexpand keyjoin
-  | rex field=keyjoin "^(?<key>[^,]+),(?<value>.*)$"
-  | eval {key} = value
-  | search responseElements.accessKey.userName = *
-  | rename identity.user.name as identity_user_name, responseElements.accessKey.userName as responseElements_accessKey_userName
-  | eval match=if(identity_user_name=responseElements_accessKey_userName,1,0)
-  | search match=0
-  | rename identity_user_name as identity.user.name , responseElements_accessKey_userName as responseElements.accessKey.userName
-  | stats count min(_time) as firstTime max(_time) as lastTime by responseElements.accessKey.userName 
-  api.operation api.service.name identity.user.account_uid identity.user.credential_uid identity.user.name 
-  identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip 
-  | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` 
-  |`asl_aws_createaccesskey_filter`'
-how_to_implement: You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format. 
+search: '`amazon_security_lake` api.operation=CreateAccessKey http_request.user_agent!=console.amazonaws.com
+  api.response.error=null | rename unmapped{}.key as unmapped_key , unmapped{}.value
+  as unmapped_value | eval keyjoin=mvzip(unmapped_key,unmapped_value) | mvexpand keyjoin
+  | rex field=keyjoin "^(?<key>[^,]+),(?<value>.*)$" | eval {key} = value | search
+  responseElements.accessKey.userName = * | rename identity.user.name as identity_user_name,
+  responseElements.accessKey.userName as responseElements_accessKey_userName | eval
+  match=if(identity_user_name=responseElements_accessKey_userName,1,0) | search match=0
+  | rename identity_user_name as identity.user.name , responseElements_accessKey_userName
+  as responseElements.accessKey.userName | stats count min(_time) as firstTime max(_time)
+  as lastTime by responseElements.accessKey.userName api.operation api.service.name
+  identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type
+  identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` |`asl_aws_createaccesskey_filter`'
+how_to_implement: You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876)
+  that includes includes a merge of all the capabilities of the Splunk Add-on for
+  Amazon Security Lake. This search works with Amazon Security Lake logs which are
+  parsed in the Open Cybersecurity Schema Framework (OCSF)format.
 known_false_positives: While this search has no known false positives, it is possible
   that an AWS admin has legitimately created keys for another user.
 references:
@@ -45,8 +49,8 @@ tags:
   asset_type: AWS Account
   confidence: 90
   impact: 70
-  message: User $responseElements.accessKey.userName$ is attempting to create access keys for $responseElements.accessKey.userName$
-    from this IP $src_endpoint.ip$
+  message: User $responseElements.accessKey.userName$ is attempting to create access
+    keys for $responseElements.accessKey.userName$ from this IP $src_endpoint.ip$
   mitre_attack_id:
   - T1078
   observable:
@@ -62,23 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.service.name
-  - api.operation
-  - identity.user.account_uid
-  - identity.user.credential_uid
-  - identity.user.name
-  - identity.user.type
-  - identity.user.uid
-  - identity.user.uuid
-  - http_request.user_agent
-  - src_endpoint.ip
-  - unmapped{}.key
-  - unmapped{}.value
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/amazon_security_lake.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/amazon_security_lake.json
     sourcetype: aws:asl
     source: aws_asl
diff --git a/detections/deprecated/asl_aws_excessive_security_scanning.yml b/detections/deprecated/asl_aws_excessive_security_scanning.yml
index 1d290c888e..10ebdd8a4a 100644
--- a/detections/deprecated/asl_aws_excessive_security_scanning.yml
+++ b/detections/deprecated/asl_aws_excessive_security_scanning.yml
@@ -9,12 +9,16 @@ description: This search looks for AWS CloudTrail events and analyse the amount
   eventNames which starts with Describe by a single user. This indicates that this
   user scans the configuration of your AWS cloud environment.
 data_source: []
-search: '`amazon_security_lake` api.operation=Describe* OR api.operation=List* OR api.operation=Get* 
-  | stats dc(api.operation) as dc_api_operations min(_time) as firstTime max(_time) as lastTime values(http_request.user_agent) as http_request.user_agent 
-  values(src_endpoint.ip) as src_endpoint.ip values(cloud.region) as cloud.region values(identity.user.account_uid) as identity.user.account_uid by identity.user.name 
-  | where dc_api_operations > 50 | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)`|`asl_aws_excessive_security_scanning_filter`'
-how_to_implement: You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format. 
+search: '`amazon_security_lake` api.operation=Describe* OR api.operation=List* OR
+  api.operation=Get* | stats dc(api.operation) as dc_api_operations min(_time) as
+  firstTime max(_time) as lastTime values(http_request.user_agent) as http_request.user_agent
+  values(src_endpoint.ip) as src_endpoint.ip values(cloud.region) as cloud.region
+  values(identity.user.account_uid) as identity.user.account_uid by identity.user.name
+  | where dc_api_operations > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`asl_aws_excessive_security_scanning_filter`'
+how_to_implement: You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876)
+  that includes includes a merge of all the capabilities of the Splunk Add-on for
+  Amazon Security Lake. This search works with Amazon Security Lake logs which are
+  parsed in the Open Cybersecurity Schema Framework (OCSF)format.
 known_false_positives: While this search has no known false positives.
 references:
 - https://github.com/aquasecurity/cloudsploit
@@ -40,10 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.operation
-  - identity.user.account_uid
-  - identity.user.name
-  - http_request.user_agent
-  - src_endpoint.ip
-  security_domain: network
\ No newline at end of file
+  security_domain: network
diff --git a/detections/deprecated/asl_aws_password_policy_changes.yml b/detections/deprecated/asl_aws_password_policy_changes.yml
index e2ac97e8a8..c20a0357aa 100644
--- a/detections/deprecated/asl_aws_password_policy_changes.yml
+++ b/detections/deprecated/asl_aws_password_policy_changes.yml
@@ -5,20 +5,23 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: deprecated
 type: Hunting
-description: This search looks for AWS CloudTrail events from Amazon Security Lake where a user is making successful
-  API calls to view/update/delete the existing password policy in an AWS organization.
-  It is unlikely for a regular user to conduct this operation. These events may potentially
-  be malicious, adversaries often use this information to gain more understanding
-  of the password defenses in place and exploit them to increase their attack surface
-  when a user account is compromised.
+description: This search looks for AWS CloudTrail events from Amazon Security Lake
+  where a user is making successful API calls to view/update/delete the existing password
+  policy in an AWS organization. It is unlikely for a regular user to conduct this
+  operation. These events may potentially be malicious, adversaries often use this
+  information to gain more understanding of the password defenses in place and exploit
+  them to increase their attack surface when a user account is compromised.
 data_source: []
-search: '`amazon_security_lake` "api.service.name"="iam.amazonaws.com" "api.operation" IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy") "api.response.error"=null 
-  | stats count min(_time) as firstTime max(_time) as lastTime by identity.user.account_uid identity.user.credential_uid identity.user.name 
-  identity.user.type identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region 
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)`  
-  | `asl_aws_password_policy_changes_filter`'
-how_to_implement: You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format. 
+search: '`amazon_security_lake` "api.service.name"="iam.amazonaws.com" "api.operation"
+  IN ("UpdateAccountPasswordPolicy","GetAccountPasswordPolicy","DeleteAccountPasswordPolicy")
+  "api.response.error"=null | stats count min(_time) as firstTime max(_time) as lastTime
+  by identity.user.account_uid identity.user.credential_uid identity.user.name identity.user.type
+  identity.user.uid identity.user.uuid http_request.user_agent src_endpoint.ip cloud.region
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `asl_aws_password_policy_changes_filter`'
+how_to_implement: You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876)
+  that includes includes a merge of all the capabilities of the Splunk Add-on for
+  Amazon Security Lake. This search works with Amazon Security Lake logs which are
+  parsed in the Open Cybersecurity Schema Framework (OCSF)format.
 known_false_positives: While this search has no known false positives, it is possible
   that an AWS admin has legitimately triggered an AWS audit tool activity which may
   trigger this event.
@@ -31,11 +34,12 @@ tags:
   asset_type: AWS Account
   confidence: 80
   impact: 90
-  message: User $identity.user.name$ is attempting to $api.operation$ the password policy for accounts
+  message: User $identity.user.name$ is attempting to $api.operation$ the password
+    policy for accounts
   mitre_attack_id:
   - T1201
   observable:
-  - name: src_endpoint.ip 
+  - name: src_endpoint.ip
     type: IP Address
     role:
     - Attacker
@@ -47,21 +51,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - api.service.name
-  - api.operation
-  - identity.user.account_uid
-  - identity.user.credential_uid
-  - identity.user.name
-  - identity.user.type
-  - identity.user.uid
-  - identity.user.uuid
-  - http_request.user_agent
-  - src_endpoint.ip
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/aws_password_policy/amazon_security_lake.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/aws_password_policy/amazon_security_lake.json
     sourcetype: aws:asl
     source: aws_asl
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml
index e848410413..7d83ed5be8 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_city.yml
@@ -5,10 +5,10 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: deprecated
 type: Anomaly
-description: 'This search looks for AWS provisioning activities from previously unseen
+description: This search looks for AWS provisioning activities from previously unseen
   cities.  Provisioning activities are defined broadly as any event that begins with
   "Run" or "Create." This search is deprecated and have been translated to use the
-  latest Change Datamodel.'
+  latest Change Datamodel.
 data_source: []
 search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
   | search City=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation
@@ -25,18 +25,17 @@ how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or late
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-known_false_positives: 'This is a strictly behavioral search, so we define "false
-  positive" slightly differently. Every time this fires, it will accurately reflect
-  the first occurrence in the time period you''re searching within, plus what is
-  stored in the cache feature. But while there are really no "false positives"
-  in a traditional sense, there is definitely lots of noise.
-  
-  This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning
-  activity. If you typically do all provisioning from tools inside of your city,
-  there should be few false positives. If you are located in countries where the
-  free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution
-  (particularly small countries in less economically powerful regions), this may
-  be much less valuable to you.'
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  city is seen in the **GeoIP** database for any kind of provisioning activity. If
+  you typically do all provisioning from tools inside of your city, there should be
+  few false positives. If you are located in countries where the free version of **MaxMind
+  GeoIP** that ships by default with Splunk has weak resolution (particularly small
+  countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 tags:
   analytic_story:
@@ -56,8 +55,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - sourceIPAddress
   security_domain: endpoint
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml
index 3470a62a3d..c923789b39 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_country.yml
@@ -5,10 +5,10 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: deprecated
 type: Anomaly
-description: 'This search looks for AWS provisioning activities from previously unseen
+description: This search looks for AWS provisioning activities from previously unseen
   countries. Provisioning activities are defined broadly as any event that begins
   with "Run" or "Create." This search is deprecated and have been translated to use
-  the latest Change Datamodel.'
+  the latest Change Datamodel.
 data_source: []
 search: '`cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress
   | search Country=* [search `cloudtrail` (eventName=Run* OR eventName=Create*) |
@@ -26,18 +26,17 @@ how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or late
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-known_false_positives: 'This is a strictly behavioral search, so we define "false
-  positive" slightly differently. Every time this fires, it will accurately reflect
-  the first occurrence in the time period you''re searching over plus what is stored
-  in the cache feature. But while there are really no \"false positives\" in a traditional
-  sense, there is definitely lots of noise.
-  
-  This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity.
-  If you typically do all provisioning from tools inside of your country, there
-  should be few false positives. If you are located in countries where the free
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching over plus what is stored
+  in the cache feature. But while there are really no \\\"false positives\\\" in a
+  traditional sense, there is definitely lots of noise.\nThis search will fire any
+  time a new country is seen in the **GeoIP** database for any kind of provisioning
+  activity. If you typically do all provisioning from tools inside of your country,
+  there should be few false positives. If you are located in countries where the free
   version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution
-  (particularly small countries in less economically powerful regions), this may
-  be much less valuable to you.'
+  (particularly small countries in less economically powerful regions), this may be
+  much less valuable to you."
 references: []
 tags:
   analytic_story:
@@ -57,8 +56,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - sourceIPAddress
   security_domain: endpoint
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml
index 396638accb..ccffc80d13 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_ip_address.yml
@@ -5,10 +5,10 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: deprecated
 type: Anomaly
-description: 'This search looks for AWS provisioning activities from previously unseen
+description: This search looks for AWS provisioning activities from previously unseen
   IP addresses. Provisioning activities are defined broadly as any event that begins
   with "Run" or "Create." This search is deprecated and have been translated to use
-  the latest Change Datamodel.'
+  the latest Change Datamodel.
 data_source: []
 search: '`cloudtrail` (eventName=Run* OR eventName=Create*) [search `cloudtrail` (eventName=Run*
   OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time)
@@ -25,18 +25,17 @@ how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or late
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-known_false_positives: 'This is a strictly behavioral search, so we define "false
-  positive" slightly differently. Every time this fires, it will accurately reflect
-  the first occurrence in the time period you''re searching within, plus what is
-  stored in the cache feature. But while there are really no "false positives"
-  in a traditional sense, there is definitely lots of noise.
-  
-  This search will fire any time a new IP address is seen in the **GeoIP** database for any kind
-  of provisioning activity. If you typically do all provisioning from tools inside
-  of your country, there should be few false positives. If you are located in countries
-  where the free version of **MaxMind GeoIP** that ships by default with Splunk
-  has weak resolution (particularly small countries in less economically powerful
-  regions), this may be much less valuable to you.'
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  IP address is seen in the **GeoIP** database for any kind of provisioning activity.
+  If you typically do all provisioning from tools inside of your country, there should
+  be few false positives. If you are located in countries where the free version of
+  **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly
+  small countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 tags:
   analytic_story:
@@ -54,8 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - sourceIPAddress
   security_domain: endpoint
diff --git a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml
index 01ea187454..c57df961c1 100644
--- a/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml
+++ b/detections/deprecated/aws_cloud_provisioning_from_previously_unseen_region.yml
@@ -25,18 +25,17 @@ how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or late
   inputs. This search works best when you run the "Previously Seen AWS Provisioning
   Activity Sources" support search once to create a history of previously seen locations
   that have provisioned AWS resources.
-known_false_positives: 'This is a strictly behavioral search, so we define "false
-  positive" slightly differently. Every time this fires, it will accurately reflect
-  the first occurrence in the time period you''re searching within, plus what is
-  stored in the cache feature. But while there are really no "false positives"
-  in a traditional sense, there is definitely lots of noise.
-  
-  This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning
-  activity. If you typically do all provisioning from tools inside of your region,
-  there should be few false positives. If you are located in regions where the free
-  version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution
-  (particularly small countries in less economically powerful regions), this may
-  be much less valuable to you.'
+known_false_positives: "This is a strictly behavioral search, so we define \"false
+  positive\" slightly differently. Every time this fires, it will accurately reflect
+  the first occurrence in the time period you're searching within, plus what is stored
+  in the cache feature. But while there are really no \"false positives\" in a traditional
+  sense, there is definitely lots of noise.\nThis search will fire any time a new
+  region is seen in the **GeoIP** database for any kind of provisioning activity.
+  If you typically do all provisioning from tools inside of your region, there should
+  be few false positives. If you are located in regions where the free version of
+  **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly
+  small countries in less economically powerful regions), this may be much less valuable
+  to you."
 references: []
 tags:
   analytic_story:
@@ -60,8 +59,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - sourceIPAddress
   security_domain: endpoint
diff --git a/detections/deprecated/aws_eks_kubernetes_cluster_sensitive_object_access.yml b/detections/deprecated/aws_eks_kubernetes_cluster_sensitive_object_access.yml
index 442a7afa48..5a64aff01e 100644
--- a/detections/deprecated/aws_eks_kubernetes_cluster_sensitive_object_access.yml
+++ b/detections/deprecated/aws_eks_kubernetes_cluster_sensitive_object_access.yml
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml b/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml
index 9b02fc2b56..f95bf12c90 100644
--- a/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml
+++ b/detections/deprecated/clients_connecting_to_multiple_dns_servers.yml
@@ -12,20 +12,16 @@ search: '| tstats `security_content_summariesonly` count, values(DNS.dest) AS de
   dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY
   by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 |
   `clients_connecting_to_multiple_dns_servers_filter`'
-how_to_implement: 'This search requires that DNS data is being ingested and populating
+how_to_implement: "This search requires that DNS data is being ingested and populating
   the `Network_Resolution` data model. This data can come from DNS logs or from solutions
-  that parse network traffic for this data, such as Splunk Stream or Bro.
-
-  This search produces fields (`dest_count`) that are not yet supported by ES Incident
-  Review and therefore cannot be viewed when a notable event is raised. These fields
-  contribute additional context to the notable. To see the additional metadata, add
-  the following fields, if not already present, to Incident Review - Event Attributes
-  (Configure > Incident Management > Incident Review Settings > Add New Entry):
-  
-  * **Label:** Distinct DNS Connections, **Field:** dest_count
-
-  Detailed documentation on how to create a new field within Incident Review may be
-  found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
+  that parse network traffic for this data, such as Splunk Stream or Bro.\nThis search
+  produces fields (`dest_count`) that are not yet supported by ES Incident Review
+  and therefore cannot be viewed when a notable event is raised. These fields contribute
+  additional context to the notable. To see the additional metadata, add the following
+  fields, if not already present, to Incident Review - Event Attributes (Configure
+  > Incident Management > Incident Review Settings > Add New Entry):\n* **Label:**
+  Distinct DNS Connections, **Field:** dest_count\nDetailed documentation on how to
+  create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
 known_false_positives: It's possible that an enterprise has more than five DNS servers
   that are configured in a round-robin rotation. Please customize the search, as appropriate.
 references: []
@@ -50,9 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.dest
-  - DNS.message_type
-  - DNS.src
   security_domain: network
diff --git a/detections/deprecated/cloud_network_access_control_list_deleted.yml b/detections/deprecated/cloud_network_access_control_list_deleted.yml
index a3fa25a4fc..bab2d3079c 100644
--- a/detections/deprecated/cloud_network_access_control_list_deleted.yml
+++ b/detections/deprecated/cloud_network_access_control_list_deleted.yml
@@ -38,14 +38,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.arn
-  - errorMessage
-  - errorCode
-  - userAgent
-  - src
-  - userName
-  - arn
   security_domain: network
diff --git a/detections/deprecated/correlation_by_repository_and_risk.yml b/detections/deprecated/correlation_by_repository_and_risk.yml
index 7daf2527d2..3a8ea2f99b 100644
--- a/detections/deprecated/correlation_by_repository_and_risk.yml
+++ b/detections/deprecated/correlation_by_repository_and_risk.yml
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: network
diff --git a/detections/deprecated/correlation_by_user_and_risk.yml b/detections/deprecated/correlation_by_user_and_risk.yml
index 2b92b67a45..ca29506e17 100644
--- a/detections/deprecated/correlation_by_user_and_risk.yml
+++ b/detections/deprecated/correlation_by_user_and_risk.yml
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: network
diff --git a/detections/deprecated/detect_activity_related_to_pass_the_hash_attacks.yml b/detections/deprecated/detect_activity_related_to_pass_the_hash_attacks.yml
index 00716f9540..22dbaf04a0 100644
--- a/detections/deprecated/detect_activity_related_to_pass_the_hash_attacks.yml
+++ b/detections/deprecated/detect_activity_related_to_pass_the_hash_attacks.yml
@@ -6,14 +6,17 @@ author: Bhavin Patel, Patrick Bareiss, Splunk
 status: deprecated
 type: Hunting
 description: This search looks for specific authentication events from the Windows
-  Security Event logs to detect potential attempts at using the Pass-the-Hash technique. This search is DEPRECATED as it is possible for event code 4624 to generate a high level of noise, as legitimate logon events may also trigger this event code. This can be especially true in environments with high levels of user activity, such as those with many concurrent logons or frequent logon attempts.
+  Security Event logs to detect potential attempts at using the Pass-the-Hash technique.
+  This search is DEPRECATED as it is possible for event code 4624 to generate a high
+  level of noise, as legitimate logon events may also trigger this event code. This
+  can be especially true in environments with high levels of user activity, such as
+  those with many concurrent logons or frequent logon attempts.
 data_source:
 - Windows Event Log Security 4624
-search: '`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) 
-  | fillnull 
-  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest 
-  | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` 
+search: '`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp
+  NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) | fillnull
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type,
+  WorkstationName, user, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `detect_activity_related_to_pass_the_hash_attacks_filter`'
 how_to_implement: To successfully implement this search, you must ingest your Windows
   Security Event logs and leverage the latest TA for Windows.
@@ -45,18 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - Logon_Process
-  - WorkstationName
-  - user
-  - dest
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
diff --git a/detections/deprecated/detect_api_activity_from_users_without_mfa.yml b/detections/deprecated/detect_api_activity_from_users_without_mfa.yml
index 5b1d007ce2..e3d816b6ca 100644
--- a/detections/deprecated/detect_api_activity_from_users_without_mfa.yml
+++ b/detections/deprecated/detect_api_activity_from_users_without_mfa.yml
@@ -18,24 +18,19 @@ search: '`cloudtrail` userIdentity.sessionContext.attributes.mfaAuthenticated=fa
   as eventName by userIdentity.arn userIdentity.type user | `security_content_ctime(firstTime)`  |
   `security_content_ctime(lastTime)` | `detect_api_activity_from_users_without_mfa_filter`'
 
-how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS 
-  (version 4.4.0 or later), then configure your AWS CloudTrail inputs. Leverage the support search `Create 
-  a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.
-
+how_to_implement: "You must install the AWS App for Splunk (version 5.1.0 or later)
+  and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail
+  inputs. Leverage the support search `Create a list of approved AWS service accounts`:
+  run it once every 30 days to create a list of service accounts and validate them.\n
   This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`)
   that are not yet supported by ES Incident Review and therefore cannot be viewed
   when a notable event is raised. These fields contribute additional context to the
   notable. To see the additional metadata, add the following fields, if not already
-  present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):
-  
-  * **Label:** AWS Event Name, **Field:** eventName
-
-  * **Label:** AWS User ARN, **Field:** userIdentity.arn
-
-  * **Label:** AWS User Type, **Field:** userIdentity.type
-
-  Detailed documentation on how to create a new field within Incident Review may be
-  found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
+  present, to Incident Review - Event Attributes (Configure > Incident Management
+  > Incident Review Settings > Add New Entry):\n* **Label:** AWS Event Name, **Field:**
+  eventName\n* **Label:** AWS User ARN, **Field:** userIdentity.arn\n* **Label:**
+  AWS User Type, **Field:** userIdentity.type\nDetailed documentation on how to create
+  a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
 known_false_positives: Many service accounts configured within an AWS infrastructure
   do not have multi factor authentication enabled. Please ignore the service accounts,
   if triggered and instead add them to the aws_service_accounts.csv file to fine tune
@@ -58,11 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.sessionContext.attributes.mfaAuthenticated
-  - eventName
-  - userIdentity.arn
-  - userIdentity.type
-  - user
   security_domain: network
diff --git a/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml b/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml
index 3523728fc7..81dd206ec7 100644
--- a/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml
+++ b/detections/deprecated/detect_aws_api_activities_from_unapproved_accounts.yml
@@ -17,29 +17,21 @@ search: '`cloudtrail` errorCode=success | rename userName as identity | search N
   min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName by
   user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `detect_aws_api_activities_from_unapproved_accounts_filter`'
-how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
+how_to_implement: "You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail
   inputs. You must also populate the `identity_lookup_expanded` lookup shipped with
   the Asset and Identity framework to be able to look up users in your identity table
-  in Enterprise Security (ES). Leverage the support search called "Create a list of
-  approved AWS service accounts": run it once every 30 days to create and validate
-  a list of service accounts.
-
-  This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet
-  supported by ES Incident Review and therefore cannot be viewed when a notable event
-  is raised. These fields contribute additional context to the notable. To see the
-  additional metadata, add the following fields, if not already present, to Incident
-  Review - Event Attributes (Configure > Incident Management > Incident Review Settings
-  > Add New Entry):
-  
-  * **Label:** AWS Event Name, **Field:** eventName
-
-  * **Label:** First Time, **Field:** firstTime
-
-  * **Label:** Last Time, **Field:** lastTime
-
-  Detailed documentation on how to create a new field within Incident Review may be
-  found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
+  in Enterprise Security (ES). Leverage the support search called \"Create a list
+  of approved AWS service accounts\": run it once every 30 days to create and validate
+  a list of service accounts.\nThis search produces fields (`eventName`,`firstTime`,`lastTime`)
+  that are not yet supported by ES Incident Review and therefore cannot be viewed
+  when a notable event is raised. These fields contribute additional context to the
+  notable. To see the additional metadata, add the following fields, if not already
+  present, to Incident Review - Event Attributes (Configure > Incident Management
+  > Incident Review Settings > Add New Entry):\n* **Label:** AWS Event Name, **Field:**
+  eventName\n* **Label:** First Time, **Field:** firstTime\n* **Label:** Last Time,
+  **Field:** lastTime\nDetailed documentation on how to create a new field within
+  Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
 known_false_positives: It's likely that you'll find activity detected by users/service
   accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv`
   file. If the user is a legitimate service account, update the `aws_service_accounts.csv`
@@ -63,10 +55,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - errorCode
-  - userName
-  - eventName
-  - user
   security_domain: access
diff --git a/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml b/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml
index 101896f340..8617b0ef99 100644
--- a/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml
+++ b/detections/deprecated/detect_dns_requests_to_phishing_sites_leveraging_evilginx2.yml
@@ -18,24 +18,19 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   [| tstats count `security_content_summariesonly` values(Web.url) as url from datamodel=Web.Web
   by Web.dest Web.site | rename "Web.*" as * | rex field=site ".*?(?<domain>[^./:]+\.(\S{2,3}|\S{2,3}.\S{2,3}))$"
   | table dest domain url] | table count src dest query answer domain url | `detect_dns_requests_to_phishing_sites_leveraging_evilginx2_filter`'
-how_to_implement: 'You need to ingest data from your DNS logs in the Network_Resolution
+how_to_implement: "You need to ingest data from your DNS logs in the Network_Resolution
   datamodel. Specifically you must ingest the domain that is being queried and the
-  IP of the host originating the request. Ideally, you should also be ingesting
-  the answer to the query and the query type. This approach allows you to also create
+  IP of the host originating the request. Ideally, you should also be ingesting the
+  answer to the query and the query type. This approach allows you to also create
   your own localized passive DNS capability which can aid you in future investigations.
   You will have to add legitimate domain names to the `legit_domains.csv` file shipped
-  with the app.
-  
-  **Splunk>Phantom Playbook Integration**
-  
-  If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain
-  Investigate` can be configured to run when any results are found by this detection
-  search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,
-  add the correct hostname to the "Phantom Instance" field in the Adaptive Response
+  with the app.\n**Splunk>Phantom Playbook Integration**\nIf Splunk>Phantom is also
+  configured in your environment, a Playbook called `Lets Encrypt Domain Investigate`
+  can be configured to run when any results are found by this detection search. To
+  use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`,
+  add the correct hostname to the \"Phantom Instance\" field in the Adaptive Response
   Actions when configuring this detection search, and set the corresponding Playbook
-  to active.
-  
-  (Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`)'
+  to active.\n(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`)"
 known_false_positives: If a known good domain is not listed in the legit_domains.csv
   file, then the search could give you false postives. Please update that lookup file
   to filter out DNS requests to legitimate domains.
@@ -58,11 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.answer
-  - DNS.dest
-  - DNS.src
-  - DNS.query
-  - host
   security_domain: network
diff --git a/detections/deprecated/detect_long_dns_txt_record_response.yml b/detections/deprecated/detect_long_dns_txt_record_response.yml
index a02ceba4ca..4531257bbd 100644
--- a/detections/deprecated/detect_long_dns_txt_record_response.yml
+++ b/detections/deprecated/detect_long_dns_txt_record_response.yml
@@ -48,11 +48,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.message_type
-  - DNS.record_type
-  - DNS.src
-  - DNS.dest
-  - DNS.answer
   security_domain: network
diff --git a/detections/deprecated/detect_mimikatz_using_loaded_images.yml b/detections/deprecated/detect_mimikatz_using_loaded_images.yml
index 26259ab821..045a72d4d2 100644
--- a/detections/deprecated/detect_mimikatz_using_loaded_images.yml
+++ b/detections/deprecated/detect_mimikatz_using_loaded_images.yml
@@ -6,13 +6,14 @@ author: Patrick Bareiss, Splunk
 status: deprecated
 type: TTP
 description: This search looks for reading loaded Images unique to credential dumping
-  with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.
+  with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon
+  Event Code.
 data_source:
 - Sysmon EventID 7
 search: '`sysmon` EventCode=7 | stats values(ImageLoaded) as ImageLoaded values(ProcessId)
   as ProcessId by dest, Image | search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll
-  ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `detect_mimikatz_using_loaded_images_filter`'
+  ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `detect_mimikatz_using_loaded_images_filter`'
 how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which
   includes EventCode 7 with powershell.exe. This search uses an input macro named
   `sysmon`. We strongly recommend that you specify your environment-specific configurations
@@ -55,17 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ImageLoaded
-  - ProcessId
-  - dest
-  - Image
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml b/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml
index 81e8da74c5..d1dcffbf54 100644
--- a/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml
+++ b/detections/deprecated/detect_mimikatz_via_powershell_and_eventcode_4703.yml
@@ -44,11 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - signature_id
-  - Process_Name
-  - Message
-  - dest
-  - Process_ID
   security_domain: access
diff --git a/detections/deprecated/detect_new_api_calls_from_user_roles.yml b/detections/deprecated/detect_new_api_calls_from_user_roles.yml
index d1631f0883..446e58fb51 100644
--- a/detections/deprecated/detect_new_api_calls_from_user_roles.yml
+++ b/detections/deprecated/detect_new_api_calls_from_user_roles.yml
@@ -45,11 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType
-  - errorCode
-  - userIdentity.type
-  - userName
-  - eventName
   security_domain: endpoint
diff --git a/detections/deprecated/detect_new_user_aws_console_login.yml b/detections/deprecated/detect_new_user_aws_console_login.yml
index 655b952eb8..7ca31decb9 100644
--- a/detections/deprecated/detect_new_user_aws_console_login.yml
+++ b/detections/deprecated/detect_new_user_aws_console_login.yml
@@ -45,8 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - userIdentity.arn
   security_domain: network
diff --git a/detections/deprecated/detect_spike_in_aws_api_activity.yml b/detections/deprecated/detect_spike_in_aws_api_activity.yml
index 56424746f5..440ceaa786 100644
--- a/detections/deprecated/detect_spike_in_aws_api_activity.yml
+++ b/detections/deprecated/detect_spike_in_aws_api_activity.yml
@@ -24,30 +24,23 @@ search: '`cloudtrail` eventType=AwsApiCall [search `cloudtrail` eventType=AwsApi
   | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName)
   as eventName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user
   | `detect_spike_in_aws_api_activity_filter`'
-how_to_implement: 'You must install the AWS App for Splunk (version 5.1.0 or later)
+how_to_implement: "You must install the AWS App for Splunk (version 5.1.0 or later)
   and Splunk Add-on for AWS (version 4.4.0 or later), then configure your AWS CloudTrail
   inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit
   your environment. The `dataPointThreshold` variable is the minimum number of data
   points required to have a statistically significant amount of data to determine.
   The `deviationThreshold` variable is the number of standard deviations away from
-  the mean that the value must be to be considered a spike.
-
-  This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`)
-  that are not yet supported by ES Incident Review and therefore cannot be viewed
-  when a notable event is raised. These fields contribute additional context to the
-  notable. To see the additional metadata, add the following fields, if not already
-  present, to Incident Review - Event Attributes (Configure > Incident Management
-  > Incident Review Settings > Add New Entry):
-  
-  * **Label:** AWS Event Name, **Field:** eventName
-  
-  * **Label:** Number of API Calls, **Field:** numberOfApiCalls
-
-  * **Label:** Unique API Calls, **Field:** uniqueApisCalled
-
-  Detailed documentation on how to create a new field within Incident Review may be
-  found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-known_false_positives: 'None.'
+  the mean that the value must be to be considered a spike.\nThis search produces
+  fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported
+  by ES Incident Review and therefore cannot be viewed when a notable event is raised.
+  These fields contribute additional context to the notable. To see the additional
+  metadata, add the following fields, if not already present, to Incident Review -
+  Event Attributes (Configure > Incident Management > Incident Review Settings > Add
+  New Entry):\n* **Label:** AWS Event Name, **Field:** eventName\n* **Label:** Number
+  of API Calls, **Field:** numberOfApiCalls\n* **Label:** Unique API Calls, **Field:**
+  uniqueApisCalled\nDetailed documentation on how to create a new field within Incident
+  Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
+known_false_positives: None.
 references: []
 tags:
   analytic_story:
@@ -67,8 +60,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType
-  - userIdentity.arn
   security_domain: network
diff --git a/detections/deprecated/detect_spike_in_network_acl_activity.yml b/detections/deprecated/detect_spike_in_network_acl_activity.yml
index 43ad1d6bcf..c730bd4802 100644
--- a/detections/deprecated/detect_spike_in_network_acl_activity.yml
+++ b/detections/deprecated/detect_spike_in_network_acl_activity.yml
@@ -54,7 +54,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - userIdentity.arn
   security_domain: network
diff --git a/detections/deprecated/detect_spike_in_security_group_activity.yml b/detections/deprecated/detect_spike_in_security_group_activity.yml
index d998353952..77f94de111 100644
--- a/detections/deprecated/detect_spike_in_security_group_activity.yml
+++ b/detections/deprecated/detect_spike_in_security_group_activity.yml
@@ -55,7 +55,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - serIdentity.arn
   security_domain: network
diff --git a/detections/deprecated/detect_usb_device_insertion.yml b/detections/deprecated/detect_usb_device_insertion.yml
index 82b74d05c9..0c5f965db3 100644
--- a/detections/deprecated/detect_usb_device_insertion.yml
+++ b/detections/deprecated/detect_usb_device_insertion.yml
@@ -42,10 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.result
-  - All_Changes.result_id
-  - All_Changes.src_priority
-  - All_Changes.dest
   security_domain: endpoint
diff --git a/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml b/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml
index 5e4f3972ba..3773477950 100644
--- a/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml
+++ b/detections/deprecated/detect_web_traffic_to_dynamic_domain_providers.yml
@@ -11,25 +11,21 @@ search: '| tstats `security_content_summariesonly` count values(Web.url) as url
   as firstTime from datamodel=Web where Web.status=200 by Web.src Web.dest Web.status
   | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `dynamic_dns_web_traffic`
   | `detect_web_traffic_to_dynamic_domain_providers_filter`'
-how_to_implement: 'This search requires you to be ingesting web-traffic logs. You
+how_to_implement: "This search requires you to be ingesting web-traffic logs. You
   can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis
   tool, such as Bro or Splunk Stream. The web data model must contain the URL being
   requested, the IP address of the host initiating the request, and the destination
   IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`,
   which contains a non-exhaustive list of dynamic DNS providers. Consider periodically
-  updating this local lookup file with new domains.
-
-  This search produces fields (`isDynDNS`) that are not yet supported by ES Incident
-  Review and therefore cannot be viewed when a notable event is raised. These fields
-  contribute additional context to the notable. To see the additional metadata, add
-  the following fields, if not already present, to Incident Review - Event Attributes
-  (Configure > Incident Management > Incident Review Settings > Add New Entry):
-  
-  * **Label:** IsDynamicDNS, **Field:** isDynDNS
-
-  Detailed documentation on how to create a new field within Incident Review may be
-  found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-  Deprecated because duplicate.'
+  updating this local lookup file with new domains.\nThis search produces fields (`isDynDNS`)
+  that are not yet supported by ES Incident Review and therefore cannot be viewed
+  when a notable event is raised. These fields contribute additional context to the
+  notable. To see the additional metadata, add the following fields, if not already
+  present, to Incident Review - Event Attributes (Configure > Incident Management
+  > Incident Review Settings > Add New Entry):\n* **Label:** IsDynamicDNS, **Field:**
+  isDynDNS\nDetailed documentation on how to create a new field within Incident Review
+  may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
+  Deprecated because duplicate."
 known_false_positives: It is possible that list of dynamic DNS providers is outdated
   and/or that the URL being requested is legitimate.
 references: []
@@ -51,10 +47,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.url
-  - Web.status
-  - Web.src
-  - Web.dest
   security_domain: network
diff --git a/detections/deprecated/detection_of_dns_tunnels.yml b/detections/deprecated/detection_of_dns_tunnels.yml
index 37cf95b31c..f475b24dde 100644
--- a/detections/deprecated/detection_of_dns_tunnels.yml
+++ b/detections/deprecated/detection_of_dns_tunnels.yml
@@ -5,18 +5,16 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: deprecated
 type: TTP
-description: 'This search is used to detect DNS tunneling, by calculating the sum
+description: "This search is used to detect DNS tunneling, by calculating the sum
   of the length of DNS queries and DNS answers. The search also filters out potential
   false positives by filtering out queries made to internal systems and the queries
   originating from internal DNS, Web, and Email servers. Endpoints using DNS as a
   method of transmission for data exfiltration, Command And Control, or evasion of
   security controls can often be detected by noting an unusually large volume of DNS
-  traffic.
-
-  NOTE:Deprecated because existing detection is doing the same. This detection is
-  replaced with two other variations, if you are using MLTK then you can use this
+  traffic.\nNOTE:Deprecated because existing detection is doing the same. This detection
+  is replaced with two other variations, if you are using MLTK then you can use this
   search `ESCU - DNS Query Length Outliers - MLTK - Rule` or use the standard deviation
-  version `ESCU - DNS Query Length With High Standard Deviation - Rule`, as an alternantive.'
+  version `ESCU - DNS Query Length With High Standard Deviation - Rule`, as an alternantive."
 data_source: []
 search: '| tstats `security_content_summariesonly` dc("DNS.query") as count  from
   datamodel=Network_Resolution  where nodename=DNS "DNS.message_type"="QUERY" NOT
@@ -64,10 +62,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.query
-  - DNS.message_type
-  - DNS.src_category
-  - DNS.src
   security_domain: network
diff --git a/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml b/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml
index 452db42510..9c716f61e0 100644
--- a/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml
+++ b/detections/deprecated/dns_query_requests_resolved_by_unauthorized_dns_servers.yml
@@ -40,10 +40,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.dest_category
-  - DNS.src_category
-  - DNS.src
-  - DNS.dest
   security_domain: network
diff --git a/detections/deprecated/dns_record_changed.yml b/detections/deprecated/dns_record_changed.yml
index c80e1ad5a0..cfd82c32f5 100644
--- a/detections/deprecated/dns_record_changed.yml
+++ b/detections/deprecated/dns_record_changed.yml
@@ -17,23 +17,18 @@ search: '| inputlookup discovered_dns_records | rename answer as discovered_answ
   | makemv delim=" " answer |  makemv delim=" " type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer
   | makemv current_answer  | mvexpand current_answer | makemv discovered_answer |
   eval n=mvfind(discovered_answer, current_answer) | where isnull(n) | `dns_record_changed_filter`'
-how_to_implement: 'To successfully implement this search you will need to ensure that
+how_to_implement: "To successfully implement this search you will need to ensure that
   DNS data is populating the `Network_Resolution` data model. It also requires that
   the `discover_dns_record` lookup table be populated by the included support search
-  "Discover DNS record".
-  
-  **Splunk>Phantom Playbook Integration**
-  
-  If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment"
-  can be configured to run when any results are found by this detection search.
-  The playbook takes in the DNS record changed and uses Geoip, whois, Censys and
-  PassiveTotal to detect if DNS issuers changed. To use this integration, install
-  the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the
-  correct hostname to the \"Phantom Instance\" field in the Adaptive Response Actions
-  when configuring this detection search, and set the corresponding Playbook to
-  active.
-  
-  (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`)'
+  \"Discover DNS record\".\n**Splunk>Phantom Playbook Integration**\nIf Splunk>Phantom
+  is also configured in your environment, a Playbook called \"DNS Hijack Enrichment\"\
+  \ can be configured to run when any results are found by this detection search.
+  The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal
+  to detect if DNS issuers changed. To use this integration, install the Phantom App
+  for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to
+  the \\\"Phantom Instance\\\" field in the Adaptive Response Actions when configuring
+  this detection search, and set the corresponding Playbook to active.\n(Playbook
+  Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`)"
 known_false_positives: Legitimate DNS changes can be detected in this search. Investigate,
   verify and update the list of provided current answers for the domains in question
   as appropriate.
@@ -56,11 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.record_type
-  - DNS.answer
-  - DNS.src
-  - DNS.message_type
-  - DNS.query
   security_domain: network
diff --git a/detections/deprecated/dump_lsass_via_procdump_rename.yml b/detections/deprecated/dump_lsass_via_procdump_rename.yml
index eafafd68f9..92db3ca2d0 100644
--- a/detections/deprecated/dump_lsass_via_procdump_rename.yml
+++ b/detections/deprecated/dump_lsass_via_procdump_rename.yml
@@ -5,21 +5,20 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: deprecated
 type: Hunting
-description: 'Detect a renamed instance of procdump.exe dumping the lsass process.
+description: "Detect a renamed instance of procdump.exe dumping the lsass process.
   This query looks for both -mm and -ma usage. -mm will produce a mini dump file and
   -ma will write a dump file with all process memory. Both are highly suspect and
-  should be reviewed. Modify the query as needed.
-
-  During triage, confirm this is procdump.exe executing. If it is the first time a
-  Sysinternals utility has been ran, it is possible there will be a -accepteula on
-  the command line. Review other endpoint data sources for cross process (injection)
-  into lsass.exe.'
+  should be reviewed. Modify the query as needed.\nDuring triage, confirm this is
+  procdump.exe executing. If it is the first time a Sysinternals utility has been
+  ran, it is possible there will be a -accepteula on the command line. Review other
+  endpoint data sources for cross process (injection) into lsass.exe."
 data_source:
 - Sysmon EventID 1
 search: '`sysmon` OriginalFileName=procdump  process_name!=procdump*.exe  EventID=1
-  (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* |  stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name,
-  process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter`'
+  (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* |  stats count min(_time)
+  as firstTime max(_time) as lastTime by dest, parent_process_name, process_name,
+  OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `dump_lsass_via_procdump_rename_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node.
@@ -57,12 +56,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OriginalFileName
-  - process_name
-  - EventID
-  - CommandLine
-  - dest
-  - parent_process_name
   security_domain: endpoint
diff --git a/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml b/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml
index ff3300c96e..b390bb4a2b 100644
--- a/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml
+++ b/detections/deprecated/ec2_instance_modified_with_previously_unseen_user.yml
@@ -44,8 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - errorCode
-  - userIdentity.arn
   security_domain: endpoint
diff --git a/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml b/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml
index e310e0f593..f97d69ec7d 100644
--- a/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml
+++ b/detections/deprecated/ec2_instance_started_in_previously_unseen_region.yml
@@ -43,7 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - awsRegion
   security_domain: network
diff --git a/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml b/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml
index 84a8377b88..25841d0aa4 100644
--- a/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml
+++ b/detections/deprecated/ec2_instance_started_with_previously_unseen_ami.yml
@@ -44,9 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - requestParameters.instancesSet.items{}.imageId
   security_domain: endpoint
diff --git a/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml b/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml
index 08a5c39246..53dcace1ed 100644
--- a/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml
+++ b/detections/deprecated/ec2_instance_started_with_previously_unseen_instance_type.yml
@@ -44,9 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - requestParameters.instanceType
   security_domain: endpoint
diff --git a/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml b/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml
index f93ac86b86..99f1ac59af 100644
--- a/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml
+++ b/detections/deprecated/ec2_instance_started_with_previously_unseen_user.yml
@@ -45,9 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventName
-  - errorCode
-  - userIdentity.arn
   security_domain: endpoint
diff --git a/detections/deprecated/execution_of_file_with_spaces_before_extension.yml b/detections/deprecated/execution_of_file_with_spaces_before_extension.yml
index 9fea7507cf..531d0635c9 100644
--- a/detections/deprecated/execution_of_file_with_spaces_before_extension.yml
+++ b/detections/deprecated/execution_of_file_with_spaces_before_extension.yml
@@ -45,11 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_path
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_name
   security_domain: endpoint
diff --git a/detections/deprecated/extended_period_without_successful_netbackup_backups.yml b/detections/deprecated/extended_period_without_successful_netbackup_backups.yml
index 5087770ffd..3d94b88c94 100644
--- a/detections/deprecated/extended_period_without_successful_netbackup_backups.yml
+++ b/detections/deprecated/extended_period_without_successful_netbackup_backups.yml
@@ -36,8 +36,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - MESSAGE
-  - COMPUTERNAME
   security_domain: endpoint
diff --git a/detections/deprecated/first_time_seen_command_line_argument.yml b/detections/deprecated/first_time_seen_command_line_argument.yml
index 2d49c269ce..fc8dc11c23 100644
--- a/detections/deprecated/first_time_seen_command_line_argument.yml
+++ b/detections/deprecated/first_time_seen_command_line_argument.yml
@@ -58,10 +58,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.dest
   security_domain: endpoint
diff --git a/detections/deprecated/gcp_detect_accounts_with_high_risk_roles_by_project.yml b/detections/deprecated/gcp_detect_accounts_with_high_risk_roles_by_project.yml
index 245e501758..d3d5c5fa6f 100644
--- a/detections/deprecated/gcp_detect_accounts_with_high_risk_roles_by_project.yml
+++ b/detections/deprecated/gcp_detect_accounts_with_high_risk_roles_by_project.yml
@@ -43,12 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - data.protoPayload.request.policy.bindings{}.role
-  - data.resource.type data.protoPayload.authenticationInfo.principalEmail
-  - data.protoPayload.authorizationInfo{}.permission
-  - data.protoPayload.authorizationInfo{}.resource
-  - data.protoPayload.response.bindings{}.role
-  - data.protoPayload.response.bindings{}.members{}
   security_domain: threat
diff --git a/detections/deprecated/gcp_detect_high_risk_permissions_by_resource_and_account.yml b/detections/deprecated/gcp_detect_high_risk_permissions_by_resource_and_account.yml
index 036c33c96b..75d6dba905 100644
--- a/detections/deprecated/gcp_detect_high_risk_permissions_by_resource_and_account.yml
+++ b/detections/deprecated/gcp_detect_high_risk_permissions_by_resource_and_account.yml
@@ -42,12 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - data.protoPayload.authorizationInfo{}.permission
-  - data.protoPayload.requestMetadata.callerIp
-  - data.protoPayload.authenticationInfo.principalEmail
-  - data.protoPayload.authorizationInfo{}.permission
-  - data.protoPayload.response.bindings{}.members{}
-  - data.resource.labels.project_id
   security_domain: threat
diff --git a/detections/deprecated/gcp_detect_oauth_token_abuse.yml b/detections/deprecated/gcp_detect_oauth_token_abuse.yml
index 96a9506193..b1ccf405cd 100644
--- a/detections/deprecated/gcp_detect_oauth_token_abuse.yml
+++ b/detections/deprecated/gcp_detect_oauth_token_abuse.yml
@@ -38,6 +38,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/gcp_kubernetes_cluster_scan_detection.yml b/detections/deprecated/gcp_kubernetes_cluster_scan_detection.yml
index 187f63a006..5b88824262 100644
--- a/detections/deprecated/gcp_kubernetes_cluster_scan_detection.yml
+++ b/detections/deprecated/gcp_kubernetes_cluster_scan_detection.yml
@@ -42,6 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/identify_new_user_accounts.yml b/detections/deprecated/identify_new_user_accounts.yml
index ded795bbb5..a577cc2a76 100644
--- a/detections/deprecated/identify_new_user_accounts.yml
+++ b/detections/deprecated/identify_new_user_accounts.yml
@@ -37,6 +37,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: access
diff --git a/detections/deprecated/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml b/detections/deprecated/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml
index 4145d19dae..6a8b0ae501 100644
--- a/detections/deprecated/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml
+++ b/detections/deprecated/kubernetes_aws_detect_most_active_service_accounts_by_pod.yml
@@ -32,6 +32,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_aws_detect_rbac_authorization_by_account.yml b/detections/deprecated/kubernetes_aws_detect_rbac_authorization_by_account.yml
index 8a75adab26..6ba012c71f 100644
--- a/detections/deprecated/kubernetes_aws_detect_rbac_authorization_by_account.yml
+++ b/detections/deprecated/kubernetes_aws_detect_rbac_authorization_by_account.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  security_domain: threat
\ No newline at end of file
+  security_domain: threat
diff --git a/detections/deprecated/kubernetes_aws_detect_sensitive_role_access.yml b/detections/deprecated/kubernetes_aws_detect_sensitive_role_access.yml
index 1416f7aa86..b32557b3df 100644
--- a/detections/deprecated/kubernetes_aws_detect_sensitive_role_access.yml
+++ b/detections/deprecated/kubernetes_aws_detect_sensitive_role_access.yml
@@ -14,8 +14,8 @@ search: '`aws_cloudwatchlogs_eks` objectRef.resource=clusterroles OR clusterrole
   user.groups{} |`kubernetes_aws_detect_sensitive_role_access_filter`'
 how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This
   search works with cloudwatch logs.
-known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
-  however source IP, namespace and user group may indicate possible malicious use.'
+known_false_positives: Sensitive role resource access is necessary for cluster operation,
+  however source IP, namespace and user group may indicate possible malicious use.
 references: []
 tags:
   analytic_story:
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml b/detections/deprecated/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml
index ed4367a905..a635334af8 100644
--- a/detections/deprecated/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml
+++ b/detections/deprecated/kubernetes_aws_detect_service_accounts_forbidden_failure_access.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_active_service_accounts_by_pod_namespace.yml b/detections/deprecated/kubernetes_azure_active_service_accounts_by_pod_namespace.yml
index 00e5f375c9..eb71b4c071 100644
--- a/detections/deprecated/kubernetes_azure_active_service_accounts_by_pod_namespace.yml
+++ b/detections/deprecated/kubernetes_azure_active_service_accounts_by_pod_namespace.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_detect_rbac_authorization_by_account.yml b/detections/deprecated/kubernetes_azure_detect_rbac_authorization_by_account.yml
index b2c28dbcfd..63c550cf1c 100644
--- a/detections/deprecated/kubernetes_azure_detect_rbac_authorization_by_account.yml
+++ b/detections/deprecated/kubernetes_azure_detect_rbac_authorization_by_account.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_detect_sensitive_object_access.yml b/detections/deprecated/kubernetes_azure_detect_sensitive_object_access.yml
index a5c11afeb1..14ebe9d148 100644
--- a/detections/deprecated/kubernetes_azure_detect_sensitive_object_access.yml
+++ b/detections/deprecated/kubernetes_azure_detect_sensitive_object_access.yml
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_detect_sensitive_role_access.yml b/detections/deprecated/kubernetes_azure_detect_sensitive_role_access.yml
index 1d80320e46..e23dd3f67f 100644
--- a/detections/deprecated/kubernetes_azure_detect_sensitive_role_access.yml
+++ b/detections/deprecated/kubernetes_azure_detect_sensitive_role_access.yml
@@ -14,8 +14,8 @@ search: '`kubernetes_azure` category=kube-audit | spath input=properties.log| se
   | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter`'
 how_to_implement: You must install the Add-on for Microsoft Cloud Services and Configure
   Kube-Audit data diagnostics
-known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
-  however source IP, namespace and user group may indicate possible malicious use.'
+known_false_positives: Sensitive role resource access is necessary for cluster operation,
+  however source IP, namespace and user group may indicate possible malicious use.
 references: []
 tags:
   analytic_story:
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml b/detections/deprecated/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml
index 96c0b75a57..ef1620a8c9 100644
--- a/detections/deprecated/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml
+++ b/detections/deprecated/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_detect_suspicious_kubectl_calls.yml b/detections/deprecated/kubernetes_azure_detect_suspicious_kubectl_calls.yml
index 9a06dad276..6e635d3185 100644
--- a/detections/deprecated/kubernetes_azure_detect_suspicious_kubectl_calls.yml
+++ b/detections/deprecated/kubernetes_azure_detect_suspicious_kubectl_calls.yml
@@ -36,6 +36,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_pod_scan_fingerprint.yml b/detections/deprecated/kubernetes_azure_pod_scan_fingerprint.yml
index 3b57b7ec9c..09705ab32d 100644
--- a/detections/deprecated/kubernetes_azure_pod_scan_fingerprint.yml
+++ b/detections/deprecated/kubernetes_azure_pod_scan_fingerprint.yml
@@ -33,6 +33,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_azure_scan_fingerprint.yml b/detections/deprecated/kubernetes_azure_scan_fingerprint.yml
index de319eab32..d2251c0eb1 100644
--- a/detections/deprecated/kubernetes_azure_scan_fingerprint.yml
+++ b/detections/deprecated/kubernetes_azure_scan_fingerprint.yml
@@ -35,6 +35,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml b/detections/deprecated/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml
index 33aa56960e..68007ab052 100644
--- a/detections/deprecated/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml
+++ b/detections/deprecated/kubernetes_gcp_detect_most_active_service_accounts_by_pod.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_gcp_detect_rbac_authorizations_by_account.yml b/detections/deprecated/kubernetes_gcp_detect_rbac_authorizations_by_account.yml
index 6b759872cd..02214a3895 100644
--- a/detections/deprecated/kubernetes_gcp_detect_rbac_authorizations_by_account.yml
+++ b/detections/deprecated/kubernetes_gcp_detect_rbac_authorizations_by_account.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_gcp_detect_sensitive_object_access.yml b/detections/deprecated/kubernetes_gcp_detect_sensitive_object_access.yml
index 7372ad6241..3f9f592e48 100644
--- a/detections/deprecated/kubernetes_gcp_detect_sensitive_object_access.yml
+++ b/detections/deprecated/kubernetes_gcp_detect_sensitive_object_access.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_gcp_detect_sensitive_role_access.yml b/detections/deprecated/kubernetes_gcp_detect_sensitive_role_access.yml
index ad75ae7237..27ead3ce83 100644
--- a/detections/deprecated/kubernetes_gcp_detect_sensitive_role_access.yml
+++ b/detections/deprecated/kubernetes_gcp_detect_sensitive_role_access.yml
@@ -14,9 +14,9 @@ search: '`google_gcp_pubsub_message` data.labels.authorization.k8s.io/reason=Clu
   | dedup src_ip src_user |`kubernetes_gcp_detect_sensitive_role_access_filter`'
 how_to_implement: You must install splunk add on for GCP. This search works with pubsub
   messaging servicelogs.
-known_false_positives: 'Sensitive role resource access is necessary for cluster operation,
+known_false_positives: Sensitive role resource access is necessary for cluster operation,
   however source IP, user agent, decision and reason may indicate possible malicious
-  use.'
+  use.
 references: []
 tags:
   analytic_story:
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml b/detections/deprecated/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml
index b1c84d451e..5129ae9455 100644
--- a/detections/deprecated/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml
+++ b/detections/deprecated/kubernetes_gcp_detect_service_accounts_forbidden_failure_access.yml
@@ -36,6 +36,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/kubernetes_gcp_detect_suspicious_kubectl_calls.yml b/detections/deprecated/kubernetes_gcp_detect_suspicious_kubectl_calls.yml
index bdbee2761d..c424132d92 100644
--- a/detections/deprecated/kubernetes_gcp_detect_suspicious_kubectl_calls.yml
+++ b/detections/deprecated/kubernetes_gcp_detect_suspicious_kubectl_calls.yml
@@ -35,6 +35,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/monitor_dns_for_brand_abuse.yml b/detections/deprecated/monitor_dns_for_brand_abuse.yml
index e4b52ee53a..e33f9439ea 100644
--- a/detections/deprecated/monitor_dns_for_brand_abuse.yml
+++ b/detections/deprecated/monitor_dns_for_brand_abuse.yml
@@ -41,6 +41,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: network
diff --git a/detections/deprecated/multiple_okta_users_with_invalid_credentials_from_the_same_ip.yml b/detections/deprecated/multiple_okta_users_with_invalid_credentials_from_the_same_ip.yml
index 70e38437f5..37fab7a112 100644
--- a/detections/deprecated/multiple_okta_users_with_invalid_credentials_from_the_same_ip.yml
+++ b/detections/deprecated/multiple_okta_users_with_invalid_credentials_from_the_same_ip.yml
@@ -5,12 +5,11 @@ date: '2024-10-17'
 author: Michael Haag, Mauricio Velazco, Rico Valdez, Splunk
 status: deprecated
 type: TTP
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `Okta Multiple Users Failing To Authenticate From Ip`.
-  This analytic identifies multiple failed logon attempts from
-  a single IP in a short period of time. Use this analytic to identify patterns of suspicious logins from a
-  single source and filter as needed or use this to drive tuning for higher fidelity
-  analytics.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `Okta Multiple Users Failing To Authenticate From Ip`. This analytic identifies
+  multiple failed logon attempts from a single IP in a short period of time. Use this
+  analytic to identify patterns of suspicious logins from a single source and filter
+  as needed or use this to drive tuning for higher fidelity analytics.'
 data_source: []
 search: '`okta` eventType=user.session.start outcome.result=FAILURE | rename client.geographicalContext.country
   as country, client.geographicalContext.state as state, client.geographicalContext.city
@@ -48,21 +47,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - outcome.reason
-  - client.geographicalContext.country
-  - client.geographicalContext.state
-  - client.geographicalContext.city
-  - user
-  - src_ip
-  - displayMessage
-  - eventType
-  - outcome.result
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/okta_multiple_users_from_ip/okta_multiple_users_from_ip.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/okta_multiple_users_from_ip/okta_multiple_users_from_ip.log
     source: Okta
-    sourcetype: OktaIM2:log
\ No newline at end of file
+    sourcetype: OktaIM2:log
diff --git a/detections/deprecated/o365_suspicious_admin_email_forwarding.yml b/detections/deprecated/o365_suspicious_admin_email_forwarding.yml
index ea3c6933fb..dcbb80b326 100644
--- a/detections/deprecated/o365_suspicious_admin_email_forwarding.yml
+++ b/detections/deprecated/o365_suspicious_admin_email_forwarding.yml
@@ -5,10 +5,9 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: deprecated
 type: Anomaly
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `O365 Mailbox Email Forwarding Enabled`.
-  This search detects when an admin configured a forwarding rule for multiple
-  mailboxes to the same destination.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `O365 Mailbox Email Forwarding Enabled`. This search detects when an admin
+  configured a forwarding rule for multiple mailboxes to the same destination.'
 data_source: []
 search: '`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters
   | rename Identity AS src_user | search ForwardingAddress=* | stats dc(src_user)
@@ -40,14 +39,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Parameters
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/deprecated/o365_suspicious_rights_delegation.yml b/detections/deprecated/o365_suspicious_rights_delegation.yml
index ae97726e02..3756c55ded 100644
--- a/detections/deprecated/o365_suspicious_rights_delegation.yml
+++ b/detections/deprecated/o365_suspicious_rights_delegation.yml
@@ -5,16 +5,40 @@ date: '2024-10-17'
 author: Patrick Bareiss, Mauricio Velazco, Splunk
 status: deprecated
 type: TTP
-description:  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `O365 Elevated Mailbox Permission Assigned`.
-  This analytic identifies instances where potentially suspicious rights are delegated within the Office 365 environment. Specifically, it detects when a user is granted FullAccess, SendAs, or SendOnBehalf permissions on another users mailbox. Such permissions can allow a user to access, send emails from, or send emails on behalf of the target mailbox. The detection leverages O365 audit logs, focusing on the Add-MailboxPermission operation. By parsing the parameters of this operation, the analytic filters for events where FullAccess, SendAs, or SendOnBehalf rights are granted. It then aggregates this data to capture the source user (who was granted the permissions), the destination user (whose mailbox was affected), the specific operation, and the type of access rights granted. Delegating mailbox rights, especially those as powerful as FullAccess, can pose significant security risks. While there are legitimate scenarios for these permissions, such as an executive assistant needing access to an executives mailbox, there are also malicious scenarios where an attacker or a compromised insider might grant themselves unauthorized access to sensitive mailboxes. Monitoring for these permissions changes is crucial to detect potential insider threats, compromised accounts, or other malicious activities.If the detection is a true positive, it indicates that a user has been granted potentially high-risk permissions on another users mailbox. This could lead to unauthorized access to sensitive emails, impersonation through sending emails as or on behalf of the mailbox owner, or data manipulation by altering or deleting emails. Immediate investigation is required to validate the legitimacy of the permission change and to assess the potential risks associated with the granted access.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `O365 Elevated Mailbox Permission Assigned`. This analytic identifies instances
+  where potentially suspicious rights are delegated within the Office 365 environment.
+  Specifically, it detects when a user is granted FullAccess, SendAs, or SendOnBehalf
+  permissions on another users mailbox. Such permissions can allow a user to access,
+  send emails from, or send emails on behalf of the target mailbox. The detection
+  leverages O365 audit logs, focusing on the Add-MailboxPermission operation. By parsing
+  the parameters of this operation, the analytic filters for events where FullAccess,
+  SendAs, or SendOnBehalf rights are granted. It then aggregates this data to capture
+  the source user (who was granted the permissions), the destination user (whose mailbox
+  was affected), the specific operation, and the type of access rights granted. Delegating
+  mailbox rights, especially those as powerful as FullAccess, can pose significant
+  security risks. While there are legitimate scenarios for these permissions, such
+  as an executive assistant needing access to an executives mailbox, there are also
+  malicious scenarios where an attacker or a compromised insider might grant themselves
+  unauthorized access to sensitive mailboxes. Monitoring for these permissions changes
+  is crucial to detect potential insider threats, compromised accounts, or other malicious
+  activities.If the detection is a true positive, it indicates that a user has been
+  granted potentially high-risk permissions on another users mailbox. This could lead
+  to unauthorized access to sensitive emails, impersonation through sending emails
+  as or on behalf of the mailbox owner, or data manipulation by altering or deleting
+  emails. Immediate investigation is required to validate the legitimacy of the permission
+  change and to assess the potential risks associated with the granted access.'
 data_source: []
 search: '`o365_management_activity` Operation=Add-MailboxPermission | spath input=Parameters
   | rename User AS src_user, Identity AS dest_user | search AccessRights=FullAccess
   OR AccessRights=SendAs OR AccessRights=SendOnBehalf | stats count earliest(_time)
   as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights
   |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`o365_suspicious_rights_delegation_filter`'
-how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events.
-known_false_positives: While there are legitimate scenarios for these permissions, such as an executive assistant needing access to an executive's mailbox, there are also malicious scenarios. Investigate and filter as needed.
+how_to_implement: You must install the Splunk Microsoft Office 365 Add-on and ingest
+  Office 365 management activity events.
+known_false_positives: While there are legitimate scenarios for these permissions,
+  such as an executive assistant needing access to an executive's mailbox, there are
+  also malicious scenarios. Investigate and filter as needed.
 references:
 - https://www.mandiant.com/resources/blog/remediation-and-hardening-strategies-for-microsoft-365-to-defend-against-unc2452
 - https://attack.mitre.org/techniques/T1098/002/
@@ -41,14 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Parameters
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/deprecated/o365_suspicious_user_email_forwarding.yml b/detections/deprecated/o365_suspicious_user_email_forwarding.yml
index aa8b76403e..e0914ce2dd 100644
--- a/detections/deprecated/o365_suspicious_user_email_forwarding.yml
+++ b/detections/deprecated/o365_suspicious_user_email_forwarding.yml
@@ -5,9 +5,33 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: deprecated
 type: Anomaly
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `O365 Mailbox Email Forwarding Enabled`.
-  The following analytic detects when multiple users have configured a forwarding rule to the same destination to proactively identify and investigate potential security risks related to email forwarding and take appropriate actions to protect the organizations data and prevent unauthorized access or data breaches. This detection is made by a Splunk query to O365 management activity logs with the operation `Set-Mailbox` to gather information about mailbox configurations. Then, the query uses the `spath` function to extract the parameters and rename the "Identity" field as "src_user" and searches for entries where the "ForwardingSmtpAddress" field is not empty, which indicates the presence of a forwarding rule. Next, the analytic uses the `stats` command to group the results by the forwarding email address and count the number of unique source users (`src_user`). Finally, it filters the results and only retains entries where the count of source users (`count_src_user`) is greater than 1, which indicates that multiple users have set up forwarding rules to the same destination. This detection is important because it suggests that multiple users are forwarding emails to the same destination without proper authorization, which can lead to the exposure of sensitive information, loss of data control, or unauthorized access to confidential emails. Investigating and addressing this issue promptly can help prevent data breaches and mitigate potential damage.indicates a potential security risk since multiple users forwarding emails to the same destination can be a sign of unauthorized access, data exfiltration, or a compromised account. Additionally, it also helps to determine if the forwarding rules are legitimate or if they indicate a security incident. False positives can occur if there are legitimate reasons for multiple users to forward emails to the same destination, such as a shared mailbox or a team collaboration scenario. Next steps include further investigation and context analysis to determine the legitimacy of the forwarding rules.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `O365 Mailbox Email Forwarding Enabled`. The following analytic detects when
+  multiple users have configured a forwarding rule to the same destination to proactively
+  identify and investigate potential security risks related to email forwarding and
+  take appropriate actions to protect the organizations data and prevent unauthorized
+  access or data breaches. This detection is made by a Splunk query to O365 management
+  activity logs with the operation `Set-Mailbox` to gather information about mailbox
+  configurations. Then, the query uses the `spath` function to extract the parameters
+  and rename the "Identity" field as "src_user" and searches for entries where the
+  "ForwardingSmtpAddress" field is not empty, which indicates the presence of a forwarding
+  rule. Next, the analytic uses the `stats` command to group the results by the forwarding
+  email address and count the number of unique source users (`src_user`). Finally,
+  it filters the results and only retains entries where the count of source users
+  (`count_src_user`) is greater than 1, which indicates that multiple users have set
+  up forwarding rules to the same destination. This detection is important because
+  it suggests that multiple users are forwarding emails to the same destination without
+  proper authorization, which can lead to the exposure of sensitive information, loss
+  of data control, or unauthorized access to confidential emails. Investigating and
+  addressing this issue promptly can help prevent data breaches and mitigate potential
+  damage.indicates a potential security risk since multiple users forwarding emails
+  to the same destination can be a sign of unauthorized access, data exfiltration,
+  or a compromised account. Additionally, it also helps to determine if the forwarding
+  rules are legitimate or if they indicate a security incident. False positives can
+  occur if there are legitimate reasons for multiple users to forward emails to the
+  same destination, such as a shared mailbox or a team collaboration scenario. Next
+  steps include further investigation and context analysis to determine the legitimacy
+  of the forwarding rules.'
 data_source: []
 search: '`o365_management_activity` Operation=Set-Mailbox | spath input=Parameters
   | rename Identity AS src_user | search ForwardingSmtpAddress=* | stats dc(src_user)
@@ -43,14 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Operation
-  - Parameters
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_mailbox_forwarding_enabled/o365_mailbox_forwarding_enabled.json
     sourcetype: o365:management:activity
     source: o365
diff --git a/detections/deprecated/okta_account_locked_out.yml b/detections/deprecated/okta_account_locked_out.yml
index 9084f592ee..5f883ff39b 100644
--- a/detections/deprecated/okta_account_locked_out.yml
+++ b/detections/deprecated/okta_account_locked_out.yml
@@ -5,15 +5,15 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: deprecated
 type: Anomaly
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `Okta Multiple Accounts Locked Out`.
-  The following analytic utilizes the user.acount.lock event to identify
-  associates who are locked out of Okta. An adversary attempting to brute force or
-  password spray account names may lock accounts out depending on the threshold.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `Okta Multiple Accounts Locked Out`. The following analytic utilizes the user.acount.lock
+  event to identify associates who are locked out of Okta. An adversary attempting
+  to brute force or password spray account names may lock accounts out depending on
+  the threshold.'
 data_source: []
 search: '`okta` eventType=user.account.lock | stats count min(_time) as firstTime
-  max(_time) as lastTime values(displayMessage) values(src_user) as user by src_ip eventType status
-  | where count >=3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  max(_time) as lastTime values(displayMessage) values(src_user) as user by src_ip
+  eventType status | where count >=3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
   `okta_account_locked_out_filter`'
 how_to_implement: This analytic is specific to Okta and requires Okta logs to be ingested.
 known_false_positives: False positives may be present. Tune Okta and tune the analytic
@@ -40,16 +40,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - src_user
-  - src_ip
-  - eventType
-  - status
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log
     source: Okta
-    sourcetype: OktaIM2:log
\ No newline at end of file
+    sourcetype: OktaIM2:log
diff --git a/detections/deprecated/okta_account_lockout_events.yml b/detections/deprecated/okta_account_lockout_events.yml
index 64d2d76c55..875947741c 100644
--- a/detections/deprecated/okta_account_lockout_events.yml
+++ b/detections/deprecated/okta_account_lockout_events.yml
@@ -5,16 +5,16 @@ date: '2024-10-17'
 author: Michael Haag, Rico Valdez, Splunk
 status: deprecated
 type: Anomaly
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `Okta Multiple Accounts Locked Out`.
-  The following anomaly will generate based on account lockout events utilizing
-  Okta eventTypes of user.account.lock.limit or user.account.lock. Per the Okta docs
-  site, this event is fired when a user account has reached the lockout limit. The
-  account will not auto-unlock and a user or client cannot gain access to the account.
-  This event indicates an account that will not be able to log in until remedial action
-  is taken by the account admin. This event can be used to understand the specifics
-  of an account lockout. Often this indicates a client application that is repeatedly
-  attempting to authenticate with invalid credentials such as an old password.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `Okta Multiple Accounts Locked Out`. The following anomaly will generate based
+  on account lockout events utilizing Okta eventTypes of user.account.lock.limit or
+  user.account.lock. Per the Okta docs site, this event is fired when a user account
+  has reached the lockout limit. The account will not auto-unlock and a user or client
+  cannot gain access to the account. This event indicates an account that will not
+  be able to log in until remedial action is taken by the account admin. This event
+  can be used to understand the specifics of an account lockout. Often this indicates
+  a client application that is repeatedly attempting to authenticate with invalid
+  credentials such as an old password.'
 data_source: []
 search: '`okta` eventType IN (user.account.lock.limit,user.account.lock) | rename
   client.geographicalContext.country as country, client.geographicalContext.state
@@ -48,18 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - displayMessage
-  - client.geographicalContext.country
-  - client.geographicalContext.state
-  - client.geographicalContext.city
-  - src_ip
-  - src_user
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/okta_multiple_accounts_lockout/okta_multiple_accounts_lockout.log
     source: Okta
-    sourcetype: OktaIM2:log
\ No newline at end of file
+    sourcetype: OktaIM2:log
diff --git a/detections/deprecated/okta_failed_sso_attempts.yml b/detections/deprecated/okta_failed_sso_attempts.yml
index 275f362f0e..24a1625e98 100644
--- a/detections/deprecated/okta_failed_sso_attempts.yml
+++ b/detections/deprecated/okta_failed_sso_attempts.yml
@@ -5,7 +5,10 @@ date: '2024-10-17'
 author: Michael Haag, Rico Valdez, Splunk
 status: deprecated
 type: Anomaly
-description: '**DEPRECATION NOTE** - This search has been deprecated and replaced with this detection `Okta Unauthorized Access to Application - DM`.  The following anomaly identifies failed Okta SSO events utilizing the legacy Okta event "unauth app access attempt".'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with this detection `Okta Unauthorized Access to Application - DM`.  The following
+  anomaly identifies failed Okta SSO events utilizing the legacy Okta event "unauth
+  app access attempt".'
 data_source: []
 search: '`okta` eventType=app.generic.unauth_app_access_attempt | stats min(_time)
   as firstTime max(_time) as lastTime values(app) as Apps count by src_user, result
@@ -36,11 +39,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - displayMessage
-  - app
-  - src_user
-  - result
-  - src_ip
   security_domain: access
diff --git a/detections/deprecated/okta_threatinsight_login_failure_with_high_unknown_users.yml b/detections/deprecated/okta_threatinsight_login_failure_with_high_unknown_users.yml
index 49a3f30c6a..ef848f89a2 100644
--- a/detections/deprecated/okta_threatinsight_login_failure_with_high_unknown_users.yml
+++ b/detections/deprecated/okta_threatinsight_login_failure_with_high_unknown_users.yml
@@ -6,16 +6,20 @@ author: Okta, Inc, Michael Haag, Splunk
 type: TTP
 status: deprecated
 data_source: []
-description:  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `Okta ThreatInsight Threat Detected`.
-  The following analytic utilizes Oktas ThreatInsight to identify Login failures with high unknown users count and any included secondary outcome reasons. 
-  This event will trigger when a brute force attempt occurs with unknown usernames attempted.'
-search: '`okta` eventType="security.threat.detected" AND outcome.reason="Login failures with high unknown users count*"
-| stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason
-| `security_content_ctime(firstTime)` 
-| `security_content_ctime(lastTime)` | `okta_threatinsight_login_failure_with_high_unknown_users_filter`'
-how_to_implement: This search is specific to Okta and requires Okta logs to be
-  ingested in your Splunk deployment.
-known_false_positives: Fidelity of this is high as it is Okta ThreatInsight. Filter and modify as needed.
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `Okta ThreatInsight Threat Detected`. The following analytic utilizes Oktas
+  ThreatInsight to identify Login failures with high unknown users count and any included
+  secondary outcome reasons. This event will trigger when a brute force attempt occurs
+  with unknown usernames attempted.'
+search: '`okta` eventType="security.threat.detected" AND outcome.reason="Login failures
+  with high unknown users count*" | stats count min(_time) as firstTime max(_time)
+  as lastTime values(displayMessage) by user eventType client.userAgent.rawUserAgent
+  client.userAgent.browser outcome.reason | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `okta_threatinsight_login_failure_with_high_unknown_users_filter`'
+how_to_implement: This search is specific to Okta and requires Okta logs to be ingested
+  in your Splunk deployment.
+known_false_positives: Fidelity of this is high as it is Okta ThreatInsight. Filter
+  and modify as needed.
 references:
 - https://help.okta.com/en-us/Content/Topics/Security/threat-insight/configure-threatinsight-system-log.htm
 tags:
@@ -38,11 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType 
-  - client.userAgent.rawUserAgent 
-  - client.userAgent.browser 
-  - outcome.reason
-  - displayMessage
   security_domain: access
diff --git a/detections/deprecated/okta_threatinsight_suspected_passwordspray_attack.yml b/detections/deprecated/okta_threatinsight_suspected_passwordspray_attack.yml
index 428aa1f0b9..bc69d5ce88 100644
--- a/detections/deprecated/okta_threatinsight_suspected_passwordspray_attack.yml
+++ b/detections/deprecated/okta_threatinsight_suspected_passwordspray_attack.yml
@@ -6,17 +6,19 @@ author: Okta, Inc, Michael Haag, Splunk
 type: TTP
 status: deprecated
 data_source: []
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `Okta ThreatInsight Threat Detected`.
-  The following analytic utilizes Oktas ThreatInsight to identify "PasswordSpray" and any included secondary outcome reasons. This event will trigger when a
-  brute force attempt occurs with unknown usernames attempted.'
-search: '`okta` eventType="security.threat.detected" AND outcome.reason="Password Spray"
-| stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage) by eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason
-| `security_content_ctime(firstTime)` 
-| `security_content_ctime(lastTime)` | `okta_threatinsight_suspected_passwordspray_attack_filter`'
-how_to_implement: This search is specific to Okta and requires Okta logs to be
-  ingested in your Splunk deployment.
-known_false_positives: Fidelity of this is high as it is Okta ThreatInsight. Filter and modify as needed.
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `Okta ThreatInsight Threat Detected`. The following analytic utilizes Oktas
+  ThreatInsight to identify "PasswordSpray" and any included secondary outcome reasons.
+  This event will trigger when a brute force attempt occurs with unknown usernames
+  attempted.'
+search: '`okta` eventType="security.threat.detected" AND outcome.reason="Password
+  Spray" | stats count min(_time) as firstTime max(_time) as lastTime values(displayMessage)
+  by eventType client.userAgent.rawUserAgent client.userAgent.browser outcome.reason
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `okta_threatinsight_suspected_passwordspray_attack_filter`'
+how_to_implement: This search is specific to Okta and requires Okta logs to be ingested
+  in your Splunk deployment.
+known_false_positives: Fidelity of this is high as it is Okta ThreatInsight. Filter
+  and modify as needed.
 references:
 - https://help.okta.com/en-us/Content/Topics/Security/threat-insight/configure-threatinsight-system-log.htm
 tags:
@@ -39,11 +41,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - eventType 
-  - client.userAgent.rawUserAgent 
-  - client.userAgent.browser 
-  - outcome.reason
-  - displayMessage
   security_domain: access
diff --git a/detections/deprecated/okta_two_or_more_rejected_okta_pushes.yml b/detections/deprecated/okta_two_or_more_rejected_okta_pushes.yml
index ef56c2379a..cff2fa7382 100644
--- a/detections/deprecated/okta_two_or_more_rejected_okta_pushes.yml
+++ b/detections/deprecated/okta_two_or_more_rejected_okta_pushes.yml
@@ -5,11 +5,10 @@ date: '2024-10-17'
 author: Michael Haag, Marissa Bower, Splunk
 status: deprecated
 type: TTP
-description: 
-  '**DEPRECATION NOTE** - This search has been deprecated and replaced with `Okta Multiple Failed MFA Requests For User`.
-  The following analytic identifies an account that has rejected more than
-  2 Push notifications in a 10 minute window. Modify this query for your environment
-  by upping the count or time window.'
+description: '**DEPRECATION NOTE** - This search has been deprecated and replaced
+  with `Okta Multiple Failed MFA Requests For User`. The following analytic identifies
+  an account that has rejected more than 2 Push notifications in a 10 minute window.
+  Modify this query for your environment by upping the count or time window.'
 data_source: []
 search: '`okta` outcome.reason="User rejected Okta push verify" OR (debugContext.debugData.factor="OKTA_VERIFY_PUSH"
   outcome.result=FAILURE legacyEventType="core.user.factor.attempt_fail" "target{}.detailEntry.methodTypeUsed"="Get
@@ -47,10 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - src_ip
-  - eventType
-  - status
   security_domain: access
diff --git a/detections/deprecated/osquery_pack___coldroot_detection.yml b/detections/deprecated/osquery_pack___coldroot_detection.yml
index 899aa6da1b..e80ee79a24 100644
--- a/detections/deprecated/osquery_pack___coldroot_detection.yml
+++ b/detections/deprecated/osquery_pack___coldroot_detection.yml
@@ -11,7 +11,8 @@ search: '| from datamodel Alerts.Alerts | search app=osquery:results (name=pack_
   OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path |
   bucket _time span=30s | stats count(path) by _time, host, user, path | `osquery_pack___coldroot_detection_filter`'
 how_to_implement: In order to properly run this search, Splunk needs to ingest data
-  from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599)
+  from your osquery deployed agents with the 
+  [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599)
   pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must
   be deployed across your indexers and universal forwarders in order to have the osquery
   data populate the Alerts data model
@@ -37,6 +38,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/processes_created_by_netsh.yml b/detections/deprecated/processes_created_by_netsh.yml
index 78a13738c4..249169c6c8 100644
--- a/detections/deprecated/processes_created_by_netsh.yml
+++ b/detections/deprecated/processes_created_by_netsh.yml
@@ -55,6 +55,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/prohibited_software_on_endpoint.yml b/detections/deprecated/prohibited_software_on_endpoint.yml
index c638c22a9e..af8b6cf90f 100644
--- a/detections/deprecated/prohibited_software_on_endpoint.yml
+++ b/detections/deprecated/prohibited_software_on_endpoint.yml
@@ -46,6 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _times
   security_domain: endpoint
diff --git a/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml b/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml
index 1bb0c0f7ee..46a2622c4a 100644
--- a/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml
+++ b/detections/deprecated/reg_exe_used_to_hide_files_directories_via_registry_keys.yml
@@ -50,6 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/remote_registry_key_modifications.yml b/detections/deprecated/remote_registry_key_modifications.yml
index 82c198f20d..d4e492e175 100644
--- a/detections/deprecated/remote_registry_key_modifications.yml
+++ b/detections/deprecated/remote_registry_key_modifications.yml
@@ -43,6 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml b/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml
index 4cc52e9d99..9cddbca6fc 100644
--- a/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml
+++ b/detections/deprecated/scheduled_tasks_used_in_badrabbit_ransomware.yml
@@ -13,8 +13,8 @@ data_source:
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime values(Processes.process) as process  from datamodel=Endpoint.Processes
   where Processes.process_name=schtasks.exe (Processes.process= "*create*"  OR Processes.process=
-  "*delete*") by Processes.parent_process Processes.process_name Processes.user Processes.dest |
-  `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  "*delete*") by Processes.parent_process Processes.process_name Processes.user Processes.dest
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
   | search (process=*rhaegal* OR process=*drogon* OR *viserion_*) | `scheduled_tasks_used_in_badrabbit_ransomware_filter`'
 how_to_implement: The detection is based on data that originates from Endpoint Detection
   and Response (EDR) agents. These agents are designed to provide security-related
@@ -49,6 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/spectre_and_meltdown_vulnerable_systems.yml b/detections/deprecated/spectre_and_meltdown_vulnerable_systems.yml
index 170a5c2438..3777034146 100644
--- a/detections/deprecated/spectre_and_meltdown_vulnerable_systems.yml
+++ b/detections/deprecated/spectre_and_meltdown_vulnerable_systems.yml
@@ -36,6 +36,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/suspicious_changes_to_file_associations.yml b/detections/deprecated/suspicious_changes_to_file_associations.yml
index d9c267da33..c544981826 100644
--- a/detections/deprecated/suspicious_changes_to_file_associations.yml
+++ b/detections/deprecated/suspicious_changes_to_file_associations.yml
@@ -51,6 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/suspicious_email___uba_anomaly.yml b/detections/deprecated/suspicious_email___uba_anomaly.yml
index 9e3ded2ed7..da737d493e 100644
--- a/detections/deprecated/suspicious_email___uba_anomaly.yml
+++ b/detections/deprecated/suspicious_email___uba_anomaly.yml
@@ -43,6 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: threat
diff --git a/detections/deprecated/suspicious_file_write.yml b/detections/deprecated/suspicious_file_write.yml
index be6dff04f9..00486d21e9 100644
--- a/detections/deprecated/suspicious_file_write.yml
+++ b/detections/deprecated/suspicious_file_write.yml
@@ -45,6 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/suspicious_powershell_command_line_arguments.yml b/detections/deprecated/suspicious_powershell_command_line_arguments.yml
index bc425f1d51..a63c872dd4 100644
--- a/detections/deprecated/suspicious_powershell_command_line_arguments.yml
+++ b/detections/deprecated/suspicious_powershell_command_line_arguments.yml
@@ -56,6 +56,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/suspicious_rundll32_rename.yml b/detections/deprecated/suspicious_rundll32_rename.yml
index 95fb4a5c9f..4a3a13ff9f 100644
--- a/detections/deprecated/suspicious_rundll32_rename.yml
+++ b/detections/deprecated/suspicious_rundll32_rename.yml
@@ -60,17 +60,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/deprecated/suspicious_writes_to_system_volume_information.yml b/detections/deprecated/suspicious_writes_to_system_volume_information.yml
index 0c657c1c67..ae8494984e 100644
--- a/detections/deprecated/suspicious_writes_to_system_volume_information.yml
+++ b/detections/deprecated/suspicious_writes_to_system_volume_information.yml
@@ -9,10 +9,10 @@ description: This search detects writes to the 'System Volume Information' folde
   by something other than the System process.
 data_source:
 - Sysmon EventID 1
-search: '(`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume
+search: (`sysmon` OR tag=process) EventCode=11 process_id!=4 file_path=*System\ Volume
   Information* | stats count min(_time) as firstTime max(_time) as lastTime by dest,
   Image, file_path | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `suspicious_writes_to_system_volume_information_filter`'
+  | `suspicious_writes_to_system_volume_information_filter`
 how_to_implement: You need to be ingesting logs with both the process name and command-line
   from your endpoints. If you are using Sysmon, you must have at least version 6.0.4
   of the Sysmon TA.
@@ -38,6 +38,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/uncommon_processes_on_endpoint.yml b/detections/deprecated/uncommon_processes_on_endpoint.yml
index 70fa651d45..2f28c3c90b 100644
--- a/detections/deprecated/uncommon_processes_on_endpoint.yml
+++ b/detections/deprecated/uncommon_processes_on_endpoint.yml
@@ -44,6 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/unsigned_image_loaded_by_lsass.yml b/detections/deprecated/unsigned_image_loaded_by_lsass.yml
index 503f8317a6..8719790c55 100644
--- a/detections/deprecated/unsigned_image_loaded_by_lsass.yml
+++ b/detections/deprecated/unsigned_image_loaded_by_lsass.yml
@@ -10,9 +10,8 @@ description: This search detects loading of unsigned images by LSASS. Deprecated
 data_source:
 - Sysmon EventID 7
 search: '`sysmon` EventID=7 Image=*lsass.exe Signed=false | stats count min(_time)
-  as firstTime max(_time) as lastTime by dest, Image, ImageLoaded, Signed, SHA1
-   | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
-  | `unsigned_image_loaded_by_lsass_filter`'
+  as firstTime max(_time) as lastTime by dest, Image, ImageLoaded, Signed, SHA1 |
+  `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `unsigned_image_loaded_by_lsass_filter`'
 how_to_implement: This search needs Sysmon Logs with a sysmon configuration, which
   includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`.
   We strongly recommend that you specify your environment-specific configurations
@@ -41,6 +40,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/unsuccessful_netbackup_backups.yml b/detections/deprecated/unsuccessful_netbackup_backups.yml
index 4c29a6c890..8d87ababc2 100644
--- a/detections/deprecated/unsuccessful_netbackup_backups.yml
+++ b/detections/deprecated/unsuccessful_netbackup_backups.yml
@@ -34,6 +34,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/web_fraud___account_harvesting.yml b/detections/deprecated/web_fraud___account_harvesting.yml
index 0b84c251e4..5f03cdc77b 100644
--- a/detections/deprecated/web_fraud___account_harvesting.yml
+++ b/detections/deprecated/web_fraud___account_harvesting.yml
@@ -53,9 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_content_type
-  - uri
-  - cookie
   security_domain: threat
diff --git a/detections/deprecated/web_fraud___anomalous_user_clickspeed.yml b/detections/deprecated/web_fraud___anomalous_user_clickspeed.yml
index 3498417248..dd78e9838a 100644
--- a/detections/deprecated/web_fraud___anomalous_user_clickspeed.yml
+++ b/detections/deprecated/web_fraud___anomalous_user_clickspeed.yml
@@ -49,8 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_content_type
-  - cookie
   security_domain: threat
diff --git a/detections/deprecated/web_fraud___password_sharing_across_accounts.yml b/detections/deprecated/web_fraud___password_sharing_across_accounts.yml
index dab627999c..5b20738ee5 100644
--- a/detections/deprecated/web_fraud___password_sharing_across_accounts.yml
+++ b/detections/deprecated/web_fraud___password_sharing_across_accounts.yml
@@ -42,8 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_content_type
-  - uri
   security_domain: threat
diff --git a/detections/deprecated/windows_connhost_exe_started_forcefully.yml b/detections/deprecated/windows_connhost_exe_started_forcefully.yml
index 226bbeff56..4849321ec6 100644
--- a/detections/deprecated/windows_connhost_exe_started_forcefully.yml
+++ b/detections/deprecated/windows_connhost_exe_started_forcefully.yml
@@ -5,11 +5,11 @@ date: '2024-10-17'
 author: Rod Soto, Jose Hernandez, Splunk
 status: deprecated
 type: TTP
-description: 'The search looks for the Console Window Host process (connhost.exe)
-  executed using the force flag -ForceV1. This is not regular behavior in the Windows
-  OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually
+description: The search looks for the Console Window Host process (connhost.exe) executed
+  using the force flag -ForceV1. This is not regular behavior in the Windows OS and
+  is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually
   seen in the windows 10 client of attack_range_local. After further testing we realized
-  this is not specific to Ryuk.'
+  this is not specific to Ryuk.
 data_source:
 - Sysmon EventID 1
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
@@ -47,6 +47,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/deprecated/windows_dll_search_order_hijacking_hunt.yml b/detections/deprecated/windows_dll_search_order_hijacking_hunt.yml
index 69d8d60287..de809d823c 100644
--- a/detections/deprecated/windows_dll_search_order_hijacking_hunt.yml
+++ b/detections/deprecated/windows_dll_search_order_hijacking_hunt.yml
@@ -27,7 +27,8 @@ search: '| tstats `security_content_summariesonly` count min(_time) as firstTime
   Processes.user Processes.parent_process_name Processes.process_name Processes.process_path
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)`
   | lookup hijacklibs library AS process_name OUTPUT islibrary | search islibrary
-  = True | rename parent_process_name as process_name , process_name AS ImageLoaded, process_path AS Module_Path | `windows_dll_search_order_hijacking_hunt_filter`'
+  = True | rename parent_process_name as process_name , process_name AS ImageLoaded,
+  process_path AS Module_Path | `windows_dll_search_order_hijacking_hunt_filter`'
 how_to_implement: The detection is based on data that originates from Endpoint Detection
   and Response (EDR) agents. These agents are designed to provide security-related
   telemetry from the endpoints where the agent is installed. To implement this search,
@@ -65,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/deprecated/windows_hosts_file_modification.yml b/detections/deprecated/windows_hosts_file_modification.yml
index f30249e4ac..55941d7789 100644
--- a/detections/deprecated/windows_hosts_file_modification.yml
+++ b/detections/deprecated/windows_hosts_file_modification.yml
@@ -39,6 +39,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/endpoint/3cx_supply_chain_attack_network_indicators.yml b/detections/endpoint/3cx_supply_chain_attack_network_indicators.yml
index fe4dc52356..10fe5e46d0 100644
--- a/detections/endpoint/3cx_supply_chain_attack_network_indicators.yml
+++ b/detections/endpoint/3cx_supply_chain_attack_network_indicators.yml
@@ -7,10 +7,23 @@ type: TTP
 status: experimental
 data_source:
 - Sysmon EventID 22
-description: The following analytic identifies DNS queries to domains associated with the 3CX supply chain attack. It leverages the Network_Resolution datamodel to detect these suspicious domain indicators. This activity is significant because it can indicate a potential compromise stemming from the 3CX supply chain attack, which is known for distributing malicious software through trusted updates. If confirmed malicious, this activity could allow attackers to establish a foothold in the network, exfiltrate sensitive data, or further propagate malware, leading to extensive damage and data breaches.
-search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | lookup 3cx_ioc_domains domain as query OUTPUT Description isIOC | search isIOC=true | `3cx_supply_chain_attack_network_indicators_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information into the `Network Resolution` datamodel in the `DNS` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA''s are installed.
-known_false_positives: False positives will be present for accessing the 3cx[.]com website. Remove from the lookup as needed.
+description: The following analytic identifies DNS queries to domains associated with
+  the 3CX supply chain attack. It leverages the Network_Resolution datamodel to detect
+  these suspicious domain indicators. This activity is significant because it can
+  indicate a potential compromise stemming from the 3CX supply chain attack, which
+  is known for distributing malicious software through trusted updates. If confirmed
+  malicious, this activity could allow attackers to establish a foothold in the network,
+  exfiltrate sensitive data, or further propagate malware, leading to extensive damage
+  and data breaches.
+search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time)
+  as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(DNS)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | lookup
+  3cx_ioc_domains domain as query OUTPUT Description isIOC | search isIOC=true | `3cx_supply_chain_attack_network_indicators_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  into the `Network Resolution` datamodel in the `DNS` node. In addition, confirm
+  the latest CIM App 4.20 or higher is installed and the latest TA''s are installed.
+known_false_positives: False positives will be present for accessing the 3cx[.]com
+  website. Remove from the lookup as needed.
 references:
 - https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
 - https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp
@@ -41,14 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - DNS.src
-  - DNS.query
-  - _time
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_network-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_network-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/7zip_commandline_to_smb_share_path.yml b/detections/endpoint/7zip_commandline_to_smb_share_path.yml
index b02f52a0ff..97d53ca01b 100644
--- a/detections/endpoint/7zip_commandline_to_smb_share_path.yml
+++ b/detections/endpoint/7zip_commandline_to_smb_share_path.yml
@@ -5,13 +5,35 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of 7z or 7za processes with command lines pointing to SMB network shares. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to archive and exfiltrate sensitive files to a network share, a technique observed in CONTI LEAK tools. If confirmed malicious, this behavior could lead to data exfiltration, compromising sensitive information and potentially aiding further attacks.
+description: The following analytic detects the execution of 7z or 7za processes with
+  command lines pointing to SMB network shares. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line arguments.
+  This activity is significant as it may indicate an attempt to archive and exfiltrate
+  sensitive files to a network share, a technique observed in CONTI LEAK tools. If
+  confirmed malicious, this behavior could lead to data exfiltration, compromising
+  sensitive information and potentially aiding further attacks.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name ="7z.exe" OR Processes.process_name = "7za.exe" OR Processes.original_file_name = "7z.exe" OR Processes.original_file_name =  "7za.exe") AND (Processes.process="*\\C$\\*" OR Processes.process="*\\Admin$\\*" OR Processes.process="*\\IPC$\\*") by Processes.original_file_name Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.parent_process_id Processes.process_id  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `7zip_commandline_to_smb_share_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name ="7z.exe"
+  OR Processes.process_name = "7za.exe" OR Processes.original_file_name = "7z.exe"
+  OR Processes.original_file_name =  "7za.exe") AND (Processes.process="*\\C$\\*"
+  OR Processes.process="*\\Admin$\\*" OR Processes.process="*\\IPC$\\*") by Processes.original_file_name
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process
+  Processes.parent_process_id Processes.process_id  Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `7zip_commandline_to_smb_share_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://threadreaderapp.com/thread/1423361119926816776.html
@@ -21,7 +43,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: archive process $process_name$ with suspicious cmdline $process$ in host $dest$
+  message: archive process $process_name$ with suspicious cmdline $process$ in host
+    $dest$
   mitre_attack_id:
   - T1560.001
   - T1560
@@ -38,20 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon_7z.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon_7z.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/access_lsass_memory_for_dump_creation.yml b/detections/endpoint/access_lsass_memory_for_dump_creation.yml
index 94003abe43..beabd83a03 100644
--- a/detections/endpoint/access_lsass_memory_for_dump_creation.yml
+++ b/detections/endpoint/access_lsass_memory_for_dump_creation.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to dump the LSASS process memory, a common technique in credential dumping attacks. It leverages Sysmon logs, specifically EventCode 10, to identify suspicious call traces to dbgcore.dll and dbghelp.dll associated with lsass.exe. This activity is significant as it often precedes the theft of sensitive login credentials, posing a high risk of unauthorized access to systems and data. If confirmed malicious, attackers could gain access to critical credentials, enabling further compromise and lateral movement within the network.
+description: The following analytic detects attempts to dump the LSASS process memory,
+  a common technique in credential dumping attacks. It leverages Sysmon logs, specifically
+  EventCode 10, to identify suspicious call traces to dbgcore.dll and dbghelp.dll
+  associated with lsass.exe. This activity is significant as it often precedes the
+  theft of sensitive login credentials, posing a high risk of unauthorized access
+  to systems and data. If confirmed malicious, attackers could gain access to critical
+  credentials, enabling further compromise and lateral movement within the network.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetImage, TargetProcessId, SourceImage, SourceProcessId  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`'
-how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives: Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll*
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetImage,
+  TargetProcessId, SourceImage, SourceProcessId  | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`'
+how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which
+  includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`.
+  We strongly recommend that you specify your environment-specific configurations
+  (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
+  with configurations for your Splunk environment. The search also uses a post-filter
+  macro designed to filter out known false positives.
+known_false_positives: Administrators can create memory dumps for debugging purposes,
+  but memory dumps of the LSASS process would be unusual.
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,10 @@ tags:
   asset_type: Windows
   confidence: 90
   impact: 70
-  message: process $SourceImage$ injected into $TargetImage$ and was attempted dump LSASS on $dest$. Adversaries tend to do this when trying to accesss credential material stored in the process memory of the Local Security Authority Subsystem Service (LSASS).
+  message: process $SourceImage$ injected into $TargetImage$ and was attempted dump
+    LSASS on $dest$. Adversaries tend to do this when trying to accesss credential
+    material stored in the process memory of the Local Security Authority Subsystem
+    Service (LSASS).
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -46,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetImage
-  - CallTrace
-  - dest
-  - TargetProcessId
-  - SourceImage
-  - SourceProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/account_discovery_with_net_app.yml b/detections/endpoint/account_discovery_with_net_app.yml
index 5dbdf9b99d..7c8391edc3 100644
--- a/detections/endpoint/account_discovery_with_net_app.yml
+++ b/detections/endpoint/account_discovery_with_net_app.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk, TheLawsOfChaos, Github Community
 status: production
 type: TTP
-description: The following analytic detects potential account discovery activities using the 'net' command, commonly employed by malware like Trickbot for reconnaissance. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line patterns and process relationships. This activity is significant as it often precedes further malicious actions, such as lateral movement or privilege escalation. If confirmed malicious, attackers could gain valuable information about user accounts, enabling them to escalate privileges or move laterally within the network, posing a significant security risk.
+description: The following analytic detects potential account discovery activities
+  using the 'net' command, commonly employed by malware like Trickbot for reconnaissance.
+  It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line
+  patterns and process relationships. This activity is significant as it often precedes
+  further malicious actions, such as lateral movement or privilege escalation. If
+  confirmed malicious, attackers could gain valuable information about user accounts,
+  enabling them to escalate privileges or move laterally within the network, posing
+  a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND (Processes.process="* user *" OR  Processes.process="*config*" OR Processes.process="*view /all*") by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.parent_process) as parent_process values(Processes.process_id)
+  as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_net` AND (Processes.process="* user *" OR  Processes.process="*config*"
+  OR Processes.process="*view /all*") by  Processes.process_name Processes.dest Processes.user
+  Processes.parent_process_name | where count >=4 | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `account_discovery_with_net_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Admin or power user may used this series of command.
 references:
 - https://labs.vipre.com/trickbot-and-its-modules/
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/active_directory_lateral_movement_identified.yml b/detections/endpoint/active_directory_lateral_movement_identified.yml
index 00b9772944..e85d062b6c 100644
--- a/detections/endpoint/active_directory_lateral_movement_identified.yml
+++ b/detections/endpoint/active_directory_lateral_movement_identified.yml
@@ -6,10 +6,37 @@ author: Michael Haag, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies potential lateral movement activities within an organization's Active Directory (AD) environment. It detects this activity by correlating multiple analytics from the Active Directory Lateral Movement analytic story within a specified time frame. This is significant for a SOC as lateral movement is a common tactic used by attackers to expand their access within a network, posing a substantial risk. If confirmed malicious, this activity could allow attackers to escalate privileges, access sensitive information, and persist within the environment, leading to severe security breaches.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active Directory Lateral Movement" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `active_directory_lateral_movement_identified_filter`'
-how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
-known_false_positives: False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed.
+description: The following analytic identifies potential lateral movement activities
+  within an organization's Active Directory (AD) environment. It detects this activity
+  by correlating multiple analytics from the Active Directory Lateral Movement analytic
+  story within a specified time frame. This is significant for a SOC as lateral movement
+  is a common tactic used by attackers to expand their access within a network, posing
+  a substantial risk. If confirmed malicious, this activity could allow attackers
+  to escalate privileges, access sensitive information, and persist within the environment,
+  leading to severe security breaches.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active
+  Directory Lateral Movement" All_Risk.risk_object_type="system" by All_Risk.risk_object
+  All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 4 | `active_directory_lateral_movement_identified_filter`'
+how_to_implement: Splunk Enterprise Security is required to utilize this correlation.
+  In addition, modify the source_count value to your environment. In our testing,
+  a count of 4 or 5 was decent in a lab, but the number may need to be increased as
+  the analytic story includes over 30 analytics. In addition, based on false positives,
+  modify any analytics to be anomaly and lower or increase risk based on organization
+  importance.
+known_false_positives: False positives will most likely be present based on risk scoring
+  and how the organization handles system to system communication. Filter, or modify
+  as needed. In addition to count by analytics, adding a risk score may be useful.
+  In our testing, with 22 events over 30 days, the risk scores ranged from 500 to
+  80,000. Your organization will be different, monitor and modify as needed.
 references:
 - https://attack.mitre.org/tactics/TA0008/
 - https://research.splunk.com/stories/active_directory_lateral_movement/
@@ -19,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Risk.calculated_risk_score
-  - All_Risk.annotations.mitre_attack.mitre_tactic_id
-  - All_Risk.annotations.mitre_attack.mitre_technique_id
-  - All_Risk.tag
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/adlm_risk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/adlm_risk.log
     source: adlm
     sourcetype: stash
diff --git a/detections/endpoint/active_directory_privilege_escalation_identified.yml b/detections/endpoint/active_directory_privilege_escalation_identified.yml
index afd58a976e..4772b60e0f 100644
--- a/detections/endpoint/active_directory_privilege_escalation_identified.yml
+++ b/detections/endpoint/active_directory_privilege_escalation_identified.yml
@@ -6,10 +6,37 @@ author: Mauricio Velazco, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies potential privilege escalation activities within an organization's Active Directory (AD) environment. It detects this activity by correlating multiple analytics from the Active Directory Privilege Escalation analytic story within a specified time frame. This is significant for a SOC as it helps identify coordinated attempts to gain elevated privileges, which could indicate a serious security threat. If confirmed malicious, this activity could allow attackers to gain unauthorized access to sensitive systems and data, leading to potential data breaches and further compromise of the network.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active Directory Privilege Escalation" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `active_directory_privilege_escalation_identified_filter`'
-how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased as the analytic story includes over 30 analytics. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
-known_false_positives: False positives will most likely be present based on risk scoring and how the organization handles system to system communication. Filter, or modify as needed. In addition to count by analytics, adding a risk score may be useful. In our testing, with 22 events over 30 days, the risk scores ranged from 500 to 80,000. Your organization will be different, monitor and modify as needed.
+description: The following analytic identifies potential privilege escalation activities
+  within an organization's Active Directory (AD) environment. It detects this activity
+  by correlating multiple analytics from the Active Directory Privilege Escalation
+  analytic story within a specified time frame. This is significant for a SOC as it
+  helps identify coordinated attempts to gain elevated privileges, which could indicate
+  a serious security threat. If confirmed malicious, this activity could allow attackers
+  to gain unauthorized access to sensitive systems and data, leading to potential
+  data breaches and further compromise of the network.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Active
+  Directory Privilege Escalation" All_Risk.risk_object_type="system" by All_Risk.risk_object
+  All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 4 | `active_directory_privilege_escalation_identified_filter`'
+how_to_implement: Splunk Enterprise Security is required to utilize this correlation.
+  In addition, modify the source_count value to your environment. In our testing,
+  a count of 4 or 5 was decent in a lab, but the number may need to be increased as
+  the analytic story includes over 30 analytics. In addition, based on false positives,
+  modify any analytics to be anomaly and lower or increase risk based on organization
+  importance.
+known_false_positives: False positives will most likely be present based on risk scoring
+  and how the organization handles system to system communication. Filter, or modify
+  as needed. In addition to count by analytics, adding a risk score may be useful.
+  In our testing, with 22 events over 30 days, the risk scores ranged from 500 to
+  80,000. Your organization will be different, monitor and modify as needed.
 references:
 - https://attack.mitre.org/tactics/TA0004/
 - https://research.splunk.com/stories/active_directory_privilege_escalation/
@@ -19,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Risk.calculated_risk_score
-  - All_Risk.annotations.mitre_attack.mitre_tactic_id
-  - All_Risk.annotations.mitre_attack.mitre_technique_id
-  - All_Risk.tag
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/attack_techniques/T1484/privesc/priv_esc.log
+  - data: 
+      https://raw.githubusercontent.com/splunk/attack_data/master/datasets/attack_techniques/T1484/privesc/priv_esc.log
     source: adlm
     sourcetype: stash
diff --git a/detections/endpoint/active_setup_registry_autostart.yml b/detections/endpoint/active_setup_registry_autostart.yml
index 54fc5de10f..462a27a787 100644
--- a/detections/endpoint/active_setup_registry_autostart.yml
+++ b/detections/endpoint/active_setup_registry_autostart.yml
@@ -5,16 +5,24 @@ date: '2024-11-14'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to the Active Setup registry for persistence and privilege escalation. It leverages data from the Endpoint.Registry data model, focusing on changes to the "StubPath" value within the "SOFTWARE\\Microsoft\\Active Setup\\Installed Components" path. This activity is significant as it is commonly used by malware, adware, and APTs to maintain persistence on compromised machines. If confirmed malicious, this could allow attackers to execute code upon system startup, potentially leading to further system compromise and unauthorized access.
+description: The following analytic detects suspicious modifications to the Active
+  Setup registry for persistence and privilege escalation. It leverages data from
+  the Endpoint.Registry data model, focusing on changes to the "StubPath" value within
+  the "SOFTWARE\\Microsoft\\Active Setup\\Installed Components" path. This activity
+  is significant as it is commonly used by malware, adware, and APTs to maintain persistence
+  on compromised machines. If confirmed malicious, this could allow attackers to execute
+  code upon system startup, potentially leading to further system compromise and unauthorized
+  access.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_value_name= "StubPath" Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Active
-  Setup\\Installed Components*") BY Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)`| `active_setup_registry_autostart_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name=
+  "StubPath" Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Active Setup\\Installed
+  Components*") BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.dest Registry.user |
+  `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `active_setup_registry_autostart_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -58,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1547.014/active_setup_stubpath/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1547.014/active_setup_stubpath/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/add_defaultuser_and_password_in_registry.yml b/detections/endpoint/add_defaultuser_and_password_in_registry.yml
index 65eaf67dd5..4f31669937 100644
--- a/detections/endpoint/add_defaultuser_and_password_in_registry.yml
+++ b/detections/endpoint/add_defaultuser_and_password_in_registry.yml
@@ -5,14 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects suspicious registry modifications that implement auto admin logon by adding DefaultUserName and DefaultPassword values. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon" registry path. This activity is significant because it is associated with BlackMatter ransomware, which uses this technique to automatically log on to compromised hosts and continue encryption after a safe mode boot. If confirmed malicious, this could allow attackers to maintain persistence and further encrypt the network, leading to significant data loss and operational disruption.
+description: The following analytic detects suspicious registry modifications that
+  implement auto admin logon by adding DefaultUserName and DefaultPassword values.
+  It leverages data from the Endpoint.Registry data model, specifically monitoring
+  changes to the "SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon" registry
+  path. This activity is significant because it is associated with BlackMatter ransomware,
+  which uses this technique to automatically log on to compromised hosts and continue
+  encryption after a safe mode boot. If confirmed malicious, this could allow attackers
+  to maintain persistence and further encrypt the network, leading to significant
+  data loss and operational disruption.
 data_source:
 - Sysmon EventID 13
 - Sysmon EventID 14
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*"
-  AND Registry.registry_value_name= DefaultPassword OR Registry.registry_value_name=
-  DefaultUserName) BY Registry.registry_path Registry.registry_key_name
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows
+  NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name= DefaultPassword
+  OR Registry.registry_value_name= DefaultUserName) BY Registry.registry_path Registry.registry_key_name
   Registry.registry_value_name Registry.dest Registry.registry_value_data Registry.process_guid
   | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `add_defaultuser_and_password_in_registry_filter`'
@@ -29,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: modified registry key $registry_key_name$ with registry value $registry_value_name$ to prepare autoadminlogon
+  message: modified registry key $registry_key_name$ with registry value $registry_value_name$
+    to prepare autoadminlogon
   mitre_attack_id:
   - T1552.002
   - T1552
@@ -51,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/add_or_set_windows_defender_exclusion.yml b/detections/endpoint/add_or_set_windows_defender_exclusion.yml
index 58eed027c5..77f36aa2e6 100644
--- a/detections/endpoint/add_or_set_windows_defender_exclusion.yml
+++ b/detections/endpoint/add_or_set_windows_defender_exclusion.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of commands to add or set exclusions in Windows Defender. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "Add-MpPreference" or "Set-MpPreference" with exclusion parameters. This activity is significant because adversaries often use it to bypass Windows Defender, allowing malicious code to execute undetected. If confirmed malicious, this behavior could enable attackers to evade antivirus detection, maintain persistence, and execute further malicious activities without interference from Windows Defender.
+description: The following analytic detects the use of commands to add or set exclusions
+  in Windows Defender. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on command-line executions involving "Add-MpPreference" or "Set-MpPreference"
+  with exclusion parameters. This activity is significant because adversaries often
+  use it to bypass Windows Defender, allowing malicious code to execute undetected.
+  If confirmed malicious, this behavior could enable attackers to evade antivirus
+  detection, maintain persistence, and execute further malicious activities without
+  interference from Windows Defender.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*Add-MpPreference *" OR Processes.process = "*Set-MpPreference *") AND Processes.process="*-exclusion*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `add_or_set_windows_defender_exclusion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Admin or user may choose to use this windows features. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*Add-MpPreference
+  *" OR Processes.process = "*Set-MpPreference *") AND Processes.process="*-exclusion*"
+  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `add_or_set_windows_defender_exclusion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Admin or user may choose to use this windows features. Filter
+  as needed.
 references:
 - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html
 - https://app.any.run/tasks/cf1245de-06a7-4366-8209-8e3006f2bfe5/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/adsisearcher_account_discovery.yml b/detections/endpoint/adsisearcher_account_discovery.yml
index 0c52ac376f..c0a9e2c9cd 100644
--- a/detections/endpoint/adsisearcher_account_discovery.yml
+++ b/detections/endpoint/adsisearcher_account_discovery.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for domain users. It leverages PowerShell Script Block Logging (EventCode=4104) to identify script blocks containing `[adsisearcher]`, `objectcategory=user`, and `.findAll()`. This activity is significant as it may indicate an attempt by adversaries or Red Teams to enumerate domain users for situational awareness and Active Directory discovery. If confirmed malicious, this could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
+description: The following analytic detects the use of the `[Adsisearcher]` type accelerator
+  in PowerShell to query Active Directory for domain users. It leverages PowerShell
+  Script Block Logging (EventCode=4104) to identify script blocks containing `[adsisearcher]`,
+  `objectcategory=user`, and `.findAll()`. This activity is significant as it may
+  indicate an attempt by adversaries or Red Teams to enumerate domain users for situational
+  awareness and Active Directory discovery. If confirmed malicious, this could lead
+  to further reconnaissance, privilege escalation, or lateral movement within the
+  network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*objectcategory=user*" ScriptBlockText = "*.findAll()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Computer ScriptBlockText UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adsisearcher_account_discovery_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText
+  = "*objectcategory=user*" ScriptBlockText = "*.findAll()*" | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode Computer ScriptBlockText UserID
+  | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `adsisearcher_account_discovery_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/002/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Powershell process having commandline "AdsiSearcher" used for user enumeration on $dest$
+  message: Powershell process having commandline "AdsiSearcher" used for user enumeration
+    on $dest$
   mitre_attack_id:
   - T1087.002
   - T1087
@@ -50,16 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - ComputerName
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher_powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher_powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml b/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml
index 312d7f81be..ba0a06b3d1 100644
--- a/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml
+++ b/detections/endpoint/allow_file_and_printing_sharing_in_firewall.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the modification of firewall settings to allow file and printer sharing. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving 'netsh' commands that enable file and printer sharing. This activity is significant because it can indicate an attempt by ransomware to discover and encrypt files on additional machines connected to the compromised host. If confirmed malicious, this could lead to widespread file encryption across the network, significantly increasing the impact of a ransomware attack.
+description: The following analytic detects the modification of firewall settings
+  to allow file and printer sharing. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on command-line executions involving 'netsh' commands
+  that enable file and printer sharing. This activity is significant because it can
+  indicate an attempt by ransomware to discover and encrypt files on additional machines
+  connected to the compromised host. If confirmed malicious, this could lead to widespread
+  file encryption across the network, significantly increasing the impact of a ransomware
+  attack.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*"  Processes.process="*enable=Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_file_and_printing_sharing_in_firewall_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network admin may modify this firewall feature that may cause this rule to be triggered.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process=
+  "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*"  Processes.process="*enable=Yes*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `allow_file_and_printing_sharing_in_firewall_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network admin may modify this firewall feature that may cause
+  this rule to be triggered.
 references:
 - https://community.fortinet.com:443/t5/FortiEDR/How-FortiEDR-detects-and-blocks-Revil-Ransomware-aka-sodinokibi/ta-p/189638?externalID=FD52469
 - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A suspicious modification of firewall to allow file and printer sharing detected on host - $dest$
+  message: A suspicious modification of firewall to allow file and printer sharing
+    detected on host - $dest$
   mitre_attack_id:
   - T1562.007
   - T1562
@@ -49,23 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml b/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml
index 5ad65b29b1..aba8f55110 100644
--- a/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml
+++ b/detections/endpoint/allow_inbound_traffic_by_firewall_rule_registry.yml
@@ -5,12 +5,19 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to firewall rule registry settings that allow inbound traffic on specific ports with a public profile. It leverages data from the Endpoint.Registry data model, focusing on registry paths and values indicative of such changes. This activity is significant as it may indicate an adversary attempting to grant remote access to a machine by modifying firewall rules. If confirmed malicious, this could enable unauthorized remote access, potentially leading to further exploitation, data exfiltration, or lateral movement within the network.
+description: The following analytic detects suspicious modifications to firewall rule
+  registry settings that allow inbound traffic on specific ports with a public profile.
+  It leverages data from the Endpoint.Registry data model, focusing on registry paths
+  and values indicative of such changes. This activity is significant as it may indicate
+  an adversary attempting to grant remote access to a machine by modifying firewall
+  rules. If confirmed malicious, this could enable unauthorized remote access, potentially
+  leading to further exploitation, data exfiltration, or lateral movement within the
+  network.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*"
   Registry.registry_value_data = "*|Action=Allow|*" Registry.registry_value_data =
   "*|Dir=In|*"  Registry.registry_value_data = "*|LPort=*") BY Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
@@ -30,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious firewall allow rule modifications were detected via the registry on endpoint $dest$ by user $user$.
+  message: Suspicious firewall allow rule modifications were detected via the registry
+    on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1021.001
   - T1021
@@ -60,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml b/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml
index fada60eed8..360b83dc9c 100644
--- a/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml
+++ b/detections/endpoint/allow_inbound_traffic_in_firewall_rule.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious PowerShell command that allows inbound traffic to a specific local port within the public profile. It leverages PowerShell script block logging (EventCode 4104) to identify commands containing keywords like "firewall," "Inbound," "Allow," and "-LocalPort." This activity is significant because it may indicate an attacker attempting to establish remote access by modifying firewall rules. If confirmed malicious, this could allow unauthorized access to the machine, potentially leading to further exploitation and data exfiltration.
+description: The following analytic detects a suspicious PowerShell command that allows
+  inbound traffic to a specific local port within the public profile. It leverages
+  PowerShell script block logging (EventCode 4104) to identify commands containing
+  keywords like "firewall," "Inbound," "Allow," and "-LocalPort." This activity is
+  significant because it may indicate an attacker attempting to establish remote access
+  by modifying firewall rules. If confirmed malicious, this could allow unauthorized
+  access to the machine, potentially leading to further exploitation and data exfiltration.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*firewall*" ScriptBlockText = "*Inbound*" ScriptBlockText = "*Allow*"  ScriptBlockText = "*-LocalPort*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_inbound_traffic_in_firewall_rule_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
-known_false_positives: administrator may allow inbound traffic in certain network or machine.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*firewall*" ScriptBlockText
+  = "*Inbound*" ScriptBlockText = "*Allow*"  ScriptBlockText = "*-LocalPort*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `allow_inbound_traffic_in_firewall_rule_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the powershell logs  from your endpoints. make sure you enable needed
+  registry to monitor this event.
+known_false_positives: administrator may allow inbound traffic in certain network
+  or machine.
 references:
 - https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/allow_inbound_traffic_in_firewall_rule/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/allow_inbound_traffic_in_firewall_rule/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/allow_network_discovery_in_firewall.yml b/detections/endpoint/allow_network_discovery_in_firewall.yml
index 2af995f13e..06a6138cd4 100644
--- a/detections/endpoint/allow_network_discovery_in_firewall.yml
+++ b/detections/endpoint/allow_network_discovery_in_firewall.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious modification to the firewall to allow network discovery on a machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving the 'netsh' command to enable network discovery. This activity is significant because it is commonly used by ransomware, such as REvil and RedDot, to discover and compromise additional machines on the network. If confirmed malicious, this could lead to widespread file encryption across multiple hosts, significantly amplifying the impact of the ransomware attack.
+description: The following analytic detects a suspicious modification to the firewall
+  to allow network discovery on a machine. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions involving the 'netsh'
+  command to enable network discovery. This activity is significant because it is
+  commonly used by ransomware, such as REvil and RedDot, to discover and compromise
+  additional machines on the network. If confirmed malicious, this could lead to widespread
+  file encryption across multiple hosts, significantly amplifying the impact of the
+  ransomware attack.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= "*firewall*" Processes.process= "*group=\"Network Discovery\"*"  Processes.process="*enable*" Processes.process="*Yes*" by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_network_discovery_in_firewall_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network admin may modify this firewall feature that may cause this rule to be triggered.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process=
+  "*firewall*" Processes.process= "*group=\"Network Discovery\"*"  Processes.process="*enable*"
+  Processes.process="*Yes*" by Processes.dest Processes.user Processes.parent_process
+  Processes.original_file_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `allow_network_discovery_in_firewall_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network admin may modify this firewall feature that may cause
+  this rule to be triggered.
 references:
 - https://community.fortinet.com:443/t5/FortiEDR/How-FortiEDR-detects-and-blocks-Revil-Ransomware-aka-sodinokibi/ta-p/189638?externalID=FD52469
 - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious modification to the firewall to allow network discovery detected on host - $dest$
+  message: Suspicious modification to the firewall to allow network discovery detected
+    on host - $dest$
   mitre_attack_id:
   - T1562.007
   - T1562
@@ -47,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/allow_operation_with_consent_admin.yml b/detections/endpoint/allow_operation_with_consent_admin.yml
index 7445a136dd..ddd8ef0eef 100644
--- a/detections/endpoint/allow_operation_with_consent_admin.yml
+++ b/detections/endpoint/allow_operation_with_consent_admin.yml
@@ -5,17 +5,25 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a registry modification that allows the 'Consent Admin' to perform operations requiring elevation without user consent or credentials. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the 'ConsentPromptBehaviorAdmin' value within the Windows Policies System registry path. This activity is significant as it indicates a potential privilege escalation attempt, which could allow an attacker to execute high-privilege tasks without user approval. If confirmed malicious, this could lead to unauthorized administrative access and control over the compromised machine, posing a severe security risk.
+description: The following analytic detects a registry modification that allows the
+  'Consent Admin' to perform operations requiring elevation without user consent or
+  credentials. It leverages data from the Endpoint.Registry data model, specifically
+  monitoring changes to the 'ConsentPromptBehaviorAdmin' value within the Windows
+  Policies System registry path. This activity is significant as it indicates a potential
+  privilege escalation attempt, which could allow an attacker to execute high-privilege
+  tasks without user approval. If confirmed malicious, this could lead to unauthorized
+  administrative access and control over the compromised machine, posing a severe
+  security risk.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*"
   Registry.registry_value_name = ConsentPromptBehaviorAdmin Registry.registry_value_data
-  = "0x00000000") BY Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `allow_operation_with_consent_admin_filter`'
+  = "0x00000000") BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.dest Registry.user |
+  `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `allow_operation_with_consent_admin_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -30,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious registry modification was performed on endpoint $dest$ by user $user$. This behavior is indicative of privilege escalation.
+  message: Suspicious registry modification was performed on endpoint $dest$ by user
+    $user$. This behavior is indicative of privilege escalation.
   mitre_attack_id:
   - T1548
   observable:
@@ -58,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/anomalous_usage_of_7zip.yml b/detections/endpoint/anomalous_usage_of_7zip.yml
index 32a263ba25..e0322b0bf1 100644
--- a/detections/endpoint/anomalous_usage_of_7zip.yml
+++ b/detections/endpoint/anomalous_usage_of_7zip.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of 7z.exe, a 7-Zip utility, spawned from rundll32.exe or dllhost.exe. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process names and parent processes. This activity is significant as it may indicate an adversary attempting to use 7-Zip for data exfiltration, often by renaming the executable to evade detection. If confirmed malicious, this could lead to unauthorized data archiving and exfiltration, compromising sensitive information and potentially leading to further system exploitation.
+description: The following analytic detects the execution of 7z.exe, a 7-Zip utility,
+  spawned from rundll32.exe or dllhost.exe. This behavior is identified using Endpoint
+  Detection and Response (EDR) telemetry, focusing on process names and parent processes.
+  This activity is significant as it may indicate an adversary attempting to use 7-Zip
+  for data exfiltration, often by renaming the executable to evade detection. If confirmed
+  malicious, this could lead to unauthorized data archiving and exfiltration, compromising
+  sensitive information and potentially leading to further system exploitation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `anomalous_usage_of_7zip_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as this behavior is not normal for `rundll32.exe` or `dllhost.exe` to spawn and run 7zip.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest
+  Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `anomalous_usage_of_7zip_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as this behavior is not normal
+  for `rundll32.exe` or `dllhost.exe` to spawn and run 7zip.
 references:
 - https://attack.mitre.org/techniques/T1560/001/
 - https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior is indicative of suspicious loading of 7zip.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$. This behavior is indicative of suspicious loading
+    of 7zip.
   mitre_attack_id:
   - T1560.001
   - T1560
@@ -61,21 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/any_powershell_downloadfile.yml b/detections/endpoint/any_powershell_downloadfile.yml
index 3963f88d3d..5193a68b57 100644
--- a/detections/endpoint/any_powershell_downloadfile.yml
+++ b/detections/endpoint/any_powershell_downloadfile.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerShell's `DownloadFile` method to download files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant as it is commonly used in malicious frameworks to download and execute additional payloads. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Analysts should investigate the source and destination of the download and review AMSI or PowerShell transaction logs for additional context.
+description: The following analytic detects the use of PowerShell's `DownloadFile`
+  method to download files. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs. This activity is significant as
+  it is commonly used in malicious frameworks to download and execute additional payloads.
+  If confirmed malicious, this could lead to unauthorized code execution, data exfiltration,
+  or further compromise of the system. Analysts should investigate the source and
+  destination of the download and review AMSI or PowerShell transaction logs for additional
+  context.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*DownloadFile* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `any_powershell_downloadfile_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*DownloadFile*
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.parent_process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `any_powershell_downloadfile_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering will need to occur
+  by parent process or command line argument. It may be required to modify this query
+  to an EDR product for more granular coverage.
 references:
 - https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadfile?view=net-5.0
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +68,9 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior identifies the use of DownloadFile within PowerShell.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$. This behavior identifies the use of DownloadFile
+    within PowerShell.
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -67,23 +96,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/any_powershell_downloadstring.yml b/detections/endpoint/any_powershell_downloadstring.yml
index 388550426c..8d1a35d1c7 100644
--- a/detections/endpoint/any_powershell_downloadstring.yml
+++ b/detections/endpoint/any_powershell_downloadstring.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerShell's `DownloadString` method to download files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because `DownloadString` is commonly used in malicious PowerShell scripts to fetch and execute remote code. If confirmed malicious, this behavior could allow an attacker to download and run arbitrary code, potentially leading to unauthorized access, data exfiltration, or further compromise of the affected system.
+description: The following analytic detects the use of PowerShell's `DownloadString`
+  method to download files. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs that include command-line details.
+  This activity is significant because `DownloadString` is commonly used in malicious
+  PowerShell scripts to fetch and execute remote code. If confirmed malicious, this
+  behavior could allow an attacker to download and run arbitrary code, potentially
+  leading to unauthorized access, data exfiltration, or further compromise of the
+  affected system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*.DownloadString*  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `any_powershell_downloadstring_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*.DownloadString*  by
+  Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `any_powershell_downloadstring_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering will need to occur
+  by parent process or command line argument. It may be required to modify this query
+  to an EDR product for more granular coverage.
 references:
 - https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadstring?view=net-5.0
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +68,9 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$. This behavior identifies the use of DownloadString within PowerShell.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$. This behavior identifies the use of DownloadString
+    within PowerShell.
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -67,23 +96,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/attacker_tools_on_endpoint.yml b/detections/endpoint/attacker_tools_on_endpoint.yml
index 275a50a887..d863edb9e3 100644
--- a/detections/endpoint/attacker_tools_on_endpoint.yml
+++ b/detections/endpoint/attacker_tools_on_endpoint.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of tools commonly exploited by cybercriminals, such as those used for unauthorized access, network scanning, or data exfiltration. It leverages process activity data from Endpoint Detection and Response (EDR) agents, focusing on known attacker tool names. This activity is significant because it serves as an early warning system for potential security incidents, enabling prompt response. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further network compromise, posing a severe threat to the organization's security infrastructure.
+description: The following analytic detects the execution of tools commonly exploited
+  by cybercriminals, such as those used for unauthorized access, network scanning,
+  or data exfiltration. It leverages process activity data from Endpoint Detection
+  and Response (EDR) agents, focusing on known attacker tool names. This activity
+  is significant because it serves as an early warning system for potential security
+  incidents, enabling prompt response. If confirmed malicious, this activity could
+  lead to unauthorized access, data theft, or further network compromise, posing a
+  severe threat to the organization's security infrastructure.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown by Processes.dest Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup attacker_tools attacker_tool_names AS process_name OUTPUT description | search description !=false| `attacker_tools_on_endpoint_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some administrator activity can be potentially triggered, please add those users to the filter macro.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Processes.process) as process values(Processes.parent_process)
+  as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown
+  Processes.user!=unknown by Processes.dest Processes.user Processes.process_name
+  Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `drop_dm_object_name(Processes)` | lookup attacker_tools attacker_tool_names AS
+  process_name OUTPUT description | search description !=false| `attacker_tools_on_endpoint_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some administrator activity can be potentially triggered, please
+  add those users to the filter macro.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An attacker tool $process_name$,listed in attacker_tools.csv is executed on host $dest$ by User $user$. This process $process_name$ is known to do- $description$
+  message: An attacker tool $process_name$,listed in attacker_tools.csv is executed
+    on host $dest$ by User $user$. This process $process_name$ is known to do- $description$
   mitre_attack_id:
   - T1036.005
   - T1036
@@ -56,15 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/attacker_scan_tools/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/attacker_scan_tools/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml b/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml
index c29e32282c..23d45433ec 100644
--- a/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml
+++ b/detections/endpoint/attempt_to_add_certificate_to_untrusted_store.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Patrick Bareiss, Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to add a certificate to the untrusted certificate store using the 'certutil -addstore' command. It leverages process activity and command-line arguments from Endpoint Detection and Response (EDR) logs mapped to the Splunk `Processes` data model. This activity is significant as it may indicate an attacker trying to disable security tools to gain unauthorized access. If confirmed malicious, this could lead to the compromise of system security, allowing attackers to bypass defenses and potentially escalate privileges or persist in the environment.
+description: The following analytic detects attempts to add a certificate to the untrusted
+  certificate store using the 'certutil -addstore' command. It leverages process activity
+  and command-line arguments from Endpoint Detection and Response (EDR) logs mapped
+  to the Splunk `Processes` data model. This activity is significant as it may indicate
+  an attacker trying to disable security tools to gain unauthorized access. If confirmed
+  malicious, this could lead to the compromise of system security, allowing attackers
+  to bypass defenses and potentially escalate privileges or persist in the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*-addstore*) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process)
+  as process max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil`
+  (Processes.process=*-addstore*) by Processes.dest Processes.user Processes.parent_process
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `attempt_to_add_certificate_to_untrusted_store_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: There may be legitimate reasons for administrators to add a
+  certificate to the untrusted certificate store. In such cases, this will typically
+  be done on a large number of systems.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1553.004/T1553.004.md
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to add a certificate to the store on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    attempting to add a certificate to the store on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1553.004
   - T1553
@@ -55,20 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/attempt_to_stop_security_service.yml b/detections/endpoint/attempt_to_stop_security_service.yml
index 460c91e134..87fa405ab4 100644
--- a/detections/endpoint/attempt_to_stop_security_service.yml
+++ b/detections/endpoint/attempt_to_stop_security_service.yml
@@ -5,14 +5,38 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to stop security-related services on an endpoint, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, specifically searching for processes involving the "sc.exe" command with the "stop" parameter. This activity is significant because disabling security services can undermine the organization's security posture, potentially leading to unauthorized access, data exfiltration, or further attacks like malware installation or privilege escalation. If confirmed malicious, this behavior could compromise the endpoint and the entire network, necessitating immediate investigation and response.
+description: The following analytic detects attempts to stop security-related services
+  on an endpoint, which may indicate malicious activity. It leverages data from Endpoint
+  Detection and Response (EDR) agents, specifically searching for processes involving
+  the "sc.exe" command with the "stop" parameter. This activity is significant because
+  disabling security services can undermine the organization's security posture, potentially
+  leading to unauthorized access, data exfiltration, or further attacks like malware
+  installation or privilege escalation. If confirmed malicious, this behavior could
+  compromise the endpoint and the entire network, necessitating immediate investigation
+  and response.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name = sc.exe Processes.process="* stop *" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |lookup security_services_lookup service as process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: None identified. Attempts to disable security-related services should be identified and understood.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_net` OR  Processes.process_name = sc.exe Processes.process="* stop
+  *" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` |lookup security_services_lookup service as
+  process OUTPUTNEW category, description | search category=security | `attempt_to_stop_security_service_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: None identified. Attempts to disable security-related services
+  should be identified and understood.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-14---disable-arbitrary-security-windows-service
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -22,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +65,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 40
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable security services on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    attempting to disable security services on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -61,23 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_defend_service_stop/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_defend_service_stop/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml b/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml
index 8c46a7d61b..f3a63b303b 100644
--- a/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml
+++ b/detections/endpoint/attempted_credential_dump_from_registry_via_reg_exe.yml
@@ -5,13 +5,36 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of reg.exe with parameters that export registry keys containing hashed credentials. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving reg.exe or cmd.exe with specific registry paths. This activity is significant because exporting these keys can allow attackers to obtain hashed credentials, which they may attempt to crack offline. If confirmed malicious, this could lead to unauthorized access to sensitive accounts, enabling further compromise and lateral movement within the network.
+description: The following analytic detects the execution of reg.exe with parameters
+  that export registry keys containing hashed credentials. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  involving reg.exe or cmd.exe with specific registry paths. This activity is significant
+  because exporting these keys can allow attackers to obtain hashed credentials, which
+  they may attempt to crack offline. If confirmed malicious, this could lead to unauthorized
+  access to sensitive accounts, enabling further compromise and lateral movement within
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg* OR Processes.process_name=cmd* Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name  Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `attempted_credential_dump_from_registry_via_reg_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg*
+  OR Processes.process_name=cmd* Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security*
+  OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System*
+  OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*)
+  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name  Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `attempted_credential_dump_from_registry_via_reg_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: None identified.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md#atomic-test-1---registry-dump-of-sam-creds-and-secrets
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export the registry keys.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to export the registry keys.
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -60,28 +89,17 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/crowdstrike_falcon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/crowdstrike_falcon.log
     source: crowdstrike
     sourcetype: crowdstrike:events:sensor
diff --git a/detections/endpoint/auto_admin_logon_registry_entry.yml b/detections/endpoint/auto_admin_logon_registry_entry.yml
index 20b67b6c7a..d6ca8f84f6 100644
--- a/detections/endpoint/auto_admin_logon_registry_entry.yml
+++ b/detections/endpoint/auto_admin_logon_registry_entry.yml
@@ -5,17 +5,25 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a suspicious registry modification that enables auto admin logon on a host. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "AutoAdminLogon" value within the "SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon" registry path. This activity is significant because it was observed in BlackMatter ransomware attacks to maintain access after a safe mode reboot, facilitating further encryption. If confirmed malicious, this could allow attackers to automatically log in and continue their operations, potentially leading to widespread network encryption and data loss.
+description: The following analytic detects a suspicious registry modification that
+  enables auto admin logon on a host. It leverages data from the Endpoint.Registry
+  data model, specifically looking for changes to the "AutoAdminLogon" value within
+  the "SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon" registry path. This
+  activity is significant because it was observed in BlackMatter ransomware attacks
+  to maintain access after a safe mode reboot, facilitating further encryption. If
+  confirmed malicious, this could allow attackers to automatically log in and continue
+  their operations, potentially leading to widespread network encryption and data
+  loss.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*"
-  AND Registry.registry_value_name=AutoAdminLogon AND Registry.registry_value_data=1)
-  BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid Registry.dest | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `auto_admin_logon_registry_entry_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*SOFTWARE\\Microsoft\\Windows
+  NT\\CurrentVersion\\Winlogon*" AND Registry.registry_value_name=AutoAdminLogon AND
+  Registry.registry_value_data=1) BY Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `auto_admin_logon_registry_entry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: modified registry key $registry_key_name$ with registry value $registry_value_name$ to prepare autoadminlogon
+  message: modified registry key $registry_key_name$ with registry value $registry_value_name$
+    to prepare autoadminlogon
   mitre_attack_id:
   - T1552.002
   - T1552
@@ -52,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/batch_file_write_to_system32.yml b/detections/endpoint/batch_file_write_to_system32.yml
index 3dbcd272a5..36f0587037 100644
--- a/detections/endpoint/batch_file_write_to_system32.yml
+++ b/detections/endpoint/batch_file_write_to_system32.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Steven Dick, Michael Haag, Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a batch file (.bat) within the Windows system directory tree, specifically in the System32 or SysWOW64 folders. It leverages data from the Endpoint datamodel, focusing on process and filesystem events to identify this behavior. This activity is significant because writing batch files to system directories can be indicative of malicious intent, such as persistence mechanisms or system manipulation. If confirmed malicious, this could allow an attacker to execute arbitrary commands with elevated privileges, potentially compromising the entire system.
+description: The following analytic detects the creation of a batch file (.bat) within
+  the Windows system directory tree, specifically in the System32 or SysWOW64 folders.
+  It leverages data from the Endpoint datamodel, focusing on process and filesystem
+  events to identify this behavior. This activity is significant because writing batch
+  files to system directories can be indicative of malicious intent, such as persistence
+  mechanisms or system manipulation. If confirmed malicious, this could allow an attacker
+  to execute arbitrary commands with elevated privileges, potentially compromising
+  the entire system.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.process_guid Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\system32\\*", "*\\syswow64\\*") Filesystem.file_name="*.bat" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)`] | table dest user file_create_time, file_name, file_path, process_name, firstTime, lastTime | dedup file_create_time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `batch_file_write_to_system32_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=* by _time span=1h Processes.process_guid Processes.process_name
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | join process_guid
+  [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\system32\\*",
+  "*\\syswow64\\*") Filesystem.file_name="*.bat" by _time span=1h Filesystem.dest
+  Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid
+  | `drop_dm_object_name(Filesystem)`] | table dest user file_create_time, file_name,
+  file_path, process_name, firstTime, lastTime | dedup file_create_time | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `batch_file_write_to_system32_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: It is possible for this search to generate a notable event
+  for a batch file write to a path that includes the string "system32", but is not
+  the actual Windows system directory. As such, you should confirm the path of the
+  batch file identified by the search. In addition, a false positive may be generated
+  by an administrator copying a legitimate batch file in this directory tree. You
+  should confirm that the activity is legitimate and modify the search to add exclusions,
+  as necessary.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -18,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A file - $file_name$ was written to system32 has occurred on endpoint $dest$ by user $user$.
+  message: A file - $file_name$ was written to system32 has occurred on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1204
   - T1204.002
@@ -48,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
-  - Processes.process_name
-  - Processes.dest
-  - Filesystem.process_guid
-  - Processes.process_guid
-  - Processes.dest
-  - Processes.process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml b/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml
index 5b90fb73ea..63234fc250 100644
--- a/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml
+++ b/detections/endpoint/bcdedit_command_back_to_normal_mode_boot.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a suspicious `bcdedit` command that reconfigures a host from safe mode back to normal boot. This detection leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions involving `bcdedit.exe` with specific parameters. This activity is significant as it may indicate the presence of ransomware, such as BlackMatter, which manipulates boot configurations to facilitate encryption processes. If confirmed malicious, this behavior could allow attackers to maintain control over the boot process, potentially leading to further system compromise and data encryption.
+description: The following analytic detects the execution of a suspicious `bcdedit`
+  command that reconfigures a host from safe mode back to normal boot. This detection
+  leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions
+  involving `bcdedit.exe` with specific parameters. This activity is significant as
+  it may indicate the presence of ransomware, such as BlackMatter, which manipulates
+  boot configurations to facilitate encryption processes. If confirmed malicious,
+  this behavior could allow attackers to maintain control over the boot process, potentially
+  leading to further system compromise and data encryption.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*/deletevalue*" Processes.process="*{current}*"  Processes.process="*safeboot*" by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_command_back_to_normal_mode_boot_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe
+  Processes.process="*/deletevalue*" Processes.process="*{current}*"  Processes.process="*safeboot*"
+  by Processes.process_name Processes.process Processes.parent_process_name Processes.dest
+  Processes.user |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `bcdedit_command_back_to_normal_mode_boot_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 50
-  message: bcdedit process with commandline $process$ to bring back to normal boot configuration the $dest$
+  message: bcdedit process with commandline $process$ to bring back to normal boot
+    configuration the $dest$
   mitre_attack_id:
   - T1490
   observable:
@@ -46,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/bcdedit_failure_recovery_modification.yml b/detections/endpoint/bcdedit_failure_recovery_modification.yml
index 832d34435d..195eec7786 100644
--- a/detections/endpoint/bcdedit_failure_recovery_modification.yml
+++ b/detections/endpoint/bcdedit_failure_recovery_modification.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications to the Windows error recovery boot configurations using bcdedit.exe with flags such as "recoveryenabled" and "no". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because ransomware often disables recovery options to prevent system restoration, making it crucial for SOC analysts to investigate. If confirmed malicious, this could hinder recovery efforts, allowing ransomware to cause extensive damage and complicate remediation.
+description: The following analytic detects modifications to the Windows error recovery
+  boot configurations using bcdedit.exe with flags such as "recoveryenabled" and "no".
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names, parent processes, and command-line executions. This activity is significant
+  because ransomware often disables recovery options to prevent system restoration,
+  making it crucial for SOC analysts to investigate. If confirmed malicious, this
+  could hinder recovery efforts, allowing ransomware to cause extensive damage and
+  complicate remediation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bcdedit_failure_recovery_modification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe
+  Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name
+  Processes.process Processes.parent_process_name Processes.dest Processes.user |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `bcdedit_failure_recovery_modification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators may modify the boot configuration.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting disable the ability to recover the endpoint.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting disable the ability to recover the
+    endpoint.
   mitre_attack_id:
   - T1490
   observable:
@@ -55,17 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/bits_job_persistence.yml b/detections/endpoint/bits_job_persistence.yml
index be88f81f67..42e18047e0 100644
--- a/detections/endpoint/bits_job_persistence.yml
+++ b/detections/endpoint/bits_job_persistence.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `bitsadmin.exe` to schedule a BITS job for persistence on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line parameters such as `create`, `addfile`, and `resume`. This activity is significant because BITS jobs can be used by attackers to maintain persistence, download malicious payloads, or exfiltrate data. If confirmed malicious, this could allow an attacker to persist in the environment, execute arbitrary code, or transfer sensitive information, necessitating further investigation and potential remediation.
+description: The following analytic detects the use of `bitsadmin.exe` to schedule
+  a BITS job for persistence on an endpoint. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on specific command-line parameters such as
+  `create`, `addfile`, and `resume`. This activity is significant because BITS jobs
+  can be used by attackers to maintain persistence, download malicious payloads, or
+  exfiltrate data. If confirmed malicious, this could allow an attacker to persist
+  in the environment, execute arbitrary code, or transfer sensitive information, necessitating
+  further investigation and potential remediation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*, *setcustomheaders*, *resume* ) by Processes.dest Processes.user Processes.original_file_name Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bits_job_persistence_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives will be present. Typically, applications will use `BitsAdmin.exe`. Any filtering should be done based on command-line arguments (legitimate applications) or parent process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process
+  IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*,
+  *setcustomheaders*, *resume* ) by Processes.dest Processes.user Processes.original_file_name
+  Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bits_job_persistence_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives will be present. Typically, applications
+  will use `BitsAdmin.exe`. Any filtering should be done based on command-line arguments
+  (legitimate applications) or parent process.
 references:
 - https://attack.mitre.org/techniques/T1197/
 - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/bitsadmin
@@ -24,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to persist using BITS.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to persist using BITS.
   mitre_attack_id:
   - T1197
   observable:
@@ -58,28 +87,17 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log
     source: crowdstrike
     sourcetype: crowdstrike:events:sensor
diff --git a/detections/endpoint/bitsadmin_download_file.yml b/detections/endpoint/bitsadmin_download_file.yml
index bbaf8f6457..4132252510 100644
--- a/detections/endpoint/bitsadmin_download_file.yml
+++ b/detections/endpoint/bitsadmin_download_file.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Sittikorn S
 status: production
 type: TTP
-description: The following analytic detects the use of `bitsadmin.exe` with the `transfer` parameter to download a remote object. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because `bitsadmin.exe` can be exploited to download and execute malicious files without immediate detection. If confirmed malicious, an attacker could use this technique to download and execute payloads, potentially leading to code execution, privilege escalation, or persistent access within the environment. Review parallel and child processes, especially `svchost.exe`, for associated artifacts.
+description: The following analytic detects the use of `bitsadmin.exe` with the `transfer`
+  parameter to download a remote object. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process and command-line telemetry. This
+  activity is significant because `bitsadmin.exe` can be exploited to download and
+  execute malicious files without immediate detection. If confirmed malicious, an
+  attacker could use this technique to download and execute payloads, potentially
+  leading to code execution, privilege escalation, or persistent access within the
+  environment. Review parallel and child processes, especially `svchost.exe`, for
+  associated artifacts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process IN ("*transfer*", "*addfile*") by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bitsadmin_download_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives, however it may be required to filter based on parent process name or network connection.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_bitsadmin` Processes.process
+  IN ("*transfer*", "*addfile*") by Processes.dest Processes.user Processes.parent_process
+  Processes.parent_process_name Processes.original_file_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `bitsadmin_download_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives, however it may be required to filter
+  based on parent process name or network connection.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/8eb52117b748d378325f7719554a896e37bccec7/atomics/T1105/T1105.md#atomic-test-9---windows---bitsadmin-bits-download
 - https://github.com/redcanaryco/atomic-red-team/blob/bc705cb7aaa5f26f2d96585fac8e4c7052df0ff9/atomics/T1197/T1197.md
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +65,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to download a file.
   mitre_attack_id:
   - T1197
   - T1105
@@ -63,28 +91,17 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/crowdstrike_falcon.log
     source: crowdstrike
     sourcetype: crowdstrike:events:sensor
diff --git a/detections/endpoint/certutil_download_with_urlcache_and_split_arguments.yml b/detections/endpoint/certutil_download_with_urlcache_and_split_arguments.yml
index b54ada68ab..a7a7e4dd7d 100644
--- a/detections/endpoint/certutil_download_with_urlcache_and_split_arguments.yml
+++ b/detections/endpoint/certutil_download_with_urlcache_and_split_arguments.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of certutil.exe to download files using the `-urlcache` and `-split` arguments. It leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions that include these specific arguments. This activity is significant because certutil.exe is typically used for certificate services, and its use to download files from remote locations is uncommon and potentially malicious. If confirmed, this behavior could indicate an attempt to download and execute malicious payloads, leading to potential system compromise and unauthorized data access.
+description: The following analytic detects the use of certutil.exe to download files
+  using the `-urlcache` and `-split` arguments. It leverages Endpoint Detection and
+  Response (EDR) data, focusing on command-line executions that include these specific
+  arguments. This activity is significant because certutil.exe is typically used for
+  certificate services, and its use to download files from remote locations is uncommon
+  and potentially malicious. If confirmed, this behavior could indicate an attempt
+  to download and execute malicious payloads, leading to potential system compromise
+  and unauthorized data access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*urlcache* Processes.process=*split*) OR Processes.process=*urlcache* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_download_with_urlcache_and_split_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*urlcache*
+  Processes.process=*split*) OR Processes.process=*urlcache* by Processes.dest Processes.user
+  Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.original_file_name Processes.parent_process_id |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `certutil_download_with_urlcache_and_split_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives in most environments, however tune
+  as needed based on parent-child relationship or network connection.
 references:
 - https://attack.mitre.org/techniques/T1105/
 - https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +65,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to download a file.
   mitre_attack_id:
   - T1105
   observable:
@@ -62,23 +90,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/certutil_download_with_verifyctl_and_split_arguments.yml b/detections/endpoint/certutil_download_with_verifyctl_and_split_arguments.yml
index 2f597123ce..097677fab5 100644
--- a/detections/endpoint/certutil_download_with_verifyctl_and_split_arguments.yml
+++ b/detections/endpoint/certutil_download_with_verifyctl_and_split_arguments.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `certutil.exe` to download files using the `-VerifyCtl` and `-split` arguments. This behavior is identified by monitoring command-line executions for these specific arguments via Endpoint Detection and Response (EDR) telemetry. This activity is significant because `certutil.exe` is a legitimate tool often abused by attackers to download and execute malicious payloads. If confirmed malicious, this could allow an attacker to download and execute arbitrary files, potentially leading to code execution, data exfiltration, or further compromise of the system.
+description: The following analytic detects the use of `certutil.exe` to download
+  files using the `-VerifyCtl` and `-split` arguments. This behavior is identified
+  by monitoring command-line executions for these specific arguments via Endpoint
+  Detection and Response (EDR) telemetry. This activity is significant because `certutil.exe`
+  is a legitimate tool often abused by attackers to download and execute malicious
+  payloads. If confirmed malicious, this could allow an attacker to download and execute
+  arbitrary files, potentially leading to code execution, data exfiltration, or further
+  compromise of the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*verifyctl* Processes.process=*split*) OR Processes.process=*verifyctl* by Processes.dest Processes.user Processes.original_file_name Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_download_with_verifyctl_and_split_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_certutil` (Processes.process=*verifyctl*
+  Processes.process=*split*) OR Processes.process=*verifyctl* by Processes.dest Processes.user
+  Processes.original_file_name Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `certutil_download_with_verifyctl_and_split_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives in most environments, however tune
+  as needed based on parent-child relationship or network connection.
 references:
 - https://attack.mitre.org/techniques/T1105/
 - https://www.hexacorn.com/blog/2020/08/23/certutil-one-more-gui-lolbin/
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to download a file.
   mitre_attack_id:
   - T1105
   observable:
@@ -59,23 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/certutil_exe_certificate_extraction.yml b/detections/endpoint/certutil_exe_certificate_extraction.yml
index 14a797e983..e27aaa024c 100644
--- a/detections/endpoint/certutil_exe_certificate_extraction.yml
+++ b/detections/endpoint/certutil_exe_certificate_extraction.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the use of certutil.exe with arguments indicating the manipulation or extraction of certificates. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because extracting certificates can allow attackers to sign new authentication tokens, particularly in federated environments like Windows ADFS. If confirmed malicious, this could enable attackers to forge authentication tokens, potentially leading to unauthorized access and privilege escalation within the network.
+description: The following analytic identifies the use of certutil.exe with arguments
+  indicating the manipulation or extraction of certificates. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process names and command-line
+  arguments. This activity is significant because extracting certificates can allow
+  attackers to sign new authentication tokens, particularly in federated environments
+  like Windows ADFS. If confirmed malicious, this could enable attackers to forge
+  authentication tokens, potentially leading to unauthorized access and privilege
+  escalation within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "*-exportPFX*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe
+  Processes.process = "*-exportPFX*" by Processes.dest Processes.user Processes.parent_process
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `certutil_exe_certificate_extraction_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unless there are specific use cases, manipulating or exporting
+  certificates using certutil is uncommon. Extraction of certificate has been observed
+  during attacks such as Golden SAML and other campaigns targeting Federated services.
 references:
 - https://blog.sygnia.co/detection-and-hunting-of-golden-saml-attack
 - https://strontic.github.io/xcyclopedia/library/certutil.exe-09A8A29BAA3A451713FD3D07943B4A43.html
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting export a certificate.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting export a certificate.
   observable:
   - name: user
     type: User
@@ -56,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/certutil_with_decode_argument.yml b/detections/endpoint/certutil_with_decode_argument.yml
index bb5eb618ff..38313937a0 100644
--- a/detections/endpoint/certutil_with_decode_argument.yml
+++ b/detections/endpoint/certutil_with_decode_argument.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of CertUtil.exe with the 'decode' argument, which may indicate an attempt to decode a previously encoded file, potentially containing malicious payloads. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving CertUtil.exe. This activity is significant because attackers often use CertUtil to decode malicious files downloaded from the internet, which are then executed to compromise the system. If confirmed malicious, this activity could lead to unauthorized code execution, further system compromise, and potential data exfiltration.
+description: The following analytic detects the use of CertUtil.exe with the 'decode'
+  argument, which may indicate an attempt to decode a previously encoded file, potentially
+  containing malicious payloads. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions involving CertUtil.exe.
+  This activity is significant because attackers often use CertUtil to decode malicious
+  files downloaded from the internet, which are then executed to compromise the system.
+  If confirmed malicious, this activity could lead to unauthorized code execution,
+  further system compromise, and potential data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process=*decode* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `certutil_with_decode_argument_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Typically seen used to `encode` files, but it is possible to see legitimate use of `decode`. Filter based on parent-child relationship, file paths, endpoint or user.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process=*decode*
+  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `certutil_with_decode_argument_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Typically seen used to `encode` files, but it is possible to
+  see legitimate use of `decode`. Filter based on parent-child relationship, file
+  paths, endpoint or user.
 references:
 - https://attack.mitre.org/techniques/T1140/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1140/T1140.md
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to decode a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to decode a file.
   mitre_attack_id:
   - T1140
   observable:
@@ -64,23 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/change_default_file_association.yml b/detections/endpoint/change_default_file_association.yml
index 17acc1ea8e..8d19cb7506 100644
--- a/detections/endpoint/change_default_file_association.yml
+++ b/detections/endpoint/change_default_file_association.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious registry modifications that change the default file association to execute a malicious payload. It leverages data from the Endpoint data model, specifically monitoring registry paths under "*\\shell\\open\\command\\*" and "*HKCR\\*". This activity is significant because altering default file associations can allow attackers to execute arbitrary scripts or payloads when a user opens a file, leading to potential code execution. If confirmed malicious, this technique can enable attackers to persist on the compromised host and execute further malicious commands, posing a severe threat to the environment.
+description: The following analytic detects suspicious registry modifications that
+  change the default file association to execute a malicious payload. It leverages
+  data from the Endpoint data model, specifically monitoring registry paths under
+  "*\\shell\\open\\command\\*" and "*HKCR\\*". This activity is significant because
+  altering default file associations can allow attackers to execute arbitrary scripts
+  or payloads when a user opens a file, leading to potential code execution. If confirmed
+  malicious, this technique can enable attackers to persist on the compromised host
+  and execute further malicious commands, posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\shell\\open\\command\\*" Registry.registry_path = "*HKCR\\*" by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `change_default_file_association_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path
+  ="*\\shell\\open\\command\\*" Registry.registry_path = "*HKCR\\*" by Registry.dest  Registry.user
+  Registry.registry_path Registry.registry_key_name Registry.registry_value_name |
+  `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`
+  | `change_default_file_association_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
 known_false_positives: unknown
 references:
 - https://dmcxblue.gitbook.io/red-team-notes-2-0/red-team-techniques/privilege-escalation/untitled-3/accessibility-features
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.001/txtfile_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.001/txtfile_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/change_to_safe_mode_with_network_config.yml b/detections/endpoint/change_to_safe_mode_with_network_config.yml
index 874fc0e64b..ae641290f0 100644
--- a/detections/endpoint/change_to_safe_mode_with_network_config.yml
+++ b/detections/endpoint/change_to_safe_mode_with_network_config.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a suspicious `bcdedit` command that configures a host to boot in safe mode with network support. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving `bcdedit.exe` with specific parameters. This activity is significant because it is a known technique used by BlackMatter ransomware to force a compromised host into safe mode for continued encryption. If confirmed malicious, this could allow attackers to bypass certain security controls, persist in the environment, and continue their malicious activities.
+description: The following analytic detects the execution of a suspicious `bcdedit`
+  command that configures a host to boot in safe mode with network support. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions involving `bcdedit.exe` with specific parameters. This activity is significant
+  because it is a known technique used by BlackMatter ransomware to force a compromised
+  host into safe mode for continued encryption. If confirmed malicious, this could
+  allow attackers to bypass certain security controls, persist in the environment,
+  and continue their malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*/set*" Processes.process="*{current}*"  Processes.process="*safeboot*" Processes.process="*network*" by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `change_to_safe_mode_with_network_config_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe
+  Processes.process="*/set*" Processes.process="*{current}*"  Processes.process="*safeboot*"
+  Processes.process="*network*" by Processes.process_name Processes.process Processes.parent_process_name
+  Processes.dest Processes.user |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `change_to_safe_mode_with_network_config_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.002/autoadminlogon/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/chcp_command_execution.yml b/detections/endpoint/chcp_command_execution.yml
index b47073c1ce..beaa9e74bd 100644
--- a/detections/endpoint/chcp_command_execution.yml
+++ b/detections/endpoint/chcp_command_execution.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the chcp.exe application, which is used to change the active code page of the console. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where chcp.exe is executed by cmd.exe with specific command-line arguments. This activity is significant because it can indicate the presence of malware, such as IcedID, which uses this technique to determine the locale region, language, or country of the compromised host. If confirmed malicious, this could lead to further system compromise and data exfiltration.
+description: The following analytic detects the execution of the chcp.exe application,
+  which is used to change the active code page of the console. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process creation
+  events where chcp.exe is executed by cmd.exe with specific command-line arguments.
+  This activity is significant because it can indicate the presence of malware, such
+  as IcedID, which uses this technique to determine the locale region, language, or
+  country of the compromised host. If confirmed malicious, this could lead to further
+  system compromise and data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=chcp.com Processes.parent_process_name = cmd.exe (Processes.parent_process=*/c* OR Processes.parent_process=*/k*) by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `chcp_command_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: other tools or script may used this to change code page to UTF-* or others
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=chcp.com
+  Processes.parent_process_name = cmd.exe (Processes.parent_process=*/c* OR Processes.parent_process=*/k*)
+  by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process
+  Processes.process_id Processes.parent_process_id Processes.dest Processes.user |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `chcp_command_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: other tools or script may used this to change code page to
+  UTF-* or others
 references:
 - https://ss64.com/nt/chcp.html
 - https://twitter.com/tccontre18/status/1419941156633329665?s=20
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: parent process $parent_process_name$ spawning chcp process $process_name$ with parent command line $parent_process$
+  message: parent process $parent_process_name$ spawning chcp process $process_name$
+    with parent command line $parent_process$
   mitre_attack_id:
   - T1059
   observable:
@@ -49,20 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - process
-  - parent_process_name
-  - parent_process
-  - process_id
-  - parent_process_id
-  - dest
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/check_elevated_cmd_using_whoami.yml b/detections/endpoint/check_elevated_cmd_using_whoami.yml
index 46fe01113f..91c505d390 100644
--- a/detections/endpoint/check_elevated_cmd_using_whoami.yml
+++ b/detections/endpoint/check_elevated_cmd_using_whoami.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of the 'whoami' command with specific parameters to check for elevated privileges. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because it is commonly used by attackers, such as FIN7, to perform reconnaissance on a compromised host. If confirmed malicious, this behavior could indicate an attacker is assessing their privilege level, potentially leading to further privilege escalation or persistence within the environment.
+description: The following analytic identifies the execution of the 'whoami' command
+  with specific parameters to check for elevated privileges. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process and command-line
+  telemetry. This activity is significant because it is commonly used by attackers,
+  such as FIN7, to perform reconnaissance on a compromised host. If confirmed malicious,
+  this behavior could indicate an attacker is assessing their privilege level, potentially
+  leading to further privilege escalation or persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*whoami*" Processes.process = "*/group*" Processes.process = "* find *" Processes.process = "*12288*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `check_elevated_cmd_using_whoami_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*whoami*"
+  Processes.process = "*/group*" Processes.process = "* find *" Processes.process
+  = "*12288*" by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `check_elevated_cmd_using_whoami_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references: []
 drilldown_searches:
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/child_processes_of_spoolsv_exe.yml b/detections/endpoint/child_processes_of_spoolsv_exe.yml
index 0aca93f1a5..8913e100f2 100644
--- a/detections/endpoint/child_processes_of_spoolsv_exe.yml
+++ b/detections/endpoint/child_processes_of_spoolsv_exe.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies child processes spawned by spoolsv.exe, the Print Spooler service in Windows, which typically runs with SYSTEM privileges. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. Monitoring this activity is crucial as it can indicate exploitation attempts, such as those associated with CVE-2018-8440, which can lead to privilege escalation. If confirmed malicious, attackers could gain SYSTEM-level access, allowing them to execute arbitrary code, escalate privileges, and potentially compromise the entire system.
+description: The following analytic identifies child processes spawned by spoolsv.exe,
+  the Print Spooler service in Windows, which typically runs with SYSTEM privileges.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process and parent process relationships. Monitoring this activity is
+  crucial as it can indicate exploitation attempts, such as those associated with
+  CVE-2018-8440, which can lead to privilege escalation. If confirmed malicious, attackers
+  could gain SYSTEM-level access, allowing them to execute arbitrary code, escalate
+  privileges, and potentially compromise the entire system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe
+  AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `child_processes_of_spoolsv_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legitimate printer-related processes may show up as children
+  of spoolsv.exe. You should confirm that any activity as legitimate and may be added
+  as exclusions in the search.
 references: []
 tags:
   analytic_story:
@@ -40,13 +62,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
-  - Processes.parent_process
-  - Processes.user
   security_domain: endpoint
diff --git a/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml b/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml
index 1d3a90c59e..69f40cb70f 100644
--- a/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml
+++ b/detections/endpoint/clear_unallocated_sector_using_cipher_app.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `cipher.exe` with the `/w` flag to clear unallocated sectors on a disk. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent processes. This activity is significant because it is a technique used by ransomware to prevent forensic recovery of deleted files. If confirmed malicious, this action could hinder incident response efforts by making it impossible to recover critical data, thereby complicating the investigation and remediation process.
+description: The following analytic detects the execution of `cipher.exe` with the
+  `/w` flag to clear unallocated sectors on a disk. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, command-line arguments,
+  and parent processes. This activity is significant because it is a technique used
+  by ransomware to prevent forensic recovery of deleted files. If confirmed malicious,
+  this action could hinder incident response efforts by making it impossible to recover
+  critical data, thereby complicating the investigation and remediation process.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe" Processes.process = "*/w:*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clear_unallocated_sector_using_cipher_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe"
+  Processes.process = "*/w:*" by Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `clear_unallocated_sector_using_cipher_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: administrator may execute this app to manage disk
 references:
 - https://unit42.paloaltonetworks.com/vatet-pyxie-defray777/3/
@@ -22,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to clear the unallocated sectors of a specific disk.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to clear the unallocated sectors
+    of a specific disk.
   mitre_attack_id:
   - T1070.004
   - T1070
@@ -56,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/clop_common_exec_parameter.yml b/detections/endpoint/clop_common_exec_parameter.yml
index ed97027cad..3af1ccbc40 100644
--- a/detections/endpoint/clop_common_exec_parameter.yml
+++ b/detections/endpoint/clop_common_exec_parameter.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of CLOP ransomware variants using specific arguments ("runrun" or "temp.dat") to trigger their malicious activities. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it indicates potential ransomware behavior, which can lead to file encryption on network shares or local machines. If confirmed malicious, this activity could result in significant data loss and operational disruption due to encrypted files, highlighting the need for immediate investigation and response.
+description: The following analytic identifies the execution of CLOP ransomware variants
+  using specific arguments ("runrun" or "temp.dat") to trigger their malicious activities.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line arguments. Monitoring this activity is
+  crucial as it indicates potential ransomware behavior, which can lead to file encryption
+  on network shares or local machines. If confirmed malicious, this activity could
+  result in significant data loss and operational disruption due to encrypted files,
+  highlighting the need for immediate investigation and response.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*"
+  Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.dest
+  Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_common_exec_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Operators can execute third party tools using these parameters.
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting using arguments to execute its main code or feature of its code related to Clop ransomware.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting using arguments to execute its main
+    code or feature of its code related to Clop ransomware.
   mitre_attack_id:
   - T1204
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/clop_ransomware_known_service_name.yml b/detections/endpoint/clop_ransomware_known_service_name.yml
index 7aecf74a05..a3eaccdbfc 100644
--- a/detections/endpoint/clop_ransomware_known_service_name.yml
+++ b/detections/endpoint/clop_ransomware_known_service_name.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras
 status: production
 type: TTP
-description: The following analytic identifies the creation of a service with a known name used by CLOP ransomware for persistence and high-privilege code execution. It detects this activity by monitoring Windows Event Logs (EventCode 7045) for specific service names ("SecurityCenterIBM", "WinCheckDRVs"). This activity is significant because the creation of such services is a common tactic used by ransomware to maintain control over infected systems. If confirmed malicious, this could allow attackers to execute code with elevated privileges, maintain persistence, and potentially disrupt or encrypt critical data.
+description: The following analytic identifies the creation of a service with a known
+  name used by CLOP ransomware for persistence and high-privilege code execution.
+  It detects this activity by monitoring Windows Event Logs (EventCode 7045) for specific
+  service names ("SecurityCenterIBM", "WinCheckDRVs"). This activity is significant
+  because the creation of such services is a common tactic used by ransomware to maintain
+  control over infected systems. If confirmed malicious, this could allow attackers
+  to execute code with elevated privileges, maintain persistence, and potentially
+  disrupt or encrypt critical data.
 data_source:
 - Windows Event Log System 7045
-search: '`wineventlog_system` EventCode=7045 ServiceName IN ("SecurityCenterIBM", "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ServiceName StartType ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
+search: '`wineventlog_system` EventCode=7045 ServiceName IN ("SecurityCenterIBM",
+  "WinCheckDRVs") | stats count min(_time) as firstTime max(_time) as lastTime by
+  Computer EventCode ServiceName StartType ServiceType | rename Computer as dest |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `clop_ransomware_known_service_name_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - cmdline
-  - _time
-  - parent_process_name
-  - process_name
-  - OriginalFileName
-  - process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/cmd_carry_out_string_command_parameter.yml b/detections/endpoint/cmd_carry_out_string_command_parameter.yml
index 84914fbcf8..e8edabef6c 100644
--- a/detections/endpoint/cmd_carry_out_string_command_parameter.yml
+++ b/detections/endpoint/cmd_carry_out_string_command_parameter.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Teoderick Contreras, Bhavin Patel, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of `cmd.exe /c` to execute commands, a technique often employed by adversaries and malware to run batch commands or invoke other shells like PowerShell. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. Monitoring this activity is crucial as it can indicate script-based attacks or unauthorized command execution. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, or persistence within the environment.
+description: The following analytic detects the use of `cmd.exe /c` to execute commands,
+  a technique often employed by adversaries and malware to run batch commands or invoke
+  other shells like PowerShell. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions and process metadata.
+  Monitoring this activity is crucial as it can indicate script-based attacks or unauthorized
+  command execution. If confirmed malicious, this behavior could lead to unauthorized
+  code execution, privilege escalation, or persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` AND Processes.process="* /c*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_carry_out_string_command_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be high based on legitimate scripted code in any environment. Filter as needed.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_cmd` AND Processes.process="*
+  /c*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `cmd_carry_out_string_command_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be high based on legitimate scripted code
+  in any environment. Filter as needed.
 references:
 - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -43,7 +63,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting spawn a new process.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting spawn a new process.
   mitre_attack_id:
   - T1059.003
   - T1059
@@ -60,21 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/cmd_carry_str_param/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/cmd_carry_str_param/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/cmd_echo_pipe___escalation.yml b/detections/endpoint/cmd_echo_pipe___escalation.yml
index 5c532bc16a..b951f0dec8 100644
--- a/detections/endpoint/cmd_echo_pipe___escalation.yml
+++ b/detections/endpoint/cmd_echo_pipe___escalation.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the use of named-pipe impersonation for privilege escalation, commonly associated with Cobalt Strike and similar frameworks. It detects command-line executions where `cmd.exe` uses `echo` to write to a named pipe, such as `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant as it indicates potential privilege escalation attempts. If confirmed malicious, attackers could gain elevated privileges, enabling further compromise and persistence within the environment.
+description: The following analytic identifies the use of named-pipe impersonation
+  for privilege escalation, commonly associated with Cobalt Strike and similar frameworks.
+  It detects command-line executions where `cmd.exe` uses `echo` to write to a named
+  pipe, such as `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process and
+  command-line telemetry. This activity is significant as it indicates potential privilege
+  escalation attempts. If confirmed malicious, attackers could gain elevated privileges,
+  enabling further compromise and persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` OR Processes.process=*%comspec%* (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_echo_pipe___escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unknown. It is possible filtering may be required to ensure fidelity.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_cmd` OR Processes.process=*%comspec%*
+  (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.dest Processes.user
+  Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_echo_pipe___escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unknown. It is possible filtering may be required to ensure
+  fidelity.
 references:
 - https://redcanary.com/threat-detection-report/threats/cobalt-strike/
 - https://github.com/rapid7/meterpreter/blob/master/source/extensions/priv/server/elevate/namedpipe.c
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +59,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ potentially performing privilege escalation using named pipes related to Cobalt Strike and other frameworks.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ potentially performing privilege escalation
+    using named pipes related to Cobalt Strike and other frameworks.
   mitre_attack_id:
   - T1059
   - T1059.003
@@ -60,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/cmdline_tool_not_executed_in_cmd_shell.yml b/detections/endpoint/cmdline_tool_not_executed_in_cmd_shell.yml
index f6e4bdda44..32e384d3f4 100644
--- a/detections/endpoint/cmdline_tool_not_executed_in_cmd_shell.yml
+++ b/detections/endpoint/cmdline_tool_not_executed_in_cmd_shell.yml
@@ -5,14 +5,42 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where `ipconfig.exe`, `systeminfo.exe`, or similar tools are executed by a non-standard parent process, excluding CMD, PowerShell, or Explorer. This detection leverages Endpoint Detection and Response (EDR) telemetry to monitor process creation events. Such behavior is significant as it may indicate adversaries using injected processes to perform system discovery, a tactic observed in FIN7's JSSLoader. If confirmed malicious, this activity could allow attackers to gather critical host information, aiding in further exploitation or lateral movement within the network.
+description: The following analytic identifies instances where `ipconfig.exe`, `systeminfo.exe`,
+  or similar tools are executed by a non-standard parent process, excluding CMD, PowerShell,
+  or Explorer. This detection leverages Endpoint Detection and Response (EDR) telemetry
+  to monitor process creation events. Such behavior is significant as it may indicate
+  adversaries using injected processes to perform system discovery, a tactic observed
+  in FIN7's JSSLoader. If confirmed malicious, this activity could allow attackers
+  to gather critical host information, aiding in further exploitation or lateral movement
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "ipconfig.exe" OR Processes.process_name = "systeminfo.exe" OR Processes.process_name = "net.exe" OR Processes.process_name = "net1.exe" OR Processes.process_name = "arp.exe" OR Processes.process_name = "nslookup.exe" OR Processes.process_name = "route.exe" OR Processes.process_name = "netstat.exe" OR Processes.process_name = "whoami.exe") AND NOT (Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell*" OR Processes.parent_process_name="pwsh.exe" OR Processes.parent_process_name = "explorer.exe") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmdline_tool_not_executed_in_cmd_shell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A network operator or systems administrator may utilize an automated host discovery application that may generate false positives. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "ipconfig.exe"
+  OR Processes.process_name = "systeminfo.exe" OR Processes.process_name = "net.exe"
+  OR Processes.process_name = "net1.exe" OR Processes.process_name = "arp.exe" OR
+  Processes.process_name = "nslookup.exe" OR Processes.process_name = "route.exe"
+  OR Processes.process_name = "netstat.exe" OR Processes.process_name = "whoami.exe")
+  AND NOT (Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name
+  = "powershell*" OR Processes.parent_process_name="pwsh.exe" OR Processes.parent_process_name
+  = "explorer.exe") by Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process_id Processes.process Processes.dest
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `cmdline_tool_not_executed_in_cmd_shell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A network operator or systems administrator may utilize an
+  automated host discovery application that may generate false positives. Filter as
+  needed.
 references:
 - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation
 - https://attack.mitre.org/groups/G0046/
@@ -23,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +72,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A non-standard parent process $parent_process_name$ spawned child process $process_name$ to execute command-line tool on $dest$.
+  message: A non-standard parent process $parent_process_name$ spawned child process
+    $process_name$ to execute command-line tool on $dest$.
   mitre_attack_id:
   - T1059
   - T1059.007
@@ -64,23 +98,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/jssloader/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/jssloader/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml b/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml
index ef13990bc1..8f1acb16d4 100644
--- a/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml
+++ b/detections/endpoint/cmlua_or_cmstplua_uac_bypass.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of COM objects like CMLUA or CMSTPLUA to bypass User Account Control (UAC). It leverages Sysmon EventCode 7 to identify the loading of specific DLLs (CMLUA.dll, CMSTPLUA.dll, CMLUAUTIL.dll) by processes not typically associated with these libraries. This activity is significant as it indicates an attempt to gain elevated privileges, a common tactic used by ransomware adversaries. If confirmed malicious, this could allow attackers to execute code with administrative rights, leading to potential system compromise and further malicious activities.
+description: The following analytic detects the use of COM objects like CMLUA or CMSTPLUA
+  to bypass User Account Control (UAC). It leverages Sysmon EventCode 7 to identify
+  the loading of specific DLLs (CMLUA.dll, CMSTPLUA.dll, CMLUAUTIL.dll) by processes
+  not typically associated with these libraries. This activity is significant as it
+  indicates an attempt to gain elevated privileges, a common tactic used by ransomware
+  adversaries. If confirmed malicious, this could allow attackers to execute code
+  with administrative rights, leading to potential system compromise and further malicious
+  activities.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7  ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by dest Image ImageLoaded process_name EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Legitimate windows application that are not on the list loading this dll. Filter as needed.
+search: '`sysmon` EventCode=7  ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll",
+  "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*",
+  "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime
+  by dest Image ImageLoaded process_name EventCode Signed ProcessId | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `cmlua_or_cmstplua_uac_bypass_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: Legitimate windows application that are not on the list loading
+  this dll. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/003/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$
+  message: The following module $ImageLoaded$ was loaded by a non-standard application
+    on endpoint $dest$
   mitre_attack_id:
   - T1218
   - T1218.003
@@ -44,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/darkside_cmstp_com/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/darkside_cmstp_com/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/cobalt_strike_named_pipes.yml b/detections/endpoint/cobalt_strike_named_pipes.yml
index 8691858d35..70a808a375 100644
--- a/detections/endpoint/cobalt_strike_named_pipes.yml
+++ b/detections/endpoint/cobalt_strike_named_pipes.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects the use of default or publicly known named
+  pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify
+  specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable
+  C2 Profiles. This activity is significant because Cobalt Strike is a popular tool
+  for adversaries to conduct post-exploitation tasks, and identifying its named pipes
+  can reveal potential malicious activity. If confirmed malicious, this could indicate
+  an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration,
+  or further lateral movement within the network.
 data_source:
 - Sysmon EventID 17
 - Sysmon EventID 18
-search: '`sysmon` EventID=17 OR EventID=18 PipeName IN (\\msagent_*, \\DserNamePipe*, \\srvsvc_*, \\postex_*, \\status_*, \\MSSE-*, \\spoolss_*, \\win_svc*, \\ntsvcs*, \\winsock*, \\UIA_PIPE*)  | stats count min(_time) as firstTime max(_time) as lastTime by dest, process_name, process_id process_path, PipeName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cobalt_strike_named_pipes_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some of the named pipes identified and added may cause false positives. Filter by process name or pipe name to reduce false positives.
+search: '`sysmon` EventID=17 OR EventID=18 PipeName IN (\\msagent_*, \\DserNamePipe*,
+  \\srvsvc_*, \\postex_*, \\status_*, \\MSSE-*, \\spoolss_*, \\win_svc*, \\ntsvcs*,
+  \\winsock*, \\UIA_PIPE*)  | stats count min(_time) as firstTime max(_time) as lastTime
+  by dest, process_name, process_id process_path, PipeName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `cobalt_strike_named_pipes_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: The idea of using named pipes with Cobalt Strike is to blend
+  in. Therefore, some of the named pipes identified and added may cause false positives.
+  Filter by process name or pipe name to reduce false positives.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes
@@ -25,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ accessing known suspicious named pipes related to Cobalt Strike.
+  message: An instance of $process_name$ was identified on endpoint $dest$ accessing
+    known suspicious named pipes related to Cobalt Strike.
   mitre_attack_id:
   - T1055
   observable:
@@ -56,18 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - PipeName
-  - dest
-  - process_name
-  - process_path
-  - process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/common_ransomware_extensions.yml b/detections/endpoint/common_ransomware_extensions.yml
index 8e34f5c48e..73b9f026e9 100644
--- a/detections/endpoint/common_ransomware_extensions.yml
+++ b/detections/endpoint/common_ransomware_extensions.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: David Dorsey, Michael Haag, Splunk, Steven Dick
 status: production
 type: Hunting
-description: The following analytic detects modifications to files with extensions commonly associated with ransomware. It leverages the Endpoint.Filesystem data model to identify changes in file extensions that match known ransomware patterns. This activity is significant because it suggests an attacker is attempting to encrypt or alter files, potentially leading to severe data loss and operational disruption. If confirmed malicious, this activity could result in the encryption of critical data, rendering it inaccessible and causing significant damage to the organization's data integrity and availability.
+description: The following analytic detects modifications to files with extensions
+  commonly associated with ransomware. It leverages the Endpoint.Filesystem data model
+  to identify changes in file extensions that match known ransomware patterns. This
+  activity is significant because it suggests an attacker is attempting to encrypt
+  or alter files, potentially leading to severe data loss and operational disruption.
+  If confirmed malicious, this activity could result in the encryption of critical
+  data, rendering it inaccessible and causing significant damage to the organization's
+  data integrity and availability.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime count latest(Filesystem.user) as user values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest _time span=1h | `drop_dm_object_name(Filesystem)` | rex field=file_name "(?<file_extension>\.[^\.]+)$" | rex field=file_path "(?<true_file_path>([^\\\]*\\\)*).*" | stats min(firstTime) as firstTime max(lastTime) as lastTime latest(user) as user dc(true_file_path) as path_count dc(file_name) as file_count latest(file_name) as file_name latest(true_file_path) as file_path by dest file_extension | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_extensions` | where path_count > 1 OR file_count > 20 | `common_ransomware_extensions_filter`'
-how_to_implement: 'You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data model node. To see the additional metadata, add the following fields, if not already present, please review the detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-known_false_positives: It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime count latest(Filesystem.user) as user values(Filesystem.file_path) as
+  file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.dest
+  _time span=1h | `drop_dm_object_name(Filesystem)` | rex field=file_name "(?<file_extension>\.[^\.]+)$"
+  | rex field=file_path "(?<true_file_path>([^\\\]*\\\)*).*" | stats min(firstTime)
+  as firstTime max(lastTime) as lastTime latest(user) as user dc(true_file_path) as
+  path_count dc(file_name) as file_count latest(file_name) as file_name latest(true_file_path)
+  as file_path by dest file_extension | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `ransomware_extensions` | where path_count > 1 OR file_count > 20 | `common_ransomware_extensions_filter`'
+how_to_implement: 'You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint Filesystem data model node. To see the
+  additional metadata, add the following fields, if not already present, please review
+  the detailed documentation on how to create a new field within Incident Review may
+  be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
+known_false_positives: It is possible for a legitimate file with these extensions
+  to be created. If this is a true ransomware attack, there will be a large number
+  of files created with these extensions.
 references:
 - https://github.com/splunk/security_content/issues/2448
 tags:
@@ -25,7 +46,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: The device $dest$ wrote $file_count$ files to $path_count$ path(s) with the $file_extension$ extension. This extension and behavior may indicate a $Name$ ransomware attack.
+  message: The device $dest$ wrote $file_count$ files to $path_count$ path(s) with
+    the $file_extension$ extension. This extension and behavior may indicate a $Name$
+    ransomware attack.
   mitre_attack_id:
   - T1485
   observable:
@@ -41,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.user
-  - Filesystem.dest
-  - Filesystem.file_path
-  - Filesystem.file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/ransom-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/ransom-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/common_ransomware_notes.yml b/detections/endpoint/common_ransomware_notes.yml
index 4dfe789f83..7786e2c326 100644
--- a/detections/endpoint/common_ransomware_notes.yml
+++ b/detections/endpoint/common_ransomware_notes.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the creation of files with names commonly associated with ransomware notes. It leverages file-system activity data from the Endpoint Filesystem data model, typically populated by endpoint detection and response (EDR) tools or Sysmon logs. This activity is significant because ransomware notes indicate a potential ransomware attack, which can lead to data encryption and extortion. If confirmed malicious, this activity could result in significant data loss, operational disruption, and financial impact due to ransom demands.
+description: The following analytic detects the creation of files with names commonly
+  associated with ransomware notes. It leverages file-system activity data from the
+  Endpoint Filesystem data model, typically populated by endpoint detection and response
+  (EDR) tools or Sysmon logs. This activity is significant because ransomware notes
+  indicate a potential ransomware attack, which can lead to data encryption and extortion.
+  If confirmed malicious, this activity could result in significant data loss, operational
+  disruption, and financial impact due to ransom demands.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes` | `common_ransomware_notes_filter`'
-how_to_implement: You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
-known_false_positives: It's possible that a legitimate file could be created with the same name used by ransomware note files.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
+  as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ransomware_notes`
+  | `common_ransomware_notes_filter`'
+how_to_implement: You must be ingesting data that records file-system activity from
+  your hosts to populate the Endpoint Filesystem data-model node. This is typically
+  populated via endpoint detection-and-response product, such as Carbon Black, or
+  via other endpoint data sources, such as Sysmon. The data used for this search is
+  typically generated via logs that report file-system reads and writes.
+known_false_positives: It's possible that a legitimate file could be created with
+  the same name used by ransomware note files.
 references: []
 tags:
   analytic_story:
@@ -24,7 +39,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: A file - $file_name$ was written to disk on endpoint $dest$ by user $user$, this is indicative of a known ransomware note file and should be reviewed immediately.
+  message: A file - $file_name$ was written to disk on endpoint $dest$ by user $user$,
+    this is indicative of a known ransomware note file and should be reviewed immediately.
   mitre_attack_id:
   - T1485
   observable:
@@ -44,16 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.user
-  - Filesystem.dest
-  - Filesystem.file_path
-  - Filesystem.file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/connectwise_screenconnect_path_traversal.yml b/detections/endpoint/connectwise_screenconnect_path_traversal.yml
index 0aaeb71a08..555ffbc2ae 100644
--- a/detections/endpoint/connectwise_screenconnect_path_traversal.yml
+++ b/detections/endpoint/connectwise_screenconnect_path_traversal.yml
@@ -7,10 +7,30 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1708 vulnerability, which allows path traversal attacks by manipulating file_path and file_name parameters in the URL. It leverages the Endpoint datamodel Filesystem node to identify suspicious file system events, specifically targeting paths and filenames associated with ScreenConnect. This activity is significant as it can lead to unauthorized access to sensitive files and directories, potentially resulting in data exfiltration or arbitrary code execution. If confirmed malicious, attackers could gain unauthorized access and control over the host system, posing a severe security risk.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\ScreenConnect\\App_Extensions\\*") Filesystem.file_name IN ("*.aspx","*.ashx") by Filesystem.file_create_time Filesystem.process_id Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_path_traversal_filter`'
-how_to_implement: This analytic utilizes the Endpoint datamodel Filesystem node to identify path traversal attempts against ScreenConnect. Note that using SACL auditing or other file system monitoring tools may also be used to detect path traversal attempts. Typically the data for this analytic will come from EDR or other properly CIM mapped data sources.
-known_false_positives: False positives are not expected, as the detection is based on the presence of file system events that indicate path traversal attempts. The analytic may be modified to look for any file writes to this path as it is not common for files to write here.
+description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect
+  CVE-2024-1708 vulnerability, which allows path traversal attacks by manipulating
+  file_path and file_name parameters in the URL. It leverages the Endpoint datamodel
+  Filesystem node to identify suspicious file system events, specifically targeting
+  paths and filenames associated with ScreenConnect. This activity is significant
+  as it can lead to unauthorized access to sensitive files and directories, potentially
+  resulting in data exfiltration or arbitrary code execution. If confirmed malicious,
+  attackers could gain unauthorized access and control over the host system, posing
+  a severe security risk.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\ScreenConnect\\App_Extensions\\*")
+  Filesystem.file_name IN ("*.aspx","*.ashx") by Filesystem.file_create_time Filesystem.process_id
+  Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.dest
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `connectwise_screenconnect_path_traversal_filter`'
+how_to_implement: This analytic utilizes the Endpoint datamodel Filesystem node to
+  identify path traversal attempts against ScreenConnect. Note that using SACL auditing
+  or other file system monitoring tools may also be used to detect path traversal
+  attempts. Typically the data for this analytic will come from EDR or other properly
+  CIM mapped data sources.
+known_false_positives: False positives are not expected, as the detection is based
+  on the presence of file system events that indicate path traversal attempts. The
+  analytic may be modified to look for any file writes to this path as it is not common
+  for files to write here.
 references:
 - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass
 - https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,13 +67,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.process_guid
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.dest
   security_domain: endpoint
   cve:
   - CVE-2024-1708
@@ -56,6 +74,7 @@ tags:
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/sysmon_app_extensions.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/sysmon_app_extensions.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml b/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml
index 2baa10c2b0..dc7b0d18f5 100644
--- a/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml
+++ b/detections/endpoint/connectwise_screenconnect_path_traversal_windows_sacl.yml
@@ -7,10 +7,26 @@ data_source:
 - Windows Event Log Security 4663
 type: TTP
 status: production
-description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1708 vulnerability using Windows SACL EventCode 4663. It identifies path traversal attacks by monitoring file system events related to the ScreenConnect service. This activity is significant as it allows unauthorized access to sensitive files and directories, potentially leading to data exfiltration or arbitrary code execution. If confirmed malicious, attackers could gain unauthorized access to critical data or execute harmful code, compromising the integrity and security of the affected system. Immediate remediation by updating to version 23.9.8 or above is recommended.
-search: '`wineventlog_security` EventCode=4663  ProcessName=*\\ScreenConnect.Service.exe file_path IN ("*\\ScreenConnect\\App_Extensions\\*") file_name IN ("*.aspx","*.ashx") | stats count min(_time) as firstTime max(_time) as lastTime by  ObjectName ObjectType ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer as dest Caller_User_Name as user ProcessName as process_name |  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_path_traversal_windows_sacl_filter`'
-how_to_implement: To implement the following query, enable SACL auditing for the ScreenConnect directory(ies). With this data, the following analytic will work correctly. A GIST is provided in the references to assist with enabling SACL Auditing.
-known_false_positives: False positives should be limited as the analytic is specific to ScreenConnect path traversal attempts. Tune as needed, or restrict to specific hosts if false positives are encountered.
+description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect
+  CVE-2024-1708 vulnerability using Windows SACL EventCode 4663. It identifies path
+  traversal attacks by monitoring file system events related to the ScreenConnect
+  service. This activity is significant as it allows unauthorized access to sensitive
+  files and directories, potentially leading to data exfiltration or arbitrary code
+  execution. If confirmed malicious, attackers could gain unauthorized access to critical
+  data or execute harmful code, compromising the integrity and security of the affected
+  system. Immediate remediation by updating to version 23.9.8 or above is recommended.
+search: '`wineventlog_security` EventCode=4663  ProcessName=*\\ScreenConnect.Service.exe
+  file_path IN ("*\\ScreenConnect\\App_Extensions\\*") file_name IN ("*.aspx","*.ashx")
+  | stats count min(_time) as firstTime max(_time) as lastTime by  ObjectName ObjectType
+  ProcessName AccessMask process_id EventCode Computer Caller_User_Name | rename Computer
+  as dest Caller_User_Name as user ProcessName as process_name |  `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `connectwise_screenconnect_path_traversal_windows_sacl_filter`'
+how_to_implement: To implement the following query, enable SACL auditing for the ScreenConnect
+  directory(ies). With this data, the following analytic will work correctly. A GIST
+  is provided in the references to assist with enabling SACL Auditing.
+known_false_positives: False positives should be limited as the analytic is specific
+  to ScreenConnect path traversal attempts. Tune as needed, or restrict to specific
+  hosts if false positives are encountered.
 references:
 - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663
 - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass
@@ -22,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,15 +64,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - ObjectName
-  - ObjectType
-  - ProcessName
-  - AccessMask
-  - process_id
-  - EventCode
-  - Computer
-  - Caller_User_Name
   security_domain: endpoint
   cve:
   - CVE-2024-1708
@@ -59,6 +71,7 @@ tags:
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/4663_connectwise_aspx_app_extensions.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/4663_connectwise_aspx_app_extensions.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Security
diff --git a/detections/endpoint/conti_common_exec_parameter.yml b/detections/endpoint/conti_common_exec_parameter.yml
index a5b0188a37..11e0af232b 100644
--- a/detections/endpoint/conti_common_exec_parameter.yml
+++ b/detections/endpoint/conti_common_exec_parameter.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of suspicious command-line arguments commonly associated with Conti ransomware, specifically targeting local drives and network shares for encryption. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential ransomware attack, which can lead to widespread data encryption and operational disruption. If confirmed malicious, the impact could be severe, resulting in data loss, system downtime, and potential ransom demands.
+description: The following analytic detects the execution of suspicious command-line
+  arguments commonly associated with Conti ransomware, specifically targeting local
+  drives and network shares for encryption. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs that include command-line
+  details. This activity is significant because it indicates a potential ransomware
+  attack, which can lead to widespread data encryption and operational disruption.
+  If confirmed malicious, the impact could be severe, resulting in data loss, system
+  downtime, and potential ransom demands.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `conti_common_exec_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: 3rd party tool may have commandline parameter that can trigger this detection.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*"
+  OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process
+  = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name
+  Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `conti_common_exec_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: 3rd party tool may have commandline parameter that can trigger
+  this detection.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.conti
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +57,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing specific Conti Ransomware related parameters.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ executing specific Conti Ransomware related
+    parameters.
   mitre_attack_id:
   - T1204
   observable:
@@ -54,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/inf1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/inf1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/control_loading_from_world_writable_directory.yml b/detections/endpoint/control_loading_from_world_writable_directory.yml
index 271e769eea..d622b7a92f 100644
--- a/detections/endpoint/control_loading_from_world_writable_directory.yml
+++ b/detections/endpoint/control_loading_from_world_writable_directory.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances of control.exe loading a .cpl or .inf file from a writable directory, which is related to CVE-2021-40444. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it may indicate an attempt to exploit a known vulnerability, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to gain control over the affected system, leading to further compromise.
+description: The following analytic identifies instances of control.exe loading a
+  .cpl or .inf file from a writable directory, which is related to CVE-2021-40444.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions mapped to the `Processes`
+  node of the `Endpoint` data model. This activity is significant as it may indicate
+  an attempt to exploit a known vulnerability, potentially leading to unauthorized
+  code execution. If confirmed malicious, this could allow an attacker to gain control
+  over the affected system, leading to further compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=control.exe OR Processes.original_file_name=CONTROL.EXE) AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `control_loading_from_world_writable_directory_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives will be present as control.exe does not natively load from writable paths as defined. One may add .cpl or .inf to the command-line if there is any false positives. Tune as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=control.exe
+  OR Processes.original_file_name=CONTROL.EXE) AND Processes.process IN ("*\\appdata\\*",
+  "*\\windows\\temp\\*", "*\\programdata\\*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `control_loading_from_world_writable_directory_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives will be present as control.exe does
+  not natively load from writable paths as defined. One may add .cpl or .inf to the
+  command-line if there is any false positives. Tune as needed.
 references:
 - https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html
 - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/
@@ -25,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +65,8 @@ tags:
   cve:
   - CVE-2021-40444
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
   mitre_attack_id:
   - T1218
   - T1218.002
@@ -62,22 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/create_local_admin_accounts_using_net_exe.yml b/detections/endpoint/create_local_admin_accounts_using_net_exe.yml
index cb649b1932..225362e4b8 100644
--- a/detections/endpoint/create_local_admin_accounts_using_net_exe.yml
+++ b/detections/endpoint/create_local_admin_accounts_using_net_exe.yml
@@ -5,13 +5,36 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of local administrator accounts using the net.exe command. It leverages Endpoint Detection and Response (EDR) data to identify processes named net.exe or net1.exe with the "/add" parameter and keywords related to administrator accounts. This activity is significant as it may indicate an attacker attempting to gain persistent access or escalate privileges. If confirmed malicious, this could lead to unauthorized access, data theft, or further system compromise. Review the process details, user context, and related artifacts to determine the legitimacy of the activity.
+description: The following analytic detects the creation of local administrator accounts
+  using the net.exe command. It leverages Endpoint Detection and Response (EDR) data
+  to identify processes named net.exe or net1.exe with the "/add" parameter and keywords
+  related to administrator accounts. This activity is significant as it may indicate
+  an attacker attempting to gain persistent access or escalate privileges. If confirmed
+  malicious, this could lead to unauthorized access, data theft, or further system
+  compromise. Review the process details, user context, and related artifacts to determine
+  the legitimacy of the activity.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process values(parent_process_name) as parent_process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND Processes.process=*/add* AND (Processes.process=*administrators* OR Processes.process=*administratoren* OR Processes.process=*administrateurs* OR Processes.process=*administrador* OR Processes.process=*amministratori* OR Processes.process=*administratorer*) by Processes.process Processes.process_name Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_local_admin_accounts_using_net_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count values(Processes.user) as
+  user values(Processes.parent_process) as parent_process values(parent_process_name)
+  as parent_process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND Processes.process=*/add*
+  AND (Processes.process=*administrators* OR Processes.process=*administratoren* OR
+  Processes.process=*administrateurs* OR Processes.process=*administrador* OR Processes.process=*amministratori*
+  OR Processes.process=*administratorer*) by Processes.process Processes.process_name
+  Processes.parent_process_name Processes.dest Processes.user| `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_local_admin_accounts_using_net_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators often leverage net.exe to create admin accounts.
 references: []
 drilldown_searches:
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a user to the local Administrators group.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to add a user to the local Administrators
+    group.
   mitre_attack_id:
   - T1136.001
   - T1136
@@ -58,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml b/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml
index 61bb5cee24..a19b57e240 100644
--- a/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml
+++ b/detections/endpoint/create_or_delete_windows_shares_using_net_exe.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation or deletion of Windows shares using the net.exe command. It leverages Endpoint Detection and Response (EDR) data to identify processes involving net.exe with actions related to share management. This activity is significant because it may indicate an attacker attempting to manipulate network shares for malicious purposes, such as data exfiltration, malware distribution, or establishing persistence. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, service disruption, or malware introduction. Immediate investigation is required to determine the intent and mitigate potential threats.
+description: The following analytic detects the creation or deletion of Windows shares
+  using the net.exe command. It leverages Endpoint Detection and Response (EDR) data
+  to identify processes involving net.exe with actions related to share management.
+  This activity is significant because it may indicate an attacker attempting to manipulate
+  network shares for malicious purposes, such as data exfiltration, malware distribution,
+  or establishing persistence. If confirmed malicious, this activity could lead to
+  unauthorized access to sensitive information, service disruption, or malware introduction.
+  Immediate investigation is required to determine the intent and mitigate potential
+  threats.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` by Processes.process Processes.process_name  Processes.parent_process_name Processes.original_file_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate.
+search: '| tstats `security_content_summariesonly` count values(Processes.user) as
+  user values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` by
+  Processes.process Processes.process_name  Processes.parent_process_name Processes.original_file_name
+  Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | search process=*share* | `create_or_delete_windows_shares_using_net_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators often leverage net.exe to create or delete network
+  shares. You should verify that the activity was intentional and is legitimate.
 references:
 - https://attack.mitre.org/techniques/T1070/005/
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ enumerating Windows file shares.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ enumerating Windows file shares.
   mitre_attack_id:
   - T1070
   - T1070.005
@@ -59,23 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/create_remote_thread_in_shell_application.yml b/detections/endpoint/create_remote_thread_in_shell_application.yml
index 4cb61d2cda..b5d9d2f2d9 100644
--- a/detections/endpoint/create_remote_thread_in_shell_application.yml
+++ b/detections/endpoint/create_remote_thread_in_shell_application.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious process injection in command shell applications, specifically targeting `cmd.exe` and `powershell.exe`. It leverages Sysmon EventCode 8 to identify the creation of remote threads within these shell processes. This activity is significant because it is a common technique used by malware, such as IcedID, to inject malicious code and execute it within legitimate processes. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to system security.
+description: The following analytic detects suspicious process injection in command
+  shell applications, specifically targeting `cmd.exe` and `powershell.exe`. It leverages
+  Sysmon EventCode 8 to identify the creation of remote threads within these shell
+  processes. This activity is significant because it is a common technique used by
+  malware, such as IcedID, to inject malicious code and execute it within legitimate
+  processes. If confirmed malicious, this behavior could allow an attacker to execute
+  arbitrary code, escalate privileges, or maintain persistence within the environment,
+  posing a severe threat to system security.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*") | stats count min(_time) as firstTime max(_time) as lastTime by  TargetImage TargetProcessId SourceProcessId  EventCode StartAddress SourceImage dest |rename SourceImage as process_name| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `create_remote_thread_in_shell_application_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*") | stats
+  count min(_time) as firstTime max(_time) as lastTime by  TargetImage TargetProcessId
+  SourceProcessId  EventCode StartAddress SourceImage dest |rename SourceImage as
+  process_name| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `create_remote_thread_in_shell_application_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: unknown
 references:
 - https://thedfirreport.com/2021/07/19/icedid-and-cobalt-strike-vs-antivirus/
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: process $process_name$ create a remote thread to shell app process $TargetImage$ in host $dest$
+  message: process $process_name$ create a remote thread to shell app process $TargetImage$
+    in host $dest$
   mitre_attack_id:
   - T1055
   observable:
@@ -46,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - TargetProcessId
-  - SourceProcessId
-  - StartAddress
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/create_remote_thread_into_lsass.yml b/detections/endpoint/create_remote_thread_into_lsass.yml
index 5a88cb4be5..7b21c0023f 100644
--- a/detections/endpoint/create_remote_thread_into_lsass.yml
+++ b/detections/endpoint/create_remote_thread_into_lsass.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a remote thread in the Local Security Authority Subsystem Service (LSASS). This behavior is identified using Sysmon EventID 8 logs, focusing on processes that create remote threads in lsass.exe. This activity is significant because it is commonly associated with credential dumping, a tactic used by adversaries to steal user authentication credentials. If confirmed malicious, this could allow attackers to gain unauthorized access to sensitive information, leading to potential compromise of the entire network. Analysts should investigate to differentiate between legitimate tools and potential threats.
+description: The following analytic detects the creation of a remote thread in the
+  Local Security Authority Subsystem Service (LSASS). This behavior is identified
+  using Sysmon EventID 8 logs, focusing on processes that create remote threads in
+  lsass.exe. This activity is significant because it is commonly associated with credential
+  dumping, a tactic used by adversaries to steal user authentication credentials.
+  If confirmed malicious, this could allow attackers to gain unauthorized access to
+  sensitive information, leading to potential compromise of the entire network. Analysts
+  should investigate to differentiate between legitimate tools and potential threats.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, EventCode, TargetImage, TargetProcessId | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter`'
-how_to_implement: This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives: Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise.
+search: '`sysmon` EventID=8 TargetImage=*lsass.exe | stats count min(_time) as firstTime
+  max(_time) as lastTime by dest, EventCode, TargetImage, TargetProcessId | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `create_remote_thread_into_lsass_filter`'
+how_to_implement: This search needs Sysmon Logs with a Sysmon configuration, which
+  includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`.
+  We strongly recommend that you specify your environment-specific configurations
+  (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
+  with configurations for your Splunk environment. The search also uses a post-filter
+  macro designed to filter out known false positives.
+known_false_positives: Other tools can access LSASS for legitimate reasons and generate
+  an event. In these cases, tweaking the search may help eliminate noise.
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Windows
   confidence: 90
   impact: 90
-  message: A process has created a remote thread into $TargetImage$ on $dest$. This behavior is indicative of credential dumping and should be investigated.
+  message: A process has created a remote thread into $TargetImage$ on $dest$. This
+    behavior is indicative of credential dumping and should be investigated.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -46,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - TargetImage
-  - dest
-  - EventCode
-  - TargetImage
-  - TargetProcessId
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml b/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml
index e3f9ebce84..98d0cbc569 100644
--- a/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml
+++ b/detections/endpoint/creation_of_lsass_dump_with_taskmgr.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of an lsass.exe process dump using Windows Task Manager. It leverages Sysmon EventID 11 to identify file creation events where the target filename matches *lsass*.dmp. This activity is significant because creating an lsass dump can be a precursor to credential theft, as the dump file contains sensitive information such as user passwords. If confirmed malicious, an attacker could use the lsass dump to extract credentials and escalate privileges, potentially compromising the entire network.
+description: The following analytic detects the creation of an lsass.exe process dump
+  using Windows Task Manager. It leverages Sysmon EventID 11 to identify file creation
+  events where the target filename matches *lsass*.dmp. This activity is significant
+  because creating an lsass dump can be a precursor to credential theft, as the dump
+  file contains sensitive information such as user passwords. If confirmed malicious,
+  an attacker could use the lsass dump to extract credentials and escalate privileges,
+  potentially compromising the entire network.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp | stats count min(_time) as firstTime max(_time) as lastTime by dest, object_category, process_name, TargetFilename  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `creation_of_lsass_dump_with_taskmgr_filter`'
-how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives: Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
+search: '`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp |
+  stats count min(_time) as firstTime max(_time) as lastTime by dest, object_category,
+  process_name, TargetFilename  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `creation_of_lsass_dump_with_taskmgr_filter`'
+how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which
+  includes EventCode 11 for detecting file create of lsass.dmp. This search uses an
+  input macro named `sysmon`. We strongly recommend that you specify your environment-specific
+  configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace
+  the macro definition with configurations for your Splunk environment. The search
+  also uses a post-filter macro designed to filter out known false positives.
+known_false_positives: Administrators can create memory dumps for debugging purposes,
+  but memory dumps of the LSASS process would be unusual.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager
 - https://attack.mitre.org/techniques/T1003/001/
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +51,8 @@ tags:
   asset_type: Windows
   confidence: 100
   impact: 80
-  message: $process_name$ was identified on endpoint $dest$ writing $TargetFilename$ to disk. This behavior is related to dumping credentials via Task Manager.
+  message: $process_name$ was identified on endpoint $dest$ writing $TargetFilename$
+    to disk. This behavior is related to dumping credentials via Task Manager.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -44,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - process_name
-  - TargetFilename
-  - dest
-  - object_category
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/creation_of_shadow_copy.yml b/detections/endpoint/creation_of_shadow_copy.yml
index 18e929919a..5bf94dca83 100644
--- a/detections/endpoint/creation_of_shadow_copy.yml
+++ b/detections/endpoint/creation_of_shadow_copy.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of shadow copies using Vssadmin or Wmic. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because creating shadow copies can be a precursor to ransomware attacks or data exfiltration, allowing attackers to bypass file locks and access sensitive data. If confirmed malicious, this behavior could enable attackers to maintain persistence, recover deleted files, or prepare for further malicious activities, posing a significant risk to the integrity and confidentiality of the system.
+description: The following analytic detects the creation of shadow copies using Vssadmin
+  or Wmic. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process execution logs that include command-line details. This activity is significant
+  because creating shadow copies can be a precursor to ransomware attacks or data
+  exfiltration, allowing attackers to bypass file locks and access sensitive data.
+  If confirmed malicious, this behavior could enable attackers to maintain persistence,
+  recover deleted files, or prepare for further malicious activities, posing a significant
+  risk to the integrity and confidentiality of the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate administrator usage of Vssadmin or Wmic will create false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe
+  Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe
+  Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user
+  Processes.process_name Processes.process  Processes.parent_process_name Processes.parent_process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate administrator usage of Vssadmin or Wmic will create
+  false positives.
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
 - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform offline password cracking.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform
+    offline password cracking.
   mitre_attack_id:
   - T1003.003
   - T1003
@@ -53,28 +82,17 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/crowdstrike_falcon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/crowdstrike_falcon.log
     source: crowdstrike
     sourcetype: crowdstrike:events:sensor
diff --git a/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml b/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml
index 1ec6cf4b8c..30982b751d 100644
--- a/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml
+++ b/detections/endpoint/creation_of_shadow_copy_with_wmic_and_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of shadow copies using "wmic" or "Powershell" commands. It leverages the Endpoint.Processes data model in Splunk to identify processes where the command includes "shadowcopy" and "create". This activity is significant because it may indicate an attacker attempting to manipulate or access data unauthorizedly, potentially leading to data theft or manipulation. If confirmed malicious, this behavior could allow attackers to backup and exfiltrate sensitive data or hide their tracks by restoring files to a previous state after an attack.
+description: The following analytic detects the creation of shadow copies using "wmic"
+  or "Powershell" commands. It leverages the Endpoint.Processes data model in Splunk
+  to identify processes where the command includes "shadowcopy" and "create". This
+  activity is significant because it may indicate an attacker attempting to manipulate
+  or access data unauthorizedly, potentially leading to data theft or manipulation.
+  If confirmed malicious, this behavior could allow attackers to backup and exfiltrate
+  sensitive data or hide their tracks by restoring files to a previous state after
+  an attack.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` OR `process_powershell` Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `creation_of_shadow_copy_with_wmic_and_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` OR `process_powershell`
+  Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name
+  Processes.original_file_name Processes.parent_process_name Processes.process Processes.dest
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `creation_of_shadow_copy_with_wmic_and_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Legtimate administrator usage of wmic to create a shadow copy.
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +58,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform offline password cracking.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to create a shadow copy to perform
+    offline password cracking.
   mitre_attack_id:
   - T1003.003
   - T1003
@@ -50,23 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml b/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml
index ea99d9090a..30d2cabefb 100644
--- a/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml
+++ b/detections/endpoint/credential_dumping_via_copy_command_from_shadow_copy.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the copy command to dump credentials from a shadow copy. It leverages Endpoint Detection and Response (EDR) data to identify processes with command lines referencing critical files like "sam", "security", "system", and "ntds.dit" in system directories. This activity is significant as it indicates an attempt to extract credentials, a common technique for unauthorized access and privilege escalation. If confirmed malicious, this could lead to attackers gaining sensitive login information, escalating privileges, moving laterally within the network, or accessing sensitive data.
+description: The following analytic detects the use of the copy command to dump credentials
+  from a shadow copy. It leverages Endpoint Detection and Response (EDR) data to identify
+  processes with command lines referencing critical files like "sam", "security",
+  "system", and "ntds.dit" in system directories. This activity is significant as
+  it indicates an attempt to extract credentials, a common technique for unauthorized
+  access and privilege escalation. If confirmed malicious, this could lead to attackers
+  gaining sensitive login information, escalating privileges, moving laterally within
+  the network, or accessing sensitive data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.original_file_name Processes.process_id Processes.parent_process_id  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_copy_command_from_shadow_copy_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_cmd` (Processes.process=*\\system32\\config\\sam*
+  OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system*
+  OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user
+  Processes.process_name Processes.process  Processes.parent_process Processes.original_file_name
+  Processes.process_id Processes.parent_process_id  Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `credential_dumping_via_copy_command_from_shadow_copy_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +57,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to copy SAM and NTDS.dit for offline password cracking.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to copy SAM and NTDS.dit for offline
+    password cracking.
   mitre_attack_id:
   - T1003.003
   - T1003
@@ -47,23 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml b/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml
index d40c1579a6..dc217a3699 100644
--- a/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml
+++ b/detections/endpoint/credential_dumping_via_symlink_to_shadow_copy.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a symlink to a shadow copy, which may indicate credential dumping attempts. It leverages the Endpoint.Processes data model in Splunk to identify processes executing commands containing "mklink" and "HarddiskVolumeShadowCopy". This activity is significant because attackers often use this technique to manipulate or delete shadow copies, hindering system backup and recovery efforts. If confirmed malicious, this could prevent data restoration, complicate incident response, and lead to data loss or compromise. Analysts should review the process details, user, parent process, and any related artifacts to identify the attack source.
+description: The following analytic detects the creation of a symlink to a shadow
+  copy, which may indicate credential dumping attempts. It leverages the Endpoint.Processes
+  data model in Splunk to identify processes executing commands containing "mklink"
+  and "HarddiskVolumeShadowCopy". This activity is significant because attackers often
+  use this technique to manipulate or delete shadow copies, hindering system backup
+  and recovery efforts. If confirmed malicious, this could prevent data restoration,
+  complicate incident response, and lead to data loss or compromise. Analysts should
+  review the process details, user, parent process, and any related artifacts to identify
+  the attack source.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.parent_process_name Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*mklink*
+  Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name
+  Processes.process  Processes.parent_process Processes.parent_process_name Processes.original_file_name
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `credential_dumping_via_symlink_to_shadow_copy_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create symlink to a shadow copy to grab credentials.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to create symlink to a shadow copy
+    to grab credentials.
   mitre_attack_id:
   - T1003.003
   - T1003
@@ -47,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/crowdstrike_admin_weak_password_policy.yml b/detections/endpoint/crowdstrike_admin_weak_password_policy.yml
index 32496ab8f9..6992a1949b 100644
--- a/detections/endpoint/crowdstrike_admin_weak_password_policy.yml
+++ b/detections/endpoint/crowdstrike_admin_weak_password_policy.yml
@@ -6,9 +6,23 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic detects CrowdStrike alerts for admin weak password policy violations, identifying instances where administrative passwords do not meet security standards. These alerts highlight significant vulnerabilities that could be exploited by attackers to gain unauthorized access. Promptly addressing these alerts is crucial for maintaining robust security and protecting critical systems and data from potential threats.
-search: '`crowdstrike_identities` primaryDisplayName = "*admin*" | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type | where risk_type = "WEAK_PASSWORD_POLICY" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_admin_weak_password_policy_filter`'
-how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for admin weak password
+  policy violations, identifying instances where administrative passwords do not meet
+  security standards. These alerts highlight significant vulnerabilities that could
+  be exploited by attackers to gain unauthorized access. Promptly addressing these
+  alerts is crucial for maintaining robust security and protecting critical systems
+  and data from potential threats.
+search: '`crowdstrike_identities` primaryDisplayName = "*admin*" | rename riskFactors{}.severity
+  as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain
+  as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count
+  min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName
+  risk_type severity riskScore riskScoreSeverity user role_type | where risk_type
+  = "WEAK_PASSWORD_POLICY" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `crowdstrike_admin_weak_password_policy_filter`'
+how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "crowdstrike:identities" event stream. Process and store the logs as needed,
+  integrating them into your logging or SIEM system for monitoring and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - riskFactors{}.severity
-  - riskFactors{}.type
-  - roles{}.type
-  - accounts{}.domain
-  - accounts{}.dn
-  - accounts{}.samAccountName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_weak_password_policy/crowdstrike_weak_password_admin_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_weak_password_policy/crowdstrike_weak_password_admin_cleaned.log
     sourcetype: crowdstrike:identities
     source: crowdstrike:identities
diff --git a/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml b/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml
index bfd6b005af..18b1272bdc 100644
--- a/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml
+++ b/detections/endpoint/crowdstrike_admin_with_duplicate_password.yml
@@ -6,9 +6,23 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic detects CrowdStrike alerts for admin accounts with duplicate password risk, identifying instances where administrative users share the same password. This practice significantly increases the risk of unauthorized access and potential breaches. Addressing these alerts promptly is crucial for maintaining strong security protocols, ensuring each admin account uses a unique, secure password to protect critical systems and data.
-search: '`crowdstrike_identities` primaryDisplayName = "*admin*" | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type | where risk_type = "DUPLICATE_PASSWORD" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_admin_with_duplicate_password_filter`'
-how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for admin accounts
+  with duplicate password risk, identifying instances where administrative users share
+  the same password. This practice significantly increases the risk of unauthorized
+  access and potential breaches. Addressing these alerts promptly is crucial for maintaining
+  strong security protocols, ensuring each admin account uses a unique, secure password
+  to protect critical systems and data.
+search: '`crowdstrike_identities` primaryDisplayName = "*admin*" | rename riskFactors{}.severity
+  as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain
+  as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count
+  min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName
+  risk_type severity riskScore riskScoreSeverity user role_type | where risk_type
+  = "DUPLICATE_PASSWORD" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `crowdstrike_admin_with_duplicate_password_filter`'
+how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "crowdstrike:identities" event stream. Process and store the logs as needed,
+  integrating them into your logging or SIEM system for monitoring and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - riskFactors{}.severity
-  - riskFactors{}.type
-  - roles{}.type
-  - accounts{}.domain
-  - accounts{}.dn
-  - accounts{}.samAccountName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_duplicate_password/crowdstrike_admin_dup_pwd_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/admin_duplicate_password/crowdstrike_admin_dup_pwd_cleaned.log
     sourcetype: crowdstrike:identities
     source: crowdstrike:identities
diff --git a/detections/endpoint/crowdstrike_high_identity_risk_severity.yml b/detections/endpoint/crowdstrike_high_identity_risk_severity.yml
index 29941dc75e..8dfca3e038 100644
--- a/detections/endpoint/crowdstrike_high_identity_risk_severity.yml
+++ b/detections/endpoint/crowdstrike_high_identity_risk_severity.yml
@@ -6,9 +6,21 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic detects CrowdStrike alerts for High Identity Risk Severity with a risk score of 70 or higher. These alerts indicate significant vulnerabilities in user identities, such as suspicious behavior or compromised credentials. Promptly investigating and addressing these alerts is crucial to prevent potential security breaches and ensure the integrity and protection of sensitive information and systems.
-search: '`crowdstrike_identities` riskScoreSeverity="HIGH" OR riskScore >= 0.70 | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_high_identity_risk_severity_filter`'
-how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for High Identity Risk
+  Severity with a risk score of 70 or higher. These alerts indicate significant vulnerabilities
+  in user identities, such as suspicious behavior or compromised credentials. Promptly
+  investigating and addressing these alerts is crucial to prevent potential security
+  breaches and ensure the integrity and protection of sensitive information and systems.
+search: '`crowdstrike_identities` riskScoreSeverity="HIGH" OR riskScore >= 0.70 |
+  rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type
+  as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName
+  as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain
+  dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_high_identity_risk_severity_filter`'
+how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "crowdstrike:identities" event stream. Process and store the logs as needed,
+  integrating them into your logging or SIEM system for monitoring and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - riskFactors{}.severity
-  - riskFactors{}.type
-  - roles{}.type
-  - accounts{}.domain
-  - accounts{}.dn
-  - accounts{}.samAccountName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/high_risk_score/crowdstrike_high_riskscore_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/high_risk_score/crowdstrike_high_riskscore_cleaned.log
     sourcetype: crowdstrike:identities
     source: crowdstrike:identities
diff --git a/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml b/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml
index a4c5197bb5..204420acee 100644
--- a/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml
+++ b/detections/endpoint/crowdstrike_medium_identity_risk_severity.yml
@@ -6,9 +6,23 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic detects CrowdStrike alerts for Medium Identity Risk Severity with a risk score of 55 or higher. These alerts indicate significant vulnerabilities in user identities, such as suspicious behavior or compromised credentials. Promptly investigating and addressing these alerts is crucial to prevent potential security breaches and ensure the integrity and protection of sensitive information and systems.
-search: '`crowdstrike_identities` riskScoreSeverity = "MEDIUM" OR riskScore >= 0.55 AND riskScore < 0.70 | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_medium_identity_risk_severity_filter`'
-how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for Medium Identity
+  Risk Severity with a risk score of 55 or higher. These alerts indicate significant
+  vulnerabilities in user identities, such as suspicious behavior or compromised credentials.
+  Promptly investigating and addressing these alerts is crucial to prevent potential
+  security breaches and ensure the integrity and protection of sensitive information
+  and systems.
+search: '`crowdstrike_identities` riskScoreSeverity = "MEDIUM" OR riskScore >= 0.55
+  AND riskScore < 0.70 | rename riskFactors{}.severity as severity, riskFactors{}.type
+  as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn
+  as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time)
+  as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity
+  user role_type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `crowdstrike_medium_identity_risk_severity_filter`'
+how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "crowdstrike:identities" event stream. Process and store the logs as needed,
+  integrating them into your logging or SIEM system for monitoring and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - riskFactors{}.severity
-  - riskFactors{}.type
-  - roles{}.type
-  - accounts{}.domain
-  - accounts{}.dn
-  - accounts{}.samAccountName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/riskscore/crowdstrike_riskscore_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/riskscore/crowdstrike_riskscore_cleaned.log
     sourcetype: crowdstrike:identities
     source: crowdstrike:identities
diff --git a/detections/endpoint/crowdstrike_medium_severity_alert.yml b/detections/endpoint/crowdstrike_medium_severity_alert.yml
index be6d16d8f4..ce35e3c4ed 100644
--- a/detections/endpoint/crowdstrike_medium_severity_alert.yml
+++ b/detections/endpoint/crowdstrike_medium_severity_alert.yml
@@ -6,9 +6,23 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: Anomaly
 status: production
-description: The following analytic detects a CrowdStrike alert with MEDIUM severity indicates a potential threat that requires prompt attention. This alert level suggests suspicious activity that may compromise security but is not immediately critical. It typically involves detectable but non-imminent risks, such as unusual behavior or attempted policy violations, which should be investigated further and mitigated quickly to prevent escalation of attacks.
-search: '`crowdstrike_stream` | rename event.EndpointIp as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, src_host, user, description, type, count_alerts, severity | where LIKE (severity, "%MEDIUM%") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_medium_severity_alert_filter`'
-how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects a CrowdStrike alert with MEDIUM severity
+  indicates a potential threat that requires prompt attention. This alert level suggests
+  suspicious activity that may compromise security but is not immediately critical.
+  It typically involves detectable but non-imminent risks, such as unusual behavior
+  or attempted policy violations, which should be investigated further and mitigated
+  quickly to prevent escalation of attacks.
+search: '`crowdstrike_stream` | rename event.EndpointIp as src_ip, event.EndpointName
+  as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType
+  as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity |
+  stats count min(_time) as firstTime max(_time) as lastTime by src_ip, src_host,
+  user, description, type, count_alerts, severity | where LIKE (severity, "%MEDIUM%")
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_medium_severity_alert_filter`'
+how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON
+  logs as needed, integrating them into your logging or SIEM system for monitoring
+  and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,20 +58,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event.EndpointIp
-  - event.EndpointName
-  - event.UserName
-  - event.IncidentDescription
-  - event.IncidentType
-  - event.NumbersOfAlerts
-  - event.SeverityName
   security_domain: endpoint
-  manual_test: This detection is marked manual test because the attack_data file and TA do not provide the event.EndpointIp and event.EndpointName fields.  event.EndpointName is required to be present for the Risk Message Validation Integration Testing. This will be investigated and is a tracked issue.
+  manual_test: This detection is marked manual test because the attack_data file and
+    TA do not provide the event.EndpointIp and event.EndpointName fields.  event.EndpointName
+    is required to be present for the Risk Message Validation Integration Testing.
+    This will be investigated and is a tracked issue.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/medium_alert/crowdstrike_medium_clean.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/medium_alert/crowdstrike_medium_clean.log
     sourcetype: CrowdStrike:Event:Streams:JSON
     source: CrowdStrike:Event:Streams
diff --git a/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml b/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml
index 8aa5ce285f..9a264835b1 100644
--- a/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml
+++ b/detections/endpoint/crowdstrike_multiple_low_severity_alerts.yml
@@ -6,9 +6,25 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: Anomaly
 status: production
-description: The following analytic detects multiple CrowdStrike LOW severity alerts, indicating a series of minor suspicious activities or policy violations. These alerts are not immediately critical but should be reviewed to prevent potential threats. They often highlight unusual behavior or low-level risks that, if left unchecked, could escalate into more significant security issues. Regular monitoring and analysis of these alerts are essential for maintaining robust security.
-search: '`crowdstrike_stream` tag=alert event.SeverityName= LOW | rename event.EndpointIp as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity | stats dc(type) as type_count, values(user) as users, values(description) as descriptions, values(type) as types, values(severity) count min(_time) as firstTime max(_time) as lastTime by src_ip src_host | where type_count >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_multiple_low_severity_alerts_filter`'
-how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects multiple CrowdStrike LOW severity alerts,
+  indicating a series of minor suspicious activities or policy violations. These alerts
+  are not immediately critical but should be reviewed to prevent potential threats.
+  They often highlight unusual behavior or low-level risks that, if left unchecked,
+  could escalate into more significant security issues. Regular monitoring and analysis
+  of these alerts are essential for maintaining robust security.
+search: '`crowdstrike_stream` tag=alert event.SeverityName= LOW | rename event.EndpointIp
+  as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription
+  as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts,
+  event.SeverityName as severity | stats dc(type) as type_count, values(user) as users,
+  values(description) as descriptions, values(type) as types, values(severity) count
+  min(_time) as firstTime max(_time) as lastTime by src_ip src_host | where type_count
+  >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `crowdstrike_multiple_low_severity_alerts_filter`'
+how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON
+  logs as needed, integrating them into your logging or SIEM system for monitoring
+  and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_host$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_host$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_host$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,19 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event.EndpointIp
-  - event.EndpointName
-  - event.UserName
-  - event.IncidentDescription
-  - event.IncidentType
-  - event.NumbersOfAlerts
-  - event.SeverityName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/multiple_low_alert/crowdstrike_multiple_low_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/multiple_low_alert/crowdstrike_multiple_low_cleaned.log
     sourcetype: CrowdStrike:Event:Streams:JSON
     source: CrowdStrike:Event:Streams
diff --git a/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml b/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml
index edad8343b3..c5046c35f7 100644
--- a/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml
+++ b/detections/endpoint/crowdstrike_privilege_escalation_for_non_admin_user.yml
@@ -6,9 +6,24 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: Anomaly
 status: production
-description: The following analytic detects CrowdStrike alerts for privilege escalation attempts by non-admin users. These alerts indicate unauthorized efforts by regular users to gain elevated permissions, posing a significant security risk. Detecting and addressing these attempts promptly helps prevent potential breaches and ensures that user privileges remain properly managed, maintaining the integrity of the organization's security protocols.
-search: '`crowdstrike_stream` tag=alert | rename event.EndpointIp as src_ip, event.EndpointName as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity | stats count min(_time) as firstTime max(_time) as lastTime by src_ip, src_host, user, description, type, count_alerts, severity | where LIKE(type,"%Privilege escalation%") AND NOT LIKE(user, "%adm%") AND NOT LIKE(user, "%svc%") AND NOT LIKE(user, "%admin%") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_privilege_escalation_for_non_admin_user_filter`'
-how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for privilege escalation
+  attempts by non-admin users. These alerts indicate unauthorized efforts by regular
+  users to gain elevated permissions, posing a significant security risk. Detecting
+  and addressing these attempts promptly helps prevent potential breaches and ensures
+  that user privileges remain properly managed, maintaining the integrity of the organization's
+  security protocols.
+search: '`crowdstrike_stream` tag=alert | rename event.EndpointIp as src_ip, event.EndpointName
+  as src_host, event.UserName as user, event.IncidentDescription as description, event.IncidentType
+  as type, event.NumbersOfAlerts as count_alerts, event.SeverityName as severity |
+  stats count min(_time) as firstTime max(_time) as lastTime by src_ip, src_host,
+  user, description, type, count_alerts, severity | where LIKE(type,"%Privilege escalation%")
+  AND NOT LIKE(user, "%adm%") AND NOT LIKE(user, "%svc%") AND NOT LIKE(user, "%admin%")
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_privilege_escalation_for_non_admin_user_filter`'
+how_to_implement: To implement CrowdStrike stream JSON logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "CrowdStrike:Event:Streams:JSON" event stream. Process and store the JSON
+  logs as needed, integrating them into your logging or SIEM system for monitoring
+  and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,20 +59,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - event.EndpointIp
-  - event.EndpointName
-  - event.UserName
-  - event.IncidentDescription
-  - event.IncidentType
-  - event.NumbersOfAlerts
-  - event.SeverityName
   security_domain: endpoint
-  manual_test: This detection is marked manual test because the attack_data file and TA do not provide the event.EndpointIp and event.EndpointName fields. event.EndpointName is required to be present for the Risk Message Validation Integration Testing. This will be investigated and is a tracked issue.
+  manual_test: This detection is marked manual test because the attack_data file and
+    TA do not provide the event.EndpointIp and event.EndpointName fields. event.EndpointName
+    is required to be present for the Risk Message Validation Integration Testing.
+    This will be investigated and is a tracked issue.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/privilege_escalation/crowdstrike_priv_esc_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/privilege_escalation/crowdstrike_priv_esc_cleaned.log
     sourcetype: CrowdStrike:Event:Streams:JSON
     source: CrowdStrike:Event:Streams
diff --git a/detections/endpoint/crowdstrike_user_weak_password_policy.yml b/detections/endpoint/crowdstrike_user_weak_password_policy.yml
index b516b4e900..6566e62e87 100644
--- a/detections/endpoint/crowdstrike_user_weak_password_policy.yml
+++ b/detections/endpoint/crowdstrike_user_weak_password_policy.yml
@@ -6,9 +6,23 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: Anomaly
 status: production
-description: The following analytic detects CrowdStrike alerts for weak password policy violations, identifying instances where passwords do not meet the required security standards. These alerts highlight potential vulnerabilities that could be exploited by attackers, emphasizing the need for stronger password practices. Addressing these alerts promptly helps to enhance overall security and protect sensitive information from unauthorized access.
-search: '`crowdstrike_identities`  primaryDisplayName != "*admin*" | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type | where risk_type = "WEAK_PASSWORD_POLICY" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_user_weak_password_policy_filter`'
-how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for weak password policy
+  violations, identifying instances where passwords do not meet the required security
+  standards. These alerts highlight potential vulnerabilities that could be exploited
+  by attackers, emphasizing the need for stronger password practices. Addressing these
+  alerts promptly helps to enhance overall security and protect sensitive information
+  from unauthorized access.
+search: '`crowdstrike_identities`  primaryDisplayName != "*admin*" | rename riskFactors{}.severity
+  as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain
+  as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count
+  min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName
+  risk_type severity riskScore riskScoreSeverity user role_type | where risk_type
+  = "WEAK_PASSWORD_POLICY" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `crowdstrike_user_weak_password_policy_filter`'
+how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "crowdstrike:identities" event stream. Process and store the logs as needed,
+  integrating them into your logging or SIEM system for monitoring and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - riskFactors{}.severity
-  - riskFactors{}.type
-  - roles{}.type
-  - accounts{}.domain
-  - accounts{}.dn
-  - accounts{}.samAccountName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/non_adminweak_password_policy/crowdstrike_user_weak_password_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/non_adminweak_password_policy/crowdstrike_user_weak_password_cleaned.log
     sourcetype: crowdstrike:identities
     source: crowdstrike:identities
diff --git a/detections/endpoint/crowdstrike_user_with_duplicate_password.yml b/detections/endpoint/crowdstrike_user_with_duplicate_password.yml
index c92c0ca633..92c2503ac2 100644
--- a/detections/endpoint/crowdstrike_user_with_duplicate_password.yml
+++ b/detections/endpoint/crowdstrike_user_with_duplicate_password.yml
@@ -6,9 +6,23 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: Anomaly
 status: production
-description: The following analytic detects CrowdStrike alerts for non-admin accounts with duplicate password risk, identifying instances where multiple non-admin users share the same password. This practice weakens security and increases the potential for unauthorized access. Addressing these alerts is essential to ensure each user account has a unique, strong password, thereby enhancing overall security and protecting sensitive information.
-search: '`crowdstrike_identities` primaryDisplayName != "*admin*" | rename riskFactors{}.severity as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName risk_type severity riskScore riskScoreSeverity user role_type | where risk_type = "DUPLICATE_PASSWORD" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `crowdstrike_user_with_duplicate_password_filter`'
-how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe to the "crowdstrike:identities" event stream. Process and store the logs as needed, integrating them into your logging or SIEM system for monitoring and analysis.
+description: The following analytic detects CrowdStrike alerts for non-admin accounts
+  with duplicate password risk, identifying instances where multiple non-admin users
+  share the same password. This practice weakens security and increases the potential
+  for unauthorized access. Addressing these alerts is essential to ensure each user
+  account has a unique, strong password, thereby enhancing overall security and protecting
+  sensitive information.
+search: '`crowdstrike_identities` primaryDisplayName != "*admin*" | rename riskFactors{}.severity
+  as severity, riskFactors{}.type as risk_type, roles{}.type as role_type, accounts{}.domain
+  as domain, accounts{}.dn as dn, accounts{}.samAccountName as user | stats count
+  min(_time) as firstTime max(_time) as lastTime by  domain dn primaryDisplayName
+  risk_type severity riskScore riskScoreSeverity user role_type | where risk_type
+  = "DUPLICATE_PASSWORD" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `crowdstrike_user_with_duplicate_password_filter`'
+how_to_implement: To implement crowdstrike:identities logs, use the Falcon Streaming
+  API. Set up an API client, authenticate with your CrowdStrike credentials, and subscribe
+  to the "crowdstrike:identities" event stream. Process and store the logs as needed,
+  integrating them into your logging or SIEM system for monitoring and analysis.
 known_false_positives: unknown
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/12/CrowdStrike-Falcon-Event-Streams-Add-on-Guide-v3.pdf
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - riskFactors{}.severity
-  - riskFactors{}.type
-  - roles{}.type
-  - accounts{}.domain
-  - accounts{}.dn
-  - accounts{}.samAccountName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/user_duplicate_password/crowdstrike_user_dup_pwd_cleaned.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/crowdstrike_stream/user_duplicate_password/crowdstrike_user_dup_pwd_cleaned.log
     sourcetype: crowdstrike:identities
     source: crowdstrike:identities
diff --git a/detections/endpoint/csc_net_on_the_fly_compilation.yml b/detections/endpoint/csc_net_on_the_fly_compilation.yml
index ac4e5462a1..bf6489da5c 100644
--- a/detections/endpoint/csc_net_on_the_fly_compilation.yml
+++ b/detections/endpoint/csc_net_on_the_fly_compilation.yml
@@ -5,14 +5,37 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of the .NET compiler csc.exe for on-the-fly compilation of potentially malicious .NET code. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with csc.exe. This activity is significant because adversaries and malware often use this technique to evade detection by compiling malicious code at runtime. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects the use of the .NET compiler csc.exe for
+  on-the-fly compilation of potentially malicious .NET code. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on specific command-line
+  patterns associated with csc.exe. This activity is significant because adversaries
+  and malware often use this technique to evade detection by compiling malicious code
+  at runtime. If confirmed malicious, this could allow attackers to execute arbitrary
+  code, potentially leading to system compromise, data exfiltration, or further lateral
+  movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_csc` Processes.process = "*/noconfig*" Processes.process = "*/fullpaths*" Processes.process = "*@*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `csc_net_on_the_fly_compilation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A network operator or systems administrator may utilize an automated powershell script taht execute .net code that may generate false positive. filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_csc` Processes.process
+  = "*/noconfig*" Processes.process = "*/fullpaths*" Processes.process = "*@*" by
+  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `csc_net_on_the_fly_compilation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A network operator or systems administrator may utilize an
+  automated powershell script taht execute .net code that may generate false positive.
+  filter is needed.
 references:
 - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/
 - https://tccontre.blogspot.com/2019/06/maicious-macro-that-compile-c-code-as.html
@@ -35,20 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/curl_download_and_bash_execution.yml b/detections/endpoint/curl_download_and_bash_execution.yml
index 097942b877..2bda4bf80c 100644
--- a/detections/endpoint/curl_download_and_bash_execution.yml
+++ b/detections/endpoint/curl_download_and_bash_execution.yml
@@ -5,7 +5,14 @@ date: '2024-09-30'
 author: Michael Haag, Splunk, DipsyTipsy
 status: production
 type: TTP
-description: The following analytic detects the use of curl on Linux or MacOS systems to download a file from a remote source and pipe it directly to bash for execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent processes. This activity is significant as it is commonly associated with malicious actions such as coinminers and exploitation of vulnerabilities like CVE-2021-44228 in Log4j. If confirmed malicious, this behavior could lead to unauthorized code execution, system compromise, and further exploitation within the environment.
+description: The following analytic detects the use of curl on Linux or MacOS systems
+  to download a file from a remote source and pipe it directly to bash for execution.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names, command-line arguments, and parent processes. This activity
+  is significant as it is commonly associated with malicious actions such as coinminers
+  and exploitation of vulnerabilities like CVE-2021-44228 in Log4j. If confirmed malicious,
+  this behavior could lead to unauthorized code execution, system compromise, and
+  further exploitation within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
@@ -38,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,7 +63,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ attempting to download a remote file and run it with bash.
+  message: An instance of $process_name$ was identified on endpoint $dest$ attempting
+    to download a remote file and run it with bash.
   mitre_attack_id:
   - T1105
   observable:
@@ -71,23 +84,14 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
-  manual_test: Due to current limitations in command line extraction capabilities with Sysmon for Linux, full CommandLine data cannot be collected for complete validation. Setting to manual test to prevent integration test failures.
+  manual_test: Due to current limitations in command line extraction capabilities
+    with Sysmon for Linux, full CommandLine data cannot be collected for complete
+    validation. Setting to manual test to prevent integration test failures.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/linux-sysmon_curlwget.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/linux-sysmon_curlwget.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/delete_shadowcopy_with_powershell.yml b/detections/endpoint/delete_shadowcopy_with_powershell.yml
index 8054eddf88..388f9fde58 100644
--- a/detections/endpoint/delete_shadowcopy_with_powershell.yml
+++ b/detections/endpoint/delete_shadowcopy_with_powershell.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerShell to delete shadow copies via the WMIC PowerShell module. It leverages EventCode 4104 and searches for specific keywords like "ShadowCopy," "Delete," or "Remove" within the ScriptBlockText. This activity is significant because deleting shadow copies is a common tactic used by ransomware, such as DarkSide, to prevent data recovery. If confirmed malicious, this action could lead to irreversible data loss and hinder recovery efforts, significantly impacting business continuity and data integrity.
+description: The following analytic detects the use of PowerShell to delete shadow
+  copies via the WMIC PowerShell module. It leverages EventCode 4104 and searches
+  for specific keywords like "ShadowCopy," "Delete," or "Remove" within the ScriptBlockText.
+  This activity is significant because deleting shadow copies is a common tactic used
+  by ransomware, such as DarkSide, to prevent data recovery. If confirmed malicious,
+  this action could lead to irreversible data loss and hinder recovery efforts, significantly
+  impacting business continuity and data integrity.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText= "*ShadowCopy*" (ScriptBlockText = "*Delete*" OR ScriptBlockText = "*Remove*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText |rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `delete_shadowcopy_with_powershell_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
+search: '`powershell` EventCode=4104 ScriptBlockText= "*ShadowCopy*" (ScriptBlockText
+  = "*Delete*" OR ScriptBlockText = "*Remove*") | stats count min(_time) as firstTime
+  max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText |rename
+  Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `delete_shadowcopy_with_powershell_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the powershell logs  from your endpoints. make sure you enable needed
+  registry to monitor this event.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An attempt to delete ShadowCopy was performed using PowerShell on $dest$ by $user$.
+  message: An attempt to delete ShadowCopy was performed using PowerShell on $dest$
+    by $user$.
   mitre_attack_id:
   - T1490
   observable:
@@ -48,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/deleting_of_net_users.yml b/detections/endpoint/deleting_of_net_users.yml
index eebcbae375..5d091ea8e5 100644
--- a/detections/endpoint/deleting_of_net_users.yml
+++ b/detections/endpoint/deleting_of_net_users.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of net.exe or net1.exe command-line to delete a user account on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line execution logs. This activity is significant as it may indicate an attempt to impair user accounts or cover tracks during lateral movement. If confirmed malicious, this could lead to unauthorized access removal, disruption of legitimate user activities, or concealment of adversarial actions, complicating incident response and forensic investigations.
+description: The following analytic detects the use of net.exe or net1.exe command-line
+  to delete a user account on a system. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process and command-line execution logs.
+  This activity is significant as it may indicate an attempt to impair user accounts
+  or cover tracks during lateral movement. If confirmed malicious, this could lead
+  to unauthorized access removal, disruption of legitimate user activities, or concealment
+  of adversarial actions, complicating incident response and forensic investigations.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="*user*" AND  Processes.process="*/delete*" by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `deleting_of_net_users_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: System administrators or scripts may delete user accounts via this technique. Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.parent_process) as parent_process values(Processes.process_id)
+  as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_net` AND Processes.process="*user*" AND  Processes.process="*/delete*"
+  by  Processes.process_name Processes.original_file_name Processes.dest Processes.user
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `deleting_of_net_users_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: System administrators or scripts may delete user accounts via
+  this technique. Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to delete accounts.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to delete accounts.
   mitre_attack_id:
   - T1531
   observable:
@@ -56,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/deleting_shadow_copies.yml b/detections/endpoint/deleting_shadow_copies.yml
index f3961d5eea..cae255c6dc 100644
--- a/detections/endpoint/deleting_shadow_copies.yml
+++ b/detections/endpoint/deleting_shadow_copies.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of shadow copies using the vssadmin.exe or wmic.exe utilities. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because deleting shadow copies is a common tactic used by attackers to prevent recovery and hide their tracks. If confirmed malicious, this action could hinder incident response efforts and allow attackers to maintain persistence and cover their activities, making it crucial for security teams to investigate promptly.
+description: The following analytic detects the deletion of shadow copies using the
+  vssadmin.exe or wmic.exe utilities. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process names and command-line arguments. This
+  activity is significant because deleting shadow copies is a common tactic used by
+  attackers to prevent recovery and hide their tracks. If confirmed malicious, this
+  action could hinder incident response efforts and allow attackers to maintain persistence
+  and cover their activities, making it crucial for security teams to investigate
+  promptly.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `deleting_shadow_copies_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe
+  OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow*
+  by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `deleting_shadow_copies_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: vssadmin.exe and wmic.exe are standard applications shipped
+  with modern versions of windows. They may be used by administrators to legitimately
+  delete old backup copies, although this is typically rare.
 references:
 - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to delete shadow copies.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to delete shadow copies.
   mitre_attack_id:
   - T1490
   observable:
@@ -63,23 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_azurehound_command_line_arguments.yml b/detections/endpoint/detect_azurehound_command_line_arguments.yml
index 29db0573ce..f302172fe1 100644
--- a/detections/endpoint/detect_azurehound_command_line_arguments.yml
+++ b/detections/endpoint/detect_azurehound_command_line_arguments.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Invoke-AzureHound` command-line argument, commonly used by the AzureHound tool. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because AzureHound is often used for reconnaissance in Azure environments, potentially exposing sensitive information. If confirmed malicious, this activity could allow an attacker to map out Azure Active Directory structures, aiding in further attacks and privilege escalation.
+description: The following analytic detects the execution of the `Invoke-AzureHound`
+  command-line argument, commonly used by the AzureHound tool. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant because AzureHound is often used for reconnaissance
+  in Azure environments, potentially exposing sensitive information. If confirmed
+  malicious, this activity could allow an attacker to map out Azure Active Directory
+  structures, aiding in further attacks and privilege escalation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*")
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.parent_process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_azurehound_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown.
 references:
 - https://attack.mitre.org/software/S0521/
@@ -24,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ using AzureHound to enumerate AzureAD.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ using AzureHound to enumerate AzureAD.
   mitre_attack_id:
   - T1087.002
   - T1069.001
@@ -63,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_azurehound_file_modifications.yml b/detections/endpoint/detect_azurehound_file_modifications.yml
index 7d9bda2ed8..7505c73bb1 100644
--- a/detections/endpoint/detect_azurehound_file_modifications.yml
+++ b/detections/endpoint/detect_azurehound_file_modifications.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of specific AzureHound-related files, such as `*-azurecollection.zip` and various `.json` files, on disk. It leverages data from the Endpoint.Filesystem datamodel, focusing on file creation events with specific filenames. This activity is significant because AzureHound is a tool used to gather information about Azure environments, similar to SharpHound for on-premises Active Directory. If confirmed malicious, this activity could indicate an attacker is collecting sensitive Azure environment data, potentially leading to further exploitation or privilege escalation within the cloud infrastructure.
+description: The following analytic detects the creation of specific AzureHound-related
+  files, such as `*-azurecollection.zip` and various `.json` files, on disk. It leverages
+  data from the Endpoint.Filesystem datamodel, focusing on file creation events with
+  specific filenames. This activity is significant because AzureHound is a tool used
+  to gather information about Azure environments, similar to SharpHound for on-premises
+  Active Directory. If confirmed malicious, this activity could indicate an attacker
+  is collecting sensitive Azure environment data, potentially leading to further exploitation
+  or privilege escalation within the cloud infrastructure.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.user | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_file_modifications_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip",
+  "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json",
+  "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name
+  Filesystem.file_path Filesystem.dest Filesystem.user | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_azurehound_file_modifications_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on file modifications that include the name of the process, and file, responsible
+  for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem`
+  node.
+known_false_positives: False positives should be limited as the analytic is specific
+  to a filename with extension .zip. Filter as needed.
 references:
 - https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350
 - https://github.com/BloodHoundAD/Legacy-AzureHound.ps1/blob/master/AzureHound.ps1
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A file - $file_name$ was written to disk that is related to AzureHound, a AzureAD enumeration utility, has occurred on endpoint $dest$ by user $user$.
+  message: A file - $file_name$ was written to disk that is related to AzureHound,
+    a AzureAD enumeration utility, has occurred on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1087.002
   - T1069.001
@@ -55,17 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - file_path
-  - dest
-  - file_name
-  - process_id
-  - file_create_time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml b/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml
index 0f9baea4c9..3be3cedcb4 100644
--- a/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml
+++ b/detections/endpoint/detect_baron_samedit_cve_2021_3156.yml
@@ -5,10 +5,20 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects attempts to exploit the Baron Samedit vulnerability (CVE-2021-3156) by identifying the use of the "sudoedit -s \\" command. This detection leverages logs from Linux systems, specifically searching for instances of the sudoedit command with the "-s" flag followed by a double quote. This activity is significant because it indicates an attempt to exploit a known vulnerability that allows attackers to gain root privileges. If confirmed malicious, this could lead to complete system compromise, unauthorized access to sensitive data, and potential data breaches.
+description: The following analytic detects attempts to exploit the Baron Samedit
+  vulnerability (CVE-2021-3156) by identifying the use of the "sudoedit -s \\" command.
+  This detection leverages logs from Linux systems, specifically searching for instances
+  of the sudoedit command with the "-s" flag followed by a double quote. This activity
+  is significant because it indicates an attempt to exploit a known vulnerability
+  that allows attackers to gain root privileges. If confirmed malicious, this could
+  lead to complete system compromise, unauthorized access to sensitive data, and potential
+  data breaches.
 data_source: []
 search: '`linux_hosts` "sudoedit -s \\" | `detect_baron_samedit_cve_2021_3156_filter`'
-how_to_implement: Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
+how_to_implement: Splunk Universal Forwarder running on Linux systems, capturing logs
+  from the /var/log directory. The vulnerability is exposed when a non privledged
+  user tries passing in a single \ character at the end of the command while using
+  the shell and edit flags.
 known_false_positives: unknown
 references: []
 tags:
@@ -31,6 +41,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml b/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml
index fc102b794e..54aa410927 100644
--- a/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml
+++ b/detections/endpoint/detect_baron_samedit_cve_2021_3156_segfault.yml
@@ -5,11 +5,24 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies a heap-based buffer overflow in sudoedit by detecting Linux logs containing both "sudoedit" and "segfault" terms. This detection leverages Splunk to monitor for more than five occurrences of these terms on a single host within a specified timeframe. This activity is significant because exploiting this vulnerability (CVE-2021-3156) can allow attackers to gain root privileges, leading to potential system compromise, unauthorized access, and data breaches. If confirmed malicious, this could result in elevated privileges and full control over the affected system, posing a severe security risk.
+description: The following analytic identifies a heap-based buffer overflow in sudoedit
+  by detecting Linux logs containing both "sudoedit" and "segfault" terms. This detection
+  leverages Splunk to monitor for more than five occurrences of these terms on a single
+  host within a specified timeframe. This activity is significant because exploiting
+  this vulnerability (CVE-2021-3156) can allow attackers to gain root privileges,
+  leading to potential system compromise, unauthorized access, and data breaches.
+  If confirmed malicious, this could result in elevated privileges and full control
+  over the affected system, posing a severe security risk.
 data_source: []
-search: '`linux_hosts` TERM(sudoedit) TERM(segfault) | stats count min(_time) as firstTime max(_time) as lastTime by host | where count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter`'
-how_to_implement: Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged.  This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host
-known_false_positives: If sudoedit is throwing segfaults for other reasons this will pick those up too.
+search: '`linux_hosts` TERM(sudoedit) TERM(segfault) | stats count min(_time) as firstTime
+  max(_time) as lastTime by host | where count > 5 | `detect_baron_samedit_cve_2021_3156_segfault_filter`'
+how_to_implement: Splunk Universal Forwarder running on Linux systems (tested on Centos
+  and Ubuntu), where segfaults are being logged.  This also captures instances where
+  the exploit has been compiled into a binary. The detection looks for greater than
+  5 instances of sudoedit combined with segfault over your search time period on a
+  single host
+known_false_positives: If sudoedit is throwing segfaults for other reasons this will
+  pick those up too.
 references: []
 tags:
   analytic_story:
@@ -31,7 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - host
   security_domain: endpoint
diff --git a/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml b/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml
index 964d56c04b..1e06613c1f 100644
--- a/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml
+++ b/detections/endpoint/detect_baron_samedit_cve_2021_3156_via_osquery.yml
@@ -5,10 +5,20 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the execution of the "sudoedit -s *" command, which is associated with the Baron Samedit CVE-2021-3156 heap-based buffer overflow vulnerability. This detection leverages the `osquery_process` data source to identify instances where this specific command is run. This activity is significant because it indicates an attempt to exploit a known vulnerability that allows privilege escalation. If confirmed malicious, an attacker could gain full control of the system, execute arbitrary code, or access sensitive data, leading to potential data breaches and system disruptions.
+description: The following analytic detects the execution of the "sudoedit -s *" command,
+  which is associated with the Baron Samedit CVE-2021-3156 heap-based buffer overflow
+  vulnerability. This detection leverages the `osquery_process` data source to identify
+  instances where this specific command is run. This activity is significant because
+  it indicates an attempt to exploit a known vulnerability that allows privilege escalation.
+  If confirmed malicious, an attacker could gain full control of the system, execute
+  arbitrary code, or access sensitive data, leading to potential data breaches and
+  system disruptions.
 data_source: []
 search: '`osquery_process` | search "columns.cmdline"="sudoedit -s \\*" | `detect_baron_samedit_cve_2021_3156_via_osquery_filter`'
-how_to_implement: OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
+how_to_implement: OSQuery installed and configured to pick up process events (info
+  at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402.
+  The vulnerability is exposed when a non privledged user tries passing in a single
+  \ character at the end of the command while using the shell and edit flags.
 known_false_positives: unknown
 references: []
 tags:
@@ -31,7 +41,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - columns.cmdline
   security_domain: endpoint
diff --git a/detections/endpoint/detect_certify_command_line_arguments.yml b/detections/endpoint/detect_certify_command_line_arguments.yml
index 56190658d0..114e78a101 100644
--- a/detections/endpoint/detect_certify_command_line_arguments.yml
+++ b/detections/endpoint/detect_certify_command_line_arguments.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the use of Certify or Certipy tools to enumerate Active Directory Certificate Services (AD CS) environments. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments associated with these tools. This activity is significant because it indicates potential reconnaissance or exploitation attempts targeting AD CS, which could lead to unauthorized access or privilege escalation. If confirmed malicious, attackers could gain insights into the AD CS infrastructure, potentially compromising sensitive certificates and escalating their privileges within the network.
+description: The following analytic detects the use of Certify or Certipy tools to
+  enumerate Active Directory Certificate Services (AD CS) environments. It leverages
+  Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments
+  associated with these tools. This activity is significant because it indicates potential
+  reconnaissance or exploitation attempts targeting AD CS, which could lead to unauthorized
+  access or privilege escalation. If confirmed malicious, attackers could gain insights
+  into the AD CS infrastructure, potentially compromising sensitive certificates and
+  escalating their privileges within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("* find *","* auth *","* request *","* req *","* download *",) AND Processes.process IN ("* /vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*","* /ca*", "* -username *","* -u *") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `detect_certify_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("* find
+  *","* auth *","* request *","* req *","* download *",) AND Processes.process IN
+  ("* /vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*","* /ca*", "*
+  -username *","* -u *") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `detect_certify_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown
 references:
 - https://github.com/GhostPack/Certify
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,19 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml b/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml
index 6056c080d1..deccf0657c 100644
--- a/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml
+++ b/detections/endpoint/detect_certify_with_powershell_script_block_logging.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the use of the Certify tool via an in-memory PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments. It leverages PowerShell Script Block Logging (EventCode 4104) to identify specific command patterns associated with Certify's enumeration and exploitation functions. This activity is significant as it indicates potential reconnaissance or exploitation attempts against AD CS, which could lead to unauthorized certificate issuance. If confirmed malicious, attackers could leverage this to escalate privileges, persist in the environment, or access sensitive information by abusing AD CS.
+description: The following analytic detects the use of the Certify tool via an in-memory
+  PowerShell function to enumerate Active Directory Certificate Services (AD CS) environments.
+  It leverages PowerShell Script Block Logging (EventCode 4104) to identify specific
+  command patterns associated with Certify's enumeration and exploitation functions.
+  This activity is significant as it indicates potential reconnaissance or exploitation
+  attempts against AD CS, which could lead to unauthorized certificate issuance. If
+  confirmed malicious, attackers could leverage this to escalate privileges, persist
+  in the environment, or access sensitive information by abusing AD CS.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText IN ("*find *") AND ScriptBlockText IN ("* /vulnerable*","* -vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*")) OR (ScriptBlockText IN (,"*auth *","*req *",) AND ScriptBlockText IN ("* -ca *","* -username *","* -u *")) OR (ScriptBlockText IN ("*request *","*download *") AND ScriptBlockText IN ("* /ca:*")) | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command Values(OpCode) as reason values(Path) as file_name values(UserID) as user by _time Computer EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval file_name = case(isnotnull(file_name),file_name,true(),"unknown") | eval signature = substr(command,0,256) | rename Computer as dest,EventCode as signature_id | `detect_certify_with_powershell_script_block_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell..
+search: '`powershell` EventCode=4104 (ScriptBlockText IN ("*find *") AND ScriptBlockText
+  IN ("* /vulnerable*","* -vulnerable*","* /enrolleeSuppliesSubject *","* /json /outfile*"))
+  OR (ScriptBlockText IN (,"*auth *","*req *",) AND ScriptBlockText IN ("* -ca *","*
+  -username *","* -u *")) OR (ScriptBlockText IN ("*request *","*download *") AND
+  ScriptBlockText IN ("* /ca:*")) | stats count min(_time) as firstTime max(_time)
+  as lastTime list(ScriptBlockText) as command Values(OpCode) as reason values(Path)
+  as file_name values(UserID) as user by _time Computer EventCode | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | eval file_name = case(isnotnull(file_name),file_name,true(),"unknown")
+  | eval signature = substr(command,0,256) | rename Computer as dest,EventCode as
+  signature_id | `detect_certify_with_powershell_script_block_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell..
 known_false_positives: Unknown, partial script block matches.
 references:
 - https://github.com/GhostPack/Certify
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - OpCode
-  - Path
-  - user
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_certipy_file_modifications.yml b/detections/endpoint/detect_certipy_file_modifications.yml
index 845ef45436..49f66e1917 100644
--- a/detections/endpoint/detect_certipy_file_modifications.yml
+++ b/detections/endpoint/detect_certipy_file_modifications.yml
@@ -5,11 +5,33 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the use of the Certipy tool to enumerate Active Directory Certificate Services (AD CS) environments by identifying unique file modifications. It leverages endpoint process and filesystem data to spot the creation of files with specific names or extensions associated with Certipy's information gathering and exfiltration activities. This activity is significant as it indicates potential reconnaissance and data exfiltration efforts by an attacker. If confirmed malicious, this could lead to unauthorized access to sensitive AD CS information, enabling further attacks or privilege escalation within the network.
+description: The following analytic detects the use of the Certipy tool to enumerate
+  Active Directory Certificate Services (AD CS) environments by identifying unique
+  file modifications. It leverages endpoint process and filesystem data to spot the
+  creation of files with specific names or extensions associated with Certipy's information
+  gathering and exfiltration activities. This activity is significant as it indicates
+  potential reconnaissance and data exfiltration efforts by an attacker. If confirmed
+  malicious, this could lead to unauthorized access to sensitive AD CS information,
+  enabling further attacks or privilege escalation within the network.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime values(Processes.process_current_directory) as process_current_directory FROM datamodel=Endpoint.Processes where Processes.action="allowed" BY _time span=1h Processes.user Processes.dest Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.parent_process_name Processes.parent_process Processes.process_guid Processes.action |`drop_dm_object_name(Processes)` | join max=0 dest process_guid [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*_certipy.zip", "*_certipy.txt", "*_certipy.json", "*.ccache") by Filesystem.file_create_time Filesystem.process_id Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` ] | fields firstTime lastTime user dest file_create_time file_name file_path parent_process_name parent_process process_name process_path process_current_directory process process_guid process_id | where isnotnull(file_name) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_certipy_file_modifications_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints as well as file creation or deletion events.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime values(Processes.process_current_directory) as process_current_directory
+  FROM datamodel=Endpoint.Processes where Processes.action="allowed" BY _time span=1h
+  Processes.user Processes.dest Processes.process_id Processes.process_name Processes.process
+  Processes.process_path Processes.parent_process_name Processes.parent_process Processes.process_guid
+  Processes.action |`drop_dm_object_name(Processes)` | join max=0 dest process_guid
+  [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*_certipy.zip",
+  "*_certipy.txt", "*_certipy.json", "*.ccache") by Filesystem.file_create_time Filesystem.process_id
+  Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.dest
+  | `drop_dm_object_name(Filesystem)` ] | fields firstTime lastTime user dest file_create_time
+  file_name file_path parent_process_name parent_process process_name process_path
+  process_current_directory process process_guid process_id | where isnotnull(file_name)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_certipy_file_modifications_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints as well as file creation or deletion events.
 known_false_positives: Unknown
 references:
 - https://github.com/ly4k/Certipy
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,28 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Processes.action
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.process_guid
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_computer_changed_with_anonymous_account.yml b/detections/endpoint/detect_computer_changed_with_anonymous_account.yml
index 5294bad184..002b2ca6b6 100644
--- a/detections/endpoint/detect_computer_changed_with_anonymous_account.yml
+++ b/detections/endpoint/detect_computer_changed_with_anonymous_account.yml
@@ -5,12 +5,25 @@ date: '2024-10-17'
 author: Rod Soto, Jose Hernandez, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects changes to computer accounts using an anonymous logon. It leverages Windows Security Event Codes 4742 (Computer Change) and 4624 (Successful Logon) with the TargetUserName set to "ANONYMOUS LOGON" and LogonType 3. This activity is significant because anonymous logons should not typically be modifying computer accounts, indicating potential unauthorized access or misconfiguration. If confirmed malicious, this could allow an attacker to alter computer accounts, potentially leading to privilege escalation or persistent access within the network.
+description: The following analytic detects changes to computer accounts using an
+  anonymous logon. It leverages Windows Security Event Codes 4742 (Computer Change)
+  and 4624 (Successful Logon) with the TargetUserName set to "ANONYMOUS LOGON" and
+  LogonType 3. This activity is significant because anonymous logons should not typically
+  be modifying computer accounts, indicating potential unauthorized access or misconfiguration.
+  If confirmed malicious, this could allow an attacker to alter computer accounts,
+  potentially leading to privilege escalation or persistent access within the network.
 data_source:
 - Windows Event Log Security 4624
 - Windows Event Log Security 4742
-search: '`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter`'
-how_to_implement: This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
+search: '`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS
+  LOGON" LogonType=3 | stats count values(host) as host, values(TargetDomainName)
+  as Domain, values(user) as user | `detect_computer_changed_with_anonymous_account_filter`'
+how_to_implement: This search requires audit computer account management to be enabled
+  on the system in order to generate Event ID 4742. We strongly recommend that you
+  specify your environment-specific configurations (index, source, sourcetype, etc.)
+  for Windows Event Logs. Replace the macro definition with configurations for your
+  Splunk environment. The search also uses a post-filter macro designed to filter
+  out known false positives.
 known_false_positives: None thus far found
 references:
 - https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/
@@ -22,7 +35,9 @@ tags:
   cve:
   - CVE-2020-1472
   impact: 70
-  message: The following $EventCode$ occurred on $dest$ by $user$ with Logon Type 3, which may be indicative of the an account or group being changed by an anonymous account.
+  message: The following $EventCode$ occurred on $dest$ by $user$ with Logon Type
+    3, which may be indicative of the an account or group being changed by an anonymous
+    account.
   mitre_attack_id:
   - T1210
   observable:
@@ -38,11 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetUserName
-  - LogonType
-  - TargetDomainName
-  - user
   security_domain: endpoint
diff --git a/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml b/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml
index 1a829c484e..f2155ba14f 100644
--- a/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml
+++ b/detections/endpoint/detect_copy_of_shadowcopy_with_script_block_logging.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerShell commands to copy the SAM, SYSTEM, or SECURITY hives, which are critical for credential theft. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command executed. This activity is significant as it indicates an attempt to exfiltrate sensitive registry hives for offline password cracking. If confirmed malicious, this could lead to unauthorized access to credentials, enabling further compromise of the system and potential lateral movement within the network.
+description: The following analytic detects the use of PowerShell commands to copy
+  the SAM, SYSTEM, or SECURITY hives, which are critical for credential theft. It
+  leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze
+  the full command executed. This activity is significant as it indicates an attempt
+  to exfiltrate sensitive registry hives for offline password cracking. If confirmed
+  malicious, this could lead to unauthorized access to credentials, enabling further
+  compromise of the system and potential lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*copy*","*[System.IO.File]::Copy*") AND ScriptBlockText IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_copy_of_shadowcopy_with_script_block_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Limited false positives as the scope is limited to SAM, SYSTEM and SECURITY hives.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*copy*","*[System.IO.File]::Copy*")
+  AND ScriptBlockText IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_copy_of_shadowcopy_with_script_block_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Limited false positives as the scope is limited to SAM, SYSTEM
+  and SECURITY hives.
 references:
 - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934
 - https://github.com/GossiTheDog/HiveNightmare
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +50,8 @@ tags:
   cve:
   - CVE-2021-36934
   impact: 80
-  message: PowerShell was identified running a script to capture the SAM hive on endpoint $dest$ by user $user$.
+  message: PowerShell was identified running a script to capture the SAM hive on endpoint
+    $dest$ by user $user$.
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -49,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - OpCode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/detect_copy_of_shadowcopy_with_script_block_logging/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/detect_copy_of_shadowcopy_with_script_block_logging/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_credential_dumping_through_lsass_access.yml b/detections/endpoint/detect_credential_dumping_through_lsass_access.yml
index 601aac8d09..bea01c3e78 100644
--- a/detections/endpoint/detect_credential_dumping_through_lsass_access.yml
+++ b/detections/endpoint/detect_credential_dumping_through_lsass_access.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to read LSASS memory, indicative of credential dumping. It leverages Sysmon EventCode 10, filtering for specific access permissions (0x1010 and 0x1410) on the lsass.exe process. This activity is significant because it suggests an attacker is trying to extract credentials from LSASS memory, potentially leading to unauthorized access, data breaches, and compromise of sensitive information. If confirmed malicious, this could enable attackers to escalate privileges, move laterally within the network, or exfiltrate data. Extensive triage is necessary to differentiate between malicious and benign activities.
+description: The following analytic detects attempts to read LSASS memory, indicative
+  of credential dumping. It leverages Sysmon EventCode 10, filtering for specific
+  access permissions (0x1010 and 0x1410) on the lsass.exe process. This activity is
+  significant because it suggests an attacker is trying to extract credentials from
+  LSASS memory, potentially leading to unauthorized access, data breaches, and compromise
+  of sensitive information. If confirmed malicious, this could enable attackers to
+  escalate privileges, move laterally within the network, or exfiltrate data. Extensive
+  triage is necessary to differentiate between malicious and benign activities.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter`'
-how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives: The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410)
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage,
+  SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `detect_credential_dumping_through_lsass_access_filter`'
+how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which
+  includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`.
+  We strongly recommend that you specify your environment-specific configurations
+  (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
+  with configurations for your Splunk environment. The search also uses a post-filter
+  macro designed to filter out known false positives.
+known_false_positives: The activity may be legitimate. Other tools can access lsass
+  for legitimate reasons, and it's possible this event could be generated in those
+  cases. In these cases, false positives should be fairly obvious and you may need
+  to tweak the search to eliminate noise.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$TargetImage$"
@@ -18,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$TargetImage$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$TargetImage$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$TargetImage$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +53,8 @@ tags:
   asset_type: Windows
   confidence: 100
   impact: 80
-  message: The $SourceImage$ has attempted access to read $TargetImage$ was identified on endpoint $dest$, this is indicative of credential dumping and should be investigated.
+  message: The $SourceImage$ has attempted access to read $TargetImage$ was identified
+    on endpoint $dest$, this is indicative of credential dumping and should be investigated.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -47,20 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetImage
-  - GrantedAccess
-  - dest
-  - SourceImage
-  - SourceProcessId
-  - TargetImage
-  - TargetProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_critical_alerts_from_security_tools.yml b/detections/endpoint/detect_critical_alerts_from_security_tools.yml
index 4cf5d5afc8..f61354e16c 100644
--- a/detections/endpoint/detect_critical_alerts_from_security_tools.yml
+++ b/detections/endpoint/detect_critical_alerts_from_security_tools.yml
@@ -8,10 +8,38 @@ type: TTP
 data_source:
 - Windows Defender Alerts
 - MS365 Defender Incident Alerts
-description: The following analytics is to detect high and critical alerts from endpoint security tools such as Microsoft Defender, Carbon Black, and Crowdstrike. This query aggregates and summarizes critical severity alerts from the Alerts data model, providing details such as the alert signature, application, description, source, destination, and timestamps, while applying custom filters and formatting for enhanced analysis in a SIEM environment.This capability allows security teams to efficiently allocate resources and maintain a strong security posture, while also supporting compliance with regulatory requirements by providing a clear record of critical security events. We tested these detections with logs from Microsoft Defender, however this detection should work for any security alerts that are ingested into the alerts data model. **Note** - We are dynamically creating the risk_score field based on the severity of the alert in the SPL and that supersedes the risk score set in the detection.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Alerts.description) as description values(Alerts.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id values(Alerts.severity) as severity values(Alerts.type) as type values(Alerts.severity_id) as severity_id values(Alerts.signature) as signature values(Alerts.signature_id) as signature_id values(Alerts.dest) as dest from datamodel=Alerts where  Alerts.severity IN ("high","critical") by Alerts.src Alerts.user Alerts.id Alerts.vendor sourcetype | `drop_dm_object_name("Alerts")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval risk_score=case(severity="informational", 2, severity="low", 5, severity="medium", 10, severity="high", 50, severity="critical" , 100) | `detect_critical_alerts_from_security_tools_filter`'
-how_to_implement: In order to properly run this search, you to ingest alerts data from other security products such as Crowdstrike, Microsoft Defender, or Carbon Black using appropriate TAs for that technology. Once ingested, the fields should be mapped to the Alerts data model. Make sure to apply transformation on the data if necessary. The risk_score field is used to calculate the risk score for the alerts and the mitre_technique_id field is used to map the alerts to the MITRE ATT&CK framework is dynamically created by the detection when this is triggered. These fields need not be set in the adaptive response actions.
-known_false_positives: False positives may vary by endpoint protection tool; monitor and filter out the alerts that are not relevant to your environment.
+description: The following analytics is to detect high and critical alerts from endpoint
+  security tools such as Microsoft Defender, Carbon Black, and Crowdstrike. This query
+  aggregates and summarizes critical severity alerts from the Alerts data model, providing
+  details such as the alert signature, application, description, source, destination,
+  and timestamps, while applying custom filters and formatting for enhanced analysis
+  in a SIEM environment.This capability allows security teams to efficiently allocate
+  resources and maintain a strong security posture, while also supporting compliance
+  with regulatory requirements by providing a clear record of critical security events.
+  We tested these detections with logs from Microsoft Defender, however this detection
+  should work for any security alerts that are ingested into the alerts data model.
+  **Note** - We are dynamically creating the risk_score field based on the severity
+  of the alert in the SPL and that supersedes the risk score set in the detection.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Alerts.description) as description values(Alerts.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id values(Alerts.severity) as severity
+  values(Alerts.type) as type values(Alerts.severity_id) as severity_id values(Alerts.signature)
+  as signature values(Alerts.signature_id) as signature_id values(Alerts.dest) as
+  dest from datamodel=Alerts where  Alerts.severity IN ("high","critical") by Alerts.src
+  Alerts.user Alerts.id Alerts.vendor sourcetype | `drop_dm_object_name("Alerts")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval
+  risk_score=case(severity="informational", 2, severity="low", 5, severity="medium",
+  10, severity="high", 50, severity="critical" , 100) | `detect_critical_alerts_from_security_tools_filter`'
+how_to_implement: In order to properly run this search, you to ingest alerts data
+  from other security products such as Crowdstrike, Microsoft Defender, or Carbon
+  Black using appropriate TAs for that technology. Once ingested, the fields should
+  be mapped to the Alerts data model. Make sure to apply transformation on the data
+  if necessary. The risk_score field is used to calculate the risk score for the alerts
+  and the mitre_technique_id field is used to map the alerts to the MITRE ATT&CK framework
+  is dynamically created by the detection when this is triggered. These fields need
+  not be set in the adaptive response actions.
+known_false_positives: False positives may vary by endpoint protection tool; monitor
+  and filter out the alerts that are not relevant to your environment.
 references:
 - https://techcommunity.microsoft.com/t5/microsoft-defender-for-cloud/accessing-microsoft-defender-for-cloud-alerts-in-splunk-using/ba-p/938228
 - https://docs.splunk.com/Documentation/CIM/5.3.2/User/Alerts
@@ -22,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,28 +80,17 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Alerts.description
-  - Alerts.mitre_technique_id
-  - Alerts.severity
-  - Alerts.type
-  - Alerts.severity_id
-  - Alerts.signature
-  - Alerts.dest
-  - Alerts.src
-  - Alerts.user
-  - Alerts.id
-  - Alerts.vendor
-  - sourcetype
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/AdvancedHunting.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/AdvancedHunting.log
     source: eventhub://windowsdefenderlogs
     sourcetype: mscs:azure:eventhub:defender:advancedhunting
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/defender_incident_alerts.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/alerts/defender_incident_alerts.log
     source: m365_defender_incident_alerts
     sourcetype: ms365:defender:incident:alerts
diff --git a/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml b/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml
index 4c6eb4e19a..598abebab1 100644
--- a/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml
+++ b/detections/endpoint/detect_empire_with_powershell_script_block_logging.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious PowerShell execution indicative of PowerShell-Empire activity. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze commands sent to PowerShell, specifically looking for patterns involving `system.net.webclient` and base64 encoding. This behavior is significant as it often represents initial stagers used by PowerShell-Empire, a known post-exploitation framework. If confirmed malicious, this activity could allow attackers to download and execute additional payloads, leading to potential code execution, data exfiltration, or further compromise of the affected system.
+description: The following analytic detects suspicious PowerShell execution indicative
+  of PowerShell-Empire activity. It leverages PowerShell Script Block Logging (EventCode=4104)
+  to capture and analyze commands sent to PowerShell, specifically looking for patterns
+  involving `system.net.webclient` and base64 encoding. This behavior is significant
+  as it often represents initial stagers used by PowerShell-Empire, a known post-exploitation
+  framework. If confirmed malicious, this activity could allow attackers to download
+  and execute additional payloads, leading to potential code execution, data exfiltration,
+  or further compromise of the affected system.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104  (ScriptBlockText=*system.net.webclient* AND ScriptBlockText=*frombase64string*) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_empire_with_powershell_script_block_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives may only pertain to it not being related to Empire, but another framework. Filter as needed if any applications use the same pattern.
+search: '`powershell` EventCode=4104  (ScriptBlockText=*system.net.webclient* AND
+  ScriptBlockText=*frombase64string*) | stats count min(_time) as firstTime max(_time)
+  as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_empire_with_powershell_script_block_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives may only pertain to it not being related to
+  Empire, but another framework. Filter as needed if any applications use the same
+  pattern.
 references:
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63
@@ -24,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$UserID$" and "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$UserID$", "$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$UserID$",
+    "$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: The following behavior was identified and typically related to PowerShell-Empire on $Computer$ by $UserID$.
+  message: The following behavior was identified and typically related to PowerShell-Empire
+    on $Computer$ by $UserID$.
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -52,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/empire.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/empire.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml b/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml
index 03b2424709..a863d9c861 100644
--- a/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml
+++ b/detections/endpoint/detect_excessive_account_lockouts_from_endpoint.yml
@@ -5,15 +5,35 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects endpoints causing a high number of account lockouts within a short period. It leverages the Windows security event logs ingested into the `Change` datamodel, specifically under the `Account_Management` node, to identify and count lockout events. This activity is significant as it may indicate a brute-force attack or misconfigured system causing repeated authentication failures. If confirmed malicious, this behavior could lead to account lockouts, disrupting user access and potentially indicating an ongoing attack attempting to compromise user credentials.
+description: The following analytic detects endpoints causing a high number of account
+  lockouts within a short period. It leverages the Windows security event logs ingested
+  into the `Change` datamodel, specifically under the `Account_Management` node, to
+  identify and count lockout events. This activity is significant as it may indicate
+  a brute-force attack or misconfigured system causing repeated authentication failures.
+  If confirmed malicious, this behavior could lead to account lockouts, disrupting
+  user access and potentially indicating an ongoing attack attempting to compromise
+  user credentials.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where All_Changes.result="*lock*" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`'
-how_to_implement: 'You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.
-
-  **Splunk>Phantom Playbook Integration** If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active.
-
-  Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`)'
-known_false_positives: It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where
+  All_Changes.result="*lock*" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")`
+  |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_account_lockouts_from_endpoint_filter`'
+how_to_implement: "You must ingest your Windows security event logs in the `Change`
+  datamodel under the nodename is `Account_Management`, for this search to execute
+  successfully. Please consider updating the cron schedule and the count of lockouts
+  you want to monitor, according to your environment.\n**Splunk>Phantom Playbook Integration**
+  If Splunk>Phantom is also configured in your environment, a Playbook called \"Excessive
+  Account Lockouts Enrichment and Response\" can be configured to run when any results
+  are found by this detection search. The Playbook executes the Contextual and Investigative
+  searches in this Story, conducts additional information gathering on Windows endpoints,
+  and takes a response action to shut down the affected endpoint. To use this integration,
+  install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add
+  the correct hostname to the \"Phantom Instance\" field in the Adaptive Response
+  Actions when configuring this detection search, and set the corresponding Playbook
+  to active.\nPlaybook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`)"
+known_false_positives: It's possible that a widely used system, such as a kiosk, could
+  cause a large number of account lockouts.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Windows
   confidence: 60
   impact: 60
-  message: Multiple accounts have been locked out. Review $dest$ and results related to $user$.
+  message: Multiple accounts have been locked out. Review $dest$ and results related
+    to $user$.
   mitre_attack_id:
   - T1078
   - T1078.002
@@ -47,22 +73,19 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.user
-  - nodename
-  - All_Changes.result
-  - All_Changes.dest
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log
     source: WinEventLog:System
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_excessive_user_account_lockouts.yml b/detections/endpoint/detect_excessive_user_account_lockouts.yml
index 19a9060cfd..79c0621d49 100644
--- a/detections/endpoint/detect_excessive_user_account_lockouts.yml
+++ b/detections/endpoint/detect_excessive_user_account_lockouts.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies user accounts experiencing an excessive number of lockouts within a short timeframe. It leverages the 'Change' data model, specifically focusing on events where the result indicates a lockout. This activity is significant as it may indicate a brute-force attack or misconfiguration, both of which require immediate attention. If confirmed malicious, this behavior could lead to account compromise, unauthorized access, and potential lateral movement within the network.
+description: The following analytic identifies user accounts experiencing an excessive
+  number of lockouts within a short timeframe. It leverages the 'Change' data model,
+  specifically focusing on events where the result indicates a lockout. This activity
+  is significant as it may indicate a brute-force attack or misconfiguration, both
+  of which require immediate attention. If confirmed malicious, this behavior could
+  lead to account compromise, unauthorized access, and potential lateral movement
+  within the network.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where  All_Changes.result="*lock*" by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search count > 5 | `detect_excessive_user_account_lockouts_filter`'
-how_to_implement: ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.
-known_false_positives: It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Change.All_Changes where  All_Changes.result="*lock*"
+  by All_Changes.user All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")`|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search
+  count > 5 | `detect_excessive_user_account_lockouts_filter`'
+how_to_implement: ou must ingest your Windows security event logs in the `Change`
+  datamodel under the nodename is `Account_Management`, for this search to execute
+  successfully. Please consider updating the cron schedule and the count of lockouts
+  you want to monitor, according to your environment.
+known_false_positives: It is possible that a legitimate user is experiencing an issue
+  causing multiple account login failures leading to lockouts.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$"
@@ -17,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,15 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.result
-  - nodename
-  - All_Changes.user
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml-1.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-xml-1.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_exchange_web_shell.yml b/detections/endpoint/detect_exchange_web_shell.yml
index edd7730d92..10a5b10e58 100644
--- a/detections/endpoint/detect_exchange_web_shell.yml
+++ b/detections/endpoint/detect_exchange_web_shell.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Shannon Davis, David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of suspicious .aspx files in known drop locations for Exchange exploitation, specifically targeting paths associated with HAFNIUM group and vulnerabilities like ProxyShell and ProxyNotShell. It leverages data from the Endpoint datamodel, focusing on process and filesystem events. This activity is significant as it may indicate a web shell deployment, a common method for persistent access and remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary commands, and potentially escalate privileges within the Exchange environment.
+description: The following analytic identifies the creation of suspicious .aspx files
+  in known drop locations for Exchange exploitation, specifically targeting paths
+  associated with HAFNIUM group and vulnerabilities like ProxyShell and ProxyNotShell.
+  It leverages data from the Endpoint datamodel, focusing on process and filesystem
+  events. This activity is significant as it may indicate a web shell deployment,
+  a common method for persistent access and remote code execution. If confirmed malicious,
+  attackers could gain unauthorized access, execute arbitrary commands, and potentially
+  escalate privileges within the Exchange environment.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name IN( "*.aspx", "*.ashx") by _time span=1h Filesystem.user Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest user file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest user file_create_time, file_name, file_path, process_name | `detect_exchange_web_shell_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
-known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | join process_guid,
+  _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*",
+  "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name
+  IN( "*.aspx", "*.ashx") by _time span=1h Filesystem.user Filesystem.dest Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` |
+  fields _time dest user file_create_time file_name file_path process_name process_path
+  process] | dedup file_create_time | table dest user file_create_time, file_name,
+  file_path, process_name | `detect_exchange_web_shell_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem`
+  node.
+known_false_positives: The query is structured in a way that `action` (read, create)
+  is not defined. Review the results of this query, filter, and tune as necessary.
+  It may be necessary to generate this query specific to your endpoint product.
 references:
 - https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Sample%20Data/Feeds/MSTICIoCs-ExchangeServerVulnerabilitiesDisclosedMarch2021.csv
 - https://www.zerodayinitiative.com/blog/2021/8/17/from-pwn2own-2021-a-new-attack-surface-on-microsoft-exchange-proxyshell
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A file - $file_name$ was written to disk that is related to IIS exploitation previously performed by HAFNIUM. Review further file modifications on endpoint $dest$ by user $user$.
+  message: A file - $file_name$ was written to disk that is related to IIS exploitation
+    previously performed by HAFNIUM. Review further file modifications on endpoint
+    $dest$ by user $user$.
   mitre_attack_id:
   - T1505
   - T1505.003
@@ -58,17 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_hash
-  - Filesystem.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_html_help_renamed.yml b/detections/endpoint/detect_html_help_renamed.yml
index 3a9a60c744..b1513ba0af 100644
--- a/detections/endpoint/detect_html_help_renamed.yml
+++ b/detections/endpoint/detect_html_help_renamed.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects instances where hh.exe (HTML Help) has been renamed and is executing a Compiled HTML Help (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because attackers can use renamed hh.exe to execute malicious scripts embedded in CHM files, potentially leading to code execution. If confirmed malicious, this technique could allow attackers to run arbitrary scripts, escalate privileges, or persist within the environment, posing a significant security risk.
+description: The following analytic detects instances where hh.exe (HTML Help) has
+  been renamed and is executing a Compiled HTML Help (CHM) file. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and original file names. This activity is significant because attackers can use
+  renamed hh.exe to execute malicious scripts embedded in CHM files, potentially leading
+  to code execution. If confirmed malicious, this technique could allow attackers
+  to run arbitrary scripts, escalate privileges, or persist within the environment,
+  posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=hh.exe AND Processes.original_file_name=HH.EXE by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=hh.exe
+  AND Processes.original_file_name=HH.EXE by Processes.dest Processes.user Processes.parent_process_name
+  Processes.original_file_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_html_help_renamed_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely a renamed instance of hh.exe will be used
+  legitimately, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/001/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md
@@ -24,7 +45,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ executed by $user$
+  message: The following $process_name$ has been identified as renamed, spawning from
+    $parent_process_name$ on $dest$ executed by $user$
   mitre_attack_id:
   - T1218
   - T1218.001
@@ -49,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_html_help_spawn_child_process.yml b/detections/endpoint/detect_html_help_spawn_child_process.yml
index 34db054882..70676e046a 100644
--- a/detections/endpoint/detect_html_help_spawn_child_process.yml
+++ b/detections/endpoint/detect_html_help_spawn_child_process.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of hh.exe (HTML Help) spawning a child process, indicating the use of a Compiled HTML Help (CHM) file to execute Windows script code. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where hh.exe is the parent process. This activity is significant as it may indicate an attempt to execute malicious scripts via CHM files, a known technique for bypassing security controls. If confirmed malicious, this could lead to unauthorized code execution, potentially compromising the system.
+description: The following analytic detects the execution of hh.exe (HTML Help) spawning
+  a child process, indicating the use of a Compiled HTML Help (CHM) file to execute
+  Windows script code. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process creation events where hh.exe is the parent process.
+  This activity is significant as it may indicate an attempt to execute malicious
+  scripts via CHM files, a known technique for bypassing security controls. If confirmed
+  malicious, this could lead to unauthorized code execution, potentially compromising
+  the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_spawn_child_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_html_help_spawn_child_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications (ex. web browsers)
+  may spawn a child process. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/001/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +62,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ spawning a child process, typically not normal behavior.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ spawning a child process, typically not normal
+    behavior.
   mitre_attack_id:
   - T1218
   - T1218.001
@@ -61,23 +89,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_html_help_url_in_command_line.yml b/detections/endpoint/detect_html_help_url_in_command_line.yml
index 11cf78f4e3..09082955b0 100644
--- a/detections/endpoint/detect_html_help_url_in_command_line.yml
+++ b/detections/endpoint/detect_html_help_url_in_command_line.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of hh.exe (HTML Help) loading a Compiled HTML Help (CHM) file from a remote URL. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing URLs. This activity is significant as it can indicate an attempt to execute malicious scripts via CHM files, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to run scripts using engines like JScript or VBScript, leading to further system compromise or data exfiltration.
+description: The following analytic detects the execution of hh.exe (HTML Help) loading
+  a Compiled HTML Help (CHM) file from a remote URL. This detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  containing URLs. This activity is significant as it can indicate an attempt to execute
+  malicious scripts via CHM files, potentially leading to unauthorized code execution.
+  If confirmed malicious, this could allow an attacker to run scripts using engines
+  like JScript or VBScript, leading to further system compromise or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*http* by Processes.dest Processes.user  Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*http*
+  by Processes.dest Processes.user  Processes.parent_process_name Processes.parent_process
+  Processes.original_file_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_html_help_url_in_command_line_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may retrieve
+  a CHM remotely, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/001/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md
@@ -26,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ contacting a remote destination to potentally download a malicious payload.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ contacting a remote destination to potentally
+    download a malicious payload.
   mitre_attack_id:
   - T1218
   - T1218.001
@@ -61,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml b/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml
index 8c13e77459..2d8dee5fb8 100644
--- a/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml
+++ b/detections/endpoint/detect_html_help_using_infotech_storage_handlers.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of hh.exe (HTML Help) using InfoTech Storage Handlers to load Windows script code from a Compiled HTML Help (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because it can be used to execute malicious scripts embedded within CHM files, potentially leading to code execution. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment.
+description: The following analytic detects the execution of hh.exe (HTML Help) using
+  InfoTech Storage Handlers to load Windows script code from a Compiled HTML Help
+  (CHM) file. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line executions. This activity is
+  significant because it can be used to execute malicious scripts embedded within
+  CHM files, potentially leading to code execution. If confirmed malicious, this technique
+  could allow an attacker to execute arbitrary code, escalate privileges, or persist
+  within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_html_help_using_infotech_storage_handlers_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process
+  IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_html_help_using_infotech_storage_handlers_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is rare to see instances of InfoTech Storage Handlers being
+  used, but it does happen in some legitimate instances. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/001/
 - https://www.kb.cert.org/vuls/id/851869
@@ -26,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 80
-  message: $process_name$ has been identified using Infotech Storage Handlers to load a specific file within a CHM on $dest$ under user $user$.
+  message: $process_name$ has been identified using Infotech Storage Handlers to load
+    a specific file within a CHM on $dest$ under user $user$.
   mitre_attack_id:
   - T1218
   - T1218.001
@@ -57,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml b/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml
index 9f5c855571..89b26a6a2a 100644
--- a/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml
+++ b/detections/endpoint/detect_mimikatz_with_powershell_script_block_logging.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of Mimikatz commands via PowerShell by leveraging PowerShell Script Block Logging (EventCode=4104). This method captures and logs the full command sent to PowerShell, allowing for the identification of suspicious activities such as Pass the Ticket, Pass the Hash, and credential dumping. This activity is significant as Mimikatz is a well-known tool used for credential theft and lateral movement. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information within the environment.
+description: The following analytic detects the execution of Mimikatz commands via
+  PowerShell by leveraging PowerShell Script Block Logging (EventCode=4104). This
+  method captures and logs the full command sent to PowerShell, allowing for the identification
+  of suspicious activities such as Pass the Ticket, Pass the Hash, and credential
+  dumping. This activity is significant as Mimikatz is a well-known tool used for
+  credential theft and lateral movement. If confirmed malicious, this could lead to
+  unauthorized access, privilege escalation, and potential compromise of sensitive
+  information within the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*, *kerberos::ptt*, *kerberos::golden*) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_mimikatz_with_powershell_script_block_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives should be limited as the commands being identifies are quite specific to EventCode 4104 and Mimikatz. Filter as needed.
+search: '`powershell` EventCode=4104 ScriptBlockText IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*,
+  *kerberos::ptt*, *kerberos::golden*) | stats count min(_time) as firstTime max(_time)
+  as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_mimikatz_with_powershell_script_block_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives should be limited as the commands being identifies
+  are quite specific to EventCode 4104 and Mimikatz. Filter as needed.
 references:
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$UserID$" and "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$UserID$", "$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$UserID$",
+    "$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: The following behavior was identified and typically related to MimiKatz being loaded within the context of PowerShell on $Computer$ by $UserID$.
+  message: The following behavior was identified and typically related to MimiKatz
+    being loaded within the context of PowerShell on $Computer$ by $UserID$.
   mitre_attack_id:
   - T1003
   - T1059.001
@@ -55,17 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/credaccess-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/credaccess-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_mshta_inline_hta_execution.yml b/detections/endpoint/detect_mshta_inline_hta_execution.yml
index 2feac8e8cb..e7dee5621d 100644
--- a/detections/endpoint/detect_mshta_inline_hta_execution.yml
+++ b/detections/endpoint/detect_mshta_inline_hta_execution.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Bhavin Patel, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of "mshta.exe" with inline protocol handlers such as "JavaScript", "VBScript", and "About". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments and process details. This activity is significant because mshta.exe can be exploited to execute malicious scripts, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or establish persistence within the environment, posing a severe security risk.
+description: The following analytic detects the execution of "mshta.exe" with inline
+  protocol handlers such as "JavaScript", "VBScript", and "About". It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments
+  and process details. This activity is significant because mshta.exe can be exploited
+  to execute malicious scripts, potentially leading to unauthorized code execution.
+  If confirmed malicious, this could allow an attacker to execute arbitrary code,
+  escalate privileges, or establish persistence within the environment, posing a severe
+  security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process=*vbscript*
+  OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user
+  Processes.process_name Processes.original_file_name Processes.parent_process_name
+  Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `detect_mshta_inline_hta_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
 references:
 - https://github.com/redcanaryco/AtomicTestHarnesses
 - https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ executing with inline HTA, indicative of defense evasion.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ executing with inline HTA, indicative of defense
+    evasion.
   mitre_attack_id:
   - T1218
   - T1218.005
@@ -59,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_mshta_renamed.yml b/detections/endpoint/detect_mshta_renamed.yml
index 5138478349..5ceae132ff 100644
--- a/detections/endpoint/detect_mshta_renamed.yml
+++ b/detections/endpoint/detect_mshta_renamed.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies instances where mshta.exe has been renamed and executed. It leverages Endpoint Detection and Response (EDR) data, specifically focusing on the original file name field to detect discrepancies. This activity is significant because renaming mshta.exe is a common tactic used by attackers to evade detection and execute malicious scripts. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies instances where mshta.exe has been
+  renamed and executed. It leverages Endpoint Detection and Response (EDR) data, specifically
+  focusing on the original file name field to detect discrepancies. This activity
+  is significant because renaming mshta.exe is a common tactic used by attackers to
+  evade detection and execute malicious scripts. If confirmed malicious, this could
+  allow an attacker to execute arbitrary code, potentially leading to system compromise,
+  data exfiltration, or further lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=mshta.exe AND Processes.original_file_name=MSHTA.EXE by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_mshta_renamed_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=mshta.exe
+  AND Processes.original_file_name=MSHTA.EXE by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_mshta_renamed_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of mshta.exe, but never renamed, triggering a false positive.
 references:
 - https://github.com/redcanaryco/AtomicTestHarnesses
 - https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -23,7 +43,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ executed by user $user$
+  message: The following $process_name$ has been identified as renamed, spawning from
+    $parent_process_name$ on $dest$ executed by user $user$
   mitre_attack_id:
   - T1218
   - T1218.005
@@ -48,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_mshta_url_in_command_line.yml b/detections/endpoint/detect_mshta_url_in_command_line.yml
index 21766a635f..93c3cfa016 100644
--- a/detections/endpoint/detect_mshta_url_in_command_line.yml
+++ b/detections/endpoint/detect_mshta_url_in_command_line.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Microsoft HTML Application Host (mshta.exe) to make remote HTTP or HTTPS connections. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments containing URLs. This activity is significant because adversaries often use mshta.exe to download and execute remote .hta files, bypassing security controls. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further network infiltration.
+description: The following analytic detects the use of Microsoft HTML Application
+  Host (mshta.exe) to make remote HTTP or HTTPS connections. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on command-line arguments
+  containing URLs. This activity is significant because adversaries often use mshta.exe
+  to download and execute remote .hta files, bypassing security controls. If confirmed
+  malicious, this behavior could allow attackers to execute arbitrary code, potentially
+  leading to system compromise, data exfiltration, or further network infiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible legitimate applications may perform this behavior and will need to be filtered.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where `process_mshta` (Processes.process="*http://*"
+  OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name
+  Processes.original_file_name Processes.dest  | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_mshta_url_in_command_line_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible legitimate applications may perform this behavior
+  and will need to be filtered.
 references:
 - https://github.com/redcanaryco/AtomicTestHarnesses
 - https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +58,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to access a remote destination to download an additional payload.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to access a remote destination to
+    download an additional payload.
   mitre_attack_id:
   - T1218
   - T1218.005
@@ -58,23 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_new_local_admin_account.yml b/detections/endpoint/detect_new_local_admin_account.yml
index b9445d82a3..1cffd6b9f5 100644
--- a/detections/endpoint/detect_new_local_admin_account.yml
+++ b/detections/endpoint/detect_new_local_admin_account.yml
@@ -5,13 +5,27 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of new accounts elevated to local administrators. It uses Windows event logs, specifically EventCode 4720 (user account creation) and EventCode 4732 (user added to Administrators group). This activity is significant as it indicates potential unauthorized privilege escalation, which is critical for SOC monitoring. If confirmed malicious, this could allow attackers to gain administrative access, leading to unauthorized data access, system modifications, and disruption of services. Immediate investigation is required to mitigate risks and prevent further unauthorized actions.
+description: The following analytic detects the creation of new accounts elevated
+  to local administrators. It uses Windows event logs, specifically EventCode 4720
+  (user account creation) and EventCode 4732 (user added to Administrators group).
+  This activity is significant as it indicates potential unauthorized privilege escalation,
+  which is critical for SOC monitoring. If confirmed malicious, this could allow attackers
+  to gain administrative access, leading to unauthorized data access, system modifications,
+  and disruption of services. Immediate investigation is required to mitigate risks
+  and prevent further unauthorized actions.
 data_source:
 - Windows Event Log Security 4732
 - Windows Event Log Security 4720
-search: '`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) | transaction src_user connected=false maxspan=180m | rename src_user as user | stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter`'
-how_to_implement: You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732
-known_false_positives: The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives
+search: '`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators)
+  | transaction src_user connected=false maxspan=180m | rename src_user as user |
+  stats count min(_time) as firstTime max(_time) as lastTime by user dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_new_local_admin_account_filter`'
+how_to_implement: You must be ingesting Windows event logs using the Splunk Windows
+  TA and collecting event code 4720 and 4732
+known_false_positives: The activity may be legitimate. For this reason, it's best
+  to verify the account with an administrator and ask whether there was a valid service
+  request for the account creation. If your local administrator group name is not
+  "Administrators", this search may generate an excessive number of false positives
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +50,8 @@ tags:
   asset_type: Windows
   confidence: 70
   impact: 60
-  message: A $user$ on $dest$ was added recently. Identify if this was legitimate behavior or not.
+  message: A $user$ on $dest$ was added recently. Identify if this was legitimate
+    behavior or not.
   mitre_attack_id:
   - T1136.001
   - T1136
@@ -48,23 +68,19 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Group_Name
-  - member_id
-  - dest
-  - user
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log
     source: WinEventLog:System
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml b/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml
index aa40759f56..927d9ba276 100644
--- a/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml
+++ b/detections/endpoint/detect_outlook_exe_writing_a_zip_file.yml
@@ -5,12 +5,36 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the execution of `outlook.exe` writing a `.zip` file to the disk. It leverages data from the Endpoint data model, specifically monitoring process and filesystem activities. This behavior is significant as it may indicate the use of Outlook to deliver malicious payloads or exfiltrate data via compressed files. If confirmed malicious, this activity could lead to unauthorized data access, data exfiltration, or the delivery of malware, potentially compromising the security of the affected system and network.
+description: The following analytic identifies the execution of `outlook.exe` writing
+  a `.zip` file to the disk. It leverages data from the Endpoint data model, specifically
+  monitoring process and filesystem activities. This behavior is significant as it
+  may indicate the use of Outlook to deliver malicious payloads or exfiltrate data
+  via compressed files. If confirmed malicious, this activity could lead to unauthorized
+  data access, data exfiltration, or the delivery of malware, potentially compromising
+  the security of the affected system and network.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly`  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name  FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*.zip*   OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by  _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] | table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name  file_path | where file_name != "" | `detect_outlook_exe_writing_a_zip_file_filter`'
-how_to_implement: You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
-known_false_positives: It is not uncommon for outlook to write legitimate zip files to the disk.
+search: '| tstats `security_content_summariesonly`  min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe
+  by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest
+  Processes.process_name Processes.parent_process_name Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename
+  process_id as malicious_id| rename parent_process_id as outlook_id| join malicious_id
+  type=inner[| tstats `security_content_summariesonly` count values(Filesystem.file_path)
+  as file_path values(Filesystem.file_name) as file_name  FROM datamodel=Endpoint.Filesystem
+  where (Filesystem.file_path=*.zip*   OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users*
+  OR Filesystem.file_path=*Local\\Temp*) by  _time span=5m Filesystem.process_id Filesystem.file_hash
+  Filesystem.dest  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | rename process_id as malicious_id| fields
+  malicious_id outlook_id dest file_path file_name file_hash count file_id] | table
+  firstTime lastTime user malicious_id outlook_id process_name parent_process_name
+  file_name  file_path | where file_name != "" | `detect_outlook_exe_writing_a_zip_file_filter`'
+how_to_implement: You must be ingesting data that records filesystem and process activity
+  from your hosts to populate the Endpoint data model. This is typically populated
+  via endpoint detection-and-response product, such as Carbon Black, or endpoint data
+  sources, such as Sysmon.
+known_false_positives: It is not uncommon for outlook to write legitimate zip files
+  to the disk.
 references: []
 tags:
   analytic_story:
@@ -37,12 +61,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.parent_process_id
-  - Processes.process_id
-  - Processes.dest
-  - Processes.parent_process_name
-  - Processes.user
   security_domain: network
diff --git a/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml b/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml
index 04e3a3ae38..674fe398aa 100644
--- a/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml
+++ b/detections/endpoint/detect_password_spray_attack_behavior_from_source.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies one source failing to authenticate with 10 or more unique users. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises and works well against any number of data sources ingested into the CIM datamodel. Environments can be very different depending on the organization. Test and customize this detections thresholds if needed.
+description: The following analytic identifies one source failing to authenticate
+  with 10 or more unique users. This behavior could represent an adversary performing
+  a Password Spraying attack to obtain initial access or elevate privileges. This
+  logic can be used for real time security monitoring as well as threat hunting exercises
+  and works well against any number of data sources ingested into the CIM datamodel.
+  Environments can be very different depending on the organization. Test and customize
+  this detections thresholds if needed.
 data_source:
 - Authentication Events (various)
-search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication where * by Authentication.action,Authentication.src,Authentication.user | `drop_dm_object_name("Authentication")` | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), failed_user=if(action="failure",user,null) | `detect_password_spray_attack_behavior_from_source_filter` | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_user) as user values(failed_user) as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) as user_dc ,sum(failure) as failure,sum(success) as success by src | fields - _time | where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
-known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners.
+search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time)
+  as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category)
+  as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication
+  where * by Authentication.action,Authentication.src,Authentication.user | `drop_dm_object_name("Authentication")`
+  | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null),
+  src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0),
+  failed_user=if(action="failure",user,null) | `detect_password_spray_attack_behavior_from_source_filter`
+  | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app)
+  as app values(src_category) as src_category values(success_user) as user values(failed_user)
+  as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user)
+  as user_dc ,sum(failure) as failure,sum(success) as success by src | fields - _time
+  | where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`'
+how_to_implement: This detection requires ingesting authentication data to the appropriate
+  accelerated datamodel. Recommend adjusting the search time window for this correlation
+  to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
+known_false_positives: Domain controllers, authentication chokepoints, and vulnerability
+  scanners.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,9 @@ tags:
   asset_type: Account
   confidence: 75
   impact: 80
-  message: The source [$src$] attempted to access $user_dc$ distinct users a total of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
+  message: The source [$src$] attempted to access $user_dc$ distinct users a total
+    of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful
+    logins detected.
   mitre_attack_id:
   - T1110.003
   - T1110
@@ -51,18 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.user_category
-  - Authentication.src_category
-  - Authentication.app
-  - Authentication.action
-  - Authentication.src
-  - Authentication.user
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml b/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml
index 2dc755e07e..a8bb109d35 100644
--- a/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml
+++ b/detections/endpoint/detect_password_spray_attack_behavior_on_user.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies any user failing to authenticate from 10 or more unique sources. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises. Environments can be very different depending on the organization. Test and customize this detections thresholds as needed
+description: The following analytic identifies any user failing to authenticate from
+  10 or more unique sources. This behavior could represent an adversary performing
+  a Password Spraying attack to obtain initial access or elevate privileges. This
+  logic can be used for real time security monitoring as well as threat hunting exercises.
+  Environments can be very different depending on the organization. Test and customize
+  this detections thresholds as needed
 data_source:
 - Authentication Events (various)
-search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication where * by Authentication.action,Authentication.src,Authentication.user | `drop_dm_object_name("Authentication")` | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), failure=if(action="failure",count,0), failed_src=if(action="failure",src,null) | `detect_password_spray_attack_behavior_on_user_filter` | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, sum(success) as success by user | fields - _time | where src_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
-known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners.
+search: '| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time)
+  as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category)
+  as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication
+  where * by Authentication.action,Authentication.src,Authentication.user | `drop_dm_object_name("Authentication")`
+  | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null),
+  success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null),
+  failure=if(action="failure",count,0), failed_src=if(action="failure",src,null) |
+  `detect_password_spray_attack_behavior_on_user_filter` | stats count min(firstTime)
+  as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as
+  src_category values(success_src) as src values(failed_src) as failed_src dc(success_src)
+  as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure,
+  sum(success) as success by user | fields - _time | where src_dc >= 10 AND .25 >
+  (success/failure) AND failed_dc > success_dc | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`'
+how_to_implement: This detection requires ingesting authentication data to the appropriate
+  accelerated datamodel. Recommend adjusting the search time window for this correlation
+  to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
+known_false_positives: Domain controllers, authentication chokepoints, and vulnerability
+  scanners.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,9 @@ tags:
   asset_type: Account
   confidence: 75
   impact: 80
-  message: A total of $src_dc$ distinct sources attempted to access the account [$user$], $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
+  message: A total of $src_dc$ distinct sources attempted to access the account [$user$],
+    $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins
+    detected.
   mitre_attack_id:
   - T1110.003
   - T1110
@@ -47,18 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Authentication.user_category
-  - Authentication.src_category
-  - Authentication.app
-  - Authentication.action
-  - Authentication.src
-  - Authentication.user
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml b/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml
index 6e0288c31e..57ac8f9a73 100644
--- a/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml
+++ b/detections/endpoint/detect_path_interception_by_creation_of_program_exe.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of a program executable in an unquoted service path, a common technique for privilege escalation. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is 'services.exe'. This activity is significant because unquoted service paths can be exploited by attackers to execute arbitrary code with elevated privileges. If confirmed malicious, this could allow an attacker to gain higher-level access, potentially leading to full system compromise and persistent control over the affected endpoint.
+description: The following analytic identifies the creation of a program executable
+  in an unquoted service path, a common technique for privilege escalation. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process creation
+  events where the parent process is 'services.exe'. This activity is significant
+  because unquoted service paths can be exploited by attackers to execute arbitrary
+  code with elevated privileges. If confirmed malicious, this could allow an attacker
+  to gain higher-level access, potentially leading to full system compromise and persistent
+  control over the affected endpoint.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?<service_process>[^\\\\]*\.(?:exe|bat|com|ps1))" | eval process_name = lower(process_name) | eval service_process = lower(service_process) | where process_name != service_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe
+  by Processes.user Processes.process_name Processes.parent_process_name Processes.process
+  Processes.dest | `drop_dm_object_name(Processes)` | rex field=process "^.*?\\\\(?<service_process>[^\\\\]*\.(?:exe|bat|com|ps1))"
+  | eval process_name = lower(process_name) | eval service_process = lower(service_process)
+  | where process_name != service_process | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `detect_path_interception_by_creation_of_program_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to perform privilege escalation by using unquoted service paths.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to perform privilege escalation by
+    using unquoted service paths.
   mitre_attack_id:
   - T1574.009
   - T1574
@@ -55,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml b/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml
index cc173f858b..e4ebfd9f18 100644
--- a/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml
+++ b/detections/endpoint/detect_processes_used_for_system_network_configuration_discovery.yml
@@ -5,14 +5,40 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the rapid execution of processes used for system network configuration discovery on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, parent processes, and command-line executions. This activity is significant as it may indicate an attacker attempting to map the network, which is a common precursor to lateral movement or further exploitation. If confirmed malicious, this behavior could allow an attacker to gain insights into the network topology, identify critical systems, and plan subsequent attacks, potentially leading to data exfiltration or system compromise.
+description: The following analytic identifies the rapid execution of processes used
+  for system network configuration discovery on an endpoint. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names,
+  parent processes, and command-line executions. This activity is significant as it
+  may indicate an attacker attempting to map the network, which is a common precursor
+  to lateral movement or further exploitation. If confirmed malicious, this behavior
+  could allow an attacker to gain insights into the network topology, identify critical
+  systems, and plan subsequent attacks, potentially leading to data exfiltration or
+  system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT Processes.user IN ("","unknown") by Processes.dest Processes.process_name Processes.parent_process_name Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process parent_process_name eventcount | `detect_processes_used_for_system_network_configuration_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where NOT Processes.user
+  IN ("","unknown") by Processes.dest Processes.process_name Processes.parent_process_name
+  Processes.user _time | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools`
+  | transaction dest connected=false maxpause=5m |where eventcount>=5 | table firstTime
+  lastTime dest user process_name process parent_process parent_process_name eventcount
+  | `detect_processes_used_for_system_network_configuration_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is uncommon for normal users to execute a series of commands
+  used for network discovery. System administrators often use scripts to execute these
+  commands. These can generate false positives.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -20,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +60,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 40
-  message: An instance of $parent_process_name$ spawning multiple $process_name$ was identified on endpoint $dest$ by user $user$ typically not a normal behavior of the process.
+  message: An instance of $parent_process_name$ spawning multiple $process_name$ was
+    identified on endpoint $dest$ by user $user$ typically not a normal behavior of
+    the process.
   mitre_attack_id:
   - T1016
   observable:
@@ -53,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml b/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml
index f7f51c1d34..1be3e2bffe 100644
--- a/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml
+++ b/detections/endpoint/detect_prohibited_applications_spawning_cmd_exe.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: production
 type: Hunting
-description: The following analytic detects executions of cmd.exe spawned by processes that are commonly abused by attackers and do not typically launch cmd.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUID, process name, parent process, and command-line executions. This activity is significant because it may indicate an attempt to execute unauthorized commands or scripts, often a precursor to further malicious actions. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, or persistence within the environment.
+description: The following analytic detects executions of cmd.exe spawned by processes
+  that are commonly abused by attackers and do not typically launch cmd.exe. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process GUID,
+  process name, parent process, and command-line executions. This activity is significant
+  because it may indicate an attempt to execute unauthorized commands or scripts,
+  often a precursor to further malicious actions. If confirmed malicious, this behavior
+  could lead to unauthorized code execution, privilege escalation, or persistence
+  within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` by Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd_macro`] | `detect_prohibited_applications_spawning_cmd_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_cmd` by Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.dest Processes.user| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` |search [`prohibited_apps_launching_cmd_macro`]
+  | `detect_prohibited_applications_spawning_cmd_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: There are circumstances where an application may legitimately
+  execute and interact with the Windows command-line interface. Investigate and modify
+  the lookup file, as appropriate.
 references: []
 tags:
   analytic_story:
@@ -23,7 +45,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running prohibited applications.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ running prohibited applications.
   mitre_attack_id:
   - T1059
   - T1059.003
@@ -48,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_psexec_with_accepteula_flag.yml b/detections/endpoint/detect_psexec_with_accepteula_flag.yml
index 2411ab3917..2921e5e546 100644
--- a/detections/endpoint/detect_psexec_with_accepteula_flag.yml
+++ b/detections/endpoint/detect_psexec_with_accepteula_flag.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of `PsExec.exe` with the `accepteula` flag in the command line. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because PsExec is commonly used by threat actors to execute code on remote systems, and the `accepteula` flag indicates first-time usage, which could signify initial compromise. If confirmed malicious, this activity could allow attackers to gain remote code execution capabilities, potentially leading to further system compromise and lateral movement within the network.
+description: The following analytic identifies the execution of `PsExec.exe` with
+  the `accepteula` flag in the command line. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs and command-line arguments.
+  This activity is significant because PsExec is commonly used by threat actors to
+  execute code on remote systems, and the `accepteula` flag indicates first-time usage,
+  which could signify initial compromise. If confirmed malicious, this activity could
+  allow attackers to gain remote code execution capabilities, potentially leading
+  to further system compromise and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_psexec` Processes.process=*accepteula* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_psexec` Processes.process=*accepteula* by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)`| `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `detect_psexec_with_accepteula_flag_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators can leverage PsExec for accessing remote systems
+  and might pass `accepteula` as an argument if they are running this tool for the
+  first time. However, it is not likely that you'd see multiple occurrences of this
+  event on a machine
 references:
 - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +69,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running the utility for possibly the first time.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ running the utility for possibly the first time.
   mitre_attack_id:
   - T1021
   - T1021.002
@@ -66,23 +95,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rare_executables.yml b/detections/endpoint/detect_rare_executables.yml
index 87aaa20848..5dcb7f0e68 100644
--- a/detections/endpoint/detect_rare_executables.yml
+++ b/detections/endpoint/detect_rare_executables.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of rare processes that appear only once across the network within a specified timeframe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant for a SOC as it helps identify potentially malicious activities or unauthorized software, which could indicate a security breach or ongoing attack. If confirmed malicious, such rare processes could lead to data theft, privilege escalation, or complete system compromise, making early detection crucial for minimizing impact.
+description: The following analytic detects the execution of rare processes that appear
+  only once across the network within a specified timeframe. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process execution logs.
+  This activity is significant for a SOC as it helps identify potentially malicious
+  activities or unauthorized software, which could indicate a security breach or ongoing
+  attack. If confirmed malicious, such rare processes could lead to data theft, privilege
+  escalation, or complete system compromise, making early detection crucial for minimizing
+  impact.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` dc(Processes.dest) as dc_dest values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | `drop_dm_object_name(Processes)` | search dc_dest < 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rare_executables_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legitimate processes may be only rarely executed in your environment.
+search: '| tstats `security_content_summariesonly` dc(Processes.dest) as dc_dest values(Processes.dest)
+  as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime
+  from datamodel=Endpoint.Processes by Processes.process_name | `drop_dm_object_name(Processes)`
+  | search dc_dest < 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_rare_executables_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legitimate processes may be only rarely executed in your
+  environment.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A rare process - [$process_name$] has been detected on less than 10 hosts in your environment.
+  message: A rare process - [$process_name$] has been detected on less than 10 hosts
+    in your environment.
   mitre_attack_id:
   - T1204
   observable:
@@ -42,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/rare_executables/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/rare_executables/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rclone_command_line_usage.yml b/detections/endpoint/detect_rclone_command_line_usage.yml
index d99186e789..6b56b12a00 100644
--- a/detections/endpoint/detect_rclone_command_line_usage.yml
+++ b/detections/endpoint/detect_rclone_command_line_usage.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the usage of `rclone.exe` with specific command-line arguments indicative of file transfer activities. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as `rclone.exe` is often used by adversaries for data exfiltration, especially during ransomware attacks. If confirmed malicious, this behavior could lead to unauthorized data transfer, resulting in data breaches and potential loss of sensitive information. Immediate isolation of the affected endpoint and further investigation are recommended.
+description: The following analytic detects the usage of `rclone.exe` with specific
+  command-line arguments indicative of file transfer activities. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  and process details. This activity is significant as `rclone.exe` is often used
+  by adversaries for data exfiltration, especially during ransomware attacks. If confirmed
+  malicious, this behavior could lead to unauthorized data transfer, resulting in
+  data breaches and potential loss of sensitive information. Immediate isolation of
+  the affected endpoint and further investigation are recommended.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rclone` Processes.process IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*", "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*")  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |  `security_content_ctime(lastTime)` | `detect_rclone_command_line_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as this is restricted to the Rclone process name. Filter or tune the analytic as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rclone` Processes.process
+  IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*",
+  "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*")  by
+  Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |  `security_content_ctime(lastTime)`
+  | `detect_rclone_command_line_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as this is restricted to
+  the Rclone process name. Filter or tune the analytic as needed.
 references:
 - https://redcanary.com/blog/rclone-mega-extortion/
 - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations
@@ -24,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +62,9 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to connect to a remote cloud service to move files or folders.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to connect to a remote cloud service
+    to move files or folders.
   mitre_attack_id:
   - T1020
   observable:
@@ -58,20 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.original_file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regasm_spawning_a_process.yml b/detections/endpoint/detect_regasm_spawning_a_process.yml
index ec73567fe0..326bc1df78 100644
--- a/detections/endpoint/detect_regasm_spawning_a_process.yml
+++ b/detections/endpoint/detect_regasm_spawning_a_process.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects regasm.exe spawning a child process. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where regasm.exe is the parent process. This activity is significant because regasm.exe spawning a process is rare and can indicate an attempt to bypass application control mechanisms. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. Immediate investigation is recommended to determine the legitimacy of the spawned process and any associated activities.
+description: The following analytic detects regasm.exe spawning a child process. This
+  behavior is identified using data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events where regasm.exe is the parent process. This
+  activity is significant because regasm.exe spawning a process is rare and can indicate
+  an attempt to bypass application control mechanisms. If confirmed malicious, this
+  could allow an attacker to execute arbitrary code, potentially leading to privilege
+  escalation or persistent access within the environment. Immediate investigation
+  is recommended to determine the legitimacy of the spawned process and any associated
+  activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe NOT (Processes.process_name IN ("conhost.exe")) by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe
+  NOT (Processes.process_name IN ("conhost.exe")) by Processes.dest Processes.user
+  Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_spawning_a_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe
+  may cause a false positive. Filter based endpoint usage, command line arguments,
+  or process lineage.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -24,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +65,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ spawning a child process, typically not normal behavior for $parent_process_name$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ spawning a child process, typically not normal
+    behavior for $parent_process_name$.
   mitre_attack_id:
   - T1218
   - T1218.009
@@ -62,19 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regasm_with_network_connection.yml b/detections/endpoint/detect_regasm_with_network_connection.yml
index 563804881a..d356b250cd 100644
--- a/detections/endpoint/detect_regasm_with_network_connection.yml
+++ b/detections/endpoint/detect_regasm_with_network_connection.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of regasm.exe establishing a network connection to a public IP address, excluding private IP ranges. This detection leverages Sysmon EventID 3 logs to identify such behavior. This activity is significant as regasm.exe is a legitimate Microsoft-signed binary that can be exploited to bypass application control mechanisms. If confirmed malicious, this behavior could indicate an adversary's attempt to establish a remote Command and Control (C2) channel, potentially leading to privilege escalation and further malicious actions within the environment.
+description: The following analytic detects the execution of regasm.exe establishing
+  a network connection to a public IP address, excluding private IP ranges. This detection
+  leverages Sysmon EventID 3 logs to identify such behavior. This activity is significant
+  as regasm.exe is a legitimate Microsoft-signed binary that can be exploited to bypass
+  application control mechanisms. If confirmed malicious, this behavior could indicate
+  an adversary's attempt to establish a remote Command and Control (C2) channel, potentially
+  leading to privilege escalation and further malicious actions within the environment.
 data_source:
 - Sysmon EventID 3
-search: '`sysmon` EventID=3 dest_ip!=10.0.0.0/8 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe  | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process_name, src_ip, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
+search: '`sysmon` EventID=3 dest_ip!=10.0.0.0/8 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16
+  process_name=regasm.exe  | stats count min(_time) as firstTime max(_time) as lastTime
+  by dest, user, process_name, src_ip, dest_ip | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_regasm_with_network_connection_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Although unlikely, limited instances of regasm.exe with a network
+  connection may cause a false positive. Filter based endpoint usage, command line
+  arguments, or process lineage.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $process_name$ contacting a remote destination was identified on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$.
+  message: An instance of $process_name$ contacting a remote destination was identified
+    on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$.
   mitre_attack_id:
   - T1218
   - T1218.009
@@ -53,20 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - dest_ip
-  - process_name
-  - dest
-  - user
-  - src_ip
-  - dest_host
-  - dest_ip
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml b/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml
index f6a0a48cd4..e27f489d7c 100644
--- a/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml
+++ b/detections/endpoint/detect_regasm_with_no_command_line_arguments.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances of regasm.exe running without command line arguments. This behavior typically indicates process injection, where another process manipulates regasm.exe. The detection leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line executions. This activity is significant as it may signal an attempt to evade detection or execute malicious code. If confirmed malicious, attackers could achieve code execution, potentially leading to privilege escalation, persistence, or access to sensitive information. Investigate network connections, parallel processes, and suspicious module loads for further context.
+description: The following analytic detects instances of regasm.exe running without
+  command line arguments. This behavior typically indicates process injection, where
+  another process manipulates regasm.exe. The detection leverages Endpoint Detection
+  and Response (EDR) data, focusing on process names and command-line executions.
+  This activity is significant as it may signal an attempt to evade detection or execute
+  malicious code. If confirmed malicious, attackers could achieve code execution,
+  potentially leading to privilege escalation, persistence, or access to sensitive
+  information. Investigate network connections, parallel processes, and suspicious
+  module loads for further context.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_regasm` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(regasm\.exe.{0,4}$)" | `detect_regasm_with_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_regasm` by _time span=1h  Processes.process_id Processes.process_name
+  Processes.dest Processes.user Processes.process_path Processes.process  Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(regasm\.exe.{0,4}$)" | `detect_regasm_with_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, limited instances of regasm.exe or may cause
+  a false positive. Filter based endpoint usage, command line arguments, or process
+  lineage.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $dest$ by $user$.
+  message: The process $process_name$ was spawned by $parent_process_name$ without
+    any command-line arguments on $dest$ by $user$.
   mitre_attack_id:
   - T1218
   - T1218.009
@@ -59,23 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regsvcs_spawning_a_process.yml b/detections/endpoint/detect_regsvcs_spawning_a_process.yml
index 9138e4dd4e..2d0b2316b1 100644
--- a/detections/endpoint/detect_regsvcs_spawning_a_process.yml
+++ b/detections/endpoint/detect_regsvcs_spawning_a_process.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies regsvcs.exe spawning a child process. This behavior is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is regsvcs.exe. This activity is significant because regsvcs.exe rarely spawns child processes, and such behavior can indicate an attempt to bypass application control mechanisms. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment. Immediate investigation is recommended to determine the legitimacy of the spawned process and any associated suspicious activities.
+description: The following analytic identifies regsvcs.exe spawning a child process.
+  This behavior is detected using Endpoint Detection and Response (EDR) telemetry,
+  focusing on process creation events where the parent process is regsvcs.exe. This
+  activity is significant because regsvcs.exe rarely spawns child processes, and such
+  behavior can indicate an attempt to bypass application control mechanisms. If confirmed
+  malicious, this could allow an attacker to execute arbitrary code, potentially leading
+  to privilege escalation or persistent access within the environment. Immediate investigation
+  is recommended to determine the legitimacy of the spawned process and any associated
+  suspicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.parent_process_name Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_spawning_a_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe
+  by Processes.parent_process_name Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_regsvcs_spawning_a_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, limited instances of regasm.exe or regsvcs.exe
+  may cause a false positive. Filter based endpoint usage, command line arguments,
+  or process lineage.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$  was identified on endpoint $dest$ by user $user$ typically not normal for this process.
+  message: An instance of $parent_process_name$ spawning $process_name$  was identified
+    on endpoint $dest$ by user $user$ typically not normal for this process.
   mitre_attack_id:
   - T1218
   - T1218.009
@@ -58,20 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regsvcs_with_network_connection.yml b/detections/endpoint/detect_regsvcs_with_network_connection.yml
index 12e80f65f5..44f9a02cec 100644
--- a/detections/endpoint/detect_regsvcs_with_network_connection.yml
+++ b/detections/endpoint/detect_regsvcs_with_network_connection.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances of Regsvcs.exe establishing a network connection to a public IP address, excluding private IP ranges. This detection leverages Sysmon EventID 3 logs to monitor network connections initiated by Regsvcs.exe. This activity is significant as Regsvcs.exe, a legitimate Microsoft-signed binary, can be exploited to bypass application control mechanisms and establish remote Command and Control (C2) channels. If confirmed malicious, this behavior could allow an attacker to escalate privileges, persist in the environment, and exfiltrate sensitive data. Immediate investigation and remediation are recommended.
+description: The following analytic identifies instances of Regsvcs.exe establishing
+  a network connection to a public IP address, excluding private IP ranges. This detection
+  leverages Sysmon EventID 3 logs to monitor network connections initiated by Regsvcs.exe.
+  This activity is significant as Regsvcs.exe, a legitimate Microsoft-signed binary,
+  can be exploited to bypass application control mechanisms and establish remote Command
+  and Control (C2) channels. If confirmed malicious, this behavior could allow an
+  attacker to escalate privileges, persist in the environment, and exfiltrate sensitive
+  data. Immediate investigation and remediation are recommended.
 data_source:
 - Sysmon EventID 3
-search: '`sysmon` EventID=3 dest_ip!=10.0.0.0/8 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe  | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process_name, src_ip, dest_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
+search: '`sysmon` EventID=3 dest_ip!=10.0.0.0/8 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16
+  process_name=regsvcs.exe  | stats count min(_time) as firstTime max(_time) as lastTime
+  by dest, user, process_name, src_ip, dest_ip | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_regsvcs_with_network_connection_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause
+  a false positive. Filter based endpoint usage, command line arguments, or process
+  lineage.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $process_name$ contacting a remote destination was identified on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$.
+  message: An instance of $process_name$ contacting a remote destination was identified
+    on endpoint $dest$ by user $user$. This behavior is not normal for $process_name$.
   mitre_attack_id:
   - T1218
   - T1218.009
@@ -52,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - dest_ip
-  - process_name
-  - dest
-  - user
-  - src_ip
-  - dest_host
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml b/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml
index b070d131ba..5289462482 100644
--- a/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml
+++ b/detections/endpoint/detect_regsvcs_with_no_command_line_arguments.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances of regsvcs.exe running without command line arguments. This behavior typically indicates process injection, where another process manipulates regsvcs.exe. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, IDs, and command-line executions. This activity is significant as it may signal an attempt to evade detection and execute malicious code. If confirmed malicious, the attacker could achieve code execution, potentially leading to privilege escalation, persistence, or access to sensitive information.
+description: The following analytic detects instances of regsvcs.exe running without
+  command line arguments. This behavior typically indicates process injection, where
+  another process manipulates regsvcs.exe. The detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, IDs, and command-line
+  executions. This activity is significant as it may signal an attempt to evade detection
+  and execute malicious code. If confirmed malicious, the attacker could achieve code
+  execution, potentially leading to privilege escalation, persistence, or access to
+  sensitive information.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_regsvcs` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(regsvcs\.exe.{0,4}$)"| `detect_regsvcs_with_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_regsvcs` by _time span=1h  Processes.process_id Processes.process_name
+  Processes.dest Processes.user Processes.process_path Processes.process  Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(regsvcs\.exe.{0,4}$)"| `detect_regsvcs_with_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, limited instances of regsvcs.exe may cause
+  a false positive. Filter based endpoint usage, command line arguments, or process
+  lineage.
 references:
 - https://attack.mitre.org/techniques/T1218/009/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $dest$ by $user$.
+  message: The process $process_name$ was spawned by $parent_process_name$ without
+    any command-line arguments on $dest$ by $user$.
   mitre_attack_id:
   - T1218
   - T1218.009
@@ -58,23 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_regsvr32_application_control_bypass.yml b/detections/endpoint/detect_regsvr32_application_control_bypass.yml
index 45e6d60eda..d1ae442afb 100644
--- a/detections/endpoint/detect_regsvr32_application_control_bypass.yml
+++ b/detections/endpoint/detect_regsvr32_application_control_bypass.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the abuse of Regsvr32.exe to proxy execution of malicious code, specifically detecting the loading of "scrobj.dll" by Regsvr32.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line executions. This activity is significant because Regsvr32.exe is a trusted, signed Microsoft binary, often used in "Squiblydoo" attacks to bypass application control mechanisms. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise and persistent access.
+description: The following analytic identifies the abuse of Regsvr32.exe to proxy
+  execution of malicious code, specifically detecting the loading of "scrobj.dll"
+  by Regsvr32.exe. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process creation events and command-line executions. This
+  activity is significant because Regsvr32.exe is a trusted, signed Microsoft binary,
+  often used in "Squiblydoo" attacks to bypass application control mechanisms. If
+  confirmed malicious, this technique could allow an attacker to execute arbitrary
+  code, potentially leading to system compromise and persistent access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives related to third party software registering .DLL's.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process=*scrobj*
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.parent_process_name Processes.process Processes.original_file_name Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `detect_regsvr32_application_control_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives related to third party software registering
+  .DLL's.
 references:
 - https://attack.mitre.org/techniques/T1218/010/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
@@ -24,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +63,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ in an attempt to bypass detection and preventative controls was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ in an attempt
+    to bypass detection and preventative controls was identified on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1218
   - T1218.010
@@ -62,23 +90,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_remote_access_software_usage_file.yml b/detections/endpoint/detect_remote_access_software_usage_file.yml
index 1916652e71..4293bebd8e 100644
--- a/detections/endpoint/detect_remote_access_software_usage_file.yml
+++ b/detections/endpoint/detect_remote_access_software_usage_file.yml
@@ -5,12 +5,37 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects the writing of files from known remote access software to disk within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on file path, file name, and user information. This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration, further compromise, or complete control over affected systems.
+description: The following analytic detects the writing of files from known remote
+  access software to disk within the environment. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on file path, file name, and user
+  information. This activity is significant as adversaries often use remote access
+  tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access.
+  If confirmed malicious, this could allow attackers to persist in the environment,
+  potentially leading to data exfiltration, further compromise, or complete control
+  over affected systems.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count, min(_time) as firstTime, max(_time) as lastTime, values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.dest, Filesystem.user, Filesystem.file_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Filesystem)` | lookup remote_access_software remote_utility AS file_name OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = TRUE | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the file path, file name, and the user that created the file. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Filesystem` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections.
-known_false_positives: Known or approved applications used by the organization or usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content
+search: '| tstats `security_content_summariesonly` count, min(_time) as firstTime,
+  max(_time) as lastTime, values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem
+  by Filesystem.dest, Filesystem.user, Filesystem.file_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `drop_dm_object_name(Filesystem)` | lookup
+  remote_access_software remote_utility AS file_name OUTPUT isutility, description
+  as signature, comment_reference as desc, category | search isutility = TRUE | `remote_access_software_usage_exceptions`
+  | `detect_remote_access_software_usage_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the file path, file name, and the user that created
+  the file. These logs must be processed using the appropriate Splunk Technology Add-ons
+  that are specific to the EDR product. The logs must also be mapped to the `Filesystem`
+  node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM)
+  to normalize the field names and speed up the data modeling process. The "exceptions"
+  macro leverages both an Assets and Identities lookup, as well as a KVStore collection
+  called "remote_software_exceptions" that lets you track and maintain device-based
+  exceptions for this set of detections.
+known_false_positives: Known or approved applications used by the organization or
+  usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv
+  lookup to globally suppress these situations across all remote access content
 references:
 - https://attack.mitre.org/techniques/T1219/
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
@@ -21,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A file for known a remote access software [$file_name$] was created on $dest$ by $user$.
+  message: A file for known a remote access software [$file_name$] was created on
+    $dest$ by $user$.
   mitre_attack_id:
   - T1219
   observable:
@@ -54,16 +85,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.user
-  - Filesystem.file_name
   security_domain: endpoint
   manual_test: This detection uses A&I lookups from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml b/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml
index ed4e64bb9a..dd4952ad55 100644
--- a/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml
+++ b/detections/endpoint/detect_remote_access_software_usage_fileinfo.yml
@@ -5,14 +5,30 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects the execution of processes with file or code signing attributes from known remote access software within the environment. It leverages Sysmon EventCode 1 data and cross-references a lookup table of remote access utilities such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries often use these tools to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration or further compromise of the network.
+description: The following analytic detects the execution of processes with file or
+  code signing attributes from known remote access software within the environment.
+  It leverages Sysmon EventCode 1 data and cross-references a lookup table of remote
+  access utilities such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity
+  is significant as adversaries often use these tools to maintain unauthorized remote
+  access. If confirmed malicious, this could allow attackers to persist in the environment,
+  potentially leading to data exfiltration or further compromise of the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`sysmon` EventCode=1 | stats count min(_time) as firstTime max(_time) as lastTime, values(Company) as Company values(Product) as Product by dest, user, parent_process_name, process_name, process | lookup remote_access_software remote_utility_fileinfo AS Product OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = True | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_fileinfo_filter`'
-how_to_implement: This analytic relies on Sysmon to be properly installed and utilized in the environment. Ensure that proper logging is setup for Sysmon and data is being ingested into Splunk. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection named "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections.
-known_false_positives: Known or approved applications used by the organization or usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content
+search: '`sysmon` EventCode=1 | stats count min(_time) as firstTime max(_time) as
+  lastTime, values(Company) as Company values(Product) as Product by dest, user, parent_process_name,
+  process_name, process | lookup remote_access_software remote_utility_fileinfo AS
+  Product OUTPUT isutility, description as signature, comment_reference as desc, category
+  | search isutility = True | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_fileinfo_filter`'
+how_to_implement: This analytic relies on Sysmon to be properly installed and utilized
+  in the environment. Ensure that proper logging is setup for Sysmon and data is being
+  ingested into Splunk. The "exceptions" macro leverages both an Assets and Identities
+  lookup, as well as a KVStore collection named "remote_software_exceptions" that
+  lets you track and maintain device-based exceptions for this set of detections.
+known_false_positives: Known or approved applications used by the organization or
+  usage of built-in functions. Known false positives can be added to the remote_access_software_usage_exception.csv
+  lookup to globally suppress these situations across all remote access content
 references:
 - https://attack.mitre.org/techniques/T1219/
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
@@ -23,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A file attributes for known a remote access software [$process_name$] was detected on $dest$
+  message: A file attributes for known a remote access software [$process_name$] was
+    detected on $dest$
   mitre_attack_id:
   - T1219
   observable:
@@ -51,21 +73,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - EventCode
-  - Company
-  - Product
-  - user
-  - parent_process_name
-  - process_name
-  - process
   security_domain: endpoint
   manual_test: This detection uses A&I lookups from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_remote_access_software_usage_process.yml b/detections/endpoint/detect_remote_access_software_usage_process.yml
index 1d58290f2b..a9327ed240 100644
--- a/detections/endpoint/detect_remote_access_software_usage_process.yml
+++ b/detections/endpoint/detect_remote_access_software_usage_process.yml
@@ -5,14 +5,42 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects the execution of known remote access software within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes mapped to the Endpoint data model. This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.
+description: The following analytic detects the execution of known remote access software
+  within the environment. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and parent processes mapped to the Endpoint data
+  model. This activity is significant as adversaries often use remote access tools
+  like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access.
+  If confirmed malicious, this could allow attackers to control systems remotely,
+  exfiltrate data, or deploy additional malware, posing a severe threat to the organization's
+  security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.process!=unknown by Processes.dest Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup remote_access_software remote_utility AS process_name OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = True | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections.
-known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes
+  where Processes.dest!=unknown Processes.process!=unknown by Processes.dest Processes.user
+  Processes.process_name Processes.process | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup remote_access_software
+  remote_utility AS process_name OUTPUT isutility, description as signature, comment_reference
+  as desc, category | search isutility = True | `remote_access_software_usage_exceptions`
+  | `detect_remote_access_software_usage_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process. The "exceptions" macro leverages both
+  an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions"
+  that lets you track and maintain device- based exceptions for this set of detections.
+known_false_positives: It is possible that legitimate remote access software is used
+  within the environment. Ensure that the lookup is reviewed and updated with any
+  additional remote access software that is used within the environment. Known false
+  positives can be added to the remote_access_software_usage_exception.csv lookup
+  to globally suppress these situations across all remote access content
 references:
 - https://attack.mitre.org/techniques/T1219/
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
@@ -23,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +69,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A process for a known remote access software $process_name$ was identified on $dest$.
+  message: A process for a known remote access software $process_name$ was identified
+    on $dest$.
   mitre_attack_id:
   - T1219
   observable:
@@ -56,19 +90,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_name
   security_domain: endpoint
   manual_test: This detection uses A&I lookups from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_renamed_7_zip.yml b/detections/endpoint/detect_renamed_7_zip.yml
index 3425cdb52f..11879dbacc 100644
--- a/detections/endpoint/detect_renamed_7_zip.yml
+++ b/detections/endpoint/detect_renamed_7_zip.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the usage of a renamed 7-Zip executable using Sysmon data. It leverages the OriginalFileName field to identify instances where the 7-Zip process has been renamed. This activity is significant as attackers often rename legitimate tools to evade detection while staging or exfiltrating data. If confirmed malicious, this behavior could indicate data exfiltration attempts or other unauthorized data manipulation, potentially leading to significant data breaches or loss of sensitive information. Analysts should validate the legitimacy of the 7-Zip executable and investigate parallel processes for further suspicious activities.
+description: The following analytic detects the usage of a renamed 7-Zip executable
+  using Sysmon data. It leverages the OriginalFileName field to identify instances
+  where the 7-Zip process has been renamed. This activity is significant as attackers
+  often rename legitimate tools to evade detection while staging or exfiltrating data.
+  If confirmed malicious, this behavior could indicate data exfiltration attempts
+  or other unauthorized data manipulation, potentially leading to significant data
+  breaches or loss of sensitive information. Analysts should validate the legitimacy
+  of the 7-Zip executable and investigate parallel processes for further suspicious
+  activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=7z*.exe AND Processes.process_name!=7z*.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_7_zip_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives, however this analytic will need to be modified for each environment if Sysmon is not used.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=7z*.exe
+  AND Processes.process_name!=7z*.exe) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_renamed_7_zip_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives, however this analytic will need to
+  be modified for each environment if Sysmon is not used.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md
 tags:
@@ -21,7 +43,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 30
-  message: The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
+  message: The following $process_name$ has been identified as renamed, spawning from
+    $parent_process_name$ on $dest$ by $user$.
   mitre_attack_id:
   - T1560.001
   - T1560
@@ -46,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_renamed_psexec.yml b/detections/endpoint/detect_renamed_psexec.yml
index 7829f7a065..383733af52 100644
--- a/detections/endpoint/detect_renamed_psexec.yml
+++ b/detections/endpoint/detect_renamed_psexec.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk, Alex Oberkircher, Github Community
 status: production
 type: Hunting
-description: The following analytic identifies instances where `PsExec.exe` has been renamed and executed on an endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because renaming `PsExec.exe` is a common tactic to evade detection. If confirmed malicious, this could allow an attacker to execute commands remotely, potentially leading to unauthorized access, lateral movement, or further compromise of the network.
+description: The following analytic identifies instances where `PsExec.exe` has been
+  renamed and executed on an endpoint. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process names and original file names. This activity
+  is significant because renaming `PsExec.exe` is a common tactic to evade detection.
+  If confirmed malicious, this could allow an attacker to execute commands remotely,
+  potentially leading to unauthorized access, lateral movement, or further compromise
+  of the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name!=psexec.exe AND Processes.process_name!=psexec64.exe) AND Processes.original_file_name=psexec.c by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_psexec_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives should be present. It is possible some third party applications may use older versions of PsExec, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name!=psexec.exe
+  AND Processes.process_name!=psexec64.exe) AND Processes.original_file_name=psexec.c
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_renamed_psexec_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives should be present. It is possible some
+  third party applications may use older versions of PsExec, filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1569.002/T1569.002.yaml
 - https://redcanary.com/blog/threat-hunting-psexec-lateral-movement/
@@ -31,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 30
-  message: The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
+  message: The following $process_name$ has been identified as renamed, spawning from
+    $parent_process_name$ on $dest$ by $user$.
   mitre_attack_id:
   - T1569
   - T1569.002
@@ -56,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_renamed_rclone.yml b/detections/endpoint/detect_renamed_rclone.yml
index ab45ec309b..e0003f9cd9 100644
--- a/detections/endpoint/detect_renamed_rclone.yml
+++ b/detections/endpoint/detect_renamed_rclone.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of a renamed `rclone.exe` process, which is commonly used for data exfiltration to remote destinations. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and original file names that do not match. This activity is significant because ransomware groups often use RClone to exfiltrate sensitive data. If confirmed malicious, this behavior could indicate an ongoing data exfiltration attempt, potentially leading to significant data loss and further compromise of the affected systems.
+description: The following analytic detects the execution of a renamed `rclone.exe`
+  process, which is commonly used for data exfiltration to remote destinations. This
+  detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on
+  process names and original file names that do not match. This activity is significant
+  because ransomware groups often use RClone to exfiltrate sensitive data. If confirmed
+  malicious, this behavior could indicate an ongoing data exfiltration attempt, potentially
+  leading to significant data loss and further compromise of the affected systems.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=rclone.exe AND Processes.process_name!=rclone.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_rclone_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as this analytic identifies renamed instances of `rclone.exe`. Filter as needed if there is a legitimate business use case.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.original_file_name=rclone.exe
+  AND Processes.process_name!=rclone.exe) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_renamed_rclone_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as this analytic identifies
+  renamed instances of `rclone.exe`. Filter as needed if there is a legitimate business
+  use case.
 references:
 - https://redcanary.com/blog/rclone-mega-extortion/
 - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations
@@ -24,7 +45,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 30
-  message: The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
+  message: The following $process_name$ has been identified as renamed, spawning from
+    $parent_process_name$ on $dest$ by $user$.
   mitre_attack_id:
   - T1020
   observable:
@@ -48,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_renamed_winrar.yml b/detections/endpoint/detect_renamed_winrar.yml
index 6439469686..87df26194f 100644
--- a/detections/endpoint/detect_renamed_winrar.yml
+++ b/detections/endpoint/detect_renamed_winrar.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies instances where `WinRAR.exe` has been renamed and executed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names within the Endpoint data model. This activity is significant because renaming executables is a common tactic used by attackers to evade detection. If confirmed malicious, this could indicate an attempt to bypass security controls, potentially leading to unauthorized data extraction or further system compromise.
+description: The following analytic identifies instances where `WinRAR.exe` has been
+  renamed and executed. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and original file names within the Endpoint data
+  model. This activity is significant because renaming executables is a common tactic
+  used by attackers to evade detection. If confirmed malicious, this could indicate
+  an attempt to bypass security controls, potentially leading to unauthorized data
+  extraction or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=WinRAR.exe (Processes.process_name!=rar.exe OR Processes.process_name!=winrar.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_renamed_winrar_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unknown. It is possible third party applications use renamed instances of WinRAR.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=WinRAR.exe
+  (Processes.process_name!=rar.exe OR Processes.process_name!=winrar.exe) by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.original_file_name |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_renamed_winrar_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unknown. It is possible third party applications use renamed
+  instances of WinRAR.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md
 tags:
@@ -22,7 +43,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 30
-  message: The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$ by $user$.
+  message: The following $process_name$ has been identified as renamed, spawning from
+    $parent_process_name$ on $dest$ by $user$.
   mitre_attack_id:
   - T1560.001
   - T1560
@@ -47,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rtlo_in_file_name.yml b/detections/endpoint/detect_rtlo_in_file_name.yml
index 2d97e5c283..b94fe362db 100644
--- a/detections/endpoint/detect_rtlo_in_file_name.yml
+++ b/detections/endpoint/detect_rtlo_in_file_name.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the use of the right-to-left override (RTLO) character in file names. It leverages data from the Endpoint.Filesystem datamodel, specifically focusing on file creation events and file names containing the RTLO character (U+202E). This activity is significant because adversaries use RTLO to disguise malicious files as benign by reversing the text that follows the character. If confirmed malicious, this technique can deceive users and security tools, leading to the execution of harmful files and potential system compromise.
+description: The following analytic identifies the use of the right-to-left override
+  (RTLO) character in file names. It leverages data from the Endpoint.Filesystem datamodel,
+  specifically focusing on file creation events and file names containing the RTLO
+  character (U+202E). This activity is significant because adversaries use RTLO to
+  disguise malicious files as benign by reversing the text that follows the character.
+  If confirmed malicious, this technique can deceive users and security tools, leading
+  to the execution of harmful files and potential system compromise.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_create_time) as file_create_time from datamodel=Endpoint.Filesystem where Filesystem.file_name!=unknown by Filesystem.dest Filesystem.user Filesystem.process_id Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex file_name =  "\\x{202E}" | rex field=file_name "(?<RTLO_file_1>.+)(?<RTLO_exist_file>\\x{202E})(?<RTLO_file_2>.+)" | eval file_name_with_RTLO=file_name | eval file_name=RTLO_file_1.RTLO_file_2 | fields - RTLO* | `detect_rtlo_in_file_name_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that includes the full command line of the process being launched on your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Implementation in regions that use right to left in native language.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.file_create_time) as file_create_time from datamodel=Endpoint.Filesystem
+  where Filesystem.file_name!=unknown by Filesystem.dest Filesystem.user Filesystem.process_id
+  Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex
+  file_name =  "\\x{202E}" | rex field=file_name "(?<RTLO_file_1>.+)(?<RTLO_exist_file>\\x{202E})(?<RTLO_file_2>.+)"
+  | eval file_name_with_RTLO=file_name | eval file_name=RTLO_file_1.RTLO_file_2 |
+  fields - RTLO* | `detect_rtlo_in_file_name_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that includes the full command line of the process being launched on
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Implementation in regions that use right to left in native
+  language.
 references:
 - https://attack.mitre.org/techniques/T1036/002/
 - https://resources.infosecinstitute.com/topic/spoof-using-right-to-left-override-rtlo-technique-2/
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.user
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rtlo_in_process.yml b/detections/endpoint/detect_rtlo_in_process.yml
index a41da5148c..567038b7cd 100644
--- a/detections/endpoint/detect_rtlo_in_process.yml
+++ b/detections/endpoint/detect_rtlo_in_process.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the abuse of the right-to-left override (RTLO) character (U+202E) in process names. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line data. This activity is significant because adversaries use the RTLO character to disguise malicious files or commands, making them appear benign. If confirmed malicious, this technique can allow attackers to execute harmful code undetected, potentially leading to unauthorized access, data exfiltration, or further system compromise.
+description: The following analytic identifies the abuse of the right-to-left override
+  (RTLO) character (U+202E) in process names. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs and command-line data.
+  This activity is significant because adversaries use the RTLO character to disguise
+  malicious files or commands, making them appear benign. If confirmed malicious,
+  this technique can allow attackers to execute harmful code undetected, potentially
+  leading to unauthorized access, data exfiltration, or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process!=unknown AND Processes.action=allowed by Processes.dest Processes.user Processes.original_file_name Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | regex process="\\x{202E}" | rex field=process "(?<RTLO_command_1>.+)(?<RTLO_exist_process>\\x{202E})(?<RTLO_command_2>.+)" | eval process_with_RTLO=process | eval process=RTLO_command_1.RTLO_command_2 | fields - RTLO* | `detect_rtlo_in_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Implementation in regions that use right to left in native language.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process!=unknown AND
+  Processes.action=allowed by Processes.dest Processes.user Processes.original_file_name
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.process_guid Processes.parent_process_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | regex
+  process="\\x{202E}" | rex field=process "(?<RTLO_command_1>.+)(?<RTLO_exist_process>\\x{202E})(?<RTLO_command_2>.+)"
+  | eval process_with_RTLO=process | eval process=RTLO_command_1.RTLO_command_2 |
+  fields - RTLO* | `detect_rtlo_in_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Implementation in regions that use right to left in native
+  language.
 references:
 - https://attack.mitre.org/techniques/T1036/002/
 - https://resources.infosecinstitute.com/topic/spoof-using-right-to-left-override-rtlo-technique-2/
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,22 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.process_guid
-  - Processes.parent_process_id
-  - Processes.parent_process_name
-  - Processes.parent_process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.002/outlook_attachment/rtlo_events.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml b/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml
index 3b8c836224..5c863d43d0 100644
--- a/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml
+++ b/detections/endpoint/detect_rundll32_application_control_bypass___advpack.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe loading advpack.dll or ieadvpack.dll via the LaunchINFSection function. This method is identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions and process details. This activity is significant as it indicates a potential application control bypass, allowing script code execution from a file. If confirmed malicious, an attacker could execute arbitrary code, potentially leading to privilege escalation, persistence, or further network compromise. Investigate script content, network connections, and any spawned child processes for further context.
+description: The following analytic detects the execution of rundll32.exe loading
+  advpack.dll or ieadvpack.dll via the LaunchINFSection function. This method is identified
+  using Endpoint Detection and Response (EDR) telemetry, focusing on command-line
+  executions and process details. This activity is significant as it indicates a potential
+  application control bypass, allowing script code execution from a file. If confirmed
+  malicious, an attacker could execute arbitrary code, potentially leading to privilege
+  escalation, persistence, or further network compromise. Investigate script content,
+  network connections, and any spawned child processes for further context.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*advpack* by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___advpack_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*advpack*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_rundll32_application_control_bypass___advpack_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use advpack.dll
+  or ieadvpack.dll, triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ loading advpack.dll
+    and ieadvpack.dll by calling the LaunchINFSection function on the command line
+    was identified on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -60,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml b/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml
index 0e584f9b2d..821130dfef 100644
--- a/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml
+++ b/detections/endpoint/detect_rundll32_application_control_bypass___setupapi.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe loading setupapi.dll and iesetupapi.dll via the LaunchINFSection function. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events and command-line arguments. This activity is significant as it indicates a potential application control bypass, allowing an attacker to execute arbitrary script code. If confirmed malicious, this technique could enable code execution, privilege escalation, or persistence within the environment, posing a severe threat to system integrity and security.
+description: The following analytic detects the execution of rundll32.exe loading
+  setupapi.dll and iesetupapi.dll via the LaunchINFSection function. This behavior
+  is identified using Endpoint Detection and Response (EDR) telemetry, focusing on
+  process creation events and command-line arguments. This activity is significant
+  as it indicates a potential application control bypass, allowing an attacker to
+  execute arbitrary script code. If confirmed malicious, this technique could enable
+  code execution, privilege escalation, or persistence within the environment, posing
+  a severe threat to system integrity and security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*setupapi* by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___setupapi_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use setupapi triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*setupapi*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_rundll32_application_control_bypass___setupapi_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use setupapi
+  triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ loading setupapi.dll
+    and iesetupapi.dll by calling the LaunchINFSection function on the command line
+    was identified on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -60,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml b/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml
index 9854ee4c48..ea85b01c05 100644
--- a/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml
+++ b/detections/endpoint/detect_rundll32_application_control_bypass___syssetup.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe loading syssetup.dll via the LaunchINFSection function. This method is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions and process details. This activity is significant as it indicates a potential application control bypass, allowing script code execution from a file. If confirmed malicious, an attacker could execute arbitrary code, potentially leading to privilege escalation, persistence, or further network compromise. Investigate the script content, network connections, and any spawned child processes for further context.
+description: The following analytic detects the execution of rundll32.exe loading
+  syssetup.dll via the LaunchINFSection function. This method is identified through
+  Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions
+  and process details. This activity is significant as it indicates a potential application
+  control bypass, allowing script code execution from a file. If confirmed malicious,
+  an attacker could execute arbitrary code, potentially leading to privilege escalation,
+  persistence, or further network compromise. Investigate the script content, network
+  connections, and any spawned child processes for further context.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*syssetup* by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_rundll32_application_control_bypass___syssetup_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*syssetup*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_rundll32_application_control_bypass___syssetup_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use syssetup.dll,
+  triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ loading syssetup.dll by calling the LaunchINFSection function on the command line was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ loading syssetup.dll
+    by calling the LaunchINFSection function on the command line was identified on
+    endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -60,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_rundll32_inline_hta_execution.yml b/detections/endpoint/detect_rundll32_inline_hta_execution.yml
index 9805fc1063..68a2d5dfdb 100644
--- a/detections/endpoint/detect_rundll32_inline_hta_execution.yml
+++ b/detections/endpoint/detect_rundll32_inline_hta_execution.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of "rundll32.exe" with inline protocol handlers such as "JavaScript", "VBScript", and "About". This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line arguments. This activity is significant as it is often associated with fileless malware or application whitelisting bypass techniques. If confirmed malicious, this could allow an attacker to execute arbitrary code, bypass security controls, and maintain persistence within the environment.
+description: The following analytic detects the execution of "rundll32.exe" with inline
+  protocol handlers such as "JavaScript", "VBScript", and "About". This behavior is
+  identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line
+  arguments. This activity is significant as it is often associated with fileless
+  malware or application whitelisting bypass techniques. If confirmed malicious, this
+  could allow an attacker to execute arbitrary code, bypass security controls, and
+  maintain persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32`
+  (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*)
+  by Processes.user Processes.process_name Processes.parent_process_name Processes.original_file_name
+  Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `detect_rundll32_inline_hta_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
 references:
 - https://github.com/redcanaryco/AtomicTestHarnesses
 - https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_sharphound_command_line_arguments.yml b/detections/endpoint/detect_sharphound_command_line_arguments.yml
index 2d21806255..7900bff6eb 100644
--- a/detections/endpoint/detect_sharphound_command_line_arguments.yml
+++ b/detections/endpoint/detect_sharphound_command_line_arguments.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of SharpHound command-line arguments, specifically `-collectionMethod` and `invoke-bloodhound`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as SharpHound is commonly used for Active Directory enumeration, which can be a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially compromising sensitive information and critical systems.
+description: The following analytic detects the execution of SharpHound command-line
+  arguments, specifically `-collectionMethod` and `invoke-bloodhound`. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant as SharpHound is commonly
+  used for Active Directory enumeration, which can be a precursor to lateral movement
+  or privilege escalation. If confirmed malicious, this activity could allow an attacker
+  to map out the network, identify high-value targets, and plan further attacks, potentially
+  compromising sensitive information and critical systems.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as the arguments used are specific to SharpHound. Filter as needed or add more command-line arguments as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*")
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as the arguments used are
+  specific to SharpHound. Filter as needed or add more command-line arguments as needed.
 references:
 - https://attack.mitre.org/software/S0521/
 - https://thedfirreport.com/?s=bloodhound
@@ -25,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,19 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_sharphound_file_modifications.yml b/detections/endpoint/detect_sharphound_file_modifications.yml
index 987df7cd69..980e400039 100644
--- a/detections/endpoint/detect_sharphound_file_modifications.yml
+++ b/detections/endpoint/detect_sharphound_file_modifications.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of files typically associated with SharpHound, a reconnaissance tool used for gathering domain and trust data. It leverages file modification events from the Endpoint.Filesystem data model, focusing on default file naming patterns like `*_BloodHound.zip` and various JSON files. This activity is significant as it indicates potential domain enumeration, which is a precursor to more targeted attacks. If confirmed malicious, an attacker could gain detailed insights into the domain structure, facilitating lateral movement and privilege escalation.
+description: The following analytic detects the creation of files typically associated
+  with SharpHound, a reconnaissance tool used for gathering domain and trust data.
+  It leverages file modification events from the Endpoint.Filesystem data model, focusing
+  on default file naming patterns like `*_BloodHound.zip` and various JSON files.
+  This activity is significant as it indicates potential domain enumeration, which
+  is a precursor to more targeted attacks. If confirmed malicious, an attacker could
+  gain detailed insights into the domain structure, facilitating lateral movement
+  and privilege escalation.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", "*_ous.json", "*_containers.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.user| `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_file_modifications_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip",
+  "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json",
+  "*_ous.json", "*_containers.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name
+  Filesystem.file_path Filesystem.dest Filesystem.user| `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_file_modifications_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on file modifications that include the name of the process, and file, responsible
+  for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem`
+  node.
+known_false_positives: False positives should be limited as the analytic is specific
+  to a filename with extension .zip. Filter as needed.
 references:
 - https://attack.mitre.org/software/S0521/
 - https://thedfirreport.com/?s=bloodhound
@@ -23,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,17 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - file_path
-  - dest
-  - file_name
-  - process_id
-  - file_create_time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_sharphound_usage.yml b/detections/endpoint/detect_sharphound_usage.yml
index f71b89309e..288c528f81 100644
--- a/detections/endpoint/detect_sharphound_usage.yml
+++ b/detections/endpoint/detect_sharphound_usage.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the usage of the SharpHound binary by identifying its original filename, `SharpHound.exe`, and the process name. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process metadata and command-line executions. SharpHound is a tool used for Active Directory enumeration, often by attackers during the reconnaissance phase. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially leading to privilege escalation and lateral movement within the environment.
+description: The following analytic detects the usage of the SharpHound binary by
+  identifying its original filename, `SharpHound.exe`, and the process name. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process metadata and command-line executions. SharpHound is a tool used for Active
+  Directory enumeration, often by attackers during the reconnaissance phase. If confirmed
+  malicious, this activity could allow an attacker to map out the network, identify
+  high-value targets, and plan further attacks, potentially leading to privilege escalation
+  and lateral movement within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sharphound.exe OR Processes.original_file_name=SharpHound.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as this is specific to a file attribute not used by anything else. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sharphound.exe
+  OR Processes.original_file_name=SharpHound.exe) by Processes.dest Processes.user
+  Processes.parent_process_name Processes.original_file_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as this is specific to a
+  file attribute not used by anything else. Filter as needed.
 references:
 - https://attack.mitre.org/software/S0521/
 - https://thedfirreport.com/?s=bloodhound
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml b/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml
index 504cda7bce..e426fd3e8e 100644
--- a/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml
+++ b/detections/endpoint/detect_suspicious_processnames_using_pretrained_model_in_dsdl.yml
@@ -7,10 +7,33 @@ type: Anomaly
 status: experimental
 data_source:
 - Sysmon EventID 1
-description: The following analytic identifies suspicious process names using a pre-trained Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry to analyze process names and predict their likelihood of being malicious. The model, a character-level Recurrent Neural Network (RNN), classifies process names as benign or suspicious based on a threshold score of 0.5. This detection is significant as it helps identify malware, such as TrickBot, which often uses randomly generated filenames to evade detection. If confirmed malicious, this activity could indicate the presence of malware capable of propagating across the network and executing harmful actions.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name Processes.parent_process_name Processes.process Processes.user Processes.dest | `drop_dm_object_name(Processes)` | rename process_name as text | fields text, parent_process_name, process, user, dest | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl | rename predicted_label as is_suspicious_score | rename text as process_name | where is_suspicious_score > 0.5 | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if a suspicious processname is similar to a benign processname.
+description: The following analytic identifies suspicious process names using a pre-trained
+  Deep Learning model. It leverages Endpoint Detection and Response (EDR) telemetry
+  to analyze process names and predict their likelihood of being malicious. The model,
+  a character-level Recurrent Neural Network (RNN), classifies process names as benign
+  or suspicious based on a threshold score of 0.5. This detection is significant as
+  it helps identify malware, such as TrickBot, which often uses randomly generated
+  filenames to evade detection. If confirmed malicious, this activity could indicate
+  the presence of malware capable of propagating across the network and executing
+  harmful actions.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes by Processes.process_name Processes.parent_process_name
+  Processes.process Processes.user Processes.dest | `drop_dm_object_name(Processes)`
+  | rename process_name as text | fields text, parent_process_name, process, user,
+  dest | apply detect_suspicious_processnames_using_pretrained_model_in_dsdl | rename
+  predicted_label as is_suspicious_score | rename text as process_name | where is_suspicious_score
+  > 0.5 | `detect_suspicious_processnames_using_pretrained_model_in_dsdl_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if a suspicious processname
+  is similar to a benign processname.
 references:
 - https://www.cisa.gov/uscert/ncas/alerts/aa20-302a
 - https://www.splunk.com/en_us/blog/security/random-words-on-entropy-and-dns.html
@@ -23,7 +46,8 @@ tags:
   - Source:Endpoint
   - Stage:Execution
   impact: 50
-  message: The process $process$ is running from an unusual place by $user$ on $dest$ with a processname that appears to be randomly generated.
+  message: The process $process$ is running from an unusual place by $user$ on $dest$
+    with a processname that appears to be randomly generated.
   mitre_attack_id:
   - T1059
   observable:
@@ -39,12 +63,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
diff --git a/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml b/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml
index 47f41f9257..2c055b4936 100644
--- a/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml
+++ b/detections/endpoint/detect_use_of_cmd_exe_to_launch_script_interpreters.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Bhavin Patel, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of cscript.exe or wscript.exe processes initiated by cmd.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes within the Endpoint data model. This activity is significant as it may indicate script-based attacks or administrative actions that could be leveraged for malicious purposes. If confirmed malicious, this behavior could allow attackers to execute scripts, potentially leading to code execution, privilege escalation, or persistence within the environment.
+description: The following analytic detects the execution of cscript.exe or wscript.exe
+  processes initiated by cmd.exe. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and parent processes within the Endpoint
+  data model. This activity is significant as it may indicate script-based attacks
+  or administrative actions that could be leveraged for malicious purposes. If confirmed
+  malicious, this behavior could allow attackers to execute scripts, potentially leading
+  to code execution, privilege escalation, or persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This detection may also be triggered by legitimate applications and numerous service accounts, which often end with a $ sign. To manage this, it's advised to check the service account's activities and, if they are valid, modify the filter macro to exclude them.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe"
+  (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process
+  Processes.process_name Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This detection may also be triggered by legitimate applications
+  and numerous service accounts, which often end with a $ sign. To manage this, it's
+  advised to check the service account's activities and, if they are valid, modify
+  the filter macro to exclude them.
 references:
 - https://attack.mitre.org/techniques/T1059/
 - https://redcanary.com/threat-detection-report/techniques/windows-command-shell/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_webshell_exploit_behavior.yml b/detections/endpoint/detect_webshell_exploit_behavior.yml
index 3db6e243c4..7230bb83cf 100644
--- a/detections/endpoint/detect_webshell_exploit_behavior.yml
+++ b/detections/endpoint/detect_webshell_exploit_behavior.yml
@@ -5,14 +5,42 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the execution of suspicious processes typically associated with webshell activity on web servers. It detects when processes like `cmd.exe`, `powershell.exe`, or `bash.exe` are spawned by web server processes such as `w3wp.exe` or `nginx.exe`. This behavior is significant as it may indicate an adversary exploiting a web application vulnerability to install a webshell, providing persistent access and command execution capabilities. If confirmed malicious, this activity could allow attackers to maintain control over the compromised server, execute arbitrary commands, and potentially escalate privileges or exfiltrate sensitive data.
+description: The following analytic identifies the execution of suspicious processes
+  typically associated with webshell activity on web servers. It detects when processes
+  like `cmd.exe`, `powershell.exe`, or `bash.exe` are spawned by web server processes
+  such as `w3wp.exe` or `nginx.exe`. This behavior is significant as it may indicate
+  an adversary exploiting a web application vulnerability to install a webshell, providing
+  persistent access and command execution capabilities. If confirmed malicious, this
+  activity could allow attackers to maintain control over the compromised server,
+  execute arbitrary commands, and potentially escalate privileges or exfiltrate sensitive
+  data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time) as firstTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("arp.exe","at.exe","bash.exe","bitsadmin.exe","certutil.exe","cmd.exe","cscript.exe", "dsget.exe","dsquery.exe","find.exe","findstr.exe","fsutil.exe","hostname.exe","ipconfig.exe","ksh.exe","nbstat.exe", "net.exe","net1.exe","netdom.exe","netsh.exe","netstat.exe","nltest.exe","nslookup.exe","ntdsutil.exe","pathping.exe", "ping.exe","powershell.exe","pwsh.exe","qprocess.exe","query.exe","qwinsta.exe","reg.exe","rundll32.exe","sc.exe", "scrcons.exe","schtasks.exe","sh.exe","systeminfo.exe","tasklist.exe","tracert.exe","ver.exe","vssadmin.exe", "wevtutil.exe","whoami.exe","wmic.exe","wscript.exe","wusa.exe","zsh.exe") AND Processes.parent_process_name IN ("w3wp.exe", "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe","tomcat*.exe")) by Processes.dest,Processes.user,Processes.parent_process,Processes.parent_process_name,Processes.process,Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_webshell_exploit_behavior_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate OS functions called by vendor applications, baseline the environment and filter before enabling. Recommend throttle by dest/process_name
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime, min(_time)
+  as firstTime from datamodel=Endpoint.Processes where (Processes.process_name IN
+  ("arp.exe","at.exe","bash.exe","bitsadmin.exe","certutil.exe","cmd.exe","cscript.exe",
+  "dsget.exe","dsquery.exe","find.exe","findstr.exe","fsutil.exe","hostname.exe","ipconfig.exe","ksh.exe","nbstat.exe",
+  "net.exe","net1.exe","netdom.exe","netsh.exe","netstat.exe","nltest.exe","nslookup.exe","ntdsutil.exe","pathping.exe",
+  "ping.exe","powershell.exe","pwsh.exe","qprocess.exe","query.exe","qwinsta.exe","reg.exe","rundll32.exe","sc.exe",
+  "scrcons.exe","schtasks.exe","sh.exe","systeminfo.exe","tasklist.exe","tracert.exe","ver.exe","vssadmin.exe",
+  "wevtutil.exe","whoami.exe","wmic.exe","wscript.exe","wusa.exe","zsh.exe") AND Processes.parent_process_name
+  IN ("w3wp.exe", "http*.exe", "nginx*.exe", "php*.exe", "php-cgi*.exe","tomcat*.exe"))
+  by Processes.dest,Processes.user,Processes.parent_process,Processes.parent_process_name,Processes.process,Processes.process_name
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `detect_webshell_exploit_behavior_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate OS functions called by vendor applications, baseline
+  the environment and filter before enabling. Recommend throttle by dest/process_name
 references:
 - https://attack.mitre.org/techniques/T1505/003/
 - https://github.com/nsacyber/Mitigating-Web-Shells
@@ -23,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +74,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: Webshell Exploit Behavior - $parent_process_name$ spawned $process_name$ on $dest$.
+  message: Webshell Exploit Behavior - $parent_process_name$ spawned $process_name$
+    on $dest$.
   mitre_attack_id:
   - T1505
   - T1505.003
@@ -62,18 +96,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process
-  - Processes.process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/generic_webshell_exploit/generic_webshell_exploit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/generic_webshell_exploit/generic_webshell_exploit.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detect_wmi_event_subscription_persistence.yml b/detections/endpoint/detect_wmi_event_subscription_persistence.yml
index e331e48144..82f8cbcb56 100644
--- a/detections/endpoint/detect_wmi_event_subscription_persistence.yml
+++ b/detections/endpoint/detect_wmi_event_subscription_persistence.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of WMI Event Subscriptions, which can be used to establish persistence or perform privilege escalation. It detects EventID 19 (EventFilter creation), EventID 20 (EventConsumer creation), and EventID 21 (FilterToConsumerBinding creation) from Sysmon logs. This activity is significant because WMI Event Subscriptions can execute code with elevated SYSTEM privileges, making it a powerful persistence mechanism. If confirmed malicious, an attacker could maintain long-term access, escalate privileges, and execute arbitrary code, posing a severe threat to the environment.
+description: The following analytic identifies the creation of WMI Event Subscriptions,
+  which can be used to establish persistence or perform privilege escalation. It detects
+  EventID 19 (EventFilter creation), EventID 20 (EventConsumer creation), and EventID
+  21 (FilterToConsumerBinding creation) from Sysmon logs. This activity is significant
+  because WMI Event Subscriptions can execute code with elevated SYSTEM privileges,
+  making it a powerful persistence mechanism. If confirmed malicious, an attacker
+  could maintain long-term access, escalate privileges, and execute arbitrary code,
+  posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 20
-search: '`sysmon` EventID=20 | stats count min(_time) as firstTime max(_time) as lastTime by Computer User Destination | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_wmi_event_subscription_persistence_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with that provide WMI Event Subscription from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID 19, 20 and 21. Tune and filter known good to limit the volume.
-known_false_positives: It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage.
+search: '`sysmon` EventID=20 | stats count min(_time) as firstTime max(_time) as lastTime
+  by Computer User Destination | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `detect_wmi_event_subscription_persistence_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with that provide WMI Event Subscription from your endpoints. If you are using
+  Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID
+  19, 20 and 21. Tune and filter known good to limit the volume.
+known_false_positives: It is possible some applications will create a consumer and
+  may be required to be filtered. For tuning, add any additional LOLBin's for further
+  depth of coverage.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md
 - https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,15 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Destination
-  - dest
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/detection_of_tools_built_by_nirsoft.yml b/detections/endpoint/detection_of_tools_built_by_nirsoft.yml
index 2223b8112e..e0c1f6a631 100644
--- a/detections/endpoint/detection_of_tools_built_by_nirsoft.yml
+++ b/detections/endpoint/detection_of_tools_built_by_nirsoft.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the execution of tools built by NirSoft by detecting specific command-line arguments such as "/stext" and "/scomma". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because NirSoft tools, while legitimate, can be exploited by attackers for malicious purposes such as credential theft or system reconnaissance. If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further compromise of the affected system.
+description: The following analytic identifies the execution of tools built by NirSoft
+  by detecting specific command-line arguments such as "/stext" and "/scomma". It
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names, parent processes, and command-line executions. This activity is significant
+  because NirSoft tools, while legitimate, can be exploited by attackers for malicious
+  purposes such as credential theft or system reconnaissance. If confirmed malicious,
+  this activity could lead to unauthorized access, data exfiltration, or further compromise
+  of the affected system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose.
+search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process)
+  as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*
+  /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `detection_of_tools_built_by_nirsoft_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: While legitimate, these NirSoft tools are prone to abuse. You
+  should verfiy that the tool was used for a legitimate purpose.
 references: []
 tags:
   analytic_story:
@@ -32,10 +52,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.user
   security_domain: endpoint
diff --git a/detections/endpoint/disable_amsi_through_registry.yml b/detections/endpoint/disable_amsi_through_registry.yml
index 8eb0a883f0..7ffca2de22 100644
--- a/detections/endpoint/disable_amsi_through_registry.yml
+++ b/detections/endpoint/disable_amsi_through_registry.yml
@@ -5,13 +5,21 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the Windows registry that disable the Antimalware Scan Interface (AMSI) by setting the "AmsiEnable" value to "0x00000000". This detection leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable". Disabling AMSI is significant as it is a common technique used by ransomware, Remote Access Trojans (RATs), and Advanced Persistent Threats (APTs) to evade detection and impair defenses. If confirmed malicious, this activity could allow attackers to execute payloads with minimal alerts, leading to potential system compromise and data exfiltration.
+description: The following analytic detects modifications to the Windows registry
+  that disable the Antimalware Scan Interface (AMSI) by setting the "AmsiEnable" value
+  to "0x00000000". This detection leverages data from the Endpoint.Registry data model,
+  specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows
+  Script\\Settings\\AmsiEnable". Disabling AMSI is significant as it is a common technique
+  used by ransomware, Remote Access Trojans (RATs), and Advanced Persistent Threats
+  (APTs) to evade detection and impair defenses. If confirmed malicious, this activity
+  could allow attackers to execute payloads with minimal alerts, leading to potential
+  system compromise and data exfiltration.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable"
-  Registry.registry_value_data = "0x00000000") BY Registry.registry_path
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows
+  Script\\Settings\\AmsiEnable" Registry.registry_value_data = "0x00000000") BY Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid Registry.dest Registry.user | `drop_dm_object_name(Registry)`
   | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
@@ -31,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_defender_antivirus_registry.yml b/detections/endpoint/disable_defender_antivirus_registry.yml
index 894e6f2ebb..e0bda8735d 100644
--- a/detections/endpoint/disable_defender_antivirus_registry.yml
+++ b/detections/endpoint/disable_defender_antivirus_registry.yml
@@ -5,17 +5,25 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of Windows Defender registry settings to disable antivirus and antispyware protections. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with Windows Defender policies. This activity is significant because disabling antivirus protections is a common tactic used by adversaries to evade detection and maintain persistence on compromised systems. If confirmed malicious, this action could allow attackers to execute further malicious activities undetected, leading to potential data breaches, system compromise, and further propagation of malware within the network.
+description: The following analytic detects the modification of Windows Defender registry
+  settings to disable antivirus and antispyware protections. It leverages data from
+  the Endpoint.Registry data model, specifically monitoring changes to registry paths
+  associated with Windows Defender policies. This activity is significant because
+  disabling antivirus protections is a common tactic used by adversaries to evade
+  detection and maintain persistence on compromised systems. If confirmed malicious,
+  this action could allow attackers to execute further malicious activities undetected,
+  leading to potential data breaches, system compromise, and further propagation of
+  malware within the network.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender*" Registry.registry_value_name
-  IN ("DisableAntiSpyware","DisableAntiVirus") Registry.registry_value_data = 0x00000001)
-  BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid Registry.user Registry.dest |
-  `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `disable_defender_antivirus_registry_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows
+  Defender*" Registry.registry_value_name IN ("DisableAntiSpyware","DisableAntiVirus")
+  Registry.registry_value_data = 0x00000001) BY Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.user Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_antivirus_registry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_defender_blockatfirstseen_feature.yml b/detections/endpoint/disable_defender_blockatfirstseen_feature.yml
index 7b01cff9e2..3a2153ef13 100644
--- a/detections/endpoint/disable_defender_blockatfirstseen_feature.yml
+++ b/detections/endpoint/disable_defender_blockatfirstseen_feature.yml
@@ -16,13 +16,13 @@ description: The following analytic detects the modification of the Windows regi
   files to bypass initial detection by Windows Defender, increasing the risk of malware
   infection. If confirmed malicious, this action could enable attackers to execute
   malicious code undetected, leading to potential system compromise and data breaches.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name
-  = DisableBlockAtFirstSeen Registry.registry_value_data = 0x00000001) BY 
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid Registry.user Registry.dest | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `disable_defender_blockatfirstseen_feature_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows
+  Defender\\SpyNet*" Registry.registry_value_name = DisableBlockAtFirstSeen Registry.registry_value_data
+  = 0x00000001) BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.user Registry.dest |
+  `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_defender_blockatfirstseen_feature_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -36,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -65,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_defender_enhanced_notification.yml b/detections/endpoint/disable_defender_enhanced_notification.yml
index d33437b15b..4c3a3b18fe 100644
--- a/detections/endpoint/disable_defender_enhanced_notification.yml
+++ b/detections/endpoint/disable_defender_enhanced_notification.yml
@@ -5,12 +5,40 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the registry to disable Windows Defender's Enhanced Notification feature. It leverages data from Endpoint Detection and Response (EDR) agents, specifically monitoring changes to the registry path associated with Windows Defender reporting. This activity is significant because disabling Enhanced Notifications can prevent users and administrators from receiving critical security alerts, potentially allowing malicious activities to go unnoticed. If confirmed malicious, this action could enable an attacker to bypass detection mechanisms, maintain persistence, and escalate their activities without triggering alerts.
+description: The following analytic detects the modification of the registry to disable
+  Windows Defender's Enhanced Notification feature. It leverages data from Endpoint
+  Detection and Response (EDR) agents, specifically monitoring changes to the registry
+  path associated with Windows Defender reporting. This activity is significant because
+  disabling Enhanced Notifications can prevent users and administrators from receiving
+  critical security alerts, potentially allowing malicious activities to go unnoticed.
+  If confirmed malicious, this action could enable an attacker to bypass detection
+  mechanisms, maintain persistence, and escalate their activities without triggering
+  alerts.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*Microsoft\\Windows Defender\\Reporting*" Registry.registry_value_name = DisableEnhancedNotifications Registry.registry_value_data = 0x00000001) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_defender_enhanced_notification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path = "*Microsoft\\Windows Defender\\Reporting*" Registry.registry_value_name
+  = DisableEnhancedNotifications Registry.registry_value_data = 0x00000001) BY _time
+  span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`]
+  | fields firstTime lastTime dest user parent_process_name parent_process process_name
+  process_path process registry_key_name registry_path registry_value_name registry_value_data
+  process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_defender_enhanced_notification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: user may choose to disable windows defender AV
 references:
 - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/
@@ -20,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,27 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_defender_mpengine_registry.yml b/detections/endpoint/disable_defender_mpengine_registry.yml
index 66a28c148a..4e218c4075 100644
--- a/detections/endpoint/disable_defender_mpengine_registry.yml
+++ b/detections/endpoint/disable_defender_mpengine_registry.yml
@@ -5,14 +5,21 @@ date: '2024-10-04'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the Windows Defender MpEngine registry value, specifically setting MpEnablePus to 0x00000000. This detection leverages endpoint registry logs, focusing on changes within the path "*\\Policies\\Microsoft\\Windows Defender\\MpEngine*". This activity is significant as it indicates an attempt to disable key Windows Defender features, potentially allowing malware to evade detection. If confirmed malicious, this could lead to undetected malware execution, persistence, and further system compromise. Immediate investigation and endpoint isolation are recommended.
+description: The following analytic detects the modification of the Windows Defender
+  MpEngine registry value, specifically setting MpEnablePus to 0x00000000. This detection
+  leverages endpoint registry logs, focusing on changes within the path "*\\Policies\\Microsoft\\Windows
+  Defender\\MpEngine*". This activity is significant as it indicates an attempt to
+  disable key Windows Defender features, potentially allowing malware to evade detection.
+  If confirmed malicious, this could lead to undetected malware execution, persistence,
+  and further system compromise. Immediate investigation and endpoint isolation are
+  recommended.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender\\MpEngine*"
-  Registry.registry_value_name = MpEnablePus Registry.registry_value_data = 0x00000000)
-  BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Policies\\Microsoft\\Windows
+  Defender\\MpEngine*" Registry.registry_value_name = MpEnablePus Registry.registry_value_data
+  = 0x00000000) BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
   Registry.registry_value_data Registry.process_guid Registry.user Registry.dest |
   `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `disable_defender_mpengine_registry_filter`'
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_defender_spynet_reporting.yml b/detections/endpoint/disable_defender_spynet_reporting.yml
index 2b28dd0fc7..b72aaa0a85 100644
--- a/detections/endpoint/disable_defender_spynet_reporting.yml
+++ b/detections/endpoint/disable_defender_spynet_reporting.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the registry to disable Windows Defender SpyNet reporting. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender SpyNet settings. This activity is significant because disabling SpyNet reporting can prevent Windows Defender from sending telemetry data, potentially allowing malicious activities to go undetected. If confirmed malicious, this action could enable an attacker to evade detection, maintain persistence, and carry out further attacks without being flagged by Windows Defender.
+description: The following analytic detects the modification of the registry to disable
+  Windows Defender SpyNet reporting. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path associated with
+  Windows Defender SpyNet settings. This activity is significant because disabling
+  SpyNet reporting can prevent Windows Defender from sending telemetry data, potentially
+  allowing malicious activities to go undetected. If confirmed malicious, this action
+  could enable an attacker to evade detection, maintain persistence, and carry out
+  further attacks without being flagged by Windows Defender.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name
-  = SpynetReporting Registry.registry_value_data = 0x00000000) BY  Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid Registry.user Registry.dest  | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `disable_defender_spynet_reporting_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows
+  Defender\\SpyNet*" Registry.registry_value_name = SpynetReporting Registry.registry_value_data
+  = 0x00000000) BY  Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.user Registry.dest  |
+  `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_defender_spynet_reporting_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -59,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_defender_submit_samples_consent_feature.yml b/detections/endpoint/disable_defender_submit_samples_consent_feature.yml
index ae9b2584ef..5d28ebe51b 100644
--- a/detections/endpoint/disable_defender_submit_samples_consent_feature.yml
+++ b/detections/endpoint/disable_defender_submit_samples_consent_feature.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk,Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the Windows registry to disable the Windows Defender Submit Samples Consent feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender SpyNet and the SubmitSamplesConsent value set to 0x00000000. This activity is significant as it indicates an attempt to bypass or evade detection by preventing Windows Defender from submitting samples for further analysis. If confirmed malicious, this could allow an attacker to execute malicious code without being detected by Windows Defender, leading to potential system compromise.
+description: The following analytic detects the modification of the Windows registry
+  to disable the Windows Defender Submit Samples Consent feature. It leverages data
+  from the Endpoint.Registry data model, specifically monitoring changes to the registry
+  path associated with Windows Defender SpyNet and the SubmitSamplesConsent value
+  set to 0x00000000. This activity is significant as it indicates an attempt to bypass
+  or evade detection by preventing Windows Defender from submitting samples for further
+  analysis. If confirmed malicious, this could allow an attacker to execute malicious
+  code without being detected by Windows Defender, leading to potential system compromise.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\Microsoft\\Windows Defender\\SpyNet*" Registry.registry_value_name
-  = SubmitSamplesConsent Registry.registry_value_data = 0x00000000) BY 
-  Registry.user Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `disable_defender_submit_samples_consent_feature_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows
+  Defender\\SpyNet*" Registry.registry_value_name = SubmitSamplesConsent Registry.registry_value_data
+  = 0x00000000) BY Registry.user Registry.dest Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_defender_submit_samples_consent_feature_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -58,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_etw_through_registry.yml b/detections/endpoint/disable_etw_through_registry.yml
index 891439df6c..f7b52f3b98 100644
--- a/detections/endpoint/disable_etw_through_registry.yml
+++ b/detections/endpoint/disable_etw_through_registry.yml
@@ -5,12 +5,19 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the registry that disable the Event Tracing for Windows (ETW) feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" with a value set to "0x00000000". This activity is significant because disabling ETW can allow attackers to evade detection mechanisms, making it harder for security tools to monitor malicious activities. If confirmed malicious, this could enable attackers to execute payloads with minimal alerts, impairing defenses and potentially leading to further compromise of the system.
+description: The following analytic detects modifications to the registry that disable
+  the Event Tracing for Windows (ETW) feature. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled"
+  with a value set to "0x00000000". This activity is significant because disabling
+  ETW can allow attackers to evade detection mechanisms, making it harder for security
+  tools to monitor malicious activities. If confirmed malicious, this could enable
+  attackers to execute payloads with minimal alerts, impairing defenses and potentially
+  leading to further compromise of the system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled"
   Registry.registry_value_data = "0x00000000") BY Registry.dest Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_logs_using_wevtutil.yml b/detections/endpoint/disable_logs_using_wevtutil.yml
index aa1be9b1be..a771f1e289 100644
--- a/detections/endpoint/disable_logs_using_wevtutil.yml
+++ b/detections/endpoint/disable_logs_using_wevtutil.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of "wevtutil.exe" with parameters to disable event logs. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because disabling event logs is a common tactic used by ransomware to evade detection and hinder forensic investigations. If confirmed malicious, this action could allow attackers to operate undetected, making it difficult to trace their activities and respond effectively to the incident.
+description: The following analytic detects the execution of "wevtutil.exe" with parameters
+  to disable event logs. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line arguments. This activity is significant
+  because disabling event logs is a common tactic used by ransomware to evade detection
+  and hinder forensic investigations. If confirmed malicious, this action could allow
+  attackers to operate undetected, making it difficult to trace their activities and
+  respond effectively to the incident.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe" Processes.process = "*sl*" Processes.process = "*/e:false*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_logs_using_wevtutil_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network operator may disable audit event logs for debugging purposes.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe"
+  Processes.process = "*sl*" Processes.process = "*/e:false*" by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.dest
+  Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_logs_using_wevtutil_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network operator may disable audit event logs for debugging
+  purposes.
 references:
 - https://www.bleepingcomputer.com/news/security/new-ransom-x-ransomware-used-in-texas-txdot-cyberattack/
 drilldown_searches:
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_registry_tool.yml b/detections/endpoint/disable_registry_tool.yml
index d092bee6d5..09b81ba1ea 100644
--- a/detections/endpoint/disable_registry_tool.yml
+++ b/detections/endpoint/disable_registry_tool.yml
@@ -5,14 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the Windows registry aimed at disabling the Registry Editor (regedit). It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" with a value of "0x00000001". This activity is significant because malware, such as RATs or trojans, often disable registry tools to prevent the removal of their entries, aiding in persistence and defense evasion. If confirmed malicious, this could hinder incident response efforts and allow the attacker to maintain control over the compromised system.
+description: The following analytic detects modifications to the Windows registry
+  aimed at disabling the Registry Editor (regedit). It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools"
+  with a value of "0x00000001". This activity is significant because malware, such
+  as RATs or trojans, often disable registry tools to prevent the removal of their
+  entries, aiding in persistence and defense evasion. If confirmed malicious, this
+  could hinder incident response efforts and allow the attacker to maintain control
+  over the compromised system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools"
-  Registry.registry_value_data = "0x00000001") BY Registry.user Registry.dest
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools"
+  Registry.registry_value_data = "0x00000001") BY Registry.user Registry.dest Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_registry_tool_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -28,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_schedule_task.yml b/detections/endpoint/disable_schedule_task.yml
index 2efdd92e2c..aeeb981ad7 100644
--- a/detections/endpoint/disable_schedule_task.yml
+++ b/detections/endpoint/disable_schedule_task.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a command to disable an existing scheduled task using 'schtasks.exe' with the '/change' and '/disable' parameters. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Disabling scheduled tasks is significant as it is a common tactic used by adversaries, including malware like IcedID, to disable security applications and evade detection. If confirmed malicious, this activity could allow attackers to persist undetected, disable critical security defenses, and further compromise the targeted host.
+description: The following analytic detects the execution of a command to disable
+  an existing scheduled task using 'schtasks.exe' with the '/change' and '/disable'
+  parameters. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line arguments. Disabling scheduled
+  tasks is significant as it is a common tactic used by adversaries, including malware
+  like IcedID, to disable security applications and evade detection. If confirmed
+  malicious, this activity could allow attackers to persist undetected, disable critical
+  security defenses, and further compromise the targeted host.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process=*/change*  Processes.process=*/disable* by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_schedule_task_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe
+  Processes.process=*/change*  Processes.process=*/disable* by Processes.user Processes.process_name
+  Processes.process Processes.parent_process_name Processes.parent_process Processes.dest
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `disable_schedule_task_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: admin may disable problematic schedule task
 references:
 - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: schtask process with commandline $process$ to disable schedule task in $dest$
+  message: schtask process with commandline $process$ to disable schedule task in
+    $dest$
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -44,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_schtask/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_schtask/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_security_logs_using_minint_registry.yml b/detections/endpoint/disable_security_logs_using_minint_registry.yml
index b3a0071da6..2f2c0a2f3f 100644
--- a/detections/endpoint/disable_security_logs_using_minint_registry.yml
+++ b/detections/endpoint/disable_security_logs_using_minint_registry.yml
@@ -5,16 +5,23 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a suspicious registry modification aimed at disabling security audit logs by adding a specific registry entry. It leverages data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" registry path. This activity is significant because it can prevent Windows from logging any events to the Security Log, effectively blinding security monitoring efforts. If confirmed malicious, this technique could allow an attacker to operate undetected, making it difficult to trace their actions and compromising the integrity of security audits.
+description: The following analytic detects a suspicious registry modification aimed
+  at disabling security audit logs by adding a specific registry entry. It leverages
+  data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt"
+  registry path. This activity is significant because it can prevent Windows from
+  logging any events to the Security Log, effectively blinding security monitoring
+  efforts. If confirmed malicious, this technique could allow an attacker to operate
+  undetected, making it difficult to trace their actions and compromising the integrity
+  of security audits.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\Control\\MiniNt\\*") BY Registry.user
-  Registry.dest Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `disable_security_logs_using_minint_registry_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Control\\MiniNt\\*")
+  BY Registry.user Registry.dest Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_security_logs_using_minint_registry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -28,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/minint_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/minint_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_show_hidden_files.yml b/detections/endpoint/disable_show_hidden_files.yml
index af500df780..48d9645370 100644
--- a/detections/endpoint/disable_show_hidden_files.yml
+++ b/detections/endpoint/disable_show_hidden_files.yml
@@ -5,16 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects modifications to the Windows registry that disable the display of hidden files. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with hidden file settings. This activity is significant because malware, such as worms and trojan spyware, often use hidden files to evade detection. If confirmed malicious, this behavior could allow an attacker to conceal malicious files on the system, making it harder for security tools and analysts to identify and remove the threat.
+description: The following analytic detects modifications to the Windows registry
+  that disable the display of hidden files. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to registry paths associated with hidden
+  file settings. This activity is significant because malware, such as worms and trojan
+  spyware, often use hidden files to evade detection. If confirmed malicious, this
+  behavior could allow an attacker to conceal malicious files on the system, making
+  it harder for security tools and analysts to identify and remove the threat.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden"
   OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt"
   Registry.registry_value_data = "0x00000001") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden"
-  Registry.registry_value_data = "0x00000000" )) BY Registry.user Registry.dest
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.registry_value_data = "0x00000000" )) BY Registry.user Registry.dest Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_show_hidden_files_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -30,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,24 +68,19 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log
     source: WinEventLog:System
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_uac_remote_restriction.yml b/detections/endpoint/disable_uac_remote_restriction.yml
index 07653d74ee..44569f943e 100644
--- a/detections/endpoint/disable_uac_remote_restriction.yml
+++ b/detections/endpoint/disable_uac_remote_restriction.yml
@@ -5,16 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the registry to disable UAC remote restriction by setting the "LocalAccountTokenFilterPolicy" value to "0x00000001". It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\CurrentVersion\\Policies\\System*". This activity is significant because disabling UAC remote restriction can allow an attacker to bypass User Account Control (UAC) protections, potentially leading to privilege escalation. If confirmed malicious, this could enable an attacker to execute unauthorized actions with elevated privileges, compromising the security of the affected system.
+description: The following analytic detects the modification of the registry to disable
+  UAC remote restriction by setting the "LocalAccountTokenFilterPolicy" value to "0x00000001".
+  It leverages data from the Endpoint.Registry data model, specifically monitoring
+  changes to the registry path "*\\CurrentVersion\\Policies\\System*". This activity
+  is significant because disabling UAC remote restriction can allow an attacker to
+  bypass User Account Control (UAC) protections, potentially leading to privilege
+  escalation. If confirmed malicious, this could enable an attacker to execute unauthorized
+  actions with elevated privileges, compromising the security of the affected system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\CurrentVersion\\Policies\\System*" Registry.registry_value_name="LocalAccountTokenFilterPolicy"
-  Registry.registry_value_data="0x00000001"  ) BY Registry.user Registry.dest
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_uac_remote_restriction_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentVersion\\Policies\\System*"
+  Registry.registry_value_name="LocalAccountTokenFilterPolicy" Registry.registry_value_data="0x00000001"  )
+  BY Registry.user Registry.dest Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_uac_remote_restriction_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -28,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/LocalAccountTokenFilterPolicy/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/LocalAccountTokenFilterPolicy/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_windows_app_hotkeys.yml b/detections/endpoint/disable_windows_app_hotkeys.yml
index fd125422e4..3a12dcbd92 100644
--- a/detections/endpoint/disable_windows_app_hotkeys.yml
+++ b/detections/endpoint/disable_windows_app_hotkeys.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a suspicious registry modification aimed at disabling Windows hotkeys for native applications. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values indicative of this behavior. This activity is significant as it can impair an analyst's ability to use essential tools like Task Manager and Command Prompt, hindering incident response efforts. If confirmed malicious, this technique can allow an attacker to maintain persistence and evade detection, complicating the remediation process.
+description: The following analytic detects a suspicious registry modification aimed
+  at disabling Windows hotkeys for native applications. It leverages data from the
+  Endpoint.Registry data model, focusing on specific registry paths and values indicative
+  of this behavior. This activity is significant as it can impair an analyst's ability
+  to use essential tools like Task Manager and Command Prompt, hindering incident
+  response efforts. If confirmed malicious, this technique can allow an attacker to
+  maintain persistence and evade detection, complicating the remediation process.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution
-  Options\\*" AND Registry.registry_value_data= "HotKey Disabled" AND Registry.registry_value_name
-  = "Debugger") BY Registry.dest Registry.user Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_windows_app_hotkeys_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows
+  NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_data=
+  "HotKey Disabled" AND Registry.registry_value_name = "Debugger") BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
+  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `disable_windows_app_hotkeys_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guidr
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_windows_behavior_monitoring.yml b/detections/endpoint/disable_windows_behavior_monitoring.yml
index df042e97f7..967fae743f 100644
--- a/detections/endpoint/disable_windows_behavior_monitoring.yml
+++ b/detections/endpoint/disable_windows_behavior_monitoring.yml
@@ -5,24 +5,30 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies modifications in the registry to disable Windows Defender's real-time behavior monitoring. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with Windows Defender settings. This activity is significant because disabling real-time protection is a common tactic used by malware such as RATs, bots, or Trojans to evade detection. If confirmed malicious, this action could allow an attacker to execute code, escalate privileges, or persist in the environment without being detected by antivirus software.
+description: The following analytic identifies modifications in the registry to disable
+  Windows Defender's real-time behavior monitoring. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to registry paths associated with Windows
+  Defender settings. This activity is significant because disabling real-time protection
+  is a common tactic used by malware such as RATs, bots, or Trojans to evade detection.
+  If confirmed malicious, this action could allow an attacker to execute code, escalate
+  privileges, or persist in the environment without being detected by antivirus software.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time
-  Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows
-  Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path=
-  "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable"
-  OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Defender\\Real-Time
-  Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= "*\\Real-Time
-  Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= "*\\Real-Time
-  Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time Protection\\DisableScriptScanning"
-  AND Registry.registry_value_data = "0x00000001") BY Registry.dest
-  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `disable_windows_behavior_monitoring_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows
+  Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path=
+  "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection"
+  OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time
+  Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows
+  Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path=
+  "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path=
+  "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time
+  Protection\\DisableScriptScanning" AND Registry.registry_value_data = "0x00000001")
+  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disable_windows_behavior_monitoring_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -36,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -64,18 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disable_windows_smartscreen_protection.yml b/detections/endpoint/disable_windows_smartscreen_protection.yml
index 75fb6d2bb6..2fed31d0f6 100644
--- a/detections/endpoint/disable_windows_smartscreen_protection.yml
+++ b/detections/endpoint/disable_windows_smartscreen_protection.yml
@@ -5,17 +5,25 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the Windows registry that disable SmartScreen protection. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with SmartScreen settings. This activity is significant because SmartScreen provides an early warning system against phishing and malware. Disabling it can indicate malicious intent, often seen in Remote Access Trojans (RATs) to evade detection while downloading additional payloads. If confirmed malicious, this action could allow attackers to bypass security measures, increasing the risk of successful phishing attacks and malware infections.
+description: The following analytic detects modifications to the Windows registry
+  that disable SmartScreen protection. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to registry paths associated with SmartScreen
+  settings. This activity is significant because SmartScreen provides an early warning
+  system against phishing and malware. Disabling it can indicate malicious intent,
+  often seen in Remote Access Trojans (RATs) to evade detection while downloading
+  additional payloads. If confirmed malicious, this action could allow attackers to
+  bypass security measures, increasing the risk of successful phishing attacks and
+  malware infections.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled",
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled",
   "*\\Microsoft\\Windows\\System\\EnableSmartScreen") Registry.registry_value_data  IN
-  ("Off", "0") BY Registry.dest Registry.user Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `disable_windows_smartscreen_protection_filter`'
+  ("Off", "0") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `disable_windows_smartscreen_protection_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml
index f7900d1ca6..7eb48e469c 100644
--- a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml
+++ b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_get_aduser.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-ADUser` PowerShell cmdlet with parameters indicating a search for domain accounts with Kerberos Pre-Authentication disabled. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific activity. This behavior is significant because discovering accounts with Kerberos Pre-Authentication disabled can allow adversaries to perform offline password cracking. If confirmed malicious, this activity could lead to unauthorized access to user accounts, potentially compromising sensitive information and escalating privileges within the network.
+description: The following analytic detects the execution of the `Get-ADUser` PowerShell
+  cmdlet with parameters indicating a search for domain accounts with Kerberos Pre-Authentication
+  disabled. It leverages PowerShell Script Block Logging (EventCode=4104) to identify
+  this specific activity. This behavior is significant because discovering accounts
+  with Kerberos Pre-Authentication disabled can allow adversaries to perform offline
+  password cracking. If confirmed malicious, this activity could lead to unauthorized
+  access to user accounts, potentially compromising sensitive information and escalating
+  privileges within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-ADUser*" AND ScriptBlockText="*4194304*") | stats count min(_time)  as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | `security_content_ctime(firstTime)` | `disabled_kerberos_pre_authentication_discovery_with_get_aduser_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use search for accounts with Kerberos Pre Authentication disabled for legitimate purposes.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-ADUser*" AND ScriptBlockText="*4194304*")
+  | stats count min(_time)  as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `disabled_kerberos_pre_authentication_discovery_with_get_aduser_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use search for accounts with
+  Kerberos Pre Authentication disabled for legitimate purposes.
 references:
 - https://attack.mitre.org/techniques/T1558/004/
 - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/getaduser/get-aduser-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/getaduser/get-aduser-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml
index 36bfdb72c3..e357eecb96 100644
--- a/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml
+++ b/detections/endpoint/disabled_kerberos_pre_authentication_discovery_with_powerview.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainUser` commandlet with the `-PreauthNotRequired` parameter using PowerShell Script Block Logging (EventCode=4104). This command is part of PowerView, a tool used for enumerating Windows Active Directory networks. Identifying domain accounts with Kerberos Pre-Authentication disabled is significant because adversaries can leverage this information to attempt offline password cracking. If confirmed malicious, this activity could lead to unauthorized access to domain accounts, potentially compromising sensitive information and escalating privileges within the network.
+description: The following analytic detects the execution of the `Get-DomainUser`
+  commandlet with the `-PreauthNotRequired` parameter using PowerShell Script Block
+  Logging (EventCode=4104). This command is part of PowerView, a tool used for enumerating
+  Windows Active Directory networks. Identifying domain accounts with Kerberos Pre-Authentication
+  disabled is significant because adversaries can leverage this information to attempt
+  offline password cracking. If confirmed malicious, this activity could lead to unauthorized
+  access to domain accounts, potentially compromising sensitive information and escalating
+  privileges within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainUser*" AND ScriptBlockText="*PreauthNotRequired*") | stats count min(_time)  as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | `security_content_ctime(firstTime)` | `disabled_kerberos_pre_authentication_discovery_with_powerview_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainUser*" AND ScriptBlockText="*PreauthNotRequired*")
+  | stats count min(_time)  as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `disabled_kerberos_pre_authentication_discovery_with_powerview_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Administrators or power users may use PowerView for troubleshooting
 references:
 - https://attack.mitre.org/techniques/T1558/004/
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdomainuser.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdomainuser.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_cmd_application.yml b/detections/endpoint/disabling_cmd_application.yml
index 586e784b58..fcdc8ea7d6 100644
--- a/detections/endpoint/disabling_cmd_application.yml
+++ b/detections/endpoint/disabling_cmd_application.yml
@@ -5,14 +5,21 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the registry that disable the CMD prompt application. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "DisableCMD" registry value. This activity is significant because disabling CMD can hinder an analyst's ability to investigate and remediate threats, a tactic often used by malware such as RATs, Trojans, or Worms. If confirmed malicious, this could prevent security teams from using CMD for directory and file traversal, complicating incident response and allowing the attacker to maintain persistence.
+description: The following analytic detects modifications to the registry that disable
+  the CMD prompt application. It leverages data from the Endpoint.Registry data model,
+  specifically looking for changes to the "DisableCMD" registry value. This activity
+  is significant because disabling CMD can hinder an analyst's ability to investigate
+  and remediate threats, a tactic often used by malware such as RATs, Trojans, or
+  Worms. If confirmed malicious, this could prevent security teams from using CMD
+  for directory and file traversal, complicating incident response and allowing the
+  attacker to maintain persistence.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD"
+  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_cmd_application_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -28,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_controlpanel.yml b/detections/endpoint/disabling_controlpanel.yml
index 41e60d9fb2..3184ab7bfa 100644
--- a/detections/endpoint/disabling_controlpanel.yml
+++ b/detections/endpoint/disabling_controlpanel.yml
@@ -5,14 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects registry modifications that disable the Control Panel on Windows systems. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" with a value of "0x00000001". This activity is significant as it is commonly used by malware to prevent users from accessing the Control Panel, thereby hindering the removal of malicious artifacts and persistence mechanisms. If confirmed malicious, this could allow attackers to maintain control over the infected machine and prevent remediation efforts.
+description: The following analytic detects registry modifications that disable the
+  Control Panel on Windows systems. It leverages data from the Endpoint.Registry data
+  model, specifically monitoring changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel"
+  with a value of "0x00000001". This activity is significant as it is commonly used
+  by malware to prevent users from accessing the Control Panel, thereby hindering
+  the removal of malicious artifacts and persistence mechanisms. If confirmed malicious,
+  this could allow attackers to maintain control over the infected machine and prevent
+  remediation efforts.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel"
+  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_controlpanel_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -28,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test (XML)
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_defender_services.yml b/detections/endpoint/disabling_defender_services.yml
index d3fe1184c8..73a2d37821 100644
--- a/detections/endpoint/disabling_defender_services.yml
+++ b/detections/endpoint/disabling_defender_services.yml
@@ -5,18 +5,25 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the disabling of Windows Defender services by monitoring registry modifications. It leverages registry event data to identify changes to specific registry paths associated with Defender services, where the 'Start' value is set to '0x00000004'. This activity is significant because disabling Defender services can indicate an attempt by an adversary to evade detection and maintain persistence on the endpoint. If confirmed malicious, this action could allow attackers to execute further malicious activities undetected, leading to potential data breaches or system compromise.
+description: The following analytic detects the disabling of Windows Defender services
+  by monitoring registry modifications. It leverages registry event data to identify
+  changes to specific registry paths associated with Defender services, where the
+  'Start' value is set to '0x00000004'. This activity is significant because disabling
+  Defender services can indicate an attempt by an adversary to evade detection and
+  maintain persistence on the endpoint. If confirmed malicious, this action could
+  allow attackers to execute further malicious activities undetected, leading to potential
+  data breaches or system compromise.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\System\\CurrentControlSet\\Services\\*" AND
-  (Registry.registry_path IN("*WdBoot*", "*WdFilter*", "*WdNisDrv*", "*WdNisSvc*","*WinDefend*",
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\System\\CurrentControlSet\\Services\\*"
+  AND (Registry.registry_path IN("*WdBoot*", "*WdFilter*", "*WdNisDrv*", "*WdNisSvc*","*WinDefend*",
   "*SecurityHealthService*")) AND Registry.registry_value_name = Start Registry.registry_value_data
-  = 0x00000004) BY Registry.dest Registry.user Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_defender_services_filter`'
+  = 0x00000004) BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disabling_defender_services_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -30,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -58,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_firewall_with_netsh.yml b/detections/endpoint/disabling_firewall_with_netsh.yml
index 616afcca36..550c57cd5b 100644
--- a/detections/endpoint/disabling_firewall_with_netsh.yml
+++ b/detections/endpoint/disabling_firewall_with_netsh.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the disabling of the firewall using the netsh application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include keywords like "firewall," "off," or "disable." This activity is significant because disabling the firewall can expose the system to external threats, allowing malware to communicate with its command and control (C2) server. If confirmed malicious, this action could lead to unauthorized data exfiltration, further malware downloads, and broader network compromise.
+description: The following analytic identifies the disabling of the firewall using
+  the netsh application. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on command-line executions that include keywords like "firewall,"
+  "off," or "disable." This activity is significant because disabling the firewall
+  can expose the system to external threats, allowing malware to communicate with
+  its command and control (C2) server. If confirmed malicious, this action could lead
+  to unauthorized data exfiltration, further malware downloads, and broader network
+  compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process= "*firewall*" (Processes.process= "*off*" OR  Processes.process= "*disable*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_firewall_with_netsh_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: admin may disable firewall during testing or fixing network problem.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process=
+  "*firewall*" (Processes.process= "*off*" OR  Processes.process= "*disable*") by
+  Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_firewall_with_netsh_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: admin may disable firewall during testing or fixing network
+  problem.
 references:
 - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_folderoptions_windows_feature.yml b/detections/endpoint/disabling_folderoptions_windows_feature.yml
index 6464e6f476..40dbe52c4d 100644
--- a/detections/endpoint/disabling_folderoptions_windows_feature.yml
+++ b/detections/endpoint/disabling_folderoptions_windows_feature.yml
@@ -5,14 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the Windows registry to disable the Folder Options feature, which prevents users from showing hidden files and file extensions. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" with a value of "0x00000001". This activity is significant as it is commonly used by malware to conceal malicious files and deceive users with fake file extensions. If confirmed malicious, this could allow an attacker to hide their presence and malicious files, making detection and remediation more difficult.
+description: The following analytic detects the modification of the Windows registry
+  to disable the Folder Options feature, which prevents users from showing hidden
+  files and file extensions. It leverages data from the Endpoint.Registry data model,
+  specifically monitoring changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions"
+  with a value of "0x00000001". This activity is significant as it is commonly used
+  by malware to conceal malicious files and deceive users with fake file extensions.
+  If confirmed malicious, this could allow an attacker to hide their presence and
+  malicious files, making detection and remediation more difficult.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions"
+  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_folderoptions_windows_feature_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -28,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_net_user_account.yml b/detections/endpoint/disabling_net_user_account.yml
index 362174ef2d..07b347db4f 100644
--- a/detections/endpoint/disabling_net_user_account.yml
+++ b/detections/endpoint/disabling_net_user_account.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `net.exe` utility to disable a user account via the command line. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate an adversary's attempt to disrupt user availability, potentially as a precursor to further malicious actions. If confirmed malicious, this could lead to denial of service for legitimate users, aiding the attacker in maintaining control or covering their tracks.
+description: The following analytic detects the use of the `net.exe` utility to disable
+  a user account via the command line. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process execution logs and command-line arguments.
+  This activity is significant as it may indicate an adversary's attempt to disrupt
+  user availability, potentially as a precursor to further malicious actions. If confirmed
+  malicious, this could lead to denial of service for legitimate users, aiding the
+  attacker in maintaining control or covering their tracks.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="*user*" AND Processes.process="*/active:no*" by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_net_user_account_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.parent_process) as parent_process values(Processes.process_id)
+  as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_net` AND Processes.process="*user*" AND Processes.process="*/active:no*"
+  by  Processes.process_name Processes.original_file_name Processes.dest Processes.user
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `disabling_net_user_account_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified disabling a user account on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    disabling a user account on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1531
   observable:
@@ -54,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_norun_windows_app.yml b/detections/endpoint/disabling_norun_windows_app.yml
index f3be484f7e..edc5298d24 100644
--- a/detections/endpoint/disabling_norun_windows_app.yml
+++ b/detections/endpoint/disabling_norun_windows_app.yml
@@ -5,14 +5,20 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of the Windows registry to disable the Run application in the Start menu. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" with a value of "0x00000001". This activity is significant because the Run application is a useful shortcut for executing known applications and scripts. If confirmed malicious, this action could hinder system cleaning efforts and make it more difficult to run essential tools, thereby aiding malware persistence.
+description: The following analytic detects the modification of the Windows registry
+  to disable the Run application in the Start menu. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun"
+  with a value of "0x00000001". This activity is significant because the Run application
+  is a useful shortcut for executing known applications and scripts. If confirmed
+  malicious, this action could hinder system cleaning efforts and make it more difficult
+  to run essential tools, thereby aiding malware persistence.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun"
+  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_norun_windows_app_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -29,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: The Windows registry was modified to disable run application in window start menu on $dest$ by $user$.
+  message: The Windows registry was modified to disable run application in window
+    start menu on $dest$ by $user$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -57,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_remote_user_account_control.yml b/detections/endpoint/disabling_remote_user_account_control.yml
index 229f445a26..56bba111b7 100644
--- a/detections/endpoint/disabling_remote_user_account_control.yml
+++ b/detections/endpoint/disabling_remote_user_account_control.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: David Dorsey, Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic identifies modifications to the registry key that controls the enforcement of Windows User Account Control (UAC). It detects changes to the registry path `HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\EnableLUA` where the value is set to `0x00000000`. This activity is significant because disabling UAC can allow unauthorized changes to the system without user consent, potentially leading to privilege escalation. If confirmed malicious, an attacker could gain elevated privileges, making it easier to execute further attacks or maintain persistence within the environment.
+description: The following analytic identifies modifications to the registry key that
+  controls the enforcement of Windows User Account Control (UAC). It detects changes
+  to the registry path `HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\EnableLUA`
+  where the value is set to `0x00000000`. This activity is significant because disabling
+  UAC can allow unauthorized changes to the system without user consent, potentially
+  leading to privilege escalation. If confirmed malicious, an attacker could gain
+  elevated privileges, making it easier to execute further attacks or maintain persistence
+  within the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_data="0x00000000" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.
-known_false_positives: This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA*
+  Registry.registry_value_data="0x00000000" by Registry.dest, Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  | `drop_dm_object_name(Registry)` | `disabling_remote_user_account_control_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black, or via other endpoint data sources, such as Sysmon.
+  The data used for this search is typically generated via logs that report registry
+  modifications.
+known_false_positives: This registry key may be modified via administrators to implement
+  a change in system policy. This type of change should be a very rare occurrence.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 70
-  message: The Windows registry keys that control the enforcement of Windows User Account Control (UAC) were modified on $dest$ by $user$.
+  message: The Windows registry keys that control the enforcement of Windows User
+    Account Control (UAC) were modified on $dest$ by $user$.
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -50,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_systemrestore_in_registry.yml b/detections/endpoint/disabling_systemrestore_in_registry.yml
index 4c489dfee4..6fadfd7272 100644
--- a/detections/endpoint/disabling_systemrestore_in_registry.yml
+++ b/detections/endpoint/disabling_systemrestore_in_registry.yml
@@ -5,19 +5,27 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of registry keys to disable System Restore on a machine. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with System Restore settings. This activity is significant because disabling System Restore can hinder recovery efforts and is a tactic often used by Remote Access Trojans (RATs) to maintain persistence on an infected system. If confirmed malicious, this action could prevent system recovery, allowing the attacker to sustain their foothold and potentially cause further damage or data loss.
+description: The following analytic detects the modification of registry keys to disable
+  System Restore on a machine. It leverages data from the Endpoint.Registry data model,
+  specifically monitoring changes to registry paths associated with System Restore
+  settings. This activity is significant because disabling System Restore can hinder
+  recovery efforts and is a tactic often used by Remote Access Trojans (RATs) to maintain
+  persistence on an infected system. If confirmed malicious, this action could prevent
+  system recovery, allowing the attacker to sustain their foothold and potentially
+  cause further damage or data loss.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR"
-  OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig"
-  OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows NT\\SystemRestore\\DisableSR"
-  OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows NT\\SystemRestore\\DisableConfig"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)`| where isnotnull(registry_value_data)
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_systemrestore_in_registry_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows
+  NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows
+  NT\\CurrentVersion\\SystemRestore\\DisableConfig" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows
+  NT\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows
+  NT\\SystemRestore\\DisableConfig" Registry.registry_value_data = "0x00000001") BY
+  Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`|
+  where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `disabling_systemrestore_in_registry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -31,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The Windows registry was modified to disable system restore on $dest$ by $user$.
+  message: The Windows registry was modified to disable system restore on $dest$ by
+    $user$.
   mitre_attack_id:
   - T1490
   observable:
@@ -58,27 +72,23 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log
     source: WinEventLog:System
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_task_manager.yml b/detections/endpoint/disabling_task_manager.yml
index f293c29854..c0bf79ad7f 100644
--- a/detections/endpoint/disabling_task_manager.yml
+++ b/detections/endpoint/disabling_task_manager.yml
@@ -5,14 +5,21 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies modifications to the Windows registry that disable Task Manager. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" with a value of "0x00000001". This activity is significant as it is commonly associated with malware such as RATs, Trojans, and worms, which disable Task Manager to prevent users from terminating malicious processes. If confirmed malicious, this could allow attackers to maintain persistence and control over the infected system.
+description: The following analytic identifies modifications to the Windows registry
+  that disable Task Manager. It leverages data from the Endpoint.Registry data model,
+  specifically looking for changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr"
+  with a value of "0x00000001". This activity is significant as it is commonly associated
+  with malware such as RATs, Trojans, and worms, which disable Task Manager to prevent
+  users from terminating malicious processes. If confirmed malicious, this could allow
+  attackers to maintain persistence and control over the infected system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr"
+  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disabling_task_manager_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml b/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml
index 9434d90a0d..55766fa0e4 100644
--- a/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml
+++ b/detections/endpoint/disabling_windows_local_security_authority_defences_via_registry.yml
@@ -7,10 +7,35 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 13 AND Sysmon EventID 1
-description: The following analytic identifies the deletion of registry keys that disable Local Security Authority (LSA) protection and Microsoft Defender Device Guard. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry actions and paths associated with LSA and Device Guard settings. This activity is significant because disabling these defenses can leave a system vulnerable to various attacks, including credential theft and unauthorized code execution. If confirmed malicious, this action could allow attackers to bypass critical security mechanisms, leading to potential system compromise and persistent access.
-search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\SYSTEM\\CurrentControlSet\\Control\\Lsa\\LsaCfgFlags", "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\DeviceGuard\\*", "*\\SYSTEM\\CurrentControlSet\\Control\\Lsa\\RunAsPPL") Registry.action IN (deleted, unknown) by Registry.action Registry.registry_path Registry.process_guid Registry.dest Registry.user| `drop_dm_object_name(Registry)` | join type=outer process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`] | table _time action dest user parent_process_name parent_process process_name process process_guid registry_path | `disabling_windows_local_security_authority_defences_via_registry_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Potential to be triggered by an administrator disabling protections for troubleshooting purposes.
+description: The following analytic identifies the deletion of registry keys that
+  disable Local Security Authority (LSA) protection and Microsoft Defender Device
+  Guard. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on registry actions and paths associated with LSA and Device Guard settings. This
+  activity is significant because disabling these defenses can leave a system vulnerable
+  to various attacks, including credential theft and unauthorized code execution.
+  If confirmed malicious, this action could allow attackers to bypass critical security
+  mechanisms, leading to potential system compromise and persistent access.
+search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry
+  where Registry.registry_path IN ("*\\SYSTEM\\CurrentControlSet\\Control\\Lsa\\LsaCfgFlags",
+  "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\DeviceGuard\\*", "*\\SYSTEM\\CurrentControlSet\\Control\\Lsa\\RunAsPPL")
+  Registry.action IN (deleted, unknown) by Registry.action Registry.registry_path
+  Registry.process_guid Registry.dest Registry.user| `drop_dm_object_name(Registry)`
+  | join type=outer process_guid [| tstats `security_content_summariesonly` count
+  FROM datamodel=Endpoint.Processes by Processes.user Processes.process_name Processes.process
+  Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid
+  | `drop_dm_object_name(Processes)`] | table _time action dest user parent_process_name
+  parent_process process_name process process_guid registry_path | `disabling_windows_local_security_authority_defences_via_registry_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Potential to be triggered by an administrator disabling protections
+  for troubleshooting purposes.
 references:
 - https://docs.microsoft.com/en-us/windows-server/security/credentials-protection-and-management/configuring-additional-lsa-protection
 - https://docs.microsoft.com/en-us/windows/security/identity-protection/credential-guard/credential-guard-manage
@@ -20,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 60
-  message: An attempt to disable Windows LSA defences was detected on $dest$. The reg key $registry_path$ was deleted by $user$.
+  message: An attempt to disable Windows LSA defences was detected on $dest$. The
+    reg key $registry_path$ was deleted by $user$.
   mitre_attack_id:
   - T1556
   observable:
@@ -46,16 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.action
-  - Registry.registry_path
-  - Registry.dest
-  - Registry.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/disable_lsa_protection/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/disable_lsa_protection/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml b/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml
index 0a707ce593..6af715f0b0 100644
--- a/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml
+++ b/detections/endpoint/dllhost_with_no_command_line_arguments_with_network.yml
@@ -5,12 +5,37 @@ date: '2024-10-17'
 author: Steven Dick, Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects instances of DLLHost.exe running without command line arguments while establishing a network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution and network activity data. It is significant because DLLHost.exe typically runs with specific arguments, and its absence can indicate malicious activity, such as Cobalt Strike usage. If confirmed malicious, this activity could allow attackers to execute code, move laterally, or exfiltrate data, posing a severe threat to the network's security.
+description: The following analytic detects instances of DLLHost.exe running without
+  command line arguments while establishing a network connection. This behavior is
+  identified using Endpoint Detection and Response (EDR) telemetry, focusing on process
+  execution and network activity data. It is significant because DLLHost.exe typically
+  runs with specific arguments, and its absence can indicate malicious activity, such
+  as Cobalt Strike usage. If confirmed malicious, this activity could allow attackers
+  to execute code, move laterally, or exfiltrate data, posing a severe threat to the
+  network's security.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(dllhost\.exe.{0,4}$)" | rename dest as src | join host process_id [| tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `dllhost_with_no_command_line_arguments_with_network_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate third party applications may use a moved copy of dllhost, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe
+  Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name
+  Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | regex process="(?i)(dllhost\.exe.{0,4}$)"
+  | rename dest as src | join host process_id [| tstats `security_content_summariesonly`
+  count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port)
+  as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port
+  != 0 by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `dllhost_with_no_command_line_arguments_with_network_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate third party applications
+  may use a moved copy of dllhost, triggering a false positive.
 references:
 - https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile
 - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/
@@ -22,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The process $process_name$ was spawned by $parent_process_name$ without any command-line arguments on $src$ by $user$.
+  message: The process $process_name$ was spawned by $parent_process_name$ without
+    any command-line arguments on $src$ by $user$.
   mitre_attack_id:
   - T1055
   observable:
@@ -46,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - process_name
-  - process_id
-  - parent_process_name
-  - dest_port
-  - process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/dns_exfiltration_using_nslookup_app.yml b/detections/endpoint/dns_exfiltration_using_nslookup_app.yml
index ed3ee5b384..bc1d934170 100644
--- a/detections/endpoint/dns_exfiltration_using_nslookup_app.yml
+++ b/detections/endpoint/dns_exfiltration_using_nslookup_app.yml
@@ -5,13 +5,35 @@ date: '2024-10-23'
 author: Teoderick Contreras, Splunk, Wouter Jansen
 status: production
 type: TTP
-description: The following analytic identifies potential DNS exfiltration using the nslookup application. It detects specific command-line parameters such as query type (TXT, A, AAAA) and retry options, which are commonly used by attackers to exfiltrate data. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process execution logs. This activity is significant as it may indicate an attempt to communicate with a Command and Control (C2) server or exfiltrate sensitive data. If confirmed malicious, this could lead to data breaches and unauthorized access to critical information.
+description: The following analytic identifies potential DNS exfiltration using the
+  nslookup application. It detects specific command-line parameters such as query
+  type (TXT, A, AAAA) and retry options, which are commonly used by attackers to exfiltrate
+  data. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing
+  on process execution logs. This activity is significant as it may indicate an attempt
+  to communicate with a Command and Control (C2) server or exfiltrate sensitive data.
+  If confirmed malicious, this could lead to data breaches and unauthorized access
+  to critical information.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="*-type=*" OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process_name  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process
+  count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*"
+  OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="*-type=*"
+  OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name
+  Processes.process Processes.parent_process_name  | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dns_exfiltration_using_nslookup_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: admin nslookup usage
 references:
 - https://www.mandiant.com/resources/fin7-spear-phishing-campaign-targets-personnel-involved-sec-filings
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing activity related to DNS exfiltration.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ performing activity related to DNS exfiltration.
   mitre_attack_id:
   - T1048
   observable:
@@ -59,23 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_account_discovery_with_dsquery.yml b/detections/endpoint/domain_account_discovery_with_dsquery.yml
index 11038804bb..ef2156d6a8 100644
--- a/detections/endpoint/domain_account_discovery_with_dsquery.yml
+++ b/detections/endpoint/domain_account_discovery_with_dsquery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `dsquery.exe` with command-line arguments used to discover domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to map out domain users, which is a common precursor to further attacks. If confirmed malicious, this behavior could allow attackers to gain insights into user accounts, facilitating subsequent actions like privilege escalation or lateral movement within the network.
+description: The following analytic identifies the execution of `dsquery.exe` with
+  command-line arguments used to discover domain users. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as it indicates potential reconnaissance
+  efforts by adversaries to map out domain users, which is a common precursor to further
+  attacks. If confirmed malicious, this behavior could allow attackers to gain insights
+  into user accounts, facilitating subsequent actions like privilege escalation or
+  lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="dsquery.exe" AND Processes.process = "*user*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_dsquery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="dsquery.exe"
+  AND Processes.process = "*user*" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `domain_account_discovery_with_dsquery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://jpcertcc.github.io/ToolAnalysisResultSheet/details/dsquery.htm
@@ -43,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_account_discovery_with_net_app.yml b/detections/endpoint/domain_account_discovery_with_net_app.yml
index 5b451eb842..717d0a5c15 100644
--- a/detections/endpoint/domain_account_discovery_with_net_app.yml
+++ b/detections/endpoint/domain_account_discovery_with_net_app.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `net.exe` or `net1.exe` with command-line arguments used to query domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt by adversaries to enumerate domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out user accounts, potentially leading to further exploitation or lateral movement within the network.
+description: The following analytic detects the execution of `net.exe` or `net1.exe`
+  with command-line arguments used to query domain users. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as it may indicate an attempt by adversaries
+  to enumerate domain users for situational awareness and Active Directory discovery.
+  If confirmed malicious, this behavior could allow attackers to map out user accounts,
+  potentially leading to further exploitation or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process = "* user*" AND Processes.process = "*/do*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_net_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process
+  = "* user*" AND Processes.process = "*/do*" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `domain_account_discovery_with_net_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://docs.microsoft.com/en-us/defender-for-identity/playbook-domain-dominance
@@ -22,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,20 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_account_discovery_with_wmic.yml b/detections/endpoint/domain_account_discovery_with_wmic.yml
index e89d599e7d..b56f4321a8 100644
--- a/detections/endpoint/domain_account_discovery_with_wmic.yml
+++ b/detections/endpoint/domain_account_discovery_with_wmic.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `wmic.exe` with command-line arguments used to query for domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of domain account discovery. This activity is significant as it often precedes lateral movement or privilege escalation attempts by adversaries. If confirmed malicious, this behavior could allow attackers to map out user accounts within the domain, facilitating further attacks and potentially compromising sensitive information.
+description: The following analytic detects the execution of `wmic.exe` with command-line
+  arguments used to query for domain users. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on specific command-line patterns indicative
+  of domain account discovery. This activity is significant as it often precedes lateral
+  movement or privilege escalation attempts by adversaries. If confirmed malicious,
+  this behavior could allow attackers to map out user accounts within the domain,
+  facilitating further attacks and potentially compromising sensitive information.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="wmic.exe" AND Processes.process = "*/NAMESPACE:\\\\root\\directory\\ldap*" AND Processes.process = "*ds_user*" AND Processes.process = "*GET*" AND Processes.process = "*ds_samaccountname*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="wmic.exe"
+  AND Processes.process = "*/NAMESPACE:\\\\root\\directory\\ldap*" AND Processes.process
+  = "*ds_user*" AND Processes.process = "*GET*" AND Processes.process = "*ds_samaccountname*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `domain_account_discovery_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/002/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,20 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_controller_discovery_with_nltest.yml b/detections/endpoint/domain_controller_discovery_with_nltest.yml
index c4637f0dd3..85267c2f67 100644
--- a/detections/endpoint/domain_controller_discovery_with_nltest.yml
+++ b/detections/endpoint/domain_controller_discovery_with_nltest.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `nltest.exe` with command-line arguments `/dclist:` or `/dsgetdc:` to discover domain controllers. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments. This activity is significant because both Red Teams and adversaries use `nltest.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out domain controllers, facilitating further attacks such as privilege escalation or lateral movement within the network.
+description: The following analytic detects the execution of `nltest.exe` with command-line
+  arguments `/dclist:` or `/dsgetdc:` to discover domain controllers. It leverages
+  Endpoint Detection and Response (EDR) data, focusing on process names and command-line
+  arguments. This activity is significant because both Red Teams and adversaries use
+  `nltest.exe` for situational awareness and Active Directory discovery. If confirmed
+  malicious, this behavior could allow attackers to map out domain controllers, facilitating
+  further attacks such as privilege escalation or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="nltest.exe") (Processes.process="*/dclist:*" OR Processes.process="*/dsgetdc:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_nltest_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="nltest.exe")
+  (Processes.process="*/dclist:*" OR Processes.process="*/dsgetdc:*") by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_nltest_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,22 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_controller_discovery_with_wmic.yml b/detections/endpoint/domain_controller_discovery_with_wmic.yml
index 81f0623221..fdd9237bf2 100644
--- a/detections/endpoint/domain_controller_discovery_with_wmic.yml
+++ b/detections/endpoint/domain_controller_discovery_with_wmic.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `wmic.exe` with command-line arguments used to discover domain controllers in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is commonly used by adversaries and Red Teams for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out the network, identify key systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration.
+description: The following analytic identifies the execution of `wmic.exe` with command-line
+  arguments used to discover domain controllers in a Windows domain. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line arguments. This activity is significant because it is commonly
+  used by adversaries and Red Teams for situational awareness and Active Directory
+  discovery. If confirmed malicious, this behavior could allow attackers to map out
+  the network, identify key systems, and plan further attacks, potentially leading
+  to unauthorized access and data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process="" OR Processes.process="*DomainControllerAddress*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe")
+  (Processes.process="" OR Processes.process="*DomainControllerAddress*") by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_controller_discovery_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -33,22 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_group_discovery_with_adsisearcher.yml b/detections/endpoint/domain_group_discovery_with_adsisearcher.yml
index 73f0bba637..9cbbc08196 100644
--- a/detections/endpoint/domain_group_discovery_with_adsisearcher.yml
+++ b/detections/endpoint/domain_group_discovery_with_adsisearcher.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for domain groups. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks containing `[adsisearcher]` and group-related queries. This activity is significant as it may indicate an attempt by adversaries or Red Teams to enumerate domain groups for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
+description: The following analytic detects the use of the `[Adsisearcher]` type accelerator
+  in PowerShell to query Active Directory for domain groups. It leverages PowerShell
+  Script Block Logging (EventCode=4104) to identify specific script blocks containing
+  `[adsisearcher]` and group-related queries. This activity is significant as it may
+  indicate an attempt by adversaries or Red Teams to enumerate domain groups for situational
+  awareness and Active Directory discovery. If confirmed malicious, this behavior
+  could lead to further reconnaissance, privilege escalation, or lateral movement
+  within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` (ScriptBlockText = "*[adsisearcher]*" AND ScriptBlockText = "*(objectcategory=group)*" AND ScriptBlockText = "*findAll()*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `domain_group_discovery_with_adsisearcher_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` (ScriptBlockText = "*[adsisearcher]*" AND ScriptBlockText =
+  "*(objectcategory=group)*" AND ScriptBlockText = "*findAll()*") | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID
+  | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)`
+  | `domain_group_discovery_with_adsisearcher_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Administrators or power users may use Adsisearcher for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,16 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/domain_group_discovery_with_adsisearcher/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/domain_group_discovery_with_adsisearcher/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_group_discovery_with_dsquery.yml b/detections/endpoint/domain_group_discovery_with_dsquery.yml
index 10d3fd6d03..b4fca270c7 100644
--- a/detections/endpoint/domain_group_discovery_with_dsquery.yml
+++ b/detections/endpoint/domain_group_discovery_with_dsquery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `dsquery.exe` with command-line arguments used to query for domain groups. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments. This activity is significant because both Red Teams and adversaries use `dsquery.exe` to enumerate domain groups, gaining situational awareness and facilitating further Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out the domain structure, identify high-value targets, and plan subsequent attacks, potentially leading to privilege escalation or data exfiltration.
+description: The following analytic identifies the execution of `dsquery.exe` with
+  command-line arguments used to query for domain groups. It leverages Endpoint Detection
+  and Response (EDR) data, focusing on process names and command-line arguments. This
+  activity is significant because both Red Teams and adversaries use `dsquery.exe`
+  to enumerate domain groups, gaining situational awareness and facilitating further
+  Active Directory discovery. If confirmed malicious, this behavior could allow attackers
+  to map out the domain structure, identify high-value targets, and plan subsequent
+  attacks, potentially leading to privilege escalation or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe") (Processes.process="*group*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_dsquery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe")
+  (Processes.process="*group*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `domain_group_discovery_with_dsquery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -34,22 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_group_discovery_with_net.yml b/detections/endpoint/domain_group_discovery_with_net.yml
index 2910eb54aa..c85e47ed00 100644
--- a/detections/endpoint/domain_group_discovery_with_net.yml
+++ b/detections/endpoint/domain_group_discovery_with_net.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `net.exe` with command-line arguments used to query domain groups, specifically `group /domain`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain groups, which is a common step in Active Directory Discovery. If confirmed malicious, this behavior could allow attackers to gain insights into the domain structure, aiding in further attacks such as privilege escalation or lateral movement.
+description: The following analytic identifies the execution of `net.exe` with command-line
+  arguments used to query domain groups, specifically `group /domain`. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line arguments. This activity is significant as it indicates potential
+  reconnaissance efforts by adversaries to enumerate domain groups, which is a common
+  step in Active Directory Discovery. If confirmed malicious, this behavior could
+  allow attackers to gain insights into the domain structure, aiding in further attacks
+  such as privilege escalation or lateral movement.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process=*group* AND Processes.process=*/do*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_net_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe") (Processes.process=*group* AND Processes.process=*/do*)
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_net_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -38,22 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/domain_group_discovery_with_wmic.yml b/detections/endpoint/domain_group_discovery_with_wmic.yml
index dd2ffe226a..9d47d75f07 100644
--- a/detections/endpoint/domain_group_discovery_with_wmic.yml
+++ b/detections/endpoint/domain_group_discovery_with_wmic.yml
@@ -5,13 +5,34 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `wmic.exe` with command-line arguments used to query for domain groups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gain situational awareness and map out Active Directory structures. If confirmed malicious, this behavior could allow attackers to identify and target specific domain groups, potentially leading to privilege escalation or lateral movement within the network.
+description: The following analytic identifies the execution of `wmic.exe` with command-line
+  arguments used to query for domain groups. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line executions.
+  This activity is significant as it indicates potential reconnaissance efforts by
+  adversaries to gain situational awareness and map out Active Directory structures.
+  If confirmed malicious, this behavior could allow attackers to identify and target
+  specific domain groups, potentially leading to privilege escalation or lateral movement
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_group* AND Processes.process="*GET ds_samaccountname*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe")
+  (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_group*
+  AND Processes.process="*GET ds_samaccountname*") by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `domain_group_discovery_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -34,22 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/download_files_using_telegram.yml b/detections/endpoint/download_files_using_telegram.yml
index 8396228a8c..818e13a3b5 100644
--- a/detections/endpoint/download_files_using_telegram.yml
+++ b/detections/endpoint/download_files_using_telegram.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious file downloads by the Telegram application on a Windows system. It leverages Sysmon EventCode 15 to identify instances where Telegram.exe creates files with a Zone.Identifier, indicating a download. This activity is significant as it may indicate an adversary using Telegram to download malicious tools, such as network scanners, for further exploitation. If confirmed malicious, this behavior could lead to network mapping, lateral movement, and potential compromise of additional systems within the network.
+description: The following analytic detects suspicious file downloads by the Telegram
+  application on a Windows system. It leverages Sysmon EventCode 15 to identify instances
+  where Telegram.exe creates files with a Zone.Identifier, indicating a download.
+  This activity is significant as it may indicate an adversary using Telegram to download
+  malicious tools, such as network scanners, for further exploitation. If confirmed
+  malicious, this behavior could lead to network mapping, lateral movement, and potential
+  compromise of additional systems within the network.
 data_source:
 - Sysmon EventID 15
-search: '`sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" |stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode process_name process_id TargetFilename Hash | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `download_files_using_telegram_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and TargetFilename from your endpoints or Events that monitor filestream events which is happened when process download something. (EventCode 15) If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: normal download of file in telegram app. (if it was a common app in network)
+search: '`sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier"
+  |stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode process_name
+  process_id TargetFilename Hash | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `download_files_using_telegram_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and TargetFilename from your endpoints or Events that
+  monitor filestream events which is happened when process download something. (EventCode
+  15) If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon
+  TA.
+known_false_positives: normal download of file in telegram app. (if it was a common
+  app in network)
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - EventCode
-  - Image
-  - process_id
-  - TargetFilename
-  - Hash
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/drop_icedid_license_dat.yml b/detections/endpoint/drop_icedid_license_dat.yml
index 62db838871..b6f47d2f6f 100644
--- a/detections/endpoint/drop_icedid_license_dat.yml
+++ b/detections/endpoint/drop_icedid_license_dat.yml
@@ -5,11 +5,24 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the dropping of a suspicious file named "license.dat" in %appdata% or %programdata%. This behavior is associated with the IcedID malware, which uses this file to inject its core bot into other processes for banking credential theft. The detection leverages Sysmon EventCode 11 to monitor file creation events in these directories. This activity is significant as it indicates a potential malware infection aiming to steal sensitive banking information. If confirmed malicious, the attacker could gain unauthorized access to financial data, leading to significant financial loss and data breaches.
+description: The following analytic detects the dropping of a suspicious file named
+  "license.dat" in %appdata% or %programdata%. This behavior is associated with the
+  IcedID malware, which uses this file to inject its core bot into other processes
+  for banking credential theft. The detection leverages Sysmon EventCode 11 to monitor
+  file creation events in these directories. This activity is significant as it indicates
+  a potential malware infection aiming to steal sensitive banking information. If
+  confirmed malicious, the attacker could gain unauthorized access to financial data,
+  leading to significant financial loss and data breaches.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventCode= 11  TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode= 11  TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*"
+  OR TargetFilename="*\\programdata\\*") |stats count min(_time) as firstTime max(_time)
+  as lastTime by TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `drop_icedid_license_dat_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.cisecurity.org/insights/white-papers/security-primer-icedid
@@ -36,12 +49,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/dsquery_domain_discovery.yml b/detections/endpoint/dsquery_domain_discovery.yml
index 941f0c866d..2957c1b328 100644
--- a/detections/endpoint/dsquery_domain_discovery.yml
+++ b/detections/endpoint/dsquery_domain_discovery.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of "dsquery.exe" with arguments targeting `TrustedDomain` queries directly from the command line. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments. This activity is significant as it often indicates domain trust discovery, a common step in lateral movement or privilege escalation by adversaries. If confirmed malicious, this could allow attackers to map domain trusts, potentially leading to further exploitation and unauthorized access to trusted domains.
+description: The following analytic detects the execution of "dsquery.exe" with arguments
+  targeting `TrustedDomain` queries directly from the command line. This behavior
+  is identified using Endpoint Detection and Response (EDR) telemetry, focusing on
+  process names and command-line arguments. This activity is significant as it often
+  indicates domain trust discovery, a common step in lateral movement or privilege
+  escalation by adversaries. If confirmed malicious, this could allow attackers to
+  map domain trusts, potentially leading to further exploitation and unauthorized
+  access to trusted domains.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dsquery.exe Processes.process=*trustedDomain* by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dsquery_domain_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives. If there is a true false positive, filter based on command-line or parent process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dsquery.exe
+  Processes.process=*trustedDomain* by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `dsquery_domain_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives. If there is a true false positive,
+  filter based on command-line or parent process.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md
 - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/
@@ -24,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified performing domain discovery on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    performing domain discovery on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1482
   observable:
@@ -58,19 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/dump_lsass_via_comsvcs_dll.yml b/detections/endpoint/dump_lsass_via_comsvcs_dll.yml
index 6833bd0d77..664a5bc9e5 100644
--- a/detections/endpoint/dump_lsass_via_comsvcs_dll.yml
+++ b/detections/endpoint/dump_lsass_via_comsvcs_dll.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the behavior of dumping credentials from memory by exploiting the Local Security Authority Subsystem Service (LSASS) using the comsvcs.dll and MiniDump via rundll32. This detection leverages process information from Endpoint Detection and Response (EDR) logs, focusing on specific command-line executions. This activity is significant because it indicates potential credential theft, which can lead to broader system compromise, persistence, lateral movement, and privilege escalation. If confirmed malicious, attackers could gain unauthorized access to sensitive information, leading to data theft, ransomware attacks, or other damaging outcomes.
+description: The following analytic detects the behavior of dumping credentials from
+  memory by exploiting the Local Security Authority Subsystem Service (LSASS) using
+  the comsvcs.dll and MiniDump via rundll32. This detection leverages process information
+  from Endpoint Detection and Response (EDR) logs, focusing on specific command-line
+  executions. This activity is significant because it indicates potential credential
+  theft, which can lead to broader system compromise, persistence, lateral movement,
+  and privilege escalation. If confirmed malicious, attackers could gain unauthorized
+  access to sensitive information, leading to data theft, ransomware attacks, or other
+  damaging outcomes.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*comsvcs.dll*
+  Processes.process=*MiniDump* by Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.dest | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_comsvcs_dll_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: None identified.
 references:
 - https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified accessing credentials using comsvcs.dll on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    accessing credentials using comsvcs.dll on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -67,23 +93,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/dump_lsass_via_procdump.yml b/detections/endpoint/dump_lsass_via_procdump.yml
index f398d92b40..db2868e098 100644
--- a/detections/endpoint/dump_lsass_via_procdump.yml
+++ b/detections/endpoint/dump_lsass_via_procdump.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of procdump.exe to dump the LSASS process, specifically looking for the -mm and -ma command-line arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and parent processes. This activity is significant because dumping LSASS can expose sensitive credentials, posing a severe security risk. If confirmed malicious, an attacker could obtain credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and further compromise of the environment.
+description: The following analytic detects the use of procdump.exe to dump the LSASS
+  process, specifically looking for the -mm and -ma command-line arguments. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names,
+  command-line executions, and parent processes. This activity is significant because
+  dumping LSASS can expose sensitive credentials, posing a severe security risk. If
+  confirmed malicious, an attacker could obtain credentials, escalate privileges,
+  and move laterally within the network, leading to potential data breaches and further
+  compromise of the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_procdump` (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_procdump` (Processes.process=*-ma*
+  OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.original_file_name Processes.dest
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `dump_lsass_via_procdump_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: None identified.
 references:
 - https://attack.mitre.org/techniques/T1003/001/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to dump lsass.exe on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    attempting to dump lsass.exe on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -60,24 +86,17 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/crowdstrike_falcon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/crowdstrike_falcon.log
     source: crowdstrike
     sourcetype: crowdstrike:events:sensor
diff --git a/detections/endpoint/elevated_group_discovery_with_net.yml b/detections/endpoint/elevated_group_discovery_with_net.yml
index b1dff481dc..8f52c0089d 100644
--- a/detections/endpoint/elevated_group_discovery_with_net.yml
+++ b/detections/endpoint/elevated_group_discovery_with_net.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `net.exe` or `net1.exe` with command-line arguments used to query elevated domain groups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to identify high-privileged users within Active Directory. If confirmed malicious, this behavior could lead to further attacks aimed at compromising privileged accounts, escalating privileges, or gaining unauthorized access to sensitive systems and data.
+description: The following analytic detects the execution of `net.exe` or `net1.exe`
+  with command-line arguments used to query elevated domain groups. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  command-line executions. This activity is significant as it indicates potential
+  reconnaissance efforts by adversaries to identify high-privileged users within Active
+  Directory. If confirmed malicious, this behavior could lead to further attacks aimed
+  at compromising privileged accounts, escalating privileges, or gaining unauthorized
+  access to sensitive systems and data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process="*group*" AND Processes.process="*/do*") (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns Admins*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_net_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe") (Processes.process="*group*" AND Processes.process="*/do*")
+  (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*"
+  OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*"
+  OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*"
+  OR Processes.process="*Dns Admins*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `elevated_group_discovery_with_net_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -24,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,22 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/elevated_group_discovery_with_powerview.yml b/detections/endpoint/elevated_group_discovery_with_powerview.yml
index 9e48be95c2..e5c13b8d44 100644
--- a/detections/endpoint/elevated_group_discovery_with_powerview.yml
+++ b/detections/endpoint/elevated_group_discovery_with_powerview.yml
@@ -5,11 +5,25 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-DomainGroupMember` cmdlet from PowerView, identified through PowerShell Script Block Logging (EventCode=4104). This cmdlet is used to enumerate members of elevated domain groups such as Domain Admins and Enterprise Admins. Monitoring this activity is crucial as it indicates potential reconnaissance efforts by adversaries to identify high-privileged users within the domain. If confirmed malicious, this activity could lead to targeted attacks on privileged accounts, facilitating further compromise and lateral movement within the network.
+description: The following analytic detects the execution of the `Get-DomainGroupMember`
+  cmdlet from PowerView, identified through PowerShell Script Block Logging (EventCode=4104).
+  This cmdlet is used to enumerate members of elevated domain groups such as Domain
+  Admins and Enterprise Admins. Monitoring this activity is crucial as it indicates
+  potential reconnaissance efforts by adversaries to identify high-privileged users
+  within the domain. If confirmed malicious, this activity could lead to targeted
+  attacks on privileged accounts, facilitating further compromise and lateral movement
+  within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroupMember*") AND ScriptBlockText IN ("*Domain Admins*","*Enterprise Admins*", "*Schema Admins*", "*Account Operators*" , "*Server Operators*", "*Protected Users*",  "*Dns Admins*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `elevated_group_discovery_with_powerview_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroupMember*")
+  AND ScriptBlockText IN ("*Domain Admins*","*Enterprise Admins*", "*Schema Admins*",
+  "*Account Operators*" , "*Server Operators*", "*Protected Users*",  "*Dns Admins*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `elevated_group_discovery_with_powerview_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Administrators or power users may use this PowerView for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -35,19 +49,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml-powerview.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml-powerview.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/elevated_group_discovery_with_wmic.yml b/detections/endpoint/elevated_group_discovery_with_wmic.yml
index 789668b6ce..5329952dc2 100644
--- a/detections/endpoint/elevated_group_discovery_with_wmic.yml
+++ b/detections/endpoint/elevated_group_discovery_with_wmic.yml
@@ -5,13 +5,36 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `wmic.exe` with command-line arguments querying specific elevated domain groups. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes that access the LDAP namespace and search for groups like "Domain Admins" or "Enterprise Admins." This activity is significant as it indicates potential reconnaissance efforts by adversaries to identify high-privilege accounts within Active Directory. If confirmed malicious, this behavior could lead to privilege escalation, allowing attackers to gain elevated access and control over critical network resources.
+description: The following analytic detects the execution of `wmic.exe` with command-line
+  arguments querying specific elevated domain groups. It leverages Endpoint Detection
+  and Response (EDR) telemetry to identify processes that access the LDAP namespace
+  and search for groups like "Domain Admins" or "Enterprise Admins." This activity
+  is significant as it indicates potential reconnaissance efforts by adversaries to
+  identify high-privilege accounts within Active Directory. If confirmed malicious,
+  this behavior could lead to privilege escalation, allowing attackers to gain elevated
+  access and control over critical network resources.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap*) (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns Admins*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe")
+  (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap*) (Processes.process="*Domain
+  Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema
+  Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server
+  Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns
+  Admins*") by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,22 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/enable_rdp_in_other_port_number.yml b/detections/endpoint/enable_rdp_in_other_port_number.yml
index 07b7b89f49..c08655588e 100644
--- a/detections/endpoint/enable_rdp_in_other_port_number.yml
+++ b/detections/endpoint/enable_rdp_in_other_port_number.yml
@@ -5,16 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the registry that enable RDP on a machine using a non-default port number. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "HKLM\SYSTEM\CurrentControlSet\Control\Terminal Server\WinStations\RDP-Tcp" and the "PortNumber" value. This activity is significant as attackers often modify RDP settings to facilitate lateral movement and maintain remote access to compromised systems. If confirmed malicious, this could allow attackers to bypass network defenses, gain persistent access, and potentially control the compromised machine.
+description: The following analytic detects modifications to the registry that enable
+  RDP on a machine using a non-default port number. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path "HKLM\SYSTEM\CurrentControlSet\Control\Terminal
+  Server\WinStations\RDP-Tcp" and the "PortNumber" value. This activity is significant
+  as attackers often modify RDP settings to facilitate lateral movement and maintain
+  remote access to compromised systems. If confirmed malicious, this could allow attackers
+  to bypass network defenses, gain persistent access, and potentially control the
+  compromised machine.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal
-  Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `enable_rdp_in_other_port_number_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal
+  Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber") BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
+  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `enable_rdp_in_other_port_number_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -28,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml b/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml
index 5ee4942793..b375ad9f09 100644
--- a/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml
+++ b/detections/endpoint/enable_wdigest_uselogoncredential_registry.yml
@@ -5,12 +5,19 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a suspicious registry modification that enables the plain text credential feature in Windows by setting the "UseLogonCredential" value to 1 in the WDigest registry path. This detection leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant because it is commonly used by malware and tools like Mimikatz to dump plain text credentials, indicating a potential credential dumping attempt. If confirmed malicious, this could allow an attacker to obtain sensitive credentials, leading to further compromise and lateral movement within the network.
+description: The following analytic detects a suspicious registry modification that
+  enables the plain text credential feature in Windows by setting the "UseLogonCredential"
+  value to 1 in the WDigest registry path. This detection leverages data from the
+  Endpoint.Registry data model, focusing on specific registry paths and values. This
+  activity is significant because it is commonly used by malware and tools like Mimikatz
+  to dump plain text credentials, indicating a potential credential dumping attempt.
+  If confirmed malicious, this could allow an attacker to obtain sensitive credentials,
+  leading to further compromise and lateral movement within the network.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SecurityProviders\\WDigest\\*"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SecurityProviders\\WDigest\\*"
   Registry.registry_value_name = "UseLogonCredential" Registry.registry_value_data=0x00000001)
   BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
   Registry.registry_value_name Registry.registry_value_data Registry.process_guid
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/wdigest_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/wdigest_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/enumerate_users_local_group_using_telegram.yml b/detections/endpoint/enumerate_users_local_group_using_telegram.yml
index 8fde0c8baf..69242f9064 100644
--- a/detections/endpoint/enumerate_users_local_group_using_telegram.yml
+++ b/detections/endpoint/enumerate_users_local_group_using_telegram.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a Telegram process enumerating all network users in a local group. It leverages EventCode 4798, which is generated when a process enumerates a user's security-enabled local groups on a computer or device. This activity is significant as it may indicate an attempt to gather information on user accounts, a common precursor to further malicious actions. If confirmed malicious, this behavior could allow an attacker to map out user accounts, potentially leading to privilege escalation or lateral movement within the network.
+description: The following analytic detects a Telegram process enumerating all network
+  users in a local group. It leverages EventCode 4798, which is generated when a process
+  enumerates a user's security-enabled local groups on a computer or device. This
+  activity is significant as it may indicate an attempt to gather information on user
+  accounts, a common precursor to further malicious actions. If confirmed malicious,
+  this behavior could allow an attacker to map out user accounts, potentially leading
+  to privilege escalation or lateral movement within the network.
 data_source:
 - Windows Event Log Security 4798
-search: '`wineventlog_security` EventCode=4798  CallerProcessName = "*\\telegram.exe" | stats count min(_time) as firstTime max(_time) as lastTime by user Computer EventCode CallerProcessName  ProcessID SubjectUserSid SubjectDomainName  SubjectLogonId | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enumerate_users_local_group_using_telegram_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment.
+search: '`wineventlog_security` EventCode=4798  CallerProcessName = "*\\telegram.exe"
+  | stats count min(_time) as firstTime max(_time) as lastTime by user Computer EventCode
+  CallerProcessName  ProcessID SubjectUserSid SubjectDomainName  SubjectLogonId |
+  rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `enumerate_users_local_group_using_telegram_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and
+  filter known instances of process like logonUI used in your environment.
 known_false_positives: unknown
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: The Telegram application has been identified enumerating local groups on $dest$ by $user$.
+  message: The Telegram application has been identified enumerating local groups on
+    $dest$ by $user$.
   mitre_attack_id:
   - T1087
   observable:
@@ -45,22 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - dest
-  - EventCode
-  - Process_Name
-  - Process_ID
-  - Account_Name
-  - Account_Domain
-  - Logon_ID
-  - Security_ID
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/enumerate_users_local_group_using_telegram/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/enumerate_users_local_group_using_telegram/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/esentutl_sam_copy.yml b/detections/endpoint/esentutl_sam_copy.yml
index 39ea944bbb..b762894198 100644
--- a/detections/endpoint/esentutl_sam_copy.yml
+++ b/detections/endpoint/esentutl_sam_copy.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of `esentutl.exe` to access credentials stored in the ntds.dit or SAM file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it may indicate an attempt to extract sensitive credential information, which is a common tactic in lateral movement and privilege escalation. If confirmed malicious, this could allow an attacker to gain unauthorized access to user credentials, potentially compromising the entire network.
+description: The following analytic detects the use of `esentutl.exe` to access credentials
+  stored in the ntds.dit or SAM file. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs that include
+  command-line details. This activity is significant because it may indicate an attempt
+  to extract sensitive credential information, which is a common tactic in lateral
+  movement and privilege escalation. If confirmed malicious, this could allow an attacker
+  to gain unauthorized access to user credentials, potentially compromising the entire
+  network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_esentutl` Processes.process IN ("*ntds*", "*SAM*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esentutl_sam_copy_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_esentutl` Processes.process
+  IN ("*ntds*", "*SAM*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `esentutl_sam_copy_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives should be limited. Filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/6a570c2a4630cf0c2bd41a2e8375b5d5ab92f700/atomics/T1003.002/T1003.002.md
@@ -23,7 +43,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user user$ attempting to capture credentials for offline cracking or observability.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user user$ attempting to capture credentials for offline
+    cracking or observability.
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -48,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/etw_registry_disabled.yml b/detections/endpoint/etw_registry_disabled.yml
index e0dd57feae..f27d331204 100644
--- a/detections/endpoint/etw_registry_disabled.yml
+++ b/detections/endpoint/etw_registry_disabled.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a registry modification that disables the Event Tracing for Windows (ETW) feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the ETWEnabled registry value under the .NETFramework path. This activity is significant because disabling ETW can allow attackers to evade Endpoint Detection and Response (EDR) tools and hide their execution from audit logs. If confirmed malicious, this action could enable attackers to operate undetected, potentially leading to further compromise and persistent access within the environment.
+description: The following analytic detects a registry modification that disables
+  the Event Tracing for Windows (ETW) feature. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the ETWEnabled registry value under
+  the .NETFramework path. This activity is significant because disabling ETW can allow
+  attackers to evade Endpoint Detection and Response (EDR) tools and hide their execution
+  from audit logs. If confirmed malicious, this action could enable attackers to operate
+  undetected, potentially leading to further compromise and persistent access within
+  the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\SOFTWARE\\Microsoft\\.NETFramework*" Registry.registry_value_name
-  = ETWEnabled Registry.registry_value_data=0x00000000) BY Registry.dest
-  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `etw_registry_disabled_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\Microsoft\\.NETFramework*"
+  Registry.registry_value_name = ETWEnabled Registry.registry_value_data=0x00000000)
+  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `etw_registry_disabled_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -61,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/etw_disable/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/etw_disable/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/eventvwr_uac_bypass.yml b/detections/endpoint/eventvwr_uac_bypass.yml
index 18b6da977d..e52a302fa9 100644
--- a/detections/endpoint/eventvwr_uac_bypass.yml
+++ b/detections/endpoint/eventvwr_uac_bypass.yml
@@ -5,13 +5,38 @@ date: '2024-09-30'
 author: Steven Dick, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Eventvwr UAC bypass by identifying suspicious registry modifications in the path that Eventvwr.msc references upon execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry changes and process execution details. This activity is significant because it indicates a potential privilege escalation attempt, allowing an attacker to execute arbitrary commands with elevated privileges. If confirmed malicious, this could lead to unauthorized code execution, persistence, and further compromise of the affected system.
+description: The following analytic detects an Eventvwr UAC bypass by identifying
+  suspicious registry modifications in the path that Eventvwr.msc references upon
+  execution. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on registry changes and process execution details. This activity
+  is significant because it indicates a potential privilege escalation attempt, allowing
+  an attacker to execute arbitrary commands with elevated privileges. If confirmed
+  malicious, this could lead to unauthorized code execution, persistence, and further
+  compromise of the affected system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*mscfile\\shell\\open\\command\\*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `eventvwr_uac_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path="*mscfile\\shell\\open\\command\\*") BY _time span=1h
+  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime
+  dest user parent_process_name parent_process process_name process_path process registry_key_name
+  registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `eventvwr_uac_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Some false positives may be present and will need to be filtered.
 references:
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
@@ -24,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: Registry values were modified to bypass UAC using Event Viewer on $dest$ by $user$.
+  message: Registry values were modified to bypass UAC using Event Viewer on $dest$
+    by $user$.
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -54,27 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excel_spawning_powershell.yml b/detections/endpoint/excel_spawning_powershell.yml
index e176914ed8..2d6c3cee7b 100644
--- a/detections/endpoint/excel_spawning_powershell.yml
+++ b/detections/endpoint/excel_spawning_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects Microsoft Excel spawning PowerShell, an uncommon and suspicious behavior. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is "excel.exe" and the child process is PowerShell. This activity is significant because it is often associated with spearphishing attacks, where malicious attachments execute encoded PowerShell commands. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to data exfiltration, privilege escalation, or persistent access within the environment.
+description: The following analytic detects Microsoft Excel spawning PowerShell, an
+  uncommon and suspicious behavior. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events where the parent
+  process is "excel.exe" and the child process is PowerShell. This activity is significant
+  because it is often associated with spearphishing attacks, where malicious attachments
+  execute encoded PowerShell commands. If confirmed malicious, this behavior could
+  allow an attacker to execute arbitrary code, potentially leading to data exfiltration,
+  privilege escalation, or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" `process_powershell` by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.user Processes.dest Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, but if any are present, filter as needed.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.parent_process_name="excel.exe" `process_powershell` by Processes.parent_process
+  Processes.parent_process_name Processes.process_name Processes.user Processes.dest
+  Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  | `excel_spawning_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, but if any are present,
+  filter as needed.
 references:
 - https://redcanary.com/threat-detection-report/techniques/powershell/
 - https://attack.mitre.org/techniques/T1566/001/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$, indicating potential suspicious macro execution.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$, indicating potential suspicious macro execution.
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -56,22 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excel_spawning_windows_script_host.yml b/detections/endpoint/excel_spawning_windows_script_host.yml
index 8042a4f79c..548131d9af 100644
--- a/detections/endpoint/excel_spawning_windows_script_host.yml
+++ b/detections/endpoint/excel_spawning_windows_script_host.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where Microsoft Excel spawns Windows Script Host processes (`cscript.exe` or `wscript.exe`). This behavior is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is `excel.exe`. This activity is significant because it is uncommon and often associated with malicious actions, such as spearphishing attacks. If confirmed malicious, this could allow an attacker to execute scripts, potentially leading to code execution, data exfiltration, or further system compromise. Immediate investigation and mitigation are recommended.
+description: The following analytic identifies instances where Microsoft Excel spawns
+  Windows Script Host processes (`cscript.exe` or `wscript.exe`). This behavior is
+  detected using Endpoint Detection and Response (EDR) telemetry, focusing on process
+  creation events where the parent process is `excel.exe`. This activity is significant
+  because it is uncommon and often associated with malicious actions, such as spearphishing
+  attacks. If confirmed malicious, this could allow an attacker to execute scripts,
+  potentially leading to code execution, data exfiltration, or further system compromise.
+  Immediate investigation and mitigation are recommended.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe")  by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `excel_spawning_windows_script_host_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, but if any are present, filter as needed. In some instances, `cscript.exe` is used for legitimate business practices.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe",
+  "wscript.exe")  by Processes.parent_process Processes.parent_process_name Processes.process_name
+  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  | `excel_spawning_windows_script_host_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, but if any are present,
+  filter as needed. In some instances, `cscript.exe` is used for legitimate business
+  practices.
 references:
 - https://app.any.run/tasks/8ecfbc29-03d0-421c-a5bf-3905d29192a2/
 - https://attack.mitre.org/techniques/T1566/001/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$, indicating potential suspicious macro execution.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$, indicating potential suspicious macro execution.
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -56,18 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - process_id
-  - parent_process_name
-  - dest
-  - user
-  - parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_attempt_to_disable_services.yml b/detections/endpoint/excessive_attempt_to_disable_services.yml
index af6ec17699..4ab20b3853 100644
--- a/detections/endpoint/excessive_attempt_to_disable_services.yml
+++ b/detections/endpoint/excessive_attempt_to_disable_services.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a suspicious series of command-line executions attempting to disable multiple services. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes where "sc.exe" is used with parameters like "config" or "Disabled" within a short time frame. This activity is significant as it may indicate an adversary's attempt to disable security or other critical services to further compromise the system. If confirmed malicious, this could lead to the attacker achieving persistence, evading detection, or disabling security mechanisms, thereby increasing the risk of further exploitation.
+description: The following analytic identifies a suspicious series of command-line
+  executions attempting to disable multiple services. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on processes where "sc.exe" is used
+  with parameters like "config" or "Disabled" within a short time frame. This activity
+  is significant as it may indicate an adversary's attempt to disable security or
+  other critical services to further compromise the system. If confirmed malicious,
+  this could lead to the attacker achieving persistence, evading detection, or disabling
+  security mechanisms, thereby increasing the risk of further exploitation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name =
+  "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name
+  Processes.parent_process_name Processes.dest Processes.user _time span=1m | where
+  count >=4 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_attempt_to_disable_services_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.
+  message: An excessive amount of $process_name$ was executed on $dest$ attempting
+    to disable services.
   mitre_attack_id:
   - T1489
   observable:
@@ -47,18 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml b/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml
index 2a4038a251..fa9d08e352 100644
--- a/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml
+++ b/detections/endpoint/excessive_distinct_processes_from_windows_temp.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Hart, Mauricio Velazco, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies an excessive number of distinct processes executing from the Windows\Temp directory. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and counts within a 20-minute window. This behavior is significant as it often indicates the presence of post-exploit frameworks like Koadic and Meterpreter, which use this technique to execute malicious actions. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and maintain persistence within the environment, posing a severe threat to system integrity and security.
+description: The following analytic identifies an excessive number of distinct processes
+  executing from the Windows\Temp directory. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process paths and counts within a 20-minute
+  window. This behavior is significant as it often indicates the presence of post-exploit
+  frameworks like Koadic and Meterpreter, which use this technique to execute malicious
+  actions. If confirmed malicious, this activity could allow attackers to execute
+  arbitrary code, escalate privileges, and maintain persistence within the environment,
+  posing a severe threat to system integrity and security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user  _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold.  Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  distinct_count(Processes.process) as distinct_process_count  min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path
+  = "*\\Windows\\Temp\\*" by Processes.dest Processes.user  _time span=20m | where
+  distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Many benign applications will create processes from executables
+  in Windows\Temp, although unlikely to exceed the given threshold.  Filter as needed.
 references:
 - https://www.offensive-security.com/metasploit-unleashed/about-meterpreter/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: Multiple processes were executed out of windows\temp within a short amount of time on $dest$.
+  message: Multiple processes were executed out of windows\temp within a short amount
+    of time on $dest$.
   mitre_attack_id:
   - T1059
   observable:
@@ -42,15 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml b/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml
index d8ee8451c2..c08c8c5fe5 100644
--- a/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml
+++ b/detections/endpoint/excessive_file_deletion_in_windefender_folder.yml
@@ -5,13 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects excessive file deletion events in the Windows Defender folder. It leverages Sysmon EventCodes 23 and 26 to identify processes deleting multiple files within this directory. This behavior is significant as it may indicate an attempt to corrupt or disable Windows Defender, a key security component. If confirmed malicious, this activity could allow an attacker to disable endpoint protection, facilitating further malicious actions without detection.
+description: The following analytic detects excessive file deletion events in the
+  Windows Defender folder. It leverages Sysmon EventCodes 23 and 26 to identify processes
+  deleting multiple files within this directory. This behavior is significant as it
+  may indicate an attempt to corrupt or disable Windows Defender, a key security component.
+  If confirmed malicious, this activity could allow an attacker to disable endpoint
+  protection, facilitating further malicious actions without detection.
 data_source:
 - Sysmon EventID 23
 - Sysmon EventID 26
-search: '`sysmon` EventCode IN ("23","26") TargetFilename = "*\\ProgramData\\Microsoft\\Windows Defender\\*" | stats count, values(TargetFilename) as deleted_files, min(_time) as firstTime, max(_time) as lastTime by user, dest, signature, signature_id, Image, process_name, process_guid | rename Image as process | where count >=50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_file_deletion_in_windefender_folder_filter`'
-how_to_implement: To successfully implement this search, you must ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed.
-known_false_positives: Windows Defender AV updates may trigger this alert. Please adjust the filter macros to mitigate false positives.
+search: '`sysmon` EventCode IN ("23","26") TargetFilename = "*\\ProgramData\\Microsoft\\Windows
+  Defender\\*" | stats count, values(TargetFilename) as deleted_files, min(_time)
+  as firstTime, max(_time) as lastTime by user, dest, signature, signature_id, Image,
+  process_name, process_guid | rename Image as process | where count >=50 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_file_deletion_in_windefender_folder_filter`'
+how_to_implement: To successfully implement this search, you must ingest logs that
+  include the process name, TargetFilename, and ProcessID executions from your endpoints.
+  If you are utilizing Sysmon, ensure you have at least version 2.0 of the Sysmon
+  TA installed.
+known_false_positives: Windows Defender AV updates may trigger this alert. Please
+  adjust the filter macros to mitigate false positives.
 references:
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
 drilldown_searches:
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Excessive file deletion events were detected in the Windows Defender folder on $dest$ by $user$. Investigate further to determine if this activity is malicious.
+  message: Excessive file deletion events were detected in the Windows Defender folder
+    on $dest$ by $user$. Investigate further to determine if this activity is malicious.
   mitre_attack_id:
   - T1485
   observable:
@@ -51,21 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - TargetFilename
-  - user
-  - dest
-  - signature
-  - signature_id
-  - Image
-  - process_name
-  - process_path
-  - process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/excessive_file_del_in_windefender_dir/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/excessive_file_del_in_windefender_dir/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml b/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml
index 9cb9f5c2c2..f9ccb76bba 100644
--- a/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml
+++ b/detections/endpoint/excessive_number_of_service_control_start_as_disabled.yml
@@ -5,14 +5,38 @@ date: '2024-09-30'
 author: Michael Hart, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects an excessive number of `sc.exe` processes launched with the command line argument `start= disabled` within a short period. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and process GUIDs. This activity is significant as it may indicate an attempt to disable critical services, potentially impairing system defenses. If confirmed malicious, this behavior could allow an attacker to disrupt security mechanisms, hinder incident response, and maintain control over the compromised system.
+description: The following analytic detects an excessive number of `sc.exe` processes
+  launched with the command line argument `start= disabled` within a short period.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names, command-line executions, and process GUIDs. This activity is significant
+  as it may indicate an attempt to disable critical services, potentially impairing
+  system defenses. If confirmed malicious, this behavior could allow an attacker to
+  disrupt security mechanisms, hinder incident response, and maintain control over
+  the compromised system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_cmdlines values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_id, _time span=30m | where distinct_cmdlines >= 8 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_service_control_start_as_disabled_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate programs and administrators will execute sc.exe with the start disabled flag.  It is possible, but unlikely from the telemetry of normal Windows operation we observed, that sc.exe will be called more than seven times in a short period of time.
+search: '| tstats `security_content_summariesonly` distinct_count(Processes.process)
+  as distinct_cmdlines values(Processes.process_id) as process_ids min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name
+  = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.parent_process_id, _time
+  span=30m | where distinct_cmdlines >= 8 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_number_of_service_control_start_as_disabled_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate programs and administrators will execute sc.exe
+  with the start disabled flag.  It is possible, but unlikely from the telemetry of
+  normal Windows operation we observed, that sc.exe will be called more than seven
+  times in a short period of time.
 references:
 - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/sc-create
 - https://attack.mitre.org/techniques/T1562/001/
@@ -22,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.
+  message: An excessive amount of $process_name$ was executed on $dest$ attempting
+    to disable services.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -48,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/sc_service_start_disabled/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/sc_service_start_disabled/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_number_of_taskhost_processes.yml b/detections/endpoint/excessive_number_of_taskhost_processes.yml
index a514656517..ce91f0b658 100644
--- a/detections/endpoint/excessive_number_of_taskhost_processes.yml
+++ b/detections/endpoint/excessive_number_of_taskhost_processes.yml
@@ -5,14 +5,39 @@ date: '2024-09-30'
 author: Michael Hart
 status: production
 type: Anomaly
-description: The following analytic identifies an excessive number of taskhost.exe and taskhostex.exe processes running within a short time frame. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and their counts. This behavior is significant as it is commonly associated with post-exploitation tools like Meterpreter and Koadic, which use multiple instances of these processes for actions such as discovery and lateral movement. If confirmed malicious, this activity could indicate an ongoing attack, allowing attackers to execute code, escalate privileges, or move laterally within the network.
+description: The following analytic identifies an excessive number of taskhost.exe
+  and taskhostex.exe processes running within a short time frame. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  their counts. This behavior is significant as it is commonly associated with post-exploitation
+  tools like Meterpreter and Koadic, which use multiple instances of these processes
+  for actions such as discovery and lateral movement. If confirmed malicious, this
+  activity could indicate an ongoing attack, allowing attackers to execute code, escalate
+  privileges, or move laterally within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process_id) as process_ids  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" BY Processes.dest Processes.process_name _time span=1h | `drop_dm_object_name(Processes)` | eval pid_count=mvcount(process_ids) | eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) | eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count by _time, dest, firstTime, lastTime | where taskhost_count > 10 or taskhostex_count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_number_of_taskhost_processes_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators, administrative actions or certain applications may run many instances of taskhost and taskhostex concurrently.  Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process_id) as
+  process_ids  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
+  WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe"
+  BY Processes.dest Processes.process_name _time span=1h | `drop_dm_object_name(Processes)`
+  | eval pid_count=mvcount(process_ids) | eval taskhost_count_=if(process_name ==
+  "taskhost.exe", pid_count, 0) | eval taskhostex_count_=if(process_name == "taskhostex.exe",
+  pid_count, 0) | stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_)
+  as taskhostex_count by _time, dest, firstTime, lastTime | where taskhost_count >
+  10 or taskhostex_count > 10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `excessive_number_of_taskhost_processes_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators, administrative actions or certain applications
+  may run many instances of taskhost and taskhostex concurrently.  Filter as needed.
 references:
 - https://attack.mitre.org/software/S0250/
 drilldown_searches:
@@ -21,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: An excessive amount of taskhost.exe and taskhostex.exe was executed on $dest$ indicative of suspicious behavior.
+  message: An excessive amount of taskhost.exe and taskhostex.exe was executed on
+    $dest$ indicative of suspicious behavior.
   mitre_attack_id:
   - T1059
   observable:
@@ -42,16 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/excessive_distinct_processes_from_windows_temp/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_service_stop_attempt.yml b/detections/endpoint/excessive_service_stop_attempt.yml
index ebfb0aa1a6..a007990729 100644
--- a/detections/endpoint/excessive_service_stop_attempt.yml
+++ b/detections/endpoint/excessive_service_stop_attempt.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects multiple attempts to stop or delete services on a system using `net.exe`, `sc.exe`, or `net1.exe`. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line executions within a one-minute window. This activity is significant as it may indicate an adversary attempting to disable security or critical services to evade detection and further their objectives. If confirmed malicious, this could lead to the attacker gaining persistence, escalating privileges, or disrupting essential services, thereby compromising the system's security posture.
+description: The following analytic detects multiple attempts to stop or delete services
+  on a system using `net.exe`, `sc.exe`, or `net1.exe`. It leverages Endpoint Detection
+  and Response (EDR) telemetry, focusing on process names and command-line executions
+  within a one-minute window. This activity is significant as it may indicate an adversary
+  attempting to disable security or critical services to evade detection and further
+  their objectives. If confirmed malicious, this could lead to the attacker gaining
+  persistence, escalating privileges, or disrupting essential services, thereby compromising
+  the system's security posture.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name = "sc.exe" OR  Processes.process_name = "net1.exe" AND Processes.process="*stop*" OR Processes.process="*delete*" by Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.dest Processes.user _time span=1m | where count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_service_stop_attempt_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name
+  = "sc.exe" OR  Processes.process_name = "net1.exe" AND Processes.process="*stop*"
+  OR Processes.process="*delete*" by Processes.process_name Processes.original_file_name
+  Processes.parent_process_name Processes.dest Processes.user _time span=1m | where
+  count >=5 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_service_stop_attempt_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An excessive amount of $process_name$ was executed on $dest$ attempting to disable services.
+  message: An excessive amount of $process_name$ was executed on $dest$ attempting
+    to disable services.
   mitre_attack_id:
   - T1489
   observable:
@@ -48,23 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_usage_of_cacls_app.yml b/detections/endpoint/excessive_usage_of_cacls_app.yml
index d7c4b6a360..97afc26f47 100644
--- a/detections/endpoint/excessive_usage_of_cacls_app.yml
+++ b/detections/endpoint/excessive_usage_of_cacls_app.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe`, or `icacls.exe` to change file or folder permissions. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an adversary attempting to restrict access to malware components or artifacts on a compromised system. If confirmed malicious, this behavior could prevent users from deleting or accessing critical files, aiding in the persistence and concealment of malicious activities.
+description: The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe`,
+  or `icacls.exe` to change file or folder permissions. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as it may indicate an adversary attempting
+  to restrict access to malware components or artifacts on a compromised system. If
+  confirmed malicious, this behavior could prevent users from deleting or accessing
+  critical files, aiding in the persistence and concealment of malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id  values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_cacls_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators or administrative scripts may use this application. Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id  values(Processes.process_name) as process_name
+  count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes
+  where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe"
+  OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process
+  Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_cacls_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators or administrative scripts may use this application.
+  Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An excessive amount of $process_name$ was executed on $dest$ attempting to modify permissions.
+  message: An excessive amount of $process_name$ was executed on $dest$ attempting
+    to modify permissions.
   mitre_attack_id:
   - T1222
   observable:
@@ -49,18 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_usage_of_net_app.yml b/detections/endpoint/excessive_usage_of_net_app.yml
index c1b6c5b89a..53d77ece06 100644
--- a/detections/endpoint/excessive_usage_of_net_app.yml
+++ b/detections/endpoint/excessive_usage_of_net_app.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects excessive usage of `net.exe` or `net1.exe` within a one-minute interval. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This behavior is significant as it may indicate an adversary attempting to create, delete, or disable multiple user accounts rapidly, a tactic observed in Monero mining incidents. If confirmed malicious, this activity could lead to unauthorized user account manipulation, potentially compromising system integrity and enabling further malicious actions.
+description: The following analytic detects excessive usage of `net.exe` or `net1.exe`
+  within a one-minute interval. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names, parent processes, and command-line executions.
+  This behavior is significant as it may indicate an adversary attempting to create,
+  delete, or disable multiple user accounts rapidly, a tactic observed in Monero mining
+  incidents. If confirmed malicious, this activity could lead to unauthorized user
+  account manipulation, potentially compromising system integrity and enabling further
+  malicious actions.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` by Processes.process_name Processes.parent_process_name Processes.original_file_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_net_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where `process_net` by Processes.process_name
+  Processes.parent_process_name Processes.original_file_name Processes.dest Processes.user
+  _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_usage_of_net_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown. Filter as needed. Modify the time span as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 40
-  message: Excessive usage of net1.exe or net.exe within 1m, with command line $process$ has been detected on $dest$ by $user$
+  message: Excessive usage of net1.exe or net.exe within 1m, with command line $process$
+    has been detected on $dest$ by $user$
   mitre_attack_id:
   - T1531
   observable:
@@ -56,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_usage_of_nslookup_app.yml b/detections/endpoint/excessive_usage_of_nslookup_app.yml
index d5ce9b9403..a0ba7e9231 100644
--- a/detections/endpoint/excessive_usage_of_nslookup_app.yml
+++ b/detections/endpoint/excessive_usage_of_nslookup_app.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Stanislav Miskovic, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects excessive usage of the nslookup application, which may indicate potential DNS exfiltration attempts. It leverages Sysmon EventCode 1 to monitor process executions, specifically focusing on nslookup.exe. The detection identifies outliers by comparing the frequency of nslookup executions against a calculated threshold. This activity is significant as it can reveal attempts by malware or APT groups to exfiltrate data via DNS queries. If confirmed malicious, this behavior could allow attackers to stealthily transfer sensitive information out of the network, bypassing traditional data exfiltration defenses.
+description: The following analytic detects excessive usage of the nslookup application,
+  which may indicate potential DNS exfiltration attempts. It leverages Sysmon EventCode
+  1 to monitor process executions, specifically focusing on nslookup.exe. The detection
+  identifies outliers by comparing the frequency of nslookup executions against a
+  calculated threshold. This activity is significant as it can reveal attempts by
+  malware or APT groups to exfiltrate data via DNS queries. If confirmed malicious,
+  this behavior could allow attackers to stealthily transfer sensitive information
+  out of the network, bypassing traditional data exfiltration defenses.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`sysmon` EventCode = 1 process_name = "nslookup.exe" | bucket _time span=1m | stats count as numNsLookup by dest, _time | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by dest | eval upperThreshold=(avgNsLookup + stdNsLookup *3) | eval isOutlier=if(numNsLookup > 20 and numNsLookup >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_nslookup_app_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of nslookup.exe may be used.
+search: '`sysmon` EventCode = 1 process_name = "nslookup.exe" | bucket _time span=1m
+  | stats count as numNsLookup by dest, _time | eventstats avg(numNsLookup) as avgNsLookup,
+  stdev(numNsLookup) as stdNsLookup, count as numSlots by dest | eval upperThreshold=(avgNsLookup
+  + stdNsLookup *3) | eval isOutlier=if(numNsLookup > 20 and numNsLookup >= upperThreshold,
+  1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `excessive_usage_of_nslookup_app_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances of nslookup.exe may be used.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/fin7-spear-phishing-campaign-targets-personnel-involved-sec-filings
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 40
-  message: Excessive usage of nslookup.exe has been detected on $dest$. This detection is triggered as as it violates the dynamic threshold
+  message: Excessive usage of nslookup.exe has been detected on $dest$. This detection
+    is triggered as as it violates the dynamic threshold
   mitre_attack_id:
   - T1048
   observable:
@@ -47,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - process_name
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_usage_of_sc_service_utility.yml b/detections/endpoint/excessive_usage_of_sc_service_utility.yml
index 8bd4c087b9..ce95bba21e 100644
--- a/detections/endpoint/excessive_usage_of_sc_service_utility.yml
+++ b/detections/endpoint/excessive_usage_of_sc_service_utility.yml
@@ -5,14 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects excessive usage of the `sc.exe` service utility on a host machine. It leverages Sysmon EventCode 1 logs to identify instances where `sc.exe` is executed more frequently than normal within a 15-minute window. This behavior is significant as it is commonly associated with ransomware, cryptocurrency miners, and other malware attempting to create, modify, delete, or disable services, potentially related to security applications or for privilege escalation. If confirmed malicious, this activity could allow attackers to manipulate critical services, leading to system compromise or disruption of security defenses.
+description: The following analytic detects excessive usage of the `sc.exe` service
+  utility on a host machine. It leverages Sysmon EventCode 1 logs to identify instances
+  where `sc.exe` is executed more frequently than normal within a 15-minute window.
+  This behavior is significant as it is commonly associated with ransomware, cryptocurrency
+  miners, and other malware attempting to create, modify, delete, or disable services,
+  potentially related to security applications or for privilege escalation. If confirmed
+  malicious, this activity could allow attackers to manipulate critical services,
+  leading to system compromise or disruption of security defenses.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`sysmon` EventCode = 1 process_name = "sc.exe" |  bucket _time span=15m | stats values(process) as process count as numScExe by dest, _time |  eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by dest |  eval upperThreshold=(avgScExe + stdScExe *3) |  eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0) |  search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_sc_service_utility_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used.
-known_false_positives: excessive execution of sc.exe is quite suspicious since it can modify or execute app in high privilege permission.
+search: '`sysmon` EventCode = 1 process_name = "sc.exe" |  bucket _time span=15m |
+  stats values(process) as process count as numScExe by dest, _time |  eventstats
+  avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by dest
+  |  eval upperThreshold=(avgScExe + stdScExe *3) |  eval isOutlier=if(avgScExe >
+  5 and avgScExe >= upperThreshold, 1, 0) |  search isOutlier=1 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_usage_of_sc_service_utility_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed taskkill.exe may be used.
+known_false_positives: excessive execution of sc.exe is quite suspicious since it
+  can modify or execute app in high privilege permission.
 references:
 - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/
 drilldown_searches:
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,15 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - process_name
-  - process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/excessive_usage_of_taskkill.yml b/detections/endpoint/excessive_usage_of_taskkill.yml
index 2e853202e6..863def6989 100644
--- a/detections/endpoint/excessive_usage_of_taskkill.yml
+++ b/detections/endpoint/excessive_usage_of_taskkill.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies excessive usage of `taskkill.exe`, a command-line utility used to terminate processes. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on instances where `taskkill.exe` is executed ten or more times within a one-minute span. This behavior is significant as adversaries often use `taskkill.exe` to disable security tools or other critical processes to evade detection. If confirmed malicious, this activity could allow attackers to bypass security defenses, maintain persistence, and further compromise the system.
+description: The following analytic identifies excessive usage of `taskkill.exe`,
+  a command-line utility used to terminate processes. The detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on instances where `taskkill.exe`
+  is executed ten or more times within a one-minute span. This behavior is significant
+  as adversaries often use `taskkill.exe` to disable security tools or other critical
+  processes to evade detection. If confirmed malicious, this activity could allow
+  attackers to bypass security defenses, maintain persistence, and further compromise
+  the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe"  by Processes.parent_process_name Processes.process_name Processes.dest Processes.user _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_taskkill_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe"  by
+  Processes.parent_process_name Processes.process_name Processes.dest Processes.user
+  _time span=1m | where count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `excessive_usage_of_taskkill_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown. Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 40
-  message: Excessive usage  of taskkill.exe with process id $process_id$ (more than 10 within 1m) has been detected on $dest$ with a parent process of $parent_process_name$.
+  message: Excessive usage  of taskkill.exe with process id $process_id$ (more than
+    10 within 1m) has been detected on $dest$ with a parent process of $parent_process_name$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -53,18 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.process
-  - Processes.process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml b/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml
index a3921cdee3..94ed799221 100644
--- a/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml
+++ b/detections/endpoint/exchange_powershell_abuse_via_ssrf.yml
@@ -5,10 +5,22 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects suspicious behavior indicative of ProxyShell exploitation against on-premise Microsoft Exchange servers. It identifies HTTP POST requests to `autodiscover.json` containing `PowerShell` in the URI, leveraging server-side request forgery (SSRF) to access backend PowerShell. This detection uses Exchange server logs ingested into Splunk. Monitoring this activity is crucial as it may indicate an attacker attempting to execute commands or scripts on the Exchange server. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent control over the Exchange environment.
+description: The following analytic detects suspicious behavior indicative of ProxyShell
+  exploitation against on-premise Microsoft Exchange servers. It identifies HTTP POST
+  requests to `autodiscover.json` containing `PowerShell` in the URI, leveraging server-side
+  request forgery (SSRF) to access backend PowerShell. This detection uses Exchange
+  server logs ingested into Splunk. Monitoring this activity is crucial as it may
+  indicate an attacker attempting to execute commands or scripts on the Exchange server.
+  If confirmed malicious, this could lead to unauthorized access, privilege escalation,
+  or persistent control over the Exchange environment.
 data_source: []
-search: '`exchange` c_uri="*//autodiscover*" cs_uri_query="*PowerShell*" cs_method="POST" | stats count min(_time) as firstTime max(_time) as lastTime by dest, cs_uri_query, cs_method, c_uri | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exchange_powershell_abuse_via_ssrf_filter`'
-how_to_implement: The following analytic requires on-premise Exchange to be logging to Splunk using the TA - https://splunkbase.splunk.com/app/3225. Ensure logs are parsed correctly, or tune the analytic for your environment.
+search: '`exchange` c_uri="*//autodiscover*" cs_uri_query="*PowerShell*" cs_method="POST"
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, cs_uri_query,
+  cs_method, c_uri | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `exchange_powershell_abuse_via_ssrf_filter`'
+how_to_implement: The following analytic requires on-premise Exchange to be logging
+  to Splunk using the TA - https://splunkbase.splunk.com/app/3225. Ensure logs are
+  parsed correctly, or tune the analytic for your environment.
 known_false_positives: Limited false positives, however, tune as needed.
 references:
 - https://github.com/GossiTheDog/ThreatHunting/blob/master/AzureSentinel/Exchange-Powershell-via-SSRF
@@ -22,7 +34,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: Activity related to ProxyShell has been identified on $dest$. Review events and take action accordingly.
+  message: Activity related to ProxyShell has been identified on $dest$. Review events
+    and take action accordingly.
   mitre_attack_id:
   - T1190
   - T1133
@@ -35,10 +48,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - cs_uri_query
-  - cs_method
-  - c_uri
   security_domain: endpoint
diff --git a/detections/endpoint/exchange_powershell_module_usage.yml b/detections/endpoint/exchange_powershell_module_usage.yml
index 36d1a825aa..8537ac6380 100644
--- a/detections/endpoint/exchange_powershell_module_usage.yml
+++ b/detections/endpoint/exchange_powershell_module_usage.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the usage of specific Exchange PowerShell modules, such as New-MailboxExportRequest, New-ManagementRoleAssignment, New-MailboxSearch, and Get-Recipient. It leverages PowerShell Script Block Logging (EventCode 4104) to identify these commands. This activity is significant because these modules can be exploited by adversaries who have gained access via ProxyShell or ProxyNotShell vulnerabilities. If confirmed malicious, attackers could export mailbox contents, assign management roles, conduct mailbox searches, or view recipient objects, potentially leading to data exfiltration, privilege escalation, or unauthorized access to sensitive information.
+description: The following analytic detects the usage of specific Exchange PowerShell
+  modules, such as New-MailboxExportRequest, New-ManagementRoleAssignment, New-MailboxSearch,
+  and Get-Recipient. It leverages PowerShell Script Block Logging (EventCode 4104)
+  to identify these commands. This activity is significant because these modules can
+  be exploited by adversaries who have gained access via ProxyShell or ProxyNotShell
+  vulnerabilities. If confirmed malicious, attackers could export mailbox contents,
+  assign management roles, conduct mailbox searches, or view recipient objects, potentially
+  leading to data exfiltration, privilege escalation, or unauthorized access to sensitive
+  information.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "Search-Mailbox") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exchange_powershell_module_usage_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*New-MailboxExportRequest*",
+  "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "Search-Mailbox")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest |rename UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exchange_powershell_module_usage_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://docs.microsoft.com/en-us/powershell/module/exchange/new-mailboxexportrequest?view=exchange-ps
 - https://docs.microsoft.com/en-us/powershell/module/exchange/new-managementroleassignment?view=exchange-ps
@@ -27,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/executable_file_written_in_administrative_smb_share.yml b/detections/endpoint/executable_file_written_in_administrative_smb_share.yml
index 6e458f7072..26ad6a3c1f 100644
--- a/detections/endpoint/executable_file_written_in_administrative_smb_share.yml
+++ b/detections/endpoint/executable_file_written_in_administrative_smb_share.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). It leverages Windows Security Event Logs with EventCode 5145 to identify this activity. This behavior is significant as it is commonly used by tools like PsExec/PaExec for staging binaries before creating and starting services on remote endpoints, a technique often employed for lateral movement and remote code execution. If confirmed malicious, this activity could allow an attacker to execute arbitrary code remotely, potentially compromising additional systems within the network.
+description: The following analytic detects executable files (.exe or .dll) being
+  written to Windows administrative SMB shares (Admin$, IPC$, C$). It leverages Windows
+  Security Event Logs with EventCode 5145 to identify this activity. This behavior
+  is significant as it is commonly used by tools like PsExec/PaExec for staging binaries
+  before creating and starting services on remote endpoints, a technique often employed
+  for lateral movement and remote code execution. If confirmed malicious, this activity
+  could allow an attacker to execute arbitrary code remotely, potentially compromising
+  additional systems within the network.
 data_source:
 - Windows Event Log Security 5145
-search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.exe","*.dll") ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode ShareName RelativeTargetName ObjectType AccessMask src_user src_port IpAddress | `security_content_ctime(firstTime)`  | `executable_file_written_in_administrative_smb_share_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.
-known_false_positives: System Administrators may use looks like PsExec for troubleshooting or administrations tasks. However, this will typically come only from certain users and certain systems that can be added to an allow list.
+search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.exe","*.dll")
+  ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask=
+  "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode
+  ShareName RelativeTargetName ObjectType AccessMask src_user src_port IpAddress |
+  `security_content_ctime(firstTime)`  | `executable_file_written_in_administrative_smb_share_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also
+  required. Also enable the object Audit access success/failure in your group policy.
+known_false_positives: System Administrators may use looks like PsExec for troubleshooting
+  or administrations tasks. However, this will typically come only from certain users
+  and certain systems that can be added to an allow list.
 references:
 - https://attack.mitre.org/techniques/T1021/002/
 - https://www.rapid7.com/blog/post/2013/03/09/psexec-demystified/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: $src_user$ dropped or created an executable file in known sensitive SMB share.  Share name=$ShareName$, Target name=$RelativeTargetName$, and Access mask=$AccessMask$
+  message: $src_user$ dropped or created an executable file in known sensitive SMB
+    share.  Share name=$ShareName$, Target name=$RelativeTargetName$, and Access mask=$AccessMask$
   mitre_attack_id:
   - T1021
   - T1021.002
@@ -53,20 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Share_Name
-  - Relative_Target_Name
-  - Object_Type
-  - Access_Mask
-  - user
-  - src_port
-  - Source_Address
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/exe_smbshare/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/exe_smbshare/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml b/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml
index 18a09337e3..42fe7316e2 100644
--- a/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml
+++ b/detections/endpoint/executables_or_script_creation_in_suspicious_path.yml
@@ -5,12 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the creation of executables or scripts in suspicious file paths on Windows systems. It leverages the Endpoint.Filesystem data model to detect files with specific extensions (e.g., .exe, .dll, .ps1) created in uncommon directories (e.g., \windows\fonts\, \users\public\). This activity is significant as adversaries often use these paths to evade detection and maintain persistence. If confirmed malicious, this behavior could allow attackers to execute unauthorized code, escalate privileges, or persist within the environment, posing a significant security threat.
+description: The following analytic identifies the creation of executables or scripts
+  in suspicious file paths on Windows systems. It leverages the Endpoint.Filesystem
+  data model to detect files with specific extensions (e.g., .exe, .dll, .ps1) created
+  in uncommon directories (e.g., \windows\fonts\, \users\public\). This activity is
+  significant as adversaries often use these paths to evade detection and maintain
+  persistence. If confirmed malicious, this behavior could allow attackers to execute
+  unauthorized code, escalate privileges, or persist within the environment, posing
+  a significant security threat.
 data_source:
 - Sysmon EventID 11
-search: '|tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name = *.js OR Filesystem.file_name = *.ps1 OR Filesystem.file_name = *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) AND ( Filesystem.file_path = *\\windows\\fonts\\* OR Filesystem.file_path = *\\windows\\temp\\* OR Filesystem.file_path = *\\users\\public\\* OR Filesystem.file_path = *\\windows\\debug\\* OR Filesystem.file_path = *\\Users\\Administrator\\Music\\* OR Filesystem.file_path = *\\Windows\\servicing\\* OR Filesystem.file_path = *\\Users\\Default\\* OR Filesystem.file_path = *Recycle.bin* OR Filesystem.file_path = *\\Windows\\Media\\* OR Filesystem.file_path = *\\Windows\\repair\\* OR Filesystem.file_path = *\\AppData\\Local\\Temp* OR Filesystem.file_path = *\\PerfLogs\\*) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `executables_or_script_creation_in_suspicious_path_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: Administrators may allow creation of script or exe in the paths specified. Filter as needed.
+search: '|tstats `security_content_summariesonly` values(Filesystem.file_path) as
+  file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem
+  where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name
+  = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name
+  = *.vbe OR Filesystem.file_name = *.js OR Filesystem.file_name = *.ps1 OR Filesystem.file_name
+  = *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) AND ( Filesystem.file_path
+  = *\\windows\\fonts\\* OR Filesystem.file_path = *\\windows\\temp\\* OR Filesystem.file_path
+  = *\\users\\public\\* OR Filesystem.file_path = *\\windows\\debug\\* OR Filesystem.file_path
+  = *\\Users\\Administrator\\Music\\* OR Filesystem.file_path = *\\Windows\\servicing\\*
+  OR Filesystem.file_path = *\\Users\\Default\\* OR Filesystem.file_path = *Recycle.bin*
+  OR Filesystem.file_path = *\\Windows\\Media\\* OR Filesystem.file_path = *\\Windows\\repair\\*
+  OR Filesystem.file_path = *\\AppData\\Local\\Temp* OR Filesystem.file_path = *\\PerfLogs\\*)
+  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `executables_or_script_creation_in_suspicious_path_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+known_false_positives: Administrators may allow creation of script or exe in the paths
+  specified. Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -22,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -64,7 +93,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 40
-  message: Suspicious executable or scripts with file name $file_name$, $file_path$ and process_id $process_id$ executed in suspicious file path in Windows by $user$
+  message: Suspicious executable or scripts with file name $file_name$, $file_path$
+    and process_id $process_id$ executed in suspicious file path in Windows by $user$
   mitre_attack_id:
   - T1036
   observable:
@@ -80,17 +110,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml b/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml
index 0760af5d9d..0440e2c2d8 100644
--- a/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml
+++ b/detections/endpoint/execute_javascript_with_jscript_com_clsid.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of JavaScript using the JScript.Encode CLSID (COM Object) by cscript.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and parent processes. This activity is significant as it is a known technique used by ransomware, such as Reddot, to execute malicious scripts and potentially disable AMSI (Antimalware Scan Interface). If confirmed malicious, this behavior could allow attackers to execute arbitrary code, evade detection, and maintain persistence within the environment.
+description: The following analytic detects the execution of JavaScript using the
+  JScript.Encode CLSID (COM Object) by cscript.exe. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, command-line executions,
+  and parent processes. This activity is significant as it is a known technique used
+  by ransomware, such as Reddot, to execute malicious scripts and potentially disable
+  AMSI (Antimalware Scan Interface). If confirmed malicious, this behavior could allow
+  attackers to execute arbitrary code, evade detection, and maintain persistence within
+  the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe" Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.parent_process_name Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `execute_javascript_with_jscript_com_clsid_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe"
+  Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.parent_process_name
+  Processes.process_name Processes.process Processes.parent_process Processes.process_id
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `execute_javascript_with_jscript_com_clsid_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,9 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: Suspicious process of cscript.exe with a parent process $parent_process_name$ where it tries to execute javascript using jscript.encode CLSID (COM OBJ), detected on $dest$ by $user$
+  message: Suspicious process of cscript.exe with a parent process $parent_process_name$
+    where it tries to execute javascript using jscript.encode CLSID (COM OBJ), detected
+    on $dest$ by $user$
   mitre_attack_id:
   - T1059
   - T1059.005
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/execution_of_file_with_multiple_extensions.yml b/detections/endpoint/execution_of_file_with_multiple_extensions.yml
index 8acf2ef0b4..a206f39b69 100644
--- a/detections/endpoint/execution_of_file_with_multiple_extensions.yml
+++ b/detections/endpoint/execution_of_file_with_multiple_extensions.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Rico Valdez, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of files with multiple extensions, such as ".doc.exe" or ".pdf.exe". This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the file name contains double extensions. This activity is significant because attackers often use double extensions to disguise malicious executables as benign documents, increasing the likelihood of user execution. If confirmed malicious, this technique can lead to unauthorized code execution, potentially compromising the endpoint and allowing further malicious activities.
+description: The following analytic detects the execution of files with multiple extensions,
+  such as ".doc.exe" or ".pdf.exe". This behavior is identified using Endpoint Detection
+  and Response (EDR) telemetry, focusing on process creation events where the file
+  name contains double extensions. This activity is significant because attackers
+  often use double extensions to disguise malicious executables as benign documents,
+  increasing the likelihood of user execution. If confirmed malicious, this technique
+  can lead to unauthorized code execution, potentially compromising the endpoint and
+  allowing further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.doc.exe", "*.xls.exe","*.ppt.exe", "*.htm.exe", "*.html.exe", "*.txt.exe", "*.pdf.exe", "*.docx.exe", "*.xlsx.exe", "*.pptx.exe","*.one.exe", "*.bat.exe", "*rtf.exe") by Processes.dest Processes.user Processes.process Processes.process_name Processes.parent_process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `execution_of_file_with_multiple_extensions_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.doc.exe",
+  "*.xls.exe","*.ppt.exe", "*.htm.exe", "*.html.exe", "*.txt.exe", "*.pdf.exe", "*.docx.exe",
+  "*.xlsx.exe", "*.pptx.exe","*.one.exe", "*.bat.exe", "*rtf.exe") by Processes.dest
+  Processes.user Processes.process Processes.process_name Processes.parent_process
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)`
+  | `execution_of_file_with_multiple_extensions_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: None identified.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: process $process$ have double extensions in the file name is executed on $dest$ by $user$
+  message: process $process$ have double extensions in the file name is executed on
+    $dest$ by $user$
   mitre_attack_id:
   - T1036
   - T1036.003
@@ -54,16 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/extraction_of_registry_hives.yml b/detections/endpoint/extraction_of_registry_hives.yml
index 9c685c2c12..2e5acc42a8 100644
--- a/detections/endpoint/extraction_of_registry_hives.yml
+++ b/detections/endpoint/extraction_of_registry_hives.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `reg.exe` to export Windows Registry hives, which may contain sensitive credentials. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving `save` or `export` actions targeting the `sam`, `system`, or `security` hives. This activity is significant as it indicates potential offline credential access attacks, often executed from untrusted processes or scripts. If confirmed malicious, attackers could gain access to credential data, enabling further compromise and lateral movement within the network.
+description: The following analytic detects the use of `reg.exe` to export Windows
+  Registry hives, which may contain sensitive credentials. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions involving `save` or `export` actions targeting the `sam`, `system`, or
+  `security` hives. This activity is significant as it indicates potential offline
+  credential access attacks, often executed from untrusted processes or scripts. If
+  confirmed malicious, attackers could gain access to credential data, enabling further
+  compromise and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` (Processes.process=*save* OR Processes.process=*export*) AND (Processes.process="*\sam *" OR Processes.process="*\system *" OR Processes.process="*\security *") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `extraction_of_registry_hives_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible some agent based products will generate false positives. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_reg` (Processes.process=*save*
+  OR Processes.process=*export*) AND (Processes.process="*\sam *" OR Processes.process="*\system
+  *" OR Processes.process="*\security *") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.parent_process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `extraction_of_registry_hives_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible some agent based products will generate false
+  positives. Filter as needed.
 references:
 - https://www.mandiant.com/resources/shining-a-light-on-darkside-ransomware-operations
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: Suspicious use of `reg.exe` exporting Windows Registry hives containing credentials executed on $dest$ by user $user$, with a parent process of $parent_process_id$
+  message: Suspicious use of `reg.exe` exporting Windows Registry hives containing
+    credentials executed on $dest$ by user $user$, with a parent process of $parent_process_id$
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -57,23 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/file_with_samsam_extension.yml b/detections/endpoint/file_with_samsam_extension.yml
index cfbae33c33..a081a3d95d 100644
--- a/detections/endpoint/file_with_samsam_extension.yml
+++ b/detections/endpoint/file_with_samsam_extension.yml
@@ -5,14 +5,31 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects file writes with extensions indicative of a SamSam ransomware attack. It leverages file-system activity data to identify file names ending in .stubbin, .berkshire, .satoshi, .sophos, or .keyxml. This activity is significant because SamSam ransomware is highly destructive, leading to file encryption and ransom demands. If confirmed malicious, the impact includes significant financial losses, operational disruptions, and reputational damage. Immediate actions should include isolating affected systems, restoring files from backups, and investigating the attack source to prevent further incidents.
+description: The following analytic detects file writes with extensions indicative
+  of a SamSam ransomware attack. It leverages file-system activity data to identify
+  file names ending in .stubbin, .berkshire, .satoshi, .sophos, or .keyxml. This activity
+  is significant because SamSam ransomware is highly destructive, leading to file
+  encryption and ransom demands. If confirmed malicious, the impact includes significant
+  financial losses, operational disruptions, and reputational damage. Immediate actions
+  should include isolating affected systems, restoring files from backups, and investigating
+  the attack source to prevent further incidents.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?<file_extension>\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter`'
-how_to_implement: You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-known_false_positives: Because these extensions are not typically used in normal operations, you should investigate all results.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
+  as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex
+  field=file_name "(?<file_extension>\.[^\.]+)$" | search file_extension=.stubbin
+  OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos
+  OR file_extension=.keyxml | `file_with_samsam_extension_filter`'
+how_to_implement: You must be ingesting data that records file-system activity from
+  your hosts to populate the Endpoint file-system data-model node. If you are using
+  Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you
+  want to collect data.
+known_false_positives: Because these extensions are not typically used in normal operations,
+  you should investigate all results.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 100
-  message: File writes $file_name$ with extensions consistent with a SamSam ransomware attack seen on $dest$
+  message: File writes $file_name$ with extensions consistent with a SamSam ransomware
+    attack seen on $dest$
   observable:
   - name: user
     type: User
@@ -47,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.user
-  - Filesystem.dest
-  - Filesystem.file_path
-  - Filesystem.file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/firewall_allowed_program_enable.yml b/detections/endpoint/firewall_allowed_program_enable.yml
index 94335bb71f..82163a4e69 100644
--- a/detections/endpoint/firewall_allowed_program_enable.yml
+++ b/detections/endpoint/firewall_allowed_program_enable.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the modification of a firewall rule to allow the execution of a specific application. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events with command-line arguments related to firewall rule changes. This activity is significant as it may indicate an attempt to bypass firewall restrictions, potentially allowing unauthorized applications to communicate over the network. If confirmed malicious, this could enable an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the target environment.
+description: The following analytic detects the modification of a firewall rule to
+  allow the execution of a specific application. This detection leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process creation events
+  with command-line arguments related to firewall rule changes. This activity is significant
+  as it may indicate an attempt to bypass firewall restrictions, potentially allowing
+  unauthorized applications to communicate over the network. If confirmed malicious,
+  this could enable an attacker to execute arbitrary code, escalate privileges, or
+  maintain persistence within the target environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*" Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `firewall_allowed_program_enable_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A network operator or systems administrator may utilize an automated or manual execution of this firewall rule that may generate false positives. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*"
+  Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `firewall_allowed_program_enable_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A network operator or systems administrator may utilize an
+  automated or manual execution of this firewall rule that may generate false positives.
+  Filter as needed.
 references:
 - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: firewall allowed program commandline $process$ of $process_name$ on $dest$ by $user$
+  message: firewall allowed program commandline $process$ of $process_name$ on $dest$
+    by $user$
   mitre_attack_id:
   - T1562.004
   - T1562
@@ -47,20 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/first_time_seen_child_process_of_zoom.yml b/detections/endpoint/first_time_seen_child_process_of_zoom.yml
index 697e7cbf6f..106d7ebf91 100644
--- a/detections/endpoint/first_time_seen_child_process_of_zoom.yml
+++ b/detections/endpoint/first_time_seen_child_process_of_zoom.yml
@@ -5,14 +5,39 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies the first-time execution of child processes spawned by Zoom (zoom.exe or zoom.us). It leverages Endpoint Detection and Response (EDR) data, specifically monitoring process creation events and comparing them against previously seen child processes. This activity is significant because the execution of unfamiliar child processes by Zoom could indicate malicious exploitation or misuse of the application. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the endpoint.
+description: The following analytic identifies the first-time execution of child processes
+  spawned by Zoom (zoom.exe or zoom.us). It leverages Endpoint Detection and Response
+  (EDR) data, specifically monitoring process creation events and comparing them against
+  previously seen child processes. This activity is significant because the execution
+  of unfamiliar child processes by Zoom could indicate malicious exploitation or misuse
+  of the application. If confirmed malicious, this could lead to unauthorized code
+  execution, data exfiltration, or further compromise of the endpoint.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name)
+  as parent_process_name values(Processes.parent_process_id) as parent_process_id
+  values(Processes.process_name) as process_name values(Processes.process) as process
+  from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe
+  OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest
+  | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as
+  dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen)
+  OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`")
+  | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name,
+  parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A new child process of zoom isn't malicious by that fact alone.
+  Further investigation of the actions of the child process is needed to verify any
+  malicious behavior is taken.
 references: []
 tags:
   analytic_story:
@@ -20,7 +45,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: Child process $process_name$ with $process_id$ spawned by zoom.exe or zoom.us which has not been previously on host $dest$
+  message: Child process $process_name$ with $process_id$ spawned by zoom.exe or zoom.us
+    which has not been previously on host $dest$
   mitre_attack_id:
   - T1068
   observable:
@@ -40,13 +66,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.process_id
-  - Processes.dest
   security_domain: endpoint
diff --git a/detections/endpoint/first_time_seen_running_windows_service.yml b/detections/endpoint/first_time_seen_running_windows_service.yml
index f74441babe..2412438627 100644
--- a/detections/endpoint/first_time_seen_running_windows_service.yml
+++ b/detections/endpoint/first_time_seen_running_windows_service.yml
@@ -5,12 +5,32 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects the first occurrence of a Windows service running in your environment. It leverages Windows system event logs, specifically EventCode 7036, to identify services entering the "running" state. This activity is significant because the appearance of a new or previously unseen service could indicate the installation of unauthorized or malicious software. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, maintain persistence, or escalate privileges within the environment. Monitoring for new services helps in early detection of potential threats.
+description: The following analytic detects the first occurrence of a Windows service
+  running in your environment. It leverages Windows system event logs, specifically
+  EventCode 7036, to identify services entering the "running" state. This activity
+  is significant because the appearance of a new or previously unseen service could
+  indicate the installation of unauthorized or malicious software. If confirmed malicious,
+  this activity could allow an attacker to execute arbitrary code, maintain persistence,
+  or escalate privileges within the environment. Monitoring for new services helps
+  in early detection of potential threats.
 data_source:
 - Windows Event Log System 7036
-search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" | where state="running" | lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) | table _time dest service | `first_time_seen_running_windows_service_filter`'
-how_to_implement: While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-known_false_positives: A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.
+search: '`wineventlog_system` EventCode=7036 | rex field=Message "The (?<service>[-\(\)\s\w]+)
+  service entered the (?<state>\w+) state" | where state="running" | lookup previously_seen_running_windows_services
+  service as service OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen
+  > relative_time(now(), `previously_seen_windows_services_window`) | table _time
+  dest service | `first_time_seen_running_windows_service_filter`'
+how_to_implement: While this search does not require you to adhere to Splunk CIM,
+  you must be ingesting your Windows system event logs in order for this search to
+  execute successfully. You should run the baseline search `Previously Seen Running
+  Windows Services - Initial` to build the initial table of child processes and hostnames
+  for this search to work. You should also schedule at the same interval as this search
+  the second baseline search `Previously Seen Running Windows Services - Update` to
+  keep this table up to date and to age out old Windows Services. Please update the
+  `previously_seen_windows_services_window` macro to adjust the time window. Please
+  ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
+known_false_positives: A previously unseen service is not necessarily malicious. Verify
+  that the service is legitimate and that was installed by a legitimate process.
 references: []
 tags:
   analytic_story:
@@ -33,9 +53,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - dest
   security_domain: endpoint
diff --git a/detections/endpoint/fodhelper_uac_bypass.yml b/detections/endpoint/fodhelper_uac_bypass.yml
index 5e7442707e..007eab2036 100644
--- a/detections/endpoint/fodhelper_uac_bypass.yml
+++ b/detections/endpoint/fodhelper_uac_bypass.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of fodhelper.exe, which is known to exploit a User Account Control (UAC) bypass by leveraging specific registry keys. The detection method uses Endpoint Detection and Response (EDR) telemetry to identify when fodhelper.exe spawns a child process and accesses the registry keys. This activity is significant because it indicates a potential privilege escalation attempt by an attacker. If confirmed malicious, the attacker could execute commands with elevated privileges, leading to unauthorized system changes and potential full system compromise.
+description: The following analytic detects the execution of fodhelper.exe, which
+  is known to exploit a User Account Control (UAC) bypass by leveraging specific registry
+  keys. The detection method uses Endpoint Detection and Response (EDR) telemetry
+  to identify when fodhelper.exe spawns a child process and accesses the registry
+  keys. This activity is significant because it indicates a potential privilege escalation
+  attempt by an attacker. If confirmed malicious, the attacker could execute commands
+  with elevated privileges, leading to unauthorized system changes and potential full
+  system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `fodhelper_uac_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe
+  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `fodhelper_uac_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Limited to no false positives are expected.
 references:
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: Suspicious registy keys added by process fodhelper.exe with a parent_process of $parent_process_name$ that has been executed on $dest$ by $user$.
+  message: Suspicious registy keys added by process fodhelper.exe with a parent_process
+    of $parent_process_name$ that has been executed on $dest$ by $user$.
   mitre_attack_id:
   - T1112
   - T1548.002
@@ -57,20 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/fsutil_zeroing_file.yml b/detections/endpoint/fsutil_zeroing_file.yml
index 6f7b85d209..9a5c960db8 100644
--- a/detections/endpoint/fsutil_zeroing_file.yml
+++ b/detections/endpoint/fsutil_zeroing_file.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the 'fsutil' command with the 'setzerodata' parameter, which zeros out a target file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is a technique used by ransomware, such as LockBit, to evade detection by erasing its malware path after encrypting the host. If confirmed malicious, this action could hinder forensic investigations and allow attackers to cover their tracks, complicating incident response efforts.
+description: The following analytic detects the execution of the 'fsutil' command
+  with the 'setzerodata' parameter, which zeros out a target file. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names and command-line arguments. This activity is significant because it is a technique
+  used by ransomware, such as LockBit, to evade detection by erasing its malware path
+  after encrypting the host. If confirmed malicious, this action could hinder forensic
+  investigations and allow attackers to cover their tracks, complicating incident
+  response efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe Processes.process="*setzerodata*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  Processes.process Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `fsutil_zeroing_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe
+  Processes.process="*setzerodata*" by Processes.user Processes.process_name Processes.parent_process_name
+  Processes.dest  Processes.process Processes.parent_process | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `fsutil_zeroing_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://app.any.run/tasks/e0ac072d-58c9-4f53-8a3b-3e491c7ac5db/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.process
-  - Processes.parent_process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/fsutil_file_zero/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/fsutil_file_zero/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml
index cb1e41f959..b10587b4e5 100644
--- a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml
+++ b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell.yml
@@ -5,13 +5,34 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` running the `Get-ADDefaultDomainPasswordPolicy` cmdlet, which is used to retrieve the password policy in a Windows domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Monitoring this activity is crucial as it can indicate attempts by adversaries to gather information about domain policies for situational awareness and Active Directory discovery. If confirmed malicious, this activity could lead to further reconnaissance and potential exploitation of domain security settings.
+description: The following analytic detects the execution of `powershell.exe` running
+  the `Get-ADDefaultDomainPasswordPolicy` cmdlet, which is used to retrieve the password
+  policy in a Windows domain. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line executions.
+  Monitoring this activity is crucial as it can indicate attempts by adversaries to
+  gather information about domain policies for situational awareness and Active Directory
+  discovery. If confirmed malicious, this activity could lead to further reconnaissance
+  and potential exploitation of domain security settings.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADDefaultDomainPasswordPolicy*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_addefaultdomainpasswordpolicy_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe"
+  OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADDefaultDomainPasswordPolicy*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `get_addefaultdomainpasswordpolicy_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -43,20 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml
index 5aecc4b0cc..be69dff742 100644
--- a/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml
+++ b/detections/endpoint/get_addefaultdomainpasswordpolicy_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-ADDefaultDomainPasswordPolicy` PowerShell cmdlet, which is used to retrieve the password policy in a Windows domain. This detection leverages PowerShell Script Block Logging (EventCode=4104) to identify the specific command execution. Monitoring this activity is significant as it can indicate an attempt to gather domain policy information, which is often a precursor to further malicious actions. If confirmed malicious, this activity could allow an attacker to understand password policies, aiding in password attacks or further domain enumeration.
+description: The following analytic detects the execution of the `Get-ADDefaultDomainPasswordPolicy`
+  PowerShell cmdlet, which is used to retrieve the password policy in a Windows domain.
+  This detection leverages PowerShell Script Block Logging (EventCode=4104) to identify
+  the specific command execution. Monitoring this activity is significant as it can
+  indicate an attempt to gather domain policy information, which is often a precursor
+  to further malicious actions. If confirmed malicious, this activity could allow
+  an attacker to understand password policies, aiding in password attacks or further
+  domain enumeration.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText ="*Get-ADDefaultDomainPasswordPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user |  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_addefaultdomainpasswordpolicy_with_powershell_script_block_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText ="*Get-ADDefaultDomainPasswordPolicy*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest, UserID as user |  `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `get_addefaultdomainpasswordpolicy_with_powershell_script_block_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -21,7 +33,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: Powershell process having commandline "Get-ADDefaultDomainPasswordPolicy" to query domain password policy on $dest$
+  message: Powershell process having commandline "Get-ADDefaultDomainPasswordPolicy"
+    to query domain password policy on $dest$
   mitre_attack_id:
   - T1201
   observable:
@@ -37,16 +50,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - ComputerName
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_aduser_with_powershell.yml b/detections/endpoint/get_aduser_with_powershell.yml
index 5a32cabecb..34e9e07893 100644
--- a/detections/endpoint/get_aduser_with_powershell.yml
+++ b/detections/endpoint/get_aduser_with_powershell.yml
@@ -5,13 +5,34 @@ date: '2024-10-17'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments used to enumerate domain users via the `Get-ADUser` cmdlet. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt by adversaries to gather information about domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, enabling attackers to identify high-value targets and plan subsequent attacks.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments used to enumerate domain users via the `Get-ADUser` cmdlet.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names and command-line executions. This activity is significant as it may
+  indicate an attempt by adversaries to gather information about domain users for
+  situational awareness and Active Directory discovery. If confirmed malicious, this
+  behavior could lead to further reconnaissance, enabling attackers to identify high-value
+  targets and plan subsequent attacks.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUser*" AND Processes.process = "*-filter*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe"
+  OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUser*"
+  AND Processes.process = "*-filter*" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://www.blackhillsinfosec.com/red-blue-purple/
@@ -45,20 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_aduser_with_powershell_script_block.yml b/detections/endpoint/get_aduser_with_powershell_script_block.yml
index d84e7b8c6b..a6eaefe724 100644
--- a/detections/endpoint/get_aduser_with_powershell_script_block.yml
+++ b/detections/endpoint/get_aduser_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-AdUser` PowerShell cmdlet, which is used to enumerate all domain users. It leverages PowerShell Script Block Logging (EventCode=4104) to identify instances where this command is executed with a filter. This activity is significant as it may indicate an attempt by adversaries or Red Teams to gather information about domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance and potential exploitation of user accounts within the domain.
+description: The following analytic detects the execution of the `Get-AdUser` PowerShell
+  cmdlet, which is used to enumerate all domain users. It leverages PowerShell Script
+  Block Logging (EventCode=4104) to identify instances where this command is executed
+  with a filter. This activity is significant as it may indicate an attempt by adversaries
+  or Red Teams to gather information about domain users for situational awareness
+  and Active Directory discovery. If confirmed malicious, this behavior could lead
+  to further reconnaissance and potential exploitation of user accounts within the
+  domain.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*get-aduser*" ScriptBlockText = "*-filter*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_script_block_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*get-aduser*" ScriptBlockText
+  = "*-filter*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode
+  ScriptBlockText Computer UserID | rename Computer as dest, UserID as user| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `get_aduser_with_powershell_script_block_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://www.blackhillsinfosec.com/red-blue-purple/
@@ -22,7 +34,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Powershell process having commandline "get-aduser" for user enumeration on $dest$
+  message: Powershell process having commandline "get-aduser" for user enumeration
+    on $dest$
   mitre_attack_id:
   - T1087.002
   - T1087
@@ -39,16 +52,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - ComputerName
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/aduser_powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/aduser_powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml
index f835bc10bb..9214443058 100644
--- a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml
+++ b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` running the `Get-ADUserResultantPasswordPolicy` cmdlet, which is used to obtain the password policy in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential enumeration of domain policies, a common tactic for situational awareness and Active Directory discovery by adversaries. If confirmed malicious, this could allow attackers to understand password policies, aiding in further attacks such as password spraying or brute force attempts.
+description: The following analytic detects the execution of `powershell.exe` running
+  the `Get-ADUserResultantPasswordPolicy` cmdlet, which is used to obtain the password
+  policy in a Windows domain. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant as it indicates potential enumeration of domain policies, a common
+  tactic for situational awareness and Active Directory discovery by adversaries.
+  If confirmed malicious, this could allow attackers to understand password policies,
+  aiding in further attacks such as password spraying or brute force attempts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUserResultantPasswordPolicy*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduserresultantpasswordpolicy_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe"
+  OR Processes.process_name="powershell*") AND Processes.process = "*Get-ADUserResultantPasswordPolicy*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `get_aduserresultantpasswordpolicy_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,20 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml
index 509d2000f0..5b7fbc51cf 100644
--- a/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml
+++ b/detections/endpoint/get_aduserresultantpasswordpolicy_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-ADUserResultantPasswordPolicy` PowerShell cmdlet, which is used to obtain the password policy in a Windows domain. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. Monitoring this behavior is significant as it may indicate an attempt to enumerate domain policies, a common tactic used by adversaries for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to understand password policies, aiding in further attacks such as password guessing or policy exploitation.
+description: The following analytic detects the execution of the `Get-ADUserResultantPasswordPolicy`
+  PowerShell cmdlet, which is used to obtain the password policy in a Windows domain.
+  It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity.
+  Monitoring this behavior is significant as it may indicate an attempt to enumerate
+  domain policies, a common tactic used by adversaries for situational awareness and
+  Active Directory discovery. If confirmed malicious, this activity could allow attackers
+  to understand password policies, aiding in further attacks such as password guessing
+  or policy exploitation.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText="*Get-ADUserResultantPasswordPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduserresultantpasswordpolicy_with_powershell_script_block_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText="*Get-ADUserResultantPasswordPolicy*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_aduserresultantpasswordpolicy_with_powershell_script_block_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: powershell process having commandline to query domain user password policy detected on host - $dest$.
+  message: powershell process having commandline to query domain user password policy
+    detected on host - $dest$.
   mitre_attack_id:
   - T1201
   observable:
@@ -47,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_domainpolicy_with_powershell.yml b/detections/endpoint/get_domainpolicy_with_powershell.yml
index 9d5f094728..44cbb5ae5b 100644
--- a/detections/endpoint/get_domainpolicy_with_powershell.yml
+++ b/detections/endpoint/get_domainpolicy_with_powershell.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` running the `Get-DomainPolicy` cmdlet, which is used to retrieve password policies in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gather domain policy information, which is crucial for planning further attacks. If confirmed malicious, this could lead to unauthorized access to sensitive domain configurations, aiding in privilege escalation and lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` running
+  the `Get-DomainPolicy` cmdlet, which is used to retrieve password policies in a
+  Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions. This activity is significant
+  as it indicates potential reconnaissance efforts by adversaries to gather domain
+  policy information, which is crucial for planning further attacks. If confirmed
+  malicious, this could lead to unauthorized access to sensitive domain configurations,
+  aiding in privilege escalation and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainPolicy*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainpolicy_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe"
+  OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainPolicy*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `get_domainpolicy_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,20 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml b/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml
index d52fd5eafd..77c938ca8c 100644
--- a/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml
+++ b/detections/endpoint/get_domainpolicy_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainPolicy` cmdlet using PowerShell Script Block Logging (EventCode=4104). It leverages logs capturing script block text to identify attempts to obtain the password policy in a Windows domain. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to gather domain policy information, which is crucial for planning further attacks. If confirmed malicious, this behavior could lead to detailed knowledge of domain security settings, aiding in privilege escalation or lateral movement within the network.
+description: The following analytic detects the execution of the `Get-DomainPolicy`
+  cmdlet using PowerShell Script Block Logging (EventCode=4104). It leverages logs
+  capturing script block text to identify attempts to obtain the password policy in
+  a Windows domain. This activity is significant as it indicates potential reconnaissance
+  efforts by adversaries or Red Teams to gather domain policy information, which is
+  crucial for planning further attacks. If confirmed malicious, this behavior could
+  lead to detailed knowledge of domain security settings, aiding in privilege escalation
+  or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText ="*Get-DomainPolicy*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainpolicy_with_powershell_script_block_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText ="*Get-DomainPolicy*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `get_domainpolicy_with_powershell_script_block_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -17,11 +29,17 @@ references:
 - https://attack.mitre.org/techniques/T1201/
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$UserID$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" UserID = "$UserID$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" UserID =
+    "$UserID$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$UserID$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 50
-  message: powershell process having commandline $ScriptBlockText$ to query domain policy.
+  message: powershell process having commandline $ScriptBlockText$ to query domain
+    policy.
   mitre_attack_id:
   - T1201
   observable:
@@ -46,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domainpolicy.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domainpolicy.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_domaintrust_with_powershell.yml b/detections/endpoint/get_domaintrust_with_powershell.yml
index d32618bb41..f58829a5c1 100644
--- a/detections/endpoint/get_domaintrust_with_powershell.yml
+++ b/detections/endpoint/get_domaintrust_with_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of the Get-DomainTrust command from PowerView using PowerShell, which is used to gather domain trust information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant as it indicates potential reconnaissance efforts by an adversary to understand domain trust relationships, which can inform lateral movement strategies. If confirmed malicious, this could allow attackers to map out the network, identify potential targets, and plan further attacks, potentially compromising additional systems within the domain.
+description: The following analytic identifies the execution of the Get-DomainTrust
+  command from PowerView using PowerShell, which is used to gather domain trust information.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process and command-line telemetry. This activity is significant as
+  it indicates potential reconnaissance efforts by an adversary to understand domain
+  trust relationships, which can inform lateral movement strategies. If confirmed
+  malicious, this could allow attackers to map out the network, identify potential
+  targets, and plan further attacks, potentially compromising additional systems within
+  the domain.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*get-domaintrust* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives as this requires an active Administrator or adversary to bring in, import, and execute.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process=*get-domaintrust*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives as this requires an active Administrator
+  or adversary to bring in, import, and execute.
 references:
 - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 30
-  message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1482
   observable:
@@ -46,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_domaintrust_with_powershell_script_block.yml b/detections/endpoint/get_domaintrust_with_powershell_script_block.yml
index 65fe1fb863..5e17b3d36e 100644
--- a/detections/endpoint/get_domaintrust_with_powershell_script_block.yml
+++ b/detections/endpoint/get_domaintrust_with_powershell_script_block.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the Get-DomainTrust command from PowerView using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, allowing for detailed inspection. Identifying this activity is significant because it may indicate an attempt to gather domain trust information, which is often a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could enable an attacker to map trust relationships within the domain, potentially leading to further exploitation and compromise of additional systems.
+description: The following analytic detects the execution of the Get-DomainTrust command
+  from PowerView using PowerShell Script Block Logging (EventCode=4104). This method
+  captures the full command sent to PowerShell, allowing for detailed inspection.
+  Identifying this activity is significant because it may indicate an attempt to gather
+  domain trust information, which is often a precursor to lateral movement or privilege
+  escalation. If confirmed malicious, this activity could enable an attacker to map
+  trust relationships within the domain, potentially leading to further exploitation
+  and compromise of additional systems.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*get-domaintrust*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: It is possible certain system management frameworks utilize this command to gather trust information.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*get-domaintrust*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `get_domaintrust_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: It is possible certain system management frameworks utilize
+  this command to gather trust information.
 references:
 - https://blog.harmj0y.net/redteaming/a-guide-to-attacking-domain-trusts/
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 30
-  message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Get-DomainTrust was identified on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1482
   observable:
@@ -48,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaintrust.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaintrust.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_domainuser_with_powershell.yml b/detections/endpoint/get_domainuser_with_powershell.yml
index 33c90a5748..09abe8254d 100644
--- a/detections/endpoint/get_domainuser_with_powershell.yml
+++ b/detections/endpoint/get_domainuser_with_powershell.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments used to enumerate domain users via the `Get-DomainUser` command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams using PowerView for Active Directory discovery. If confirmed malicious, this could allow attackers to gain situational awareness and identify valuable targets within the domain, potentially leading to further exploitation.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments used to enumerate domain users via the `Get-DomainUser` command.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions mapped to the `Processes`
+  node of the `Endpoint` data model. This activity is significant as it indicates
+  potential reconnaissance efforts by adversaries or Red Teams using PowerView for
+  Active Directory discovery. If confirmed malicious, this could allow attackers to
+  gain situational awareness and identify valuable targets within the domain, potentially
+  leading to further exploitation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainUser*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainuser_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe"
+  OR Processes.process_name="powershell*") AND Processes.process = "*Get-DomainUser*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `get_domainuser_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainUser/
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,20 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_domainuser_with_powershell_script_block.yml b/detections/endpoint/get_domainuser_with_powershell_script_block.yml
index a67261c3da..a8a1588a36 100644
--- a/detections/endpoint/get_domainuser_with_powershell_script_block.yml
+++ b/detections/endpoint/get_domainuser_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainUser` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part of PowerView, a tool often used for domain enumeration. The detection leverages PowerShell operational logs to identify instances where this command is executed. Monitoring this activity is crucial as it may indicate an adversary's attempt to gather information about domain users, which is a common step in Active Directory Discovery. If confirmed malicious, this activity could lead to further reconnaissance and potential exploitation of domain resources.
+description: The following analytic detects the execution of the `Get-DomainUser`
+  cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part
+  of PowerView, a tool often used for domain enumeration. The detection leverages
+  PowerShell operational logs to identify instances where this command is executed.
+  Monitoring this activity is crucial as it may indicate an adversary's attempt to
+  gather information about domain users, which is a common step in Active Directory
+  Discovery. If confirmed malicious, this activity could lead to further reconnaissance
+  and potential exploitation of domain resources.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-DomainUser*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_domainuser_with_powershell_script_block_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-DomainUser*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest, UserID as user| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `get_domainuser_with_powershell_script_block_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainUser/
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Powershell process having commandline "*Get-DomainUser*" for user enumeration on $dest$
+  message: Powershell process having commandline "*Get-DomainUser*" for user enumeration
+    on $dest$
   mitre_attack_id:
   - T1087.002
   - T1087
@@ -46,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - ComputerName
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_foresttrust_with_powershell.yml b/detections/endpoint/get_foresttrust_with_powershell.yml
index f00a6300f5..cb66249b46 100644
--- a/detections/endpoint/get_foresttrust_with_powershell.yml
+++ b/detections/endpoint/get_foresttrust_with_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the Get-ForestTrust command via PowerShell, commonly used by adversaries to gather domain trust information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Identifying this activity is crucial as it indicates potential reconnaissance efforts to map out domain trusts, which can inform further attacks. If confirmed malicious, this activity could allow attackers to understand domain relationships, aiding in lateral movement and privilege escalation within the network.
+description: The following analytic detects the execution of the Get-ForestTrust command
+  via PowerShell, commonly used by adversaries to gather domain trust information.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions. Identifying this activity
+  is crucial as it indicates potential reconnaissance efforts to map out domain trusts,
+  which can inform further attacks. If confirmed malicious, this activity could allow
+  attackers to understand domain relationships, aiding in lateral movement and privilege
+  escalation within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe Processes.process=*get-foresttrust* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives as this requires an active Administrator or adversary to bring in, import, and execute.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe
+  OR Processes.process_name=cmd.exe Processes.process=*get-foresttrust* by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives as this requires an active Administrator
+  or adversary to bring in, import, and execute.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestTrust/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 30
-  message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1482
   observable:
@@ -46,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_foresttrust_with_powershell_script_block.yml b/detections/endpoint/get_foresttrust_with_powershell_script_block.yml
index 78f28a2dd5..ff7dfd115b 100644
--- a/detections/endpoint/get_foresttrust_with_powershell_script_block.yml
+++ b/detections/endpoint/get_foresttrust_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the Get-ForestTrust command from PowerSploit using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, providing detailed visibility into potentially suspicious activities. Monitoring this behavior is crucial as it can indicate an attempt to gather domain trust information, which is often a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to map trust relationships within the domain, facilitating further exploitation and access to sensitive resources.
+description: The following analytic detects the execution of the Get-ForestTrust command
+  from PowerSploit using PowerShell Script Block Logging (EventCode=4104). This method
+  captures the full command sent to PowerShell, providing detailed visibility into
+  potentially suspicious activities. Monitoring this behavior is crucial as it can
+  indicate an attempt to gather domain trust information, which is often a precursor
+  to lateral movement or privilege escalation. If confirmed malicious, this activity
+  could allow an attacker to map trust relationships within the domain, facilitating
+  further exploitation and access to sensitive resources.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*get-foresttrust*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*get-foresttrust*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `get_foresttrust_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives may be present. Tune as needed.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestTrust/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 30
-  message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Get-ForestTrust was identified on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1482
   - T1059.001
@@ -46,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Path
-  - Opcode
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_wmiobject_group_discovery.yml b/detections/endpoint/get_wmiobject_group_discovery.yml
index cf16d99be8..0e34cd84a3 100644
--- a/detections/endpoint/get_wmiobject_group_discovery.yml
+++ b/detections/endpoint/get_wmiobject_group_discovery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of the `Get-WMIObject Win32_Group` command executed via PowerShell to enumerate local groups on an endpoint. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Identifying local groups can be a precursor to privilege escalation or lateral movement. If confirmed malicious, this activity could allow an attacker to map out group memberships, aiding in further exploitation or unauthorized access to sensitive resources.
+description: The following analytic detects the use of the `Get-WMIObject Win32_Group`
+  command executed via PowerShell to enumerate local groups on an endpoint. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names and command-line executions. Identifying local groups can be a precursor to
+  privilege escalation or lateral movement. If confirmed malicious, this activity
+  could allow an attacker to map out group memberships, aiding in further exploitation
+  or unauthorized access to sensitive resources.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe OR processes.process_name=cmd.exe) (Processes.process="*Get-WMIObject*" AND Processes.process="*Win32_Group*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`  | `get_wmiobject_group_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe
+  OR processes.process_name=cmd.exe) (Processes.process="*Get-WMIObject*" AND Processes.process="*Win32_Group*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`  |
+  `get_wmiobject_group_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present. Tune as needed.
 references:
 - https://attack.mitre.org/techniques/T1069/001/
@@ -39,23 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml b/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml
index e66ab0b2fb..94aee4d428 100644
--- a/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml
+++ b/detections/endpoint/get_wmiobject_group_discovery_with_script_block_logging.yml
@@ -5,11 +5,22 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-WMIObject Win32_Group` command using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, allowing for detailed analysis. Identifying group information on an endpoint is not inherently malicious but can be suspicious based on context such as time, endpoint, and user. This activity is significant as it may indicate reconnaissance efforts by an attacker. If confirmed malicious, it could lead to further enumeration and potential lateral movement within the network.
+description: The following analytic detects the execution of the `Get-WMIObject Win32_Group`
+  command using PowerShell Script Block Logging (EventCode=4104). This method captures
+  the full command sent to PowerShell, allowing for detailed analysis. Identifying
+  group information on an endpoint is not inherently malicious but can be suspicious
+  based on context such as time, endpoint, and user. This activity is significant
+  as it may indicate reconnaissance efforts by an attacker. If confirmed malicious,
+  it could lead to further enumeration and potential lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-WMIObject*" AND ScriptBlockText = "*Win32_Group*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_wmiobject_group_discovery_with_script_block_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-WMIObject*" AND ScriptBlockText
+  = "*Win32_Group*" | stats count min(_time) as firstTime max(_time) as lastTime by
+  EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `get_wmiobject_group_discovery_with_script_block_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives may be present. Tune as needed.
 references:
 - https://www.splunk.com/en_us/blog/security/powershell-detections-threat-research-release-august-2021.html
@@ -41,16 +52,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - ComputerName
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getadcomputer_with_powershell.yml b/detections/endpoint/getadcomputer_with_powershell.yml
index 821f7a82ed..df40d07ff1 100644
--- a/detections/endpoint/getadcomputer_with_powershell.yml
+++ b/detections/endpoint/getadcomputer_with_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with the `Get-AdComputer` commandlet, which is used to discover remote systems within a domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it indicates potential reconnaissance efforts by adversaries to map out domain computers, which is a common step in the attack lifecycle. If confirmed malicious, this behavior could allow attackers to gain situational awareness and plan further attacks, potentially leading to unauthorized access and data exfiltration.
+description: The following analytic detects the execution of `powershell.exe` with
+  the `Get-AdComputer` commandlet, which is used to discover remote systems within
+  a domain. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line arguments. This activity is significant
+  because it indicates potential reconnaissance efforts by adversaries to map out
+  domain computers, which is a common step in the attack lifecycle. If confirmed malicious,
+  this behavior could allow attackers to gain situational awareness and plan further
+  attacks, potentially leading to unauthorized access and data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-AdComputer*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadcomputer_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-AdComputer*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getadcomputer_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -33,22 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getadcomputer_with_powershell_script_block.yml b/detections/endpoint/getadcomputer_with_powershell_script_block.yml
index b67c0bd681..f4473853ee 100644
--- a/detections/endpoint/getadcomputer_with_powershell_script_block.yml
+++ b/detections/endpoint/getadcomputer_with_powershell_script_block.yml
@@ -5,12 +5,24 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-AdComputer` PowerShell commandlet using PowerShell Script Block Logging (EventCode=4104). This detection leverages script block text to identify when this commandlet is run. The `Get-AdComputer` commandlet is significant as it can be used by adversaries to enumerate all domain computers, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to map the network, identify targets, and plan further attacks, potentially leading to unauthorized access and data exfiltration.
+description: The following analytic detects the execution of the `Get-AdComputer`
+  PowerShell commandlet using PowerShell Script Block Logging (EventCode=4104). This
+  detection leverages script block text to identify when this commandlet is run. The
+  `Get-AdComputer` commandlet is significant as it can be used by adversaries to enumerate
+  all domain computers, aiding in situational awareness and Active Directory discovery.
+  If confirmed malicious, this activity could allow attackers to map the network,
+  identify targets, and plan further attacks, potentially leading to unauthorized
+  access and data exfiltration.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-AdComputer*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getadcomputer_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-AdComputer*") | stats
+  count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `getadcomputer_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
 - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps
@@ -34,17 +46,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getadgroup_with_powershell.yml b/detections/endpoint/getadgroup_with_powershell.yml
index 93142a8fd9..63980e40e3 100644
--- a/detections/endpoint/getadgroup_with_powershell.yml
+++ b/detections/endpoint/getadgroup_with_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with the `Get-AdGroup` commandlet, which is used to query domain groups in a Windows Domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it may indicate an adversary or Red Team enumerating domain groups for situational awareness and Active Directory discovery. If confirmed malicious, this activity could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  the `Get-AdGroup` commandlet, which is used to query domain groups in a Windows
+  Domain. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line arguments. Monitoring this activity
+  is crucial as it may indicate an adversary or Red Team enumerating domain groups
+  for situational awareness and Active Directory discovery. If confirmed malicious,
+  this activity could lead to further reconnaissance, privilege escalation, or lateral
+  movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-AdGroup*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-AdGroup*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getadgroup_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -35,22 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getadgroup_with_powershell_script_block.yml b/detections/endpoint/getadgroup_with_powershell_script_block.yml
index d4969f1688..8264f44b95 100644
--- a/detections/endpoint/getadgroup_with_powershell_script_block.yml
+++ b/detections/endpoint/getadgroup_with_powershell_script_block.yml
@@ -5,12 +5,24 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-AdGroup` PowerShell cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is used to enumerate all domain groups, which adversaries may exploit for situational awareness and Active Directory discovery. Monitoring this activity is crucial as it can indicate reconnaissance efforts within the network. If confirmed malicious, this behavior could lead to further exploitation, such as privilege escalation or lateral movement, by providing attackers with detailed information about the domain's group structure.
+description: The following analytic detects the execution of the `Get-AdGroup` PowerShell
+  cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is used
+  to enumerate all domain groups, which adversaries may exploit for situational awareness
+  and Active Directory discovery. Monitoring this activity is crucial as it can indicate
+  reconnaissance efforts within the network. If confirmed malicious, this behavior
+  could lead to further exploitation, such as privilege escalation or lateral movement,
+  by providing attackers with detailed information about the domain's group structure.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-ADGroup*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-ADGroup*" | stats count
+  min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer
+  UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `getadgroup_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
 - https://docs.microsoft.com/en-us/powershell/module/activedirectory/get-adgroup?view=windowsserver2019-ps
@@ -33,16 +45,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getcurrent_user_with_powershell.yml b/detections/endpoint/getcurrent_user_with_powershell.yml
index 085bb563ae..4c67ebf677 100644
--- a/detections/endpoint/getcurrent_user_with_powershell.yml
+++ b/detections/endpoint/getcurrent_user_with_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments invoking the `GetCurrent` method of the WindowsIdentity .NET class. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use this method to identify the logged-in user on a compromised endpoint, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this could allow attackers to gain insights into user context, potentially facilitating further exploitation and lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments invoking the `GetCurrent` method of the WindowsIdentity .NET
+  class. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line executions. This activity is
+  significant as adversaries may use this method to identify the logged-in user on
+  a compromised endpoint, aiding in situational awareness and Active Directory discovery.
+  If confirmed malicious, this could allow attackers to gain insights into user context,
+  potentially facilitating further exploitation and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*System.Security.Principal.WindowsIdentity* OR Processes.process=*GetCurrent()*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getcurrent_user_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*System.Security.Principal.WindowsIdentity* OR Processes.process=*GetCurrent()*)
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getcurrent_user_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1033/
@@ -33,23 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getcurrent_user_with_powershell_script_block.yml b/detections/endpoint/getcurrent_user_with_powershell_script_block.yml
index 4dfaa09866..cbe7342610 100644
--- a/detections/endpoint/getcurrent_user_with_powershell_script_block.yml
+++ b/detections/endpoint/getcurrent_user_with_powershell_script_block.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `GetCurrent` method from the WindowsIdentity .NET class using PowerShell Script Block Logging (EventCode=4104). This method identifies the current Windows user. The detection leverages PowerShell script block logs to identify when this method is called. This activity is significant because adversaries and Red Teams may use it to gain situational awareness and perform Active Directory discovery on compromised endpoints. If confirmed malicious, this could allow attackers to map out user accounts and potentially escalate privileges or move laterally within the network.
+description: The following analytic detects the execution of the `GetCurrent` method
+  from the WindowsIdentity .NET class using PowerShell Script Block Logging (EventCode=4104).
+  This method identifies the current Windows user. The detection leverages PowerShell
+  script block logs to identify when this method is called. This activity is significant
+  because adversaries and Red Teams may use it to gain situational awareness and perform
+  Active Directory discovery on compromised endpoints. If confirmed malicious, this
+  could allow attackers to map out user accounts and potentially escalate privileges
+  or move laterally within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*[System.Security.Principal.WindowsIdentity]*"  ScriptBlockText = "*GetCurrent()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getcurrent_user_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*[System.Security.Principal.WindowsIdentity]*"  ScriptBlockText
+  = "*GetCurrent()*" | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as
+  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getcurrent_user_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1033/
 - https://docs.microsoft.com/en-us/dotnet/api/system.security.principal.windowsidentity.getcurrent?view=net-6.0&viewFallbackFrom=net-5.0
@@ -32,18 +46,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Path
-  - Message
-  - OpCode
-  - ComputerName
-  - User
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getdomaincomputer_with_powershell.yml b/detections/endpoint/getdomaincomputer_with_powershell.yml
index ca2c205684..428ddb88f7 100644
--- a/detections/endpoint/getdomaincomputer_with_powershell.yml
+++ b/detections/endpoint/getdomaincomputer_with_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments that utilize `Get-DomainComputer` to discover remote systems. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as `Get-DomainComputer` is part of PowerView, a tool often used by adversaries for domain enumeration and situational awareness. If confirmed malicious, this activity could allow attackers to map out the network, identify critical systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments that utilize `Get-DomainComputer` to discover remote systems.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions. This activity is significant
+  as `Get-DomainComputer` is part of PowerView, a tool often used by adversaries for
+  domain enumeration and situational awareness. If confirmed malicious, this activity
+  could allow attackers to map out the network, identify critical systems, and plan
+  further attacks, potentially leading to unauthorized access and data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-DomainComputer*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaincomputer_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-DomainComputer*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getdomaincomputer_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use PowerView for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,22 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml b/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml
index 36d47f53f0..7b7757eb44 100644
--- a/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml
+++ b/detections/endpoint/getdomaincomputer_with_powershell_script_block.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainComputer` commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet is part of PowerView, a tool often used for enumerating domain computers within Windows environments. The detection leverages script block text analysis to identify this specific command. Monitoring this activity is crucial as it can indicate an adversary's attempt to gather information about domain computers, which is a common step in Active Directory reconnaissance. If confirmed malicious, this activity could lead to further network enumeration and potential lateral movement within the domain.
+description: The following analytic detects the execution of the `Get-DomainComputer`
+  commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet
+  is part of PowerView, a tool often used for enumerating domain computers within
+  Windows environments. The detection leverages script block text analysis to identify
+  this specific command. Monitoring this activity is crucial as it can indicate an
+  adversary's attempt to gather information about domain computers, which is a common
+  step in Active Directory reconnaissance. If confirmed malicious, this activity could
+  lead to further network enumeration and potential lateral movement within the domain.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `getdomaincomputer_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*") |
+  stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID
+  EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `getdomaincomputer_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Administrators or power users may use PowerView for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,17 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getdomaincontroller_with_powershell.yml b/detections/endpoint/getdomaincontroller_with_powershell.yml
index 44a57f4342..2951493f1b 100644
--- a/detections/endpoint/getdomaincontroller_with_powershell.yml
+++ b/detections/endpoint/getdomaincontroller_with_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with the `Get-DomainController` command, which is used to discover remote systems within a Windows domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is crucial as it may indicate an attempt to enumerate domain controllers, a common tactic in Active Directory discovery. If confirmed malicious, this activity could allow attackers to gain situational awareness, potentially leading to further exploitation and lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  the `Get-DomainController` command, which is used to discover remote systems within
+  a Windows domain. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line arguments. Monitoring this
+  activity is crucial as it may indicate an attempt to enumerate domain controllers,
+  a common tactic in Active Directory discovery. If confirmed malicious, this activity
+  could allow attackers to gain situational awareness, potentially leading to further
+  exploitation and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-DomainController*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaincontroller_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-DomainController*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getdomaincontroller_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use PowerView for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -34,22 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml b/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml
index 117d22c040..91367922f1 100644
--- a/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml
+++ b/detections/endpoint/getdomaincontroller_with_powershell_script_block.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainController` commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet is part of PowerView, a tool often used for domain enumeration. The detection leverages script block text to identify this specific activity. Monitoring this behavior is crucial as it may indicate an adversary or Red Team performing reconnaissance to map out domain controllers. If confirmed malicious, this activity could lead to further domain enumeration, potentially exposing sensitive information and aiding in lateral movement within the network.
+description: The following analytic detects the execution of the `Get-DomainController`
+  commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet
+  is part of PowerView, a tool often used for domain enumeration. The detection leverages
+  script block text to identify this specific activity. Monitoring this behavior is
+  crucial as it may indicate an adversary or Red Team performing reconnaissance to
+  map out domain controllers. If confirmed malicious, this activity could lead to
+  further domain enumeration, potentially exposing sensitive information and aiding
+  in lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainController*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaincontroller_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainController*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaincontroller_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainController/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,17 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdc.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getdc.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getdomaingroup_with_powershell.yml b/detections/endpoint/getdomaingroup_with_powershell.yml
index 04ee575c9f..e099a3faa7 100644
--- a/detections/endpoint/getdomaingroup_with_powershell.yml
+++ b/detections/endpoint/getdomaingroup_with_powershell.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments that query for domain groups using `Get-DomainGroup`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. Monitoring this activity is crucial as `Get-DomainGroup` is part of PowerView, a tool often used by adversaries for domain enumeration and situational awareness. If confirmed malicious, this activity could allow attackers to gain insights into domain group structures, aiding in further exploitation and privilege escalation.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments that query for domain groups using `Get-DomainGroup`. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line executions mapped to the `Processes` node of the
+  `Endpoint` data model. Monitoring this activity is crucial as `Get-DomainGroup`
+  is part of PowerView, a tool often used by adversaries for domain enumeration and
+  situational awareness. If confirmed malicious, this activity could allow attackers
+  to gain insights into domain group structures, aiding in further exploitation and
+  privilege escalation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-DomainGroup*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getdomaingroup_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-DomainGroup*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getdomaingroup_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,22 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getdomaingroup_with_powershell_script_block.yml b/detections/endpoint/getdomaingroup_with_powershell_script_block.yml
index 4d4207e0bb..1596e290cb 100644
--- a/detections/endpoint/getdomaingroup_with_powershell_script_block.yml
+++ b/detections/endpoint/getdomaingroup_with_powershell_script_block.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainGroup` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet, part of the PowerView tool, is used to enumerate domain groups within a Windows domain. The detection leverages script block text to identify this specific command. Monitoring this activity is crucial as it may indicate an adversary or Red Team performing reconnaissance to gain situational awareness and map out Active Directory structures. If confirmed malicious, this activity could lead to further exploitation, including privilege escalation and lateral movement within the network.
+description: The following analytic detects the execution of the `Get-DomainGroup`
+  cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet, part
+  of the PowerView tool, is used to enumerate domain groups within a Windows domain.
+  The detection leverages script block text to identify this specific command. Monitoring
+  this activity is crucial as it may indicate an adversary or Red Team performing
+  reconnaissance to gain situational awareness and map out Active Directory structures.
+  If confirmed malicious, this activity could lead to further exploitation, including
+  privilege escalation and lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroup*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaingroup_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerView functions for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroup*") | stats
+  count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `getdomaingroup_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerView functions
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainGroup/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaingroup.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/domaingroup.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getlocaluser_with_powershell.yml b/detections/endpoint/getlocaluser_with_powershell.yml
index be90ac677a..be366bce9a 100644
--- a/detections/endpoint/getlocaluser_with_powershell.yml
+++ b/detections/endpoint/getlocaluser_with_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with the `Get-LocalUser` commandlet, which is used to query local user accounts. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is significant because adversaries and Red Teams may use it to enumerate local users for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to identify potential targets for further exploitation or privilege escalation within the environment.
+description: The following analytic detects the execution of `powershell.exe` with
+  the `Get-LocalUser` commandlet, which is used to query local user accounts. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments. Monitoring this activity is significant
+  because adversaries and Red Teams may use it to enumerate local users for situational
+  awareness and Active Directory discovery. If confirmed malicious, this activity
+  could allow attackers to identify potential targets for further exploitation or
+  privilege escalation within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-LocalUser*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getlocaluser_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-LocalUser*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getlocaluser_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/001/
 tags:
@@ -34,12 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getlocaluser_with_powershell_script_block.yml b/detections/endpoint/getlocaluser_with_powershell_script_block.yml
index 97dd53b765..829a5a0056 100644
--- a/detections/endpoint/getlocaluser_with_powershell_script_block.yml
+++ b/detections/endpoint/getlocaluser_with_powershell_script_block.yml
@@ -5,12 +5,25 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-LocalUser` PowerShell commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet lists all local users on a system. The detection leverages script block text from PowerShell logs to identify this activity. Monitoring this behavior is significant as adversaries and Red Teams may use it to enumerate local users for situational awareness and Active Directory discovery. If confirmed malicious, this activity could lead to further reconnaissance, enabling attackers to identify potential targets for privilege escalation or lateral movement.
+description: The following analytic detects the execution of the `Get-LocalUser` PowerShell
+  commandlet using PowerShell Script Block Logging (EventCode=4104). This commandlet
+  lists all local users on a system. The detection leverages script block text from
+  PowerShell logs to identify this activity. Monitoring this behavior is significant
+  as adversaries and Red Teams may use it to enumerate local users for situational
+  awareness and Active Directory discovery. If confirmed malicious, this activity
+  could lead to further reconnaissance, enabling attackers to identify potential targets
+  for privilege escalation or lateral movement.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-LocalUser*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getlocaluser_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-LocalUser*") | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getlocaluser_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/001/
 - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html
@@ -35,16 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getnettcpconnection_with_powershell.yml b/detections/endpoint/getnettcpconnection_with_powershell.yml
index edde0e813e..e5fbed30b9 100644
--- a/detections/endpoint/getnettcpconnection_with_powershell.yml
+++ b/detections/endpoint/getnettcpconnection_with_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `powershell.exe` with the `Get-NetTcpConnection` command, which lists current TCP connections on a system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Monitoring this activity is significant as it may indicate an adversary or Red Team performing network reconnaissance or situational awareness. If confirmed malicious, this activity could allow attackers to map network connections, aiding in lateral movement or further exploitation within the network.
+description: The following analytic identifies the execution of `powershell.exe` with
+  the `Get-NetTcpConnection` command, which lists current TCP connections on a system.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions. Monitoring this activity
+  is significant as it may indicate an adversary or Red Team performing network reconnaissance
+  or situational awareness. If confirmed malicious, this activity could allow attackers
+  to map network connections, aiding in lateral movement or further exploitation within
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-NetTcpConnection*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getnettcpconnection_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-NetTcpConnection*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getnettcpconnection_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1049/
@@ -34,22 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml b/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml
index ddcaa0b775..9969bb8ee1 100644
--- a/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml
+++ b/detections/endpoint/getnettcpconnection_with_powershell_script_block.yml
@@ -5,12 +5,24 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-NetTcpconnection` PowerShell cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet lists network connections on a system, which adversaries may use for situational awareness and Active Directory discovery. Monitoring this activity is crucial as it can indicate reconnaissance efforts by an attacker. If confirmed malicious, this behavior could allow an attacker to map the network, identify critical systems, and plan further attacks, potentially leading to data exfiltration or lateral movement within the network.
+description: The following analytic detects the execution of the `Get-NetTcpconnection`
+  PowerShell cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet
+  lists network connections on a system, which adversaries may use for situational
+  awareness and Active Directory discovery. Monitoring this activity is crucial as
+  it can indicate reconnaissance efforts by an attacker. If confirmed malicious, this
+  behavior could allow an attacker to map the network, identify critical systems,
+  and plan further attacks, potentially leading to data exfiltration or lateral movement
+  within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-NetTcpconnection*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getnettcpconnection_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-NetTcpconnection*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getnettcpconnection_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1049/
 - https://docs.microsoft.com/en-us/powershell/module/nettcpip/get-nettcpconnection?view=windowsserver2019-ps
@@ -32,17 +44,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/nettcpconnection.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/nettcpconnection.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml b/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml
index 527f9cd358..ccb44da9b6 100644
--- a/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml
+++ b/detections/endpoint/getwmiobject_ds_computer_with_powershell.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments that utilize the `Get-WmiObject` cmdlet to discover remote systems, specifically targeting the `DS_Computer` parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain computers and gather situational awareness within Active Directory. If confirmed malicious, this behavior could allow attackers to map the network, identify critical systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments that utilize the `Get-WmiObject` cmdlet to discover remote
+  systems, specifically targeting the `DS_Computer` parameter. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant as it indicates potential
+  reconnaissance efforts by adversaries to enumerate domain computers and gather situational
+  awareness within Active Directory. If confirmed malicious, this behavior could allow
+  attackers to map the network, identify critical systems, and plan further attacks,
+  potentially leading to unauthorized access and data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" AND Processes.process="*class ds_computer*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_computer_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*"
+  AND Processes.process="*class ds_computer*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getwmiobject_ds_computer_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,22 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml
index 4334db23f1..f2294185ea 100644
--- a/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml
+++ b/detections/endpoint/getwmiobject_ds_computer_with_powershell_script_block.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-WmiObject` cmdlet with the `DS_Computer` class parameter via PowerShell Script Block Logging (EventCode=4104). This detection leverages script block text to identify queries targeting domain computers using WMI. Monitoring this activity is crucial as adversaries and Red Teams may use it for Active Directory Discovery and situational awareness. If confirmed malicious, this behavior could allow attackers to map out domain computers, facilitating further attacks such as lateral movement or privilege escalation.
+description: The following analytic detects the execution of the `Get-WmiObject` cmdlet
+  with the `DS_Computer` class parameter via PowerShell Script Block Logging (EventCode=4104).
+  This detection leverages script block text to identify queries targeting domain
+  computers using WMI. Monitoring this activity is crucial as adversaries and Red
+  Teams may use it for Active Directory Discovery and situational awareness. If confirmed
+  malicious, this behavior could allow attackers to map out domain computers, facilitating
+  further attacks such as lateral movement or privilege escalation.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace root\\directory\\ldap*" AND ScriptBlockText="*class ds_computer*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `getwmiobject_ds_computer_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace
+  root\\directory\\ldap*" AND ScriptBlockText="*class ds_computer*") | stats count
+  min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `getwmiobject_ds_computer_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
 - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,17 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_ds_group_with_powershell.yml b/detections/endpoint/getwmiobject_ds_group_with_powershell.yml
index 58c8df9be5..ebd8d4eaf8 100644
--- a/detections/endpoint/getwmiobject_ds_group_with_powershell.yml
+++ b/detections/endpoint/getwmiobject_ds_group_with_powershell.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of `powershell.exe` with command-line arguments used to query domain groups via the `Get-WmiObject` cmdlet and the `-class ds_group` parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain groups, which is a common step in Active Directory Discovery. If confirmed malicious, this could allow attackers to gain insights into the domain structure, aiding in further attacks and privilege escalation.
+description: The following analytic identifies the execution of `powershell.exe` with
+  command-line arguments used to query domain groups via the `Get-WmiObject` cmdlet
+  and the `-class ds_group` parameter. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as it indicates potential reconnaissance
+  efforts by adversaries to enumerate domain groups, which is a common step in Active
+  Directory Discovery. If confirmed malicious, this could allow attackers to gain
+  insights into the domain structure, aiding in further attacks and privilege escalation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*" AND Processes.process="*class ds_group*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_group_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-WmiObject* AND Processes.process="*namespace root\\directory\\ldap*"
+  AND Processes.process="*class ds_group*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `getwmiobject_ds_group_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,22 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml
index 5606861024..2debc8dc7d 100644
--- a/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml
+++ b/detections/endpoint/getwmiobject_ds_group_with_powershell_script_block.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-WmiObject` commandlet with the `DS_Group` parameter via PowerShell Script Block Logging (EventCode=4104). This method leverages WMI to query all domain groups. Monitoring this activity is crucial as adversaries and Red Teams may use it for domain group enumeration, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to map out the domain structure, potentially leading to further exploitation and privilege escalation within the network.
+description: The following analytic detects the execution of the `Get-WmiObject` commandlet
+  with the `DS_Group` parameter via PowerShell Script Block Logging (EventCode=4104).
+  This method leverages WMI to query all domain groups. Monitoring this activity is
+  crucial as adversaries and Red Teams may use it for domain group enumeration, aiding
+  in situational awareness and Active Directory discovery. If confirmed malicious,
+  this activity could allow attackers to map out the domain structure, potentially
+  leading to further exploitation and privilege escalation within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace root\\directory\\ldap*" AND ScriptBlockText="*class ds_group*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`getwmiobject_ds_group_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText=*Get-WmiObject* AND ScriptBlockText="*namespace
+  root\\directory\\ldap*" AND ScriptBlockText="*class ds_group*") | stats count min(_time)
+  as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText
+  | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`|`getwmiobject_ds_group_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
 - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/get-wmiobject?view=powershell-5.1
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_ds_user_with_powershell.yml b/detections/endpoint/getwmiobject_ds_user_with_powershell.yml
index 72233a025f..db459d3302 100644
--- a/detections/endpoint/getwmiobject_ds_user_with_powershell.yml
+++ b/detections/endpoint/getwmiobject_ds_user_with_powershell.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments used to query domain users via the `Get-WmiObject` cmdlet and `-class ds_user` parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate domain users, which is a common step in Active Directory Discovery. If confirmed malicious, this could lead to further attacks, including privilege escalation and lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments used to query domain users via the `Get-WmiObject` cmdlet
+  and `-class ds_user` parameter. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line executions.
+  This activity is significant as it indicates potential reconnaissance efforts by
+  adversaries to enumerate domain users, which is a common step in Active Directory
+  Discovery. If confirmed malicious, this could lead to further attacks, including
+  privilege escalation and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe" OR Processes.process_name="powershell*") AND Processes.process = "*get-wmiobject*" AND Processes.process = "*ds_user*" AND Processes.process = "*root\\directory\\ldap*" AND Processes.process = "*-namespace*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="cmd.exe"
+  OR Processes.process_name="powershell*") AND Processes.process = "*get-wmiobject*"
+  AND Processes.process = "*ds_user*" AND Processes.process = "*root\\directory\\ldap*"
+  AND Processes.process = "*-namespace*" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://jpcertcc.github.io/ToolAnalysisResultSheet/details/dsquery.htm
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,20 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml
index 8f3f909ffd..5d01773601 100644
--- a/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml
+++ b/detections/endpoint/getwmiobject_ds_user_with_powershell_script_block.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-WmiObject` cmdlet with the `DS_User` class parameter via PowerShell Script Block Logging (EventCode=4104). It leverages logs to identify attempts to query all domain users using WMI. This activity is significant as it may indicate an adversary or Red Team operation attempting to enumerate domain users for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, enabling attackers to map out the network and identify potential targets for privilege escalation or lateral movement.
+description: The following analytic detects the execution of the `Get-WmiObject` cmdlet
+  with the `DS_User` class parameter via PowerShell Script Block Logging (EventCode=4104).
+  It leverages logs to identify attempts to query all domain users using WMI. This
+  activity is significant as it may indicate an adversary or Red Team operation attempting
+  to enumerate domain users for situational awareness and Active Directory discovery.
+  If confirmed malicious, this behavior could lead to further reconnaissance, enabling
+  attackers to map out the network and identify potential targets for privilege escalation
+  or lateral movement.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*get-wmiobject*" ScriptBlockText = "*ds_user*" ScriptBlockText = "*-namespace*" ScriptBlockText = "*root\\directory\\ldap*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_script_block_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*get-wmiobject*" ScriptBlockText
+  = "*ds_user*" ScriptBlockText = "*-namespace*" ScriptBlockText = "*root\\directory\\ldap*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_ds_user_with_powershell_script_block_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://www.blackhillsinfosec.com/red-blue-purple/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: powershell process having commandline for user enumeration detected on host - $dest$
+  message: powershell process having commandline for user enumeration detected on
+    host - $dest$
   mitre_attack_id:
   - T1087.002
   - T1087
@@ -46,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_user_account_with_powershell.yml b/detections/endpoint/getwmiobject_user_account_with_powershell.yml
index d26e2547a4..4e98f5b45e 100644
--- a/detections/endpoint/getwmiobject_user_account_with_powershell.yml
+++ b/detections/endpoint/getwmiobject_user_account_with_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments that utilize the `Get-WmiObject` cmdlet and the `Win32_UserAccount` parameter to query local user accounts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt by adversaries to enumerate user accounts for situational awareness or Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments that utilize the `Get-WmiObject` cmdlet and the `Win32_UserAccount`
+  parameter to query local user accounts. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line executions.
+  This activity is significant as it may indicate an attempt by adversaries to enumerate
+  user accounts for situational awareness or Active Directory discovery. If confirmed
+  malicious, this behavior could lead to further reconnaissance, privilege escalation,
+  or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process=*Get-WmiObject* AND Processes.process=*Win32_UserAccount*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_user_account_with_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process=*Get-WmiObject* AND Processes.process=*Win32_UserAccount*) by
+  Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `getwmiobject_user_account_with_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/001/
 tags:
@@ -35,12 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml b/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml
index cb410b387c..c02f4843cc 100644
--- a/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml
+++ b/detections/endpoint/getwmiobject_user_account_with_powershell_script_block.yml
@@ -5,12 +5,23 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `Get-WmiObject` commandlet with the `Win32_UserAccount` parameter via PowerShell Script Block Logging (EventCode=4104). This method leverages script block text to identify when a list of all local users is being enumerated. This activity is significant as it may indicate an adversary or Red Team operation attempting to gather user information for situational awareness and Active Directory discovery. If confirmed malicious, this could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
+description: The following analytic detects the execution of the `Get-WmiObject` commandlet
+  with the `Win32_UserAccount` parameter via PowerShell Script Block Logging (EventCode=4104).
+  This method leverages script block text to identify when a list of all local users
+  is being enumerated. This activity is significant as it may indicate an adversary
+  or Red Team operation attempting to gather user information for situational awareness
+  and Active Directory discovery. If confirmed malicious, this could lead to further
+  reconnaissance, privilege escalation, or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText="*Get-WmiObject*" AND ScriptBlockText="*Win32_UserAccount*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `getwmiobject_user_account_with_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText="*Get-WmiObject*" AND ScriptBlockText="*Win32_UserAccount*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | `security_content_ctime(firstTime)` | `getwmiobject_user_account_with_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/001/
 - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html
@@ -36,16 +47,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml b/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml
index 96948b0901..55a94a8c02 100644
--- a/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml
+++ b/detections/endpoint/gpupdate_with_no_command_line_arguments_with_network.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of gpupdate.exe without command line arguments and with an active network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution and network traffic data. It is significant because gpupdate.exe typically runs with specific arguments, and its execution without them, especially with network activity, is often associated with malicious software like Cobalt Strike. If confirmed malicious, this activity could indicate an attacker leveraging gpupdate.exe for lateral movement, command and control, or other nefarious purposes, potentially leading to system compromise.
+description: The following analytic detects the execution of gpupdate.exe without
+  command line arguments and with an active network connection. This behavior is identified
+  using Endpoint Detection and Response (EDR) telemetry, focusing on process execution
+  and network traffic data. It is significant because gpupdate.exe typically runs
+  with specific arguments, and its execution without them, especially with network
+  activity, is often associated with malicious software like Cobalt Strike. If confirmed
+  malicious, this activity could indicate an attacker leveraging gpupdate.exe for
+  lateral movement, command and control, or other nefarious purposes, potentially
+  leading to system compromise.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(gpupdate\.exe.{0,4}$)"| join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 | `gpupdate_with_no_command_line_arguments_with_network_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=gpupdate.exe by _time span=1h  Processes.process_id
+  Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | regex process="(?i)(gpupdate\.exe.{0,4}$)"|
+  join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port
+  | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest
+  parent_process_name process_name process_path process process_id dest_port C2 |
+  `gpupdate_with_no_command_line_arguments_with_network_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives may be present in small environments.
+  Tuning may be required based on parent process.
 references:
 - https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile
 - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/
@@ -20,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +62,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: Process gpupdate.exe  with parent_process $parent_process_name$ is executed on $dest$ by user $user$, followed by an outbound network connection to $C2$ on port $dest_port$. This behaviour is seen with cobaltstrike.
+  message: Process gpupdate.exe  with parent_process $parent_process_name$ is executed
+    on $dest$ by user $user$, followed by an outbound network connection to $C2$ on
+    port $dest_port$. This behaviour is seen with cobaltstrike.
   mitre_attack_id:
   - T1055
   observable:
@@ -55,18 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventID
-  - process_name
-  - process_id
-  - parent_process_name
-  - dest_port
-  - process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml b/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml
index 49f0ee4a62..77ce4e2c4a 100644
--- a/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml
+++ b/detections/endpoint/headless_browser_mockbin_or_mocky_request.yml
@@ -9,10 +9,28 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects headless browser activity accessing mockbin.org or mocky.io. It identifies processes with the "--headless" and "--disable-gpu" command line arguments, along with references to mockbin.org or mocky.io. This behavior is significant as headless browsers are often used for automated tasks, including malicious activities like web scraping or automated attacks. If confirmed malicious, this activity could indicate an attempt to bypass traditional browser security measures, potentially leading to data exfiltration or further exploitation of web applications.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*" AND (Processes.process="*mockbin.org/*" OR Processes.process="*mocky.io/*")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `headless_browser_mockbin_or_mocky_request_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives are not expected with this detection, unless within the organization there is a legitimate need for headless browsing accessing mockbin.org or mocky.io.
+description: The following analytic detects headless browser activity accessing mockbin.org
+  or mocky.io. It identifies processes with the "--headless" and "--disable-gpu" command
+  line arguments, along with references to mockbin.org or mocky.io. This behavior
+  is significant as headless browsers are often used for automated tasks, including
+  malicious activities like web scraping or automated attacks. If confirmed malicious,
+  this activity could indicate an attempt to bypass traditional browser security measures,
+  potentially leading to data exfiltration or further exploitation of web applications.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*"
+  AND Processes.process="*--disable-gpu*" AND (Processes.process="*mockbin.org/*"
+  OR Processes.process="*mocky.io/*")) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `headless_browser_mockbin_or_mocky_request_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives are not expected with this detection, unless
+  within the organization there is a legitimate need for headless browsing accessing
+  mockbin.org or mocky.io.
 references:
 - https://mockbin.org/
 - https://www.mocky.io/
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +55,8 @@ tags:
   atomic_guid: []
   confidence: 70
   impact: 80
-  message: Headless browser activity accessing mockbin.org or mocky.io detected on $dest$ by $user$.
+  message: Headless browser activity accessing mockbin.org or mocky.io detected on
+    $dest$ by $user$.
   mitre_attack_id:
   - T1564.003
   observable:
@@ -48,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/headless_browser_usage.yml b/detections/endpoint/headless_browser_usage.yml
index 51cdec466a..1824ad244f 100644
--- a/detections/endpoint/headless_browser_usage.yml
+++ b/detections/endpoint/headless_browser_usage.yml
@@ -9,10 +9,28 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the usage of headless browsers within an organization. It identifies processes containing the "--headless" and "--disable-gpu" command line arguments, which are indicative of headless browsing. This detection leverages data from the Endpoint.Processes datamodel to identify such processes. Monitoring headless browser usage is significant as these tools can be exploited by adversaries for malicious activities like web scraping, automated testing, and undetected web interactions. If confirmed malicious, this activity could lead to unauthorized data extraction, automated attacks, or other covert operations on web applications.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `headless_browser_usage_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: This hunting analytic is meant to assist with baselining and understanding headless browsing in use. Filter as needed.
+description: The following analytic detects the usage of headless browsers within
+  an organization. It identifies processes containing the "--headless" and "--disable-gpu"
+  command line arguments, which are indicative of headless browsing. This detection
+  leverages data from the Endpoint.Processes datamodel to identify such processes.
+  Monitoring headless browser usage is significant as these tools can be exploited
+  by adversaries for malicious activities like web scraping, automated testing, and
+  undetected web interactions. If confirmed malicious, this activity could lead to
+  unauthorized data extraction, automated attacks, or other covert operations on web
+  applications.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*"
+  AND Processes.process="*--disable-gpu*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `headless_browser_usage_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: This hunting analytic is meant to assist with baselining and
+  understanding headless browsing in use. Filter as needed.
 references:
 - https://cert.gov.ua/article/5702579
 tags:
@@ -38,19 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - sourcetype
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1185/headlessbrowser/headless_mockbin.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/hide_user_account_from_sign_in_screen.yml b/detections/endpoint/hide_user_account_from_sign_in_screen.yml
index 1081e6abe7..dc2eccbf5c 100644
--- a/detections/endpoint/hide_user_account_from_sign_in_screen.yml
+++ b/detections/endpoint/hide_user_account_from_sign_in_screen.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects a suspicious registry modification that hides a user account from the Windows Login screen. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" with a value of "0x00000000". This activity is significant as it may indicate an adversary attempting to create a hidden admin account to avoid detection and maintain persistence on the compromised machine. If confirmed malicious, this could allow the attacker to maintain undetected access and control over the system, posing a severe security risk.
+description: The following analytic detects a suspicious registry modification that
+  hides a user account from the Windows Login screen. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path "*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*"
+  with a value of "0x00000000". This activity is significant as it may indicate an
+  adversary attempting to create a hidden admin account to avoid detection and maintain
+  persistence on the compromised machine. If confirmed malicious, this could allow
+  the attacker to maintain undetected access and control over the system, posing a
+  severe security risk.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*"
-  AND Registry.registry_value_data = "0x00000000") BY Registry.dest
-  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
-  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
-  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `hide_user_account_from_sign_in_screen_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows
+  NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_data
+  = "0x00000000") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `hide_user_account_from_sign_in_screen_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +53,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: Suspicious registry modification ($registry_value_name$) which is used go hide a user account on the Windows Login screen detected on $dest$ executed by $user$
+  message: Suspicious registry modification ($registry_value_name$) which is used
+    go hide a user account on the Windows Login screen detected on $dest$ executed
+    by $user$
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -62,18 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml b/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml
index 7c86fa4949..83e1cfac83 100644
--- a/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml
+++ b/detections/endpoint/hiding_files_and_directories_with_attrib_exe.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the Windows binary attrib.exe to hide files or directories by marking them with specific flags. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments that include the "+h" flag. This activity is significant because hiding files can be a tactic used by attackers to conceal malicious files or tools from users and security software. If confirmed malicious, this behavior could allow an attacker to persist in the environment undetected, potentially leading to further compromise or data exfiltration.
+description: The following analytic detects the use of the Windows binary attrib.exe
+  to hide files or directories by marking them with specific flags. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments
+  that include the "+h" flag. This activity is significant because hiding files can
+  be a tactic used by attackers to conceal malicious files or tools from users and
+  security software. If confirmed malicious, this behavior could allow an attacker
+  to persist in the environment undetected, potentially leading to further compromise
+  or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process_name Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` |`hiding_files_and_directories_with_attrib_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some applications and users may legitimately use attrib.exe to interact with the files.
+search: '| tstats `security_content_summariesonly` count min(_time) values(Processes.process)
+  as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe
+  (Processes.process=*+h*) by Processes.parent_process_name Processes.process_name
+  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  |`hiding_files_and_directories_with_attrib_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some applications and users may legitimately use attrib.exe
+  to interact with the files.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$user$" and "$dest$"
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml b/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml
index 3676edad88..d4780c68c6 100644
--- a/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml
+++ b/detections/endpoint/high_frequency_copy_of_files_in_network_share.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a high frequency of file copying or moving within network shares, which may indicate potential data sabotage or exfiltration attempts. It leverages Windows Security Event Logs (EventCode 5145) to monitor access to specific file types and network shares. This activity is significant as it can reveal insider threats attempting to transfer classified or internal files, potentially leading to data breaches or evidence tampering. If confirmed malicious, this behavior could result in unauthorized data access, data loss, or compromised sensitive information.
+description: The following analytic detects a high frequency of file copying or moving
+  within network shares, which may indicate potential data sabotage or exfiltration
+  attempts. It leverages Windows Security Event Logs (EventCode 5145) to monitor access
+  to specific file types and network shares. This activity is significant as it can
+  reveal insider threats attempting to transfer classified or internal files, potentially
+  leading to data breaches or evidence tampering. If confirmed malicious, this behavior
+  could result in unauthorized data access, data loss, or compromised sensitive information.
 data_source:
 - Windows Event Log Security 5145
-search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.doc","*.docx","*.xls","*.xlsx","*.ppt","*.pptx","*.log","*.txt","*.db","*.7z","*.zip","*.rar","*.tar","*.gz","*.jpg","*.gif","*.png","*.bmp","*.pdf","*.rtf","*.key") ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= "0x2" |  bucket _time span=5m | stats values(RelativeTargetName) as valRelativeTargetName, values(ShareName) as valShareName, values(ObjectType) as valObjectType, values(AccessMask) as valAccessmask, values(src_port) as valSrcPort, values(SourceAddress) as valSrcAddress count as numShareName by dest, _time, EventCode, src_user, src_ip | eventstats avg(numShareName) as avgShareName, stdev(numShareName) as stdShareName, count as numSlots by dest, _time, EventCode, src_user |  eval upperThreshold=(avgShareName + stdShareName *3) |  eval isOutlier=if(avgShareName > 20 and avgShareName >= upperThreshold, 1, 0) |  search isOutlier=1  | `high_frequency_copy_of_files_in_network_share_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.
-known_false_positives: This behavior may seen in normal transfer of file within network if network share is common place for sharing documents.
+search: '`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.doc","*.docx","*.xls","*.xlsx","*.ppt","*.pptx","*.log","*.txt","*.db","*.7z","*.zip","*.rar","*.tar","*.gz","*.jpg","*.gif","*.png","*.bmp","*.pdf","*.rtf","*.key")
+  ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask=
+  "0x2" |  bucket _time span=5m | stats values(RelativeTargetName) as valRelativeTargetName,
+  values(ShareName) as valShareName, values(ObjectType) as valObjectType, values(AccessMask)
+  as valAccessmask, values(src_port) as valSrcPort, values(SourceAddress) as valSrcAddress
+  count as numShareName by dest, _time, EventCode, src_user, src_ip | eventstats avg(numShareName)
+  as avgShareName, stdev(numShareName) as stdShareName, count as numSlots by dest,
+  _time, EventCode, src_user |  eval upperThreshold=(avgShareName + stdShareName *3)
+  |  eval isOutlier=if(avgShareName > 20 and avgShareName >= upperThreshold, 1, 0)
+  |  search isOutlier=1  | `high_frequency_copy_of_files_in_network_share_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also
+  required. Also enable the object Audit access success/failure in your group policy.
+known_false_positives: This behavior may seen in normal transfer of file within network
+  if network share is common place for sharing documents.
 references:
 - https://attack.mitre.org/techniques/T1537/
 drilldown_searches:
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Share_Name
-  - Relative_Target_Name
-  - Object_Type
-  - Access_Mask
-  - user
-  - src_port
-  - Source_Address
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/high_frequency_copy_of_files_in_network_share/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/high_frequency_copy_of_files_in_network_share/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/high_process_termination_frequency.yml b/detections/endpoint/high_process_termination_frequency.yml
index be7b05f755..791e14e624 100644
--- a/detections/endpoint/high_process_termination_frequency.yml
+++ b/detections/endpoint/high_process_termination_frequency.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Teoderick Contreras
 status: production
 type: Anomaly
-description: The following analytic identifies a high frequency of process termination events on a computer within a short period. It leverages Sysmon EventCode 5 logs to detect instances where 15 or more processes are terminated within a 3-second window. This behavior is significant as it is commonly associated with ransomware attempting to avoid exceptions during file encryption. If confirmed malicious, this activity could indicate an active ransomware attack, potentially leading to widespread file encryption and significant data loss.
+description: The following analytic identifies a high frequency of process termination
+  events on a computer within a short period. It leverages Sysmon EventCode 5 logs
+  to detect instances where 15 or more processes are terminated within a 3-second
+  window. This behavior is significant as it is commonly associated with ransomware
+  attempting to avoid exceptions during file encryption. If confirmed malicious, this
+  activity could indicate an active ransomware attack, potentially leading to widespread
+  file encryption and significant data loss.
 data_source:
 - Sysmon EventID 5
-search: '`sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime  count by _time dest EventCode ProcessID | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `high_process_termination_frequency_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=5 |bin _time span=3s |stats values(Image) as proc_terminated
+  min(_time) as firstTime max(_time) as lastTime  count by _time dest EventCode ProcessID
+  | where count >= 15 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `high_process_termination_frequency_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Image (process full path of terminated process) from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: admin or user tool that can terminate multiple process.
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: High frequency process termination (more than 15 processes within 3s) detected on host $dest$
+  message: High frequency process termination (more than 15 processes within 3s) detected
+    on host $dest$
   mitre_attack_id:
   - T1486
   observable:
@@ -49,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - Image
-  - dest
-  - _time
-  - ProcessID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/hunting_3cxdesktopapp_software.yml b/detections/endpoint/hunting_3cxdesktopapp_software.yml
index 787657234f..72f16404ea 100644
--- a/detections/endpoint/hunting_3cxdesktopapp_software.yml
+++ b/detections/endpoint/hunting_3cxdesktopapp_software.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the presence of any version of the 3CXDesktopApp, also known as the 3CX Desktop App, on Mac or Windows systems. It leverages the Endpoint data model's Processes node to identify instances of the application running, although it does not provide file version information. This activity is significant because 3CX has identified vulnerabilities in versions 18.12.407 and 18.12.416, which could be exploited by attackers. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the affected systems.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=3CXDesktopApp.exe OR Processes.process_name="3CX Desktop App" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hunting_3cxdesktopapp_software_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: There may be false positives generated due to the reliance on version numbers for identification purposes. Despite this limitation, the primary goal of this approach is to aid in the detection of the software within the environment.
+description: The following analytic detects the presence of any version of the 3CXDesktopApp,
+  also known as the 3CX Desktop App, on Mac or Windows systems. It leverages the Endpoint
+  data model's Processes node to identify instances of the application running, although
+  it does not provide file version information. This activity is significant because
+  3CX has identified vulnerabilities in versions 18.12.407 and 18.12.416, which could
+  be exploited by attackers. If confirmed malicious, this could lead to unauthorized
+  access, data exfiltration, or further compromise of the affected systems.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=3CXDesktopApp.exe
+  OR Processes.process_name="3CX Desktop App" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `hunting_3cxdesktopapp_software_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: There may be false positives generated due to the reliance
+  on version numbers for identification purposes. Despite this limitation, the primary
+  goal of this approach is to aid in the detection of the software within the environment.
 references:
 - https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
 - https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp
@@ -43,23 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/icacls_deny_command.yml b/detections/endpoint/icacls_deny_command.yml
index 5460c73791..1bc4a2b58f 100644
--- a/detections/endpoint/icacls_deny_command.yml
+++ b/detections/endpoint/icacls_deny_command.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances where an adversary modifies security permissions of a file or directory using commands like "icacls.exe", "cacls.exe", or "xcacls.exe" with deny options. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it is commonly used by Advanced Persistent Threats (APTs) and coinminer scripts to evade detection and impede access to critical files. If confirmed malicious, this could allow attackers to maintain persistence and hinder incident response efforts.
+description: The following analytic detects instances where an adversary modifies
+  security permissions of a file or directory using commands like "icacls.exe", "cacls.exe",
+  or "xcacls.exe" with deny options. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process names and command-line executions. This
+  activity is significant as it is commonly used by Advanced Persistent Threats (APTs)
+  and coinminer scripts to evade detection and impede access to critical files. If
+  confirmed malicious, this could allow attackers to maintain persistence and hinder
+  incident response efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/deny*", "*/D*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_deny_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unknown. It is possible some administrative scripts use ICacls. Filter as needed.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe",
+  "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/deny*", "*/D*") by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `icacls_deny_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unknown. It is possible some administrative scripts use ICacls.
+  Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: Process name $process_name$ with deny argument executed by $user$ to change security permission of a specific file or directory on host $dest$
+  message: Process name $process_name$ with deny argument executed by $user$ to change
+    security permission of a specific file or directory on host $dest$
   mitre_attack_id:
   - T1222
   observable:
@@ -48,18 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/icacls_grant_command.yml b/detections/endpoint/icacls_grant_command.yml
index bed1381f5d..5b90ddd6df 100644
--- a/detections/endpoint/icacls_grant_command.yml
+++ b/detections/endpoint/icacls_grant_command.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the ICACLS command to grant additional access permissions to files or directories. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process names and command-line arguments. This activity is significant because it is commonly used by Advanced Persistent Threats (APTs) and coinminer scripts to evade detection and maintain control over compromised systems. If confirmed malicious, this behavior could allow attackers to manipulate file permissions, potentially leading to unauthorized access, data exfiltration, or further system compromise.
+description: The following analytic detects the use of the ICACLS command to grant
+  additional access permissions to files or directories. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on specific process names and command-line
+  arguments. This activity is significant because it is commonly used by Advanced
+  Persistent Threats (APTs) and coinminer scripts to evade detection and maintain
+  control over compromised systems. If confirmed malicious, this behavior could allow
+  attackers to manipulate file permissions, potentially leading to unauthorized access,
+  data exfiltration, or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/grant*", "*/G*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icacls_grant_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe",
+  "cacls.exe", "xcacls.exe") AND Processes.process IN ("*/grant*", "*/G*") by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `icacls_grant_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown. Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Process name $process_name$ with grant argument executed by $user$ to change security permission of a specific file or directory on host $dest$
+  message: Process name $process_name$ with grant argument executed by $user$ to change
+    security permission of a specific file or directory on host $dest$
   mitre_attack_id:
   - T1222
   observable:
@@ -47,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml b/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml
index 2772e5a379..e0ddce0324 100644
--- a/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml
+++ b/detections/endpoint/icedid_exfiltrated_archived_file_creation.yml
@@ -5,13 +5,26 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the creation of suspicious files named passff.tar and cookie.tar, which are indicative of archived stolen browser information such as history and cookies on a machine compromised with IcedID. It leverages Sysmon EventCode 11 to identify these specific filenames. This activity is significant because it suggests that sensitive browser data has been exfiltrated, which could lead to further exploitation or data breaches. If confirmed malicious, this could allow attackers to access personal information, conduct further phishing attacks, or escalate their presence within the network.
+description: The following analytic detects the creation of suspicious files named
+  passff.tar and cookie.tar, which are indicative of archived stolen browser information
+  such as history and cookies on a machine compromised with IcedID. It leverages Sysmon
+  EventCode 11 to identify these specific filenames. This activity is significant
+  because it suggests that sensitive browser data has been exfiltrated, which could
+  lead to further exploitation or data breaches. If confirmed malicious, this could
+  allow attackers to access personal information, conduct further phishing attacks,
+  or escalate their presence within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`sysmon` EventCode= 11  (TargetFilename = "*\\passff.tar" OR TargetFilename = "*\\cookie.tar") |stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode= 11  (TargetFilename = "*\\passff.tar" OR TargetFilename
+  = "*\\cookie.tar") |stats count min(_time) as firstTime max(_time) as lastTime by
+  TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `icedid_exfiltrated_archived_file_creation_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.cisecurity.org/insights/white-papers/security-primer-icedid
@@ -38,17 +51,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - TargetFilename
-  - EventCode
-  - process_id
-  - process_name
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml b/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml
index 6e1e737ead..42085de288 100644
--- a/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml
+++ b/detections/endpoint/impacket_lateral_movement_commandline_parameters.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the use of suspicious command-line parameters associated with Impacket tools, such as `wmiexec.py`, `smbexec.py`, `dcomexec.py`, and `atexec.py`, which are used for lateral movement and remote code execution. It detects these activities by analyzing process execution logs from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns. This activity is significant because Impacket tools are commonly used by adversaries and Red Teams to move laterally within a network. If confirmed malicious, this could allow attackers to execute commands remotely, potentially leading to further compromise and data exfiltration.
+description: The following analytic identifies the use of suspicious command-line
+  parameters associated with Impacket tools, such as `wmiexec.py`, `smbexec.py`, `dcomexec.py`,
+  and `atexec.py`, which are used for lateral movement and remote code execution.
+  It detects these activities by analyzing process execution logs from Endpoint Detection
+  and Response (EDR) agents, focusing on specific command-line patterns. This activity
+  is significant because Impacket tools are commonly used by adversaries and Red Teams
+  to move laterally within a network. If confirmed malicious, this could allow attackers
+  to execute commands remotely, potentially leading to further compromise and data
+  exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process = "*/Q /c * \\\\127.0.0.1\\*$*" AND Processes.process IN ("*2>&1*","*2&gt;&amp;1*")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_commandline_parameters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe
+  (Processes.process = "*/Q /c * \\\\127.0.0.1\\*$*" AND Processes.process IN ("*2>&1*","*2&gt;&amp;1*"))
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_commandline_parameters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although uncommon, Administrators may leverage Impackets tools
+  to start a process on remote systems for system administration or automation use
+  cases.
 references:
 - https://attack.mitre.org/techniques/T1021/002/
 - https://attack.mitre.org/techniques/T1021/003/
@@ -29,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,7 +74,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 90
-  message: Suspicious command line parameters on $dest$ may represent a lateral movement attack with Impackets tools
+  message: Suspicious command line parameters on $dest$ may represent a lateral movement
+    attack with Impackets tools
   mitre_attack_id:
   - T1021
   - T1021.002
@@ -62,23 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/impacket/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/impacket/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml b/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml
index 2e5a329ad3..4710cc147b 100644
--- a/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml
+++ b/detections/endpoint/impacket_lateral_movement_smbexec_commandline_parameters.yml
@@ -9,10 +9,34 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process,"(?i)echo\s+cd") AND match(process, "(?i)\\__output") AND  match(process, "(?i)C:\\\\Windows\\\\[a-zA-Z]{1,8}\\.bat")  AND match(process, "\\\\127\.0\.0\.1\\.*") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `impacket_lateral_movement_smbexec_commandline_parameters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.
+description: The following analytic identifies suspicious command-line parameters
+  associated with the use of Impacket's smbexec.py for lateral movement. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line
+  patterns indicative of Impacket tool usage. This activity is significant as both
+  Red Teams and adversaries use Impacket for remote code execution and lateral movement.
+  If confirmed malicious, this activity could allow attackers to execute commands
+  on remote endpoints, potentially leading to unauthorized access, data exfiltration,
+  or further compromise of the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process,"(?i)echo\s+cd")
+  AND match(process, "(?i)\\__output") AND  match(process, "(?i)C:\\\\Windows\\\\[a-zA-Z]{1,8}\\.bat")  AND
+  match(process, "\\\\127\.0\.0\.1\\.*") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `impacket_lateral_movement_smbexec_commandline_parameters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although uncommon, Administrators may leverage Impackets tools
+  to start a process on remote systems for system administration or automation use
+  cases.
 references:
 - https://attack.mitre.org/techniques/T1021/002/
 - https://attack.mitre.org/techniques/T1021/003/
@@ -29,7 +53,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,7 +75,8 @@ tags:
   atomic_guid: []
   confidence: 70
   impact: 90
-  message: Suspicious command-line parameters on $dest$ may represent lateral movement using smbexec.
+  message: Suspicious command-line parameters on $dest$ may represent lateral movement
+    using smbexec.
   mitre_attack_id:
   - T1021
   - T1021.002
@@ -62,18 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/smbexec_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/smbexec_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml b/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml
index 483ea9f354..3a9840906f 100644
--- a/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml
+++ b/detections/endpoint/impacket_lateral_movement_wmiexec_commandline_parameters.yml
@@ -9,10 +9,33 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the use of Impacket's `wmiexec.py` tool for lateral movement by identifying specific command-line parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes spawned by `wmiprvse.exe` with command-line patterns indicative of Impacket usage. This activity is significant as Impacket tools are commonly used by adversaries for remote code execution and lateral movement within a network. If confirmed malicious, this could allow attackers to execute arbitrary commands on remote systems, potentially leading to further compromise and data exfiltration.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process, "\\\\127\.0\.0\.1\\.*") AND match(process, "__\\d{1,10}\\.\\d{1,10}") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `impacket_lateral_movement_wmiexec_commandline_parameters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.
+description: The following analytic detects the use of Impacket's `wmiexec.py` tool
+  for lateral movement by identifying specific command-line parameters. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on processes spawned
+  by `wmiprvse.exe` with command-line patterns indicative of Impacket usage. This
+  activity is significant as Impacket tools are commonly used by adversaries for remote
+  code execution and lateral movement within a network. If confirmed malicious, this
+  could allow attackers to execute arbitrary commands on remote systems, potentially
+  leading to further compromise and data exfiltration.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | where match(process, "(?i)cmd\.exe\s+\/Q\s+\/c") AND match(process, "\\\\127\.0\.0\.1\\.*")
+  AND match(process, "__\\d{1,10}\\.\\d{1,10}") | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `impacket_lateral_movement_wmiexec_commandline_parameters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although uncommon, Administrators may leverage Impackets tools
+  to start a process on remote systems for system administration or automation use
+  cases.
 references:
 - https://attack.mitre.org/techniques/T1021/002/
 - https://attack.mitre.org/techniques/T1021/003/
@@ -29,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,7 +75,8 @@ tags:
   atomic_guid: []
   confidence: 70
   impact: 90
-  message: Suspicious command-line parameters on $dest$ may represent lateral movement using wmiexec.
+  message: Suspicious command-line parameters on $dest$ may represent lateral movement
+    using wmiexec.
   mitre_attack_id:
   - T1021
   - T1021.002
@@ -63,18 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/wmiexec_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/wmiexec_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml b/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml
index 38e6cef5a9..51fd821cfc 100644
--- a/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml
+++ b/detections/endpoint/interactive_session_on_remote_endpoint_with_powershell.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `Enter-PSSession` cmdlet to establish an interactive session on a remote endpoint via the WinRM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity by searching for specific script block text patterns. This behavior is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this activity could allow attackers to execute commands remotely, potentially leading to further compromise of the network and unauthorized access to sensitive information.
+description: The following analytic detects the use of the `Enter-PSSession` cmdlet
+  to establish an interactive session on a remote endpoint via the WinRM protocol.
+  It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity
+  by searching for specific script block text patterns. This behavior is significant
+  as it may indicate lateral movement or remote code execution attempts by adversaries.
+  If confirmed malicious, this activity could allow attackers to execute commands
+  remotely, potentially leading to further compromise of the network and unauthorized
+  access to sensitive information.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText="*Enter-PSSession*" AND ScriptBlockText="*-ComputerName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `interactive_session_on_remote_endpoint_with_powershell_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators may leverage WinRM and `Enter-PSSession` for administrative and troubleshooting tasks. This activity is usually limited to a small set of hosts or users. In certain environments, tuning may not be possible.
+search: '`powershell` EventCode=4104 (ScriptBlockText="*Enter-PSSession*" AND ScriptBlockText="*-ComputerName*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `interactive_session_on_remote_endpoint_with_powershell_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup instructions
+  can be found 
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators may leverage WinRM and `Enter-PSSession` for
+  administrative and troubleshooting tasks. This activity is usually limited to a
+  small set of hosts or users. In certain environments, tuning may not be possible.
 references:
 - https://attack.mitre.org/techniques/T1021/006/
 - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.core/enter-pssession?view=powershell-7.2
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - ComputerName
-  - User
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_pssession/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_pssession/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/java_writing_jsp_file.yml b/detections/endpoint/java_writing_jsp_file.yml
index 41861c3b66..47828b8b72 100644
--- a/detections/endpoint/java_writing_jsp_file.yml
+++ b/detections/endpoint/java_writing_jsp_file.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the Java process writing a .jsp file to disk, which may indicate a web shell being deployed. It leverages data from the Endpoint datamodel, specifically monitoring process and filesystem activities. This activity is significant because web shells can provide attackers with remote control over the compromised server, leading to further exploitation. If confirmed malicious, this could allow unauthorized access, data exfiltration, or further compromise of the affected system, posing a severe security risk.
+description: The following analytic detects the Java process writing a .jsp file to
+  disk, which may indicate a web shell being deployed. It leverages data from the
+  Endpoint datamodel, specifically monitoring process and filesystem activities. This
+  activity is significant because web shells can provide attackers with remote control
+  over the compromised server, leading to further exploitation. If confirmed malicious,
+  this could allow unauthorized access, data exfiltration, or further compromise of
+  the affected system, posing a severe security risk.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("java","java.exe", "javaw.exe") by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.jsp*" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.user | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time) as lastTime by dest process_name process_guid file_name file_path file_create_time user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `java_writing_jsp_file_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives are possible and filtering may be required. Restrict by assets or filter known jsp files that are common for the environment.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name IN ("java","java.exe", "javaw.exe") by _time Processes.process_id
+  Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)`
+  | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_name="*.jsp*" by _time Filesystem.dest Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.user
+  | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name
+  file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time)
+  as lastTime by dest process_name process_guid file_name file_path file_create_time
+  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `java_writing_jsp_file_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem`
+  node. In addition, confirm the latest CIM App 4.20 or higher is installed and the
+  latest TA for the endpoint product.
+known_false_positives: False positives are possible and filtering may be required.
+  Restrict by assets or filter known jsp files that are common for the environment.
 references:
 - https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/
 - https://github.com/TheGejr/SpringShell
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   cve:
   - CVE-2022-22965
   impact: 60
-  message: An instance of $process_name$ was identified on endpoint $dest$ writing a jsp file $file_name$ to disk, potentially indicative of exploitation.
+  message: An instance of $process_name$ was identified on endpoint $dest$ writing
+    a jsp file $file_name$ to disk, potentially indicative of exploitation.
   mitre_attack_id:
   - T1190
   - T1133
@@ -51,29 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.process_guid
-  - Filesystem.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/java_write_jsp-linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/java_write_jsp-linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/jscript_execution_using_cscript_app.yml b/detections/endpoint/jscript_execution_using_cscript_app.yml
index c7273b8666..d6baeba732 100644
--- a/detections/endpoint/jscript_execution_using_cscript_app.yml
+++ b/detections/endpoint/jscript_execution_using_cscript_app.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of JScript using the cscript.exe process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This behavior is significant because JScript files are typically executed by wscript.exe, making cscript.exe execution unusual and potentially indicative of malicious activity, such as the FIN7 group's tactics. If confirmed malicious, this activity could allow attackers to execute arbitrary scripts, leading to code execution, data exfiltration, or further system compromise.
+description: The following analytic detects the execution of JScript using the cscript.exe
+  process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process and command-line telemetry. This behavior is significant because JScript
+  files are typically executed by wscript.exe, making cscript.exe execution unusual
+  and potentially indicative of malicious activity, such as the FIN7 group's tactics.
+  If confirmed malicious, this activity could allow attackers to execute arbitrary
+  scripts, leading to code execution, data exfiltration, or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "cscript.exe" AND Processes.parent_process = "*//e:jscript*") OR (Processes.process_name = "cscript.exe" AND Processes.process = "*//e:jscript*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jscript_execution_using_cscript_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  = "cscript.exe" AND Processes.parent_process = "*//e:jscript*") OR (Processes.process_name
+  = "cscript.exe" AND Processes.process = "*//e:jscript*") by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process_id Processes.process
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `jscript_execution_using_cscript_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Process name $process_name$ with commandline $process$ to execute jscript in $dest$
+  message: Process name $process_name$ with commandline $process$ to execute jscript
+    in $dest$
   mitre_attack_id:
   - T1059
   - T1059.007
@@ -49,19 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml b/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml
index 48fcd5daf8..7498601251 100644
--- a/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml
+++ b/detections/endpoint/kerberoasting_spn_request_with_rc4_encryption.yml
@@ -5,21 +5,27 @@ date: '2024-10-16'
 author: Jose Hernandez, Patrick Bareiss, Mauricio Velazco, Dean Luxton, Splunk
 status: production
 type: TTP
-description: The following analytic detects potential Kerberoasting attacks by identifying Kerberos service ticket requests with RC4 encryption through Event ID 4769. It leverages specific Ticket_Options values commonly used by Kerberoasting tools. This activity is significant as Kerberoasting allows attackers to request service tickets for domain accounts, typically service accounts, and crack them offline to gain privileged access. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and further compromise of the Active Directory environment.
+description: The following analytic detects potential Kerberoasting attacks by identifying
+  Kerberos service ticket requests with RC4 encryption through Event ID 4769. It leverages
+  specific Ticket_Options values commonly used by Kerberoasting tools. This activity
+  is significant as Kerberoasting allows attackers to request service tickets for
+  domain accounts, typically service accounts, and crack them offline to gain privileged
+  access. If confirmed malicious, this could lead to unauthorized access, privilege
+  escalation, and further compromise of the Active Directory environment.
 data_source:
 - Windows Event Log Security 4769
 search: '`wineventlog_security` EventCode=4769 ServiceName!="*$" (TicketOptions=0x40810000
   OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17
-  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, user, service_id,
-  service, TicketEncryptionType, TicketOptions | rename Computer as dest | `security_content_ctime(lastTime)`
-  | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter`'
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, user,
+  service_id, service, TicketEncryptionType, TicketOptions | rename Computer as dest
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberoasting_spn_request_with_rc4_encryption_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   Domain Controller and Kerberos events. The Advanced Security Audit policy setting
   `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
   Whithin environments where this type of communication is common, consider dropping
-  the risk score and add throttling based on the user and service_id for 30 days. 
+  the risk score and add throttling based on the user and service_id for 30 days.
   This will allow RBA to alert when there is an anomalous spike of these kerberoastable
-  SPN requests within a short period of time. 
+  SPN requests within a short period of time.
 known_false_positives: Older systems that support kerberos RC4 by default like NetApp
   may generate false positives. Filter as needed
 references:
@@ -31,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TicketOptions
-  - TicketEncryptionType
-  - Computer
-  - service
-  - service_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/kerberoasting_spn_request_with_rc4_encryption/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/kerberoasting_spn_request_with_rc4_encryption/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml b/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml
index 07b019ccd3..5ca0a7df26 100644
--- a/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml
+++ b/detections/endpoint/kerberos_pre_authentication_flag_disabled_in_useraccountcontrol.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects when the Kerberos Pre-Authentication flag is disabled in a user account, using Windows Security Event 4738. This event indicates a change in the UserAccountControl property of a domain user object. Disabling this flag allows adversaries to perform offline brute force attacks on the user's password using the AS-REP Roasting technique. This activity is significant as it can be used by attackers with existing privileges to escalate their access or maintain persistence. If confirmed malicious, this could lead to unauthorized access and potential compromise of sensitive information.
+description: The following analytic detects when the Kerberos Pre-Authentication flag
+  is disabled in a user account, using Windows Security Event 4738. This event indicates
+  a change in the UserAccountControl property of a domain user object. Disabling this
+  flag allows adversaries to perform offline brute force attacks on the user's password
+  using the AS-REP Roasting technique. This activity is significant as it can be used
+  by attackers with existing privileges to escalate their access or maintain persistence.
+  If confirmed malicious, this could lead to unauthorized access and potential compromise
+  of sensitive information.
 data_source:
 - Windows Event Log Security 4738
-search: '`wineventlog_security` EventCode=4738 MSADChangedAttributes="*Don''t Require Preauth'' - Enabled*" |rename Account_Name as user | table EventCode, user, dest, Security_ID, MSADChangedAttributes | `kerberos_pre_authentication_flag_disabled_in_useraccountcontrol_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `User Account Management` within `Account Management` needs to be enabled.
+search: "`wineventlog_security` EventCode=4738 MSADChangedAttributes=\"*Don't Require
+  Preauth' - Enabled*\" |rename Account_Name as user | table EventCode, user, dest,
+  Security_ID, MSADChangedAttributes | `kerberos_pre_authentication_flag_disabled_in_useraccountcontrol_filter`"
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events. The Advanced Security Audit policy setting `User Account
+  Management` within `Account Management` needs to be enabled.
 known_false_positives: Unknown.
 references:
 - https://docs.microsoft.com/en-us/troubleshoot/windows-server/identity/useraccountcontrol-manipulate-account-properties
@@ -21,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,16 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Account_Name
-  - Security_ID
-  - MSADChangedAttributes
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/powershell/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/powershell/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
diff --git a/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml b/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml
index 5ba9e1799f..d71f17db7e 100644
--- a/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml
+++ b/detections/endpoint/kerberos_pre_authentication_flag_disabled_with_powershell.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `Set-ADAccountControl` PowerShell cmdlet with parameters that disable Kerberos Pre-Authentication. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific command execution. Disabling Kerberos Pre-Authentication is significant because it allows adversaries to perform offline brute force attacks against user passwords using the AS-REP Roasting technique. If confirmed malicious, this activity could enable attackers to escalate privileges or maintain persistence within an Active Directory environment, posing a severe security risk.
+description: The following analytic detects the use of the `Set-ADAccountControl`
+  PowerShell cmdlet with parameters that disable Kerberos Pre-Authentication. It leverages
+  PowerShell Script Block Logging (EventCode=4104) to identify this specific command
+  execution. Disabling Kerberos Pre-Authentication is significant because it allows
+  adversaries to perform offline brute force attacks against user passwords using
+  the AS-REP Roasting technique. If confirmed malicious, this activity could enable
+  attackers to escalate privileges or maintain persistence within an Active Directory
+  environment, posing a severe security risk.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Set-ADAccountControl*" AND ScriptBlockText="*DoesNotRequirePreAuth:$true*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kerberos_pre_authentication_flag_disabled_with_powershell_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Although unlikely, Administrators may need to set this flag for legitimate purposes.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Set-ADAccountControl*" AND
+  ScriptBlockText="*DoesNotRequirePreAuth:$true*") | stats count min(_time) as firstTime
+  max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer
+  as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `kerberos_pre_authentication_flag_disabled_with_powershell_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Although unlikely, Administrators may need to set this flag
+  for legitimate purposes.
 references:
 - https://docs.microsoft.com/en-us/troubleshoot/windows-server/identity/useraccountcontrol-manipulate-account-properties
 - https://m0chan.github.io/2019/07/31/How-To-Attack-Kerberos-101.html
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/powershell/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.004/powershell/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml b/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml
index 5afbdc7094..e8bfb04956 100644
--- a/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml
+++ b/detections/endpoint/kerberos_service_ticket_request_using_rc4_encryption.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: 'The following analytic detects Kerberos service ticket requests using RC4 encryption, leveraging Kerberos Event 4769. This method identifies potential Golden Ticket attacks, where adversaries forge Kerberos Granting Tickets (TGT) using the Krbtgt account NTLM password hash to gain unrestricted access to an Active Directory environment. Monitoring for RC4 encryption usage is significant as it is rare in modern networks, indicating possible malicious activity. If confirmed malicious, attackers could move laterally and execute code on remote systems, compromising the entire network. Note: This detection may be bypassed if attackers use the AES key instead of the NTLM hash.'
+description: 'The following analytic detects Kerberos service ticket requests using
+  RC4 encryption, leveraging Kerberos Event 4769. This method identifies potential
+  Golden Ticket attacks, where adversaries forge Kerberos Granting Tickets (TGT) using
+  the Krbtgt account NTLM password hash to gain unrestricted access to an Active Directory
+  environment. Monitoring for RC4 encryption usage is significant as it is rare in
+  modern networks, indicating possible malicious activity. If confirmed malicious,
+  attackers could move laterally and execute code on remote systems, compromising
+  the entire network. Note: This detection may be bypassed if attackers use the AES
+  key instead of the NTLM hash.'
 data_source:
 - Windows Event Log Security 4769
-search: '`wineventlog_security` EventCode=4769 ServiceName="*$" (TicketOptions=0x40810000 OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17 | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, TicketEncryptionType, TicketOptions | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `kerberos_service_ticket_request_using_rc4_encryption_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: Based on Microsoft documentation, legacy systems or applications will use RC4-HMAC as the default encryption for Kerberos Service Ticket requests. Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256.
+search: '`wineventlog_security` EventCode=4769 ServiceName="*$" (TicketOptions=0x40810000
+  OR TicketOptions=0x40800000 OR TicketOptions=0x40810010) TicketEncryptionType=0x17
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id,
+  TicketEncryptionType, TicketOptions | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `kerberos_service_ticket_request_using_rc4_encryption_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: Based on Microsoft documentation, legacy systems or applications
+  will use RC4-HMAC as the default encryption for Kerberos Service Ticket requests.
+  Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems
+  will use AES128 or AES256.
 references:
 - https://attack.mitre.org/techniques/T1558/001/
 - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4769
@@ -23,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 90
-  message: A Kerberos Service TTicket request with RC4 encryption was requested from $dest$
+  message: A Kerberos Service TTicket request with RC4 encryption was requested from
+    $dest$
   mitre_attack_id:
   - T1558
   - T1558.001
@@ -46,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Ticket_Options
-  - Ticket_Encryption_Type
-  - dest
-  - service
-  - service_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.001/kerberos_service_ticket_request_using_rc4_encryption/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.001/kerberos_service_ticket_request_using_rc4_encryption/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml b/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml
index b28dca4f42..938ce6c956 100644
--- a/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml
+++ b/detections/endpoint/kerberos_tgt_request_using_rc4_encryption.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects a Kerberos Ticket Granting Ticket (TGT) request using RC4-HMAC encryption (type 0x17) by leveraging Event 4768. This encryption type is outdated and its presence may indicate an OverPass The Hash attack. Monitoring this activity is crucial as it can signify credential theft, allowing adversaries to authenticate to the Kerberos Distribution Center (KDC) using a stolen NTLM hash. If confirmed malicious, this could enable unauthorized access to systems and resources, potentially leading to lateral movement and further compromise within the network.
+description: The following analytic detects a Kerberos Ticket Granting Ticket (TGT)
+  request using RC4-HMAC encryption (type 0x17) by leveraging Event 4768. This encryption
+  type is outdated and its presence may indicate an OverPass The Hash attack. Monitoring
+  this activity is crucial as it can signify credential theft, allowing adversaries
+  to authenticate to the Kerberos Distribution Center (KDC) using a stolen NTLM hash.
+  If confirmed malicious, this could enable unauthorized access to systems and resources,
+  potentially leading to lateral movement and further compromise within the network.
 data_source:
 - Windows Event Log Security 4768
-search: '`wineventlog_security` EventCode=4768 TicketEncryptionType=0x17 ServiceName!=*$ | stats count min(_time) as firstTime max(_time) as lastTime by ServiceName src_ip dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `kerberos_tgt_request_using_rc4_encryption_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: Based on Microsoft documentation, legacy systems or applications will use RC4-HMAC as the default encryption for TGT requests. Specifically, systems before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256.
+search: '`wineventlog_security` EventCode=4768 TicketEncryptionType=0x17 ServiceName!=*$
+  | stats count min(_time) as firstTime max(_time) as lastTime by ServiceName src_ip
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `kerberos_tgt_request_using_rc4_encryption_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: Based on Microsoft documentation, legacy systems or applications
+  will use RC4-HMAC as the default encryption for TGT requests. Specifically, systems
+  before Windows Server 2008 and Windows Vista. Newer systems will use AES128 or AES256.
 references:
 - https://stealthbits.com/blog/how-to-detect-overpass-the-hash-attacks/
 - https://www.thehacker.recipes/ad/movement/kerberos/ptk
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A Kerberos TGT request with RC4 encryption was requested for $ServiceName$ from $src_ip$
+  message: A Kerberos TGT request with RC4 encryption was requested for $ServiceName$
+    from $src_ip$
   mitre_attack_id:
   - T1550
   observable:
@@ -42,15 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TicketEncryptionType
-  - ServiceName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/kerberos_tgt_request_using_rc4_encryption/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/kerberos_tgt_request_using_rc4_encryption/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/kerberos_user_enumeration.yml b/detections/endpoint/kerberos_user_enumeration.yml
index 60fd4ebbf4..b8eae32e76 100644
--- a/detections/endpoint/kerberos_user_enumeration.yml
+++ b/detections/endpoint/kerberos_user_enumeration.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects an unusual number of Kerberos Ticket Granting Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This behavior is significant as it may indicate an adversary performing a user enumeration attack against Active Directory. If confirmed malicious, the attacker could validate a list of usernames, potentially leading to further attacks such as brute force or credential stuffing, compromising the security of the environment.
+description: The following analytic detects an unusual number of Kerberos Ticket Granting
+  Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages
+  Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This
+  behavior is significant as it may indicate an adversary performing a user enumeration
+  attack against Active Directory. If confirmed malicious, the attacker could validate
+  a list of usernames, potentially leading to further attacks such as brute force
+  or credential stuffing, compromising the security of the environment.
 data_source:
 - Windows Event Log Security 4768
-search: '`wineventlog_security` EventCode=4768 Status=0x6 TargetUserName!="*$" | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, src_ip | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by src_ip | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1| `kerberos_user_enumeration_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
+search: '`wineventlog_security` EventCode=4768 Status=0x6 TargetUserName!="*$" | bucket
+  span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, src_ip | eventstats avg(unique_accounts) as comp_avg
+  , stdev(unique_accounts) as comp_std by src_ip | eval upperBound=(comp_avg+comp_std*3)
+  | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0)
+  | search isOutlier=1| `kerberos_user_enumeration_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: Possible false positive scenarios include but are not limited
+  to vulnerability scanners and missconfigured systems.
 references:
 - https://github.com/ropnop/kerbrute
 - https://attack.mitre.org/techniques/T1589/002/
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Result_Code
-  - Account_Name
-  - Client_Address
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1589.002/kerberos_user_enumeration/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1589.002/kerberos_user_enumeration/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/known_services_killed_by_ransomware.yml b/detections/endpoint/known_services_killed_by_ransomware.yml
index 1e579305d4..5d51633f16 100644
--- a/detections/endpoint/known_services_killed_by_ransomware.yml
+++ b/detections/endpoint/known_services_killed_by_ransomware.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious termination of known services commonly targeted by ransomware before file encryption. It leverages Windows System Event Logs (EventCode 7036) to identify when critical services such as Volume Shadow Copy, backup, and antivirus services are stopped. This activity is significant because ransomware often disables these services to avoid errors and ensure successful file encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering files inaccessible and potentially causing significant operational disruption and data loss.
+description: The following analytic detects the suspicious termination of known services
+  commonly targeted by ransomware before file encryption. It leverages Windows System
+  Event Logs (EventCode 7036) to identify when critical services such as Volume Shadow
+  Copy, backup, and antivirus services are stopped. This activity is significant because
+  ransomware often disables these services to avoid errors and ensure successful file
+  encryption. If confirmed malicious, this behavior could lead to widespread data
+  encryption, rendering files inaccessible and potentially causing significant operational
+  disruption and data loss.
 data_source:
 - Windows Event Log System 7036
-search: '`wineventlog_system` EventCode=7036 param1 IN ("*Volume Shadow Copy*","*VSS*", "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*", "DefWatch", "ccEvtMgr", "ccSetMgr", "SavRoam", "RTVscan", "QBFCService", "QBIDPService", "Intuit.QuickBooks.FCS", "QBCFMonitorService" "YooBackup", "YooIT", "*Veeam*", "PDVFSService", "BackupExecVSSProvider", "BackupExecAgentAccelerator", "BackupExec*", "WdBoot", "WdFilter", "WdNisDrv", "WdNisSvc", "WinDefend", "wscsvc", "Sense", "sppsvc", "SecurityHealthService") param2="stopped" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode param1 dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `known_services_killed_by_ransomware_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the 7036 EventCode ScManager in System audit Logs from your endpoints.
-known_false_positives: Admin activities or installing related updates may do a sudden stop to list of services we monitor.
+search: '`wineventlog_system` EventCode=7036 param1 IN ("*Volume Shadow Copy*","*VSS*",
+  "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*", "DefWatch",
+  "ccEvtMgr", "ccSetMgr", "SavRoam", "RTVscan", "QBFCService", "QBIDPService", "Intuit.QuickBooks.FCS",
+  "QBCFMonitorService" "YooBackup", "YooIT", "*Veeam*", "PDVFSService", "BackupExecVSSProvider",
+  "BackupExecAgentAccelerator", "BackupExec*", "WdBoot", "WdFilter", "WdNisDrv", "WdNisSvc",
+  "WinDefend", "wscsvc", "Sense", "sppsvc", "SecurityHealthService") param2="stopped"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode param1
+  dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `known_services_killed_by_ransomware_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the 7036 EventCode ScManager in System audit Logs from your endpoints.
+known_false_positives: Admin activities or installing related updates may do a sudden
+  stop to list of services we monitor.
 references:
 - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/
 - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/
@@ -22,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,16 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - dest
-  - Type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/known_services_killed_by_ransomware/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/known_services_killed_by_ransomware/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml b/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml
index c6b1744c59..e5d8f8ab4c 100644
--- a/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml
+++ b/detections/endpoint/linux_account_manipulation_of_ssh_config_and_keys.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of SSH keys on a Linux machine. It leverages filesystem event logs to identify when files within "/etc/ssh/*" or "~/.ssh/*" are deleted. This activity is significant because attackers may delete or modify SSH keys to evade security measures or as part of a destructive payload, similar to the AcidRain malware. If confirmed malicious, this behavior could lead to impaired security features, hindered forensic investigations, or further unauthorized access, necessitating immediate investigation to identify the responsible process and user.
+description: The following analytic detects the deletion of SSH keys on a Linux machine.
+  It leverages filesystem event logs to identify when files within "/etc/ssh/*" or
+  "~/.ssh/*" are deleted. This activity is significant because attackers may delete
+  or modify SSH keys to evade security measures or as part of a destructive payload,
+  similar to the AcidRain malware. If confirmed malicious, this behavior could lead
+  to impaired security features, hindered forensic investigations, or further unauthorized
+  access, necessitating immediate investigation to identify the responsible process
+  and user.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted AND Filesystem.file_path IN ("/etc/ssh/*", "~/.ssh/*") by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_account_manipulation_of_ssh_config_and_keys_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted AND
+  Filesystem.file_path IN ("/etc/ssh/*", "~/.ssh/*") by _time span=1h Filesystem.file_name
+  Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action |
+  `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_account_manipulation_of_ssh_config_and_keys_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/
 drilldown_searches:
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_add_files_in_known_crontab_directories.yml b/detections/endpoint/linux_add_files_in_known_crontab_directories.yml
index 5b14ce0cf2..6f5cdb9f14 100644
--- a/detections/endpoint/linux_add_files_in_known_crontab_directories.yml
+++ b/detections/endpoint/linux_add_files_in_known_crontab_directories.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unauthorized file creation in known crontab directories on Unix-based systems. It leverages filesystem data to identify new files in directories such as /etc/cron* and /var/spool/cron/*. This activity is significant as it may indicate an attempt by threat actors or malware to establish persistence on a compromised host. If confirmed malicious, this could allow attackers to execute arbitrary code at scheduled intervals, potentially leading to further system compromise and unauthorized access to sensitive information.
+description: The following analytic detects unauthorized file creation in known crontab
+  directories on Unix-based systems. It leverages filesystem data to identify new
+  files in directories such as /etc/cron* and /var/spool/cron/*. This activity is
+  significant as it may indicate an attempt by threat actors or malware to establish
+  persistence on a compromised host. If confirmed malicious, this could allow attackers
+  to execute arbitrary code at scheduled intervals, potentially leading to further
+  system compromise and unauthorized access to sensitive information.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/cron*", "*/var/spool/cron/*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_add_files_in_known_crontab_directories_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can create file in crontab folders for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/cron*",
+  "*/var/spool/cron/*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_add_files_in_known_crontab_directories_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints.
+  If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
+known_false_positives: Administrator or network operator can create file in crontab
+  folders for automation purposes. Please update the filter macros to remove false
+  positives.
 references:
 - https://www.sandflysecurity.com/blog/detecting-cronrat-malware-on-linux-instantly/
 - https://www.cyberciti.biz/faq/how-do-i-add-jobs-to-cron-under-linux-or-unix-oses/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_add_user_account.yml b/detections/endpoint/linux_add_user_account.yml
index 83139fbcba..14d54e8bca 100644
--- a/detections/endpoint/linux_add_user_account.yml
+++ b/detections/endpoint/linux_add_user_account.yml
@@ -5,12 +5,32 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk.
+description: The following analytic detects the creation of new user accounts on Linux
+  systems using commands like "useradd" or "adduser." It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as adversaries often create new user accounts
+  to establish persistence on compromised hosts. If confirmed malicious, this could
+  allow attackers to maintain access, escalate privileges, and further compromise
+  the system, posing a severe security risk.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name IN ("useradd", "adduser") OR Processes.process IN ("*useradd *", "*adduser *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_add_user_account_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes
+  where Processes.process_name IN ("useradd", "adduser") OR Processes.process IN ("*useradd
+  *", "*adduser *") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_add_user_account_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://linuxize.com/post/how-to-create-users-in-linux-using-the-useradd-command/
 tags:
@@ -33,19 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_adduser/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_adduser/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_adding_crontab_using_list_parameter.yml b/detections/endpoint/linux_adding_crontab_using_list_parameter.yml
index a635b9c165..e93cd78d79 100644
--- a/detections/endpoint/linux_adding_crontab_using_list_parameter.yml
+++ b/detections/endpoint/linux_adding_crontab_using_list_parameter.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects suspicious modifications to cron jobs on Linux systems using the crontab command with list parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt to establish persistence or execute malicious code on a schedule. If confirmed malicious, the impact could include unauthorized code execution, data destruction, or other damaging outcomes. Further investigation should analyze the added cron job, its associated command, and any related processes.
+description: The following analytic detects suspicious modifications to cron jobs
+  on Linux systems using the crontab command with list parameters. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  command-line executions. This activity is significant as it may indicate an attempt
+  to establish persistence or execute malicious code on a schedule. If confirmed malicious,
+  the impact could include unauthorized code execution, data destruction, or other
+  damaging outcomes. Further investigation should analyze the added cron job, its
+  associated command, and any related processes.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "crontab" Processes.process= "* -l*" by  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_adding_crontab_using_list_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "crontab"
+  Processes.process= "* -l*" by  Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.dest
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_adding_crontab_using_list_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -39,19 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_list_parameter/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_list_parameter/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_apt_get_privilege_escalation.yml b/detections/endpoint/linux_apt_get_privilege_escalation.yml
index 0ebabe0ef9..f957c3b4d9 100644
--- a/detections/endpoint/linux_apt_get_privilege_escalation.yml
+++ b/detections/endpoint/linux_apt_get_privilege_escalation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'apt-get' command with elevated privileges using 'sudo' on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a user may be attempting to escalate privileges to root, which could lead to unauthorized system control. If confirmed malicious, an attacker could gain root access, allowing them to execute arbitrary commands, install or remove software, and potentially compromise the entire system.
+description: The following analytic detects the execution of the 'apt-get' command
+  with elevated privileges using 'sudo' on a Linux system. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process execution logs
+  that include command-line details. This activity is significant because it indicates
+  a user may be attempting to escalate privileges to root, which could lead to unauthorized
+  system control. If confirmed malicious, an attacker could gain root access, allowing
+  them to execute arbitrary commands, install or remove software, and potentially
+  compromise the entire system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt-get*" AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_apt_get_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt-get*"
+  AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_apt_get_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/apt-get/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt_get/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_apt_privilege_escalation.yml b/detections/endpoint/linux_apt_privilege_escalation.yml
index 32853c028b..e733e081cb 100644
--- a/detections/endpoint/linux_apt_privilege_escalation.yml
+++ b/detections/endpoint/linux_apt_privilege_escalation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the Advanced Package Tool (APT) with elevated privileges via sudo on Linux systems. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes where APT commands are executed with sudo rights. This activity is significant because it indicates a user can run system commands as root, potentially leading to unauthorized root shell access. If confirmed malicious, this could allow an attacker to escalate privileges, execute arbitrary commands, and gain full control over the affected system, posing a severe security risk.
+description: The following analytic detects the use of the Advanced Package Tool (APT)
+  with elevated privileges via sudo on Linux systems. It leverages Endpoint Detection
+  and Response (EDR) telemetry to identify processes where APT commands are executed
+  with sudo rights. This activity is significant because it indicates a user can run
+  system commands as root, potentially leading to unauthorized root shell access.
+  If confirmed malicious, this could allow an attacker to escalate privileges, execute
+  arbitrary commands, and gain full control over the affected system, posing a severe
+  security risk.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt*" AND Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_apt_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*apt*" AND
+  Processes.process="*APT::Update::Pre-Invoke::*" AND Processes.process="*sudo*" by
+  Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_apt_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/apt/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/apt/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_at_allow_config_file_creation.yml b/detections/endpoint/linux_at_allow_config_file_creation.yml
index 831a6c533d..878276b261 100644
--- a/detections/endpoint/linux_at_allow_config_file_creation.yml
+++ b/detections/endpoint/linux_at_allow_config_file_creation.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of the /etc/at.allow or /etc/at.deny configuration files in Linux. It leverages file creation events from the Endpoint datamodel to identify when these files are created. This activity is significant as these files control user permissions for the "at" scheduling application and can be abused by attackers to establish persistence. If confirmed malicious, this could allow unauthorized execution of malicious code, leading to potential data theft or further system compromise. Analysts should review the file path, creation time, and associated processes to assess the threat.
+description: The following analytic detects the creation of the /etc/at.allow or /etc/at.deny
+  configuration files in Linux. It leverages file creation events from the Endpoint
+  datamodel to identify when these files are created. This activity is significant
+  as these files control user permissions for the "at" scheduling application and
+  can be abused by attackers to establish persistence. If confirmed malicious, this
+  could allow unauthorized execution of malicious code, leading to potential data
+  theft or further system compromise. Analysts should review the file path, creation
+  time, and associated processes to assess the threat.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/at.allow", "*/etc/at.deny") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_at_allow_config_file_creation_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can create this file for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/at.allow",
+  "*/etc/at.deny") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_at_allow_config_file_creation_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints
+  into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for
+  Linux Sysmon from Splunkbase.
+known_false_positives: Administrator or network operator can create this file for
+  automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://linuxize.com/post/at-command-in-linux/
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_at_application_execution.yml b/detections/endpoint/linux_at_application_execution.yml
index d3309ddfab..0819b2a5c6 100644
--- a/detections/endpoint/linux_at_application_execution.yml
+++ b/detections/endpoint/linux_at_application_execution.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names associated with "at" or "atd". This activity is significant because the "At" application can be exploited to maintain unauthorized access or deliver additional malicious payloads. If confirmed malicious, this behavior could lead to data theft, ransomware attacks, or other severe consequences. Immediate investigation is required to determine the legitimacy of the execution and mitigate potential risks.
+description: The following analytic detects the execution of the "At" application
+  in Linux, which can be used by attackers to create persistence entries on a compromised
+  host. This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and parent process names associated with "at" or "atd".
+  This activity is significant because the "At" application can be exploited to maintain
+  unauthorized access or deliver additional malicious payloads. If confirmed malicious,
+  this behavior could lead to data theft, ransomware attacks, or other severe consequences.
+  Immediate investigation is required to determine the legitimacy of the execution
+  and mitigate potential risks.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where  Processes.process_name IN ("at", "atd") OR Processes.parent_process_name IN ("at", "atd") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_at_application_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes
+  where  Processes.process_name IN ("at", "atd") OR Processes.parent_process_name
+  IN ("at", "atd") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_at_application_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/001/
 - https://www.linkedin.com/pulse/getting-attacker-ip-address-from-malicious-linux-job-craig-rowland/
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_auditd_add_user_account.yml b/detections/endpoint/linux_auditd_add_user_account.yml
index a80987dec3..1079dd49e3 100644
--- a/detections/endpoint/linux_auditd_add_user_account.yml
+++ b/detections/endpoint/linux_auditd_add_user_account.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk.
+description: The following analytic detects the creation of new user accounts on Linux
+  systems using commands like "useradd" or "adduser." It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as adversaries often create new user accounts
+  to establish persistence on compromised hosts. If confirmed malicious, this could
+  allow attackers to maintain access, escalate privileges, and further compromise
+  the system, posing a severe security risk.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process`| rename host as dest | where LIKE (process_exec, "%useradd%") OR LIKE (process_exec, "%adduser%") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle dest  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_add_user_account_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process`| rename host as
+  dest | where LIKE (process_exec, "%useradd%") OR LIKE (process_exec, "%adduser%")
+  | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle
+  dest  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_add_user_account_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://linuxize.com/post/how-to-create-users-in-linux-using-the-useradd-command/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,13 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user/linux_auditd_add_user.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user/linux_auditd_add_user.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_add_user_account_type.yml b/detections/endpoint/linux_auditd_add_user_account_type.yml
index 34634bf18d..b6f1ad0f43 100644
--- a/detections/endpoint/linux_auditd_add_user_account_type.yml
+++ b/detections/endpoint/linux_auditd_add_user_account_type.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the suspicious add user account type. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
+description: The following analytic detects the suspicious add user account type.
+  This behavior is critical for a SOC to monitor because it may indicate attempts
+  to gain unauthorized access or maintain control over a system. Such actions could
+  be signs of malicious activity. If confirmed, this could lead to serious consequences,
+  including a compromised system, unauthorized access to sensitive data, or even a
+  wider breach affecting the entire network. Detecting and responding to these signs
+  early is essential to prevent potential security incidents.
 data_source:
 - Linux Auditd Add User
-search: '`linux_auditd` type=ADD_USER | rename hostname as dest| stats count min(_time) as firstTime max(_time) as lastTime by exe pid dest res UID type | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_add_user_account_type_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=ADD_USER | rename hostname as dest| stats count min(_time)
+  as firstTime max(_time) as lastTime by exe pid dest res UID type | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_add_user_account_type_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - exe
-  - pid
-  - hostname
-  - res
-  - UID
-  - type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user_type/linux_auditd_add_user_type.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/linux_auditd_add_user_type/linux_auditd_add_user_type.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_at_application_execution.yml b/detections/endpoint/linux_auditd_at_application_execution.yml
index f6ca1c20ed..1cf94c2705 100644
--- a/detections/endpoint/linux_auditd_at_application_execution.yml
+++ b/detections/endpoint/linux_auditd_at_application_execution.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names associated with "at" or "atd". This activity is significant because the "At" application can be exploited to maintain unauthorized access or deliver additional malicious payloads. If confirmed malicious, this behavior could lead to data theft, ransomware attacks, or other severe consequences. Immediate investigation is required to determine the legitimacy of the execution and mitigate potential risks.
+description: The following analytic detects the execution of the "At" application
+  in Linux, which can be used by attackers to create persistence entries on a compromised
+  host. This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and parent process names associated with "at" or "atd".
+  This activity is significant because the "At" application can be exploited to maintain
+  unauthorized access or deliver additional malicious payloads. If confirmed malicious,
+  this behavior could lead to data theft, ransomware attacks, or other severe consequences.
+  Immediate investigation is required to determine the legitimacy of the execution
+  and mitigate potential risks.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm IN ("at", "atd") OR exe IN ("/usr/bin/at","/usr/bin/atd") AND NOT (UID IN("daemon")) | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_at_application_execution_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm IN ("at", "atd") OR exe IN ("/usr/bin/at","/usr/bin/atd")
+  AND NOT (UID IN("daemon")) | rename host as dest | stats count min(_time) as firstTime
+  max(_time) as lastTime by comm exe  SYSCALL UID ppid pid dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_at_application_execution_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/001/
 - https://www.linkedin.com/pulse/getting-attacker-ip-address-from-malicious-linux-job-craig-rowland/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the "at" application.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the
+    "at" application.
   mitre_attack_id:
   - T1053.002
   - T1053
@@ -46,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_auditd_at/linux_auditd_at_execution.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_auditd_at/linux_auditd_at_execution.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_auditd_service_stop.yml b/detections/endpoint/linux_auditd_auditd_service_stop.yml
index 6e319b0c91..496a8501f4 100644
--- a/detections/endpoint/linux_auditd_auditd_service_stop.yml
+++ b/detections/endpoint/linux_auditd_auditd_service_stop.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
+description: The following analytic detects the suspicious auditd service stop. This
+  behavior is critical for a SOC to monitor because it may indicate attempts to gain
+  unauthorized access or maintain control over a system. Such actions could be signs
+  of malicious activity. If confirmed, this could lead to serious consequences, including
+  a compromised system, unauthorized access to sensitive data, or even a wider breach
+  affecting the entire network. Detecting and responding to these signs early is essential
+  to prevent potential security incidents.
 data_source:
 - Linux Auditd Service Stop
-search: '`linux_auditd` type=SERVICE_STOP unit IN ("auditd") | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_auditd_service_stop_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SERVICE_STOP unit IN ("auditd") | rename host as dest
+  | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm
+  exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_auditd_service_stop_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - type
-  - pid
-  - UID
-  - comm
-  - exe
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_auditd_service_stop/linux_auditd_auditd_service_stop.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_auditd_service_stop/linux_auditd_auditd_service_stop.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_base64_decode_files.yml b/detections/endpoint/linux_auditd_base64_decode_files.yml
index 1773a63279..770cb47143 100644
--- a/detections/endpoint/linux_auditd_base64_decode_files.yml
+++ b/detections/endpoint/linux_auditd_base64_decode_files.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious Base64 decode operations that may indicate malicious activity, such as data exfiltration or execution of encoded commands. Base64 is commonly used to encode data for safe transmission, but attackers may abuse it to conceal malicious payloads. This detection focuses on identifying unusual or unexpected Base64 decoding processes, particularly when associated with critical files or directories. By monitoring these activities, the analytic helps uncover potential threats, enabling security teams to respond promptly and mitigate risks associated with encoded malware or unauthorized data access.
+description: The following analytic detects suspicious Base64 decode operations that
+  may indicate malicious activity, such as data exfiltration or execution of encoded
+  commands. Base64 is commonly used to encode data for safe transmission, but attackers
+  may abuse it to conceal malicious payloads. This detection focuses on identifying
+  unusual or unexpected Base64 decoding processes, particularly when associated with
+  critical files or directories. By monitoring these activities, the analytic helps
+  uncover potential threats, enabling security teams to respond promptly and mitigate
+  risks associated with encoded malware or unauthorized data access.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE(process_exec, "%base64%") AND (LIKE(process_exec, "%-d %") OR LIKE(process_exec, "% --d%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_base64_decode_files_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE(process_exec, "%base64%") AND (LIKE(process_exec, "%-d %") OR
+  LIKE(process_exec, "% --d%")) | stats count min(_time) as firstTime max(_time) as
+  lastTime by argc process_exec dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_base64_decode_files_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://gtfobins.github.io/gtfobins/dd/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to decode a file using base64.
+  message: A [$process_exec$] event occurred on host - [$dest$] to decode a file using
+    base64.
   mitre_attack_id:
   - T1140
   observable:
@@ -44,14 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/linux_auditd_base64/linux_auditd_base64.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/linux_auditd_base64/linux_auditd_base64.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_change_file_owner_to_root.yml b/detections/endpoint/linux_auditd_change_file_owner_to_root.yml
index 6c16348207..40cfa0aa8d 100644
--- a/detections/endpoint/linux_auditd_change_file_owner_to_root.yml
+++ b/detections/endpoint/linux_auditd_change_file_owner_to_root.yml
@@ -5,12 +5,31 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the 'chown' command to change a file owner to 'root' on a Linux system. It leverages Linux Auditd telemetry, specifically monitoring command-line executions and process details. This activity is significant as it may indicate an attempt to escalate privileges by adversaries, malware, or red teamers. If confirmed malicious, this action could allow an attacker to gain root-level access, leading to full control over the compromised host and potential persistence within the environment.
+description: The following analytic detects the use of the 'chown' command to change
+  a file owner to 'root' on a Linux system. It leverages Linux Auditd telemetry, specifically
+  monitoring command-line executions and process details. This activity is significant
+  as it may indicate an attempt to escalate privileges by adversaries, malware, or
+  red teamers. If confirmed malicious, this action could allow an attacker to gain
+  root-level access, leading to full control over the compromised host and potential
+  persistence within the environment.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process`| rename host as dest | where LIKE (process_exec, "%chown %root%") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_change_file_owner_to_root_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process`| rename host as
+  dest | where LIKE (process_exec, "%chown %root%") | stats count min(_time) as firstTime
+  max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_change_file_owner_to_root_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://unix.stackexchange.com/questions/101073/how-to-change-permissions-from-root-user-to-all-users
 - https://askubuntu.com/questions/617850/changing-from-user-to-superuser
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A [$process_exec$] event occurred on host - [$dest$] to change a file owner to root.
+  message: A [$process_exec$] event occurred on host - [$dest$] to change a file owner
+    to root.
   mitre_attack_id:
   - T1222.002
   - T1222
@@ -45,13 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_auditd_chown_root/linux_auditd_chown_root.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/linux_auditd_chown_root/linux_auditd_chown_root.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_clipboard_data_copy.yml b/detections/endpoint/linux_auditd_clipboard_data_copy.yml
index a9d3ce2382..488bd91aa6 100644
--- a/detections/endpoint/linux_auditd_clipboard_data_copy.yml
+++ b/detections/endpoint/linux_auditd_clipboard_data_copy.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Linux Auditd telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment.
+description: The following analytic detects the use of the Linux 'xclip' command to
+  copy data from the clipboard. It leverages Linux Auditd telemetry, focusing on process
+  names and command-line arguments related to clipboard operations. This activity
+  is significant because adversaries can exploit clipboard data to capture sensitive
+  information such as passwords or IP addresses. If confirmed malicious, this technique
+  could lead to unauthorized data exfiltration, compromising sensitive information
+  and potentially aiding further attacks within the environment.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE(process_exec, "%xclip%") AND (LIKE(process_exec, "%clipboard%") OR LIKE(process_exec, "%-o%") OR LIKE(process_exec, "%clip %") OR LIKE(process_exec, "%-selection %") OR LIKE(process_exec, "%sel %")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_clipboard_data_copy_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: False positives may be present on Linux desktop as it may commonly be used by administrators or end users. Filter as needed.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE(process_exec, "%xclip%") AND (LIKE(process_exec, "%clipboard%")
+  OR LIKE(process_exec, "%-o%") OR LIKE(process_exec, "%clip %") OR LIKE(process_exec,
+  "%-selection %") OR LIKE(process_exec, "%sel %")) | stats count min(_time) as firstTime
+  max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `linux_auditd_clipboard_data_copy_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: False positives may be present on Linux desktop as it may commonly
+  be used by administrators or end users. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1115/
 - https://linux.die.net/man/1/xclip
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 40
-  message: A [$process_exec$] event occurred on host - [$dest$] to copy data from the clipboard.
+  message: A [$process_exec$] event occurred on host - [$dest$] to copy data from
+    the clipboard.
   mitre_attack_id:
   - T1115
   observable:
@@ -42,14 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/linux_auditd_xclip/linux_auditd_xclip.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/linux_auditd_xclip/linux_auditd_xclip.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_data_destruction_command.yml b/detections/endpoint/linux_auditd_data_destruction_command.yml
index 005764584e..d9fc5e7c92 100644
--- a/detections/endpoint/linux_auditd_data_destruction_command.yml
+++ b/detections/endpoint/linux_auditd_data_destruction_command.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a Unix shell command designed to wipe root directories on a Linux host. It leverages data from Linux Auditd, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root' option. This activity is significant as it indicates potential data destruction attempts, often associated with malware like Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, system instability, and compromised integrity of the affected Linux host. Immediate investigation and response are crucial to mitigate potential damage.
+description: The following analytic detects the execution of a Unix shell command
+  designed to wipe root directories on a Linux host. It leverages data from Linux
+  Auditd, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root'
+  option. This activity is significant as it indicates potential data destruction
+  attempts, often associated with malware like Awfulshred. If confirmed malicious,
+  this behavior could lead to severe data loss, system instability, and compromised
+  integrity of the affected Linux host. Immediate investigation and response are crucial
+  to mitigate potential damage.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE (process_exec, "%rm %") AND LIKE (process_exec, "% -rf %") AND LIKE (process_exec, "%--no-preserve-root%") | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_data_destruction_command_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE (process_exec, "%rm %") AND LIKE (process_exec, "% -rf %") AND
+  LIKE (process_exec, "%--no-preserve-root%") | stats count min(_time) as firstTime
+  max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_data_destruction_command_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
 known_false_positives: unknown
 references:
 - https://cert.gov.ua/article/3718487
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,14 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_no_preserve_root/linux_auditd_no_preserve_root.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_no_preserve_root/linux_auditd_no_preserve_root.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml
index 8da72aeac3..e4c1cddc8a 100644
--- a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml
+++ b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious data transfer activities that involve the use of the `split` syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.
+description: The following analytic detects suspicious data transfer activities that
+  involve the use of the `split` syscall, potentially indicating an attempt to evade
+  detection by breaking large files into smaller parts. Attackers may use this technique
+  to bypass size-based security controls, facilitating the covert exfiltration of
+  sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall,
+  this analytic helps identify potential data exfiltration attempts, allowing security
+  teams to intervene and prevent the unauthorized transfer of critical information
+  from the network.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE(process_exec, "%split %") AND LIKE(process_exec, "% -b %") | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_data_transfer_size_limits_via_split_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE(process_exec, "%split %") AND LIKE(process_exec, "% -b %") | stats
+  count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_data_transfer_size_limits_via_split_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,14 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_b_exec/linux_auditd_split_b_exec.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_b_exec/linux_auditd_split_b_exec.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml
index 3801c130b7..0d491f09a1 100644
--- a/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml
+++ b/detections/endpoint/linux_auditd_data_transfer_size_limits_via_split_syscall.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious data transfer activities that involve the use of the `split` syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.
+description: The following analytic detects suspicious data transfer activities that
+  involve the use of the `split` syscall, potentially indicating an attempt to evade
+  detection by breaking large files into smaller parts. Attackers may use this technique
+  to bypass size-based security controls, facilitating the covert exfiltration of
+  sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall,
+  this analytic helps identify potential data exfiltration attempts, allowing security
+  teams to intervene and prevent the unauthorized transfer of critical information
+  from the network.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=split OR exe= "*/split" | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_data_transfer_size_limits_via_split_syscall_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm=split OR exe= "*/split" | rename host as
+  dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL
+  UID ppid pid success dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_data_transfer_size_limits_via_split_syscall_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] that limits the size of data transfer.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] that limits
+    the size of data transfer.
   mitre_attack_id:
   - T1030
   observable:
@@ -43,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_syscall/linux_auditd_split_syscall.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/linux_auditd_split_syscall/linux_auditd_split_syscall.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml b/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml
index 693417262f..c70e4307d1 100644
--- a/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml
+++ b/detections/endpoint/linux_auditd_database_file_and_directory_discovery.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious database file and directory discovery activities, which may signal an attacker attempt to locate and assess critical database assets on a compromised system. This behavior is often a precursor to data theft, unauthorized access, or privilege escalation, as attackers seek to identify valuable information stored in databases. By monitoring for unusual or unauthorized attempts to locate database files and directories, this analytic aids in early detection of potential reconnaissance or data breach efforts, enabling security teams to respond swiftly and mitigate the risk of further compromise.
+description: The following analytic detects suspicious database file and directory
+  discovery activities, which may signal an attacker attempt to locate and assess
+  critical database assets on a compromised system. This behavior is often a precursor
+  to data theft, unauthorized access, or privilege escalation, as attackers seek to
+  identify valuable information stored in databases. By monitoring for unusual or
+  unauthorized attempts to locate database files and directories, this analytic aids
+  in early detection of potential reconnaissance or data breach efforts, enabling
+  security teams to respond swiftly and mitigate the risk of further compromise.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.db%") OR LIKE (process_exec, "%.sql%") OR LIKE (process_exec, "%.sqlite%") OR LIKE (process_exec, "%.mdb%")OR LIKE (process_exec, "%.accdb%")OR LIKE (process_exec, "%.mdf%")OR LIKE (process_exec, "%.ndf%")OR LIKE (process_exec, "%.ldf%")OR LIKE (process_exec, "%.frm%")OR LIKE (process_exec, "%.idb%")OR LIKE (process_exec, "%.myd%")OR LIKE (process_exec, "%.myi%")OR LIKE (process_exec, "%.dbf%")OR LIKE (process_exec, "%.db2%")OR LIKE (process_exec, "%.dbc%")OR LIKE (process_exec, "%.fpt%")OR LIKE (process_exec, "%.ora%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_database_file_and_directory_discovery_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%.db%") OR LIKE (process_exec, "%.sql%") OR LIKE (process_exec,
+  "%.sqlite%") OR LIKE (process_exec, "%.mdb%")OR LIKE (process_exec, "%.accdb%")OR
+  LIKE (process_exec, "%.mdf%")OR LIKE (process_exec, "%.ndf%")OR LIKE (process_exec,
+  "%.ldf%")OR LIKE (process_exec, "%.frm%")OR LIKE (process_exec, "%.idb%")OR LIKE
+  (process_exec, "%.myd%")OR LIKE (process_exec, "%.myi%")OR LIKE (process_exec, "%.dbf%")OR
+  LIKE (process_exec, "%.db2%")OR LIKE (process_exec, "%.dbc%")OR LIKE (process_exec,
+  "%.fpt%")OR LIKE (process_exec, "%.ora%")) | stats count min(_time) as firstTime
+  max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_database_file_and_directory_discovery_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to discover database files and directories.
+  message: A [$process_exec$] event occurred on host - [$dest$] to discover database
+    files and directories.
   mitre_attack_id:
   - T1083
   observable:
@@ -44,14 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_db/linux_auditd_find_db.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_db/linux_auditd_find_db.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_dd_file_overwrite.yml b/detections/endpoint/linux_auditd_dd_file_overwrite.yml
index ce12c18355..1c62a122f4 100644
--- a/detections/endpoint/linux_auditd_dd_file_overwrite.yml
+++ b/detections/endpoint/linux_auditd_dd_file_overwrite.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Linux Auditd telemetry, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions.
+description: The following analytic detects the use of the 'dd' command to overwrite
+  files on a Linux system. It leverages data from Linux Auditd telemetry, focusing
+  on process execution logs that include command-line details. This activity is significant
+  because adversaries often use the 'dd' command to destroy or irreversibly overwrite
+  files, disrupting system availability and services. If confirmed malicious, this
+  behavior could lead to data destruction, making recovery difficult and potentially
+  causing significant operational disruptions.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where LIKE(process_exec, "%dd %") AND LIKE(process_exec, "% of=%") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_dd_file_overwrite_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where LIKE(process_exec, "%dd %") AND LIKE(process_exec, "% of=%") | stats
+  count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter
+  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_dd_file_overwrite_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://gtfobins.github.io/gtfobins/dd/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1485/T1485.md
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,13 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_dd_overwrite/linux_auditd_dd_overwrite.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_dd_overwrite/linux_auditd_dd_overwrite.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml b/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml
index f87a70b292..c5286f1a30 100644
--- a/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml
+++ b/detections/endpoint/linux_auditd_disable_or_modify_system_firewall.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the suspicious disable or modify system firewall. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
+description: The following analytic detects the suspicious disable or modify system
+  firewall. This behavior is critical for a SOC to monitor because it may indicate
+  attempts to gain unauthorized access or maintain control over a system. Such actions
+  could be signs of malicious activity. If confirmed, this could lead to serious consequences,
+  including a compromised system, unauthorized access to sensitive data, or even a
+  wider breach affecting the entire network. Detecting and responding to these signs
+  early is essential to prevent potential security incidents.
 data_source:
 - Linux Auditd Service Stop
-search: '`linux_auditd` type=SERVICE_STOP unit IN ("firewalld", "ufw") | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe unit dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_disable_or_modify_system_firewall_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SERVICE_STOP unit IN ("firewalld", "ufw") | rename host
+  as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid
+  UID comm exe unit dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_disable_or_modify_system_firewall_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A service event - [$type$] to disable or modify system firewall occured on host - [$dest$] .
+  message: A service event - [$type$] to disable or modify system firewall occured
+    on host - [$dest$] .
   mitre_attack_id:
   - T1562.004
   - T1562
@@ -44,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - type
-  - pid
-  - UID
-  - comm
-  - exe
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/linux_auditd_disable_firewall/linux_auditd_disable_firewall.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/linux_auditd_disable_firewall/linux_auditd_disable_firewall.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_doas_conf_file_creation.yml b/detections/endpoint/linux_auditd_doas_conf_file_creation.yml
index 1bc402f554..bcde751ddd 100644
--- a/detections/endpoint/linux_auditd_doas_conf_file_creation.yml
+++ b/detections/endpoint/linux_auditd_doas_conf_file_creation.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of the doas.conf file on a Linux host. This file is used by the doas utility to allow standard users to perform tasks as root, similar to sudo. The detection leverages Linux Auditd data, focusing on the creation of the doas.conf file. This activity is significant because it can indicate an attempt to gain elevated privileges, potentially by an adversary. If confirmed malicious, this could allow an attacker to execute commands with root commands with root privileges, leading to full system compromise.
+description: The following analytic detects the creation of the doas.conf file on
+  a Linux host. This file is used by the doas utility to allow standard users to perform
+  tasks as root, similar to sudo. The detection leverages Linux Auditd data, focusing
+  on the creation of the doas.conf file. This activity is significant because it can
+  indicate an attempt to gain elevated privileges, potentially by an adversary. If
+  confirmed malicious, this could allow an attacker to execute commands with root
+  commands with root privileges, leading to full system compromise.
 data_source:
 - Linux Auditd Path
-search: '`linux_auditd` type=PATH name ="/etc/doas.conf*" | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_doas_conf_file_creation_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=PATH name ="/etc/doas.conf*" | rename host as dest |
+  stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID
+  type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_doas_conf_file_creation_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://wiki.gentoo.org/wiki/Doas
 - https://www.makeuseof.com/how-to-install-and-use-doas/
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,15 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - name
-  - nametype
-  - OGID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_audited_doas_conf/linux_audited_doas_conf.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_audited_doas_conf/linux_audited_doas_conf.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_doas_tool_execution.yml b/detections/endpoint/linux_auditd_doas_tool_execution.yml
index d2d53daf24..e5375c577d 100644
--- a/detections/endpoint/linux_auditd_doas_tool_execution.yml
+++ b/detections/endpoint/linux_auditd_doas_tool_execution.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'doas' tool on a Linux host. This tool allows standard users to perform tasks with root privileges, similar to 'sudo'. The detection leverages data from Linux Auditd, focusing on process names and command-line executions. This activity is significant as 'doas' can be exploited by adversaries to gain elevated privileges on a compromised host. If confirmed malicious, this could lead to unauthorized administrative access, potentially compromising the entire system.
+description: The following analytic detects the execution of the 'doas' tool on a
+  Linux host. This tool allows standard users to perform tasks with root privileges,
+  similar to 'sudo'. The detection leverages data from Linux Auditd, focusing on process
+  names and command-line executions. This activity is significant as 'doas' can be
+  exploited by adversaries to gain elevated privileges on a compromised host. If confirmed
+  malicious, this could lead to unauthorized administrative access, potentially compromising
+  the entire system.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=doas | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_doas_tool_execution_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm=doas | rename host as dest | stats count
+  min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid
+  success dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_doas_tool_execution_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://wiki.gentoo.org/wiki/Doas
 - https://www.makeuseof.com/how-to-install-and-use-doas/
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the "doas" tool.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to execute the
+    "doas" tool.
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -44,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_doas/linux_auditd_doas.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_doas/linux_auditd_doas.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml b/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml
index c2f019bc93..ed255b616b 100644
--- a/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml
+++ b/detections/endpoint/linux_auditd_edit_cron_table_parameter.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious editing of cron jobs in Linux using the crontab command-line parameter (-e). It identifies this activity by monitoring command-line executions involving 'crontab' and the edit parameter. This behavior is significant for a SOC as cron job manipulations can indicate unauthorized persistence attempts or scheduled malicious actions. If confirmed malicious, this activity could lead to system compromise, unauthorized access, or broader network compromise.
+description: The following analytic detects the suspicious editing of cron jobs in
+  Linux using the crontab command-line parameter (-e). It identifies this activity
+  by monitoring command-line executions involving 'crontab' and the edit parameter.
+  This behavior is significant for a SOC as cron job manipulations can indicate unauthorized
+  persistence attempts or scheduled malicious actions. If confirmed malicious, this
+  activity could lead to system compromise, unauthorized access, or broader network
+  compromise.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL SYSCALL=rename (comm IN ("crontab") OR exe IN ("*/crontab")) success=yes AND NOT (UID IN("daemon")) | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_edit_cron_table_parameter_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL SYSCALL=rename (comm IN ("crontab") OR exe IN
+  ("*/crontab")) success=yes AND NOT (UID IN("daemon")) | rename host as dest | stats
+  count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid
+  pid dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_edit_cron_table_parameter_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/003/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to edit the cron table.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to edit the
+    cron table.
   mitre_attack_id:
   - T1053.003
   - T1053
@@ -45,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/linux_auditd_crontab_edit/linux_auditd_crontab_edit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/linux_auditd_crontab_edit/linux_auditd_crontab_edit.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_file_and_directory_discovery.yml b/detections/endpoint/linux_auditd_file_and_directory_discovery.yml
index ceee8b1cfe..8f65f35acb 100644
--- a/detections/endpoint/linux_auditd_file_and_directory_discovery.yml
+++ b/detections/endpoint/linux_auditd_file_and_directory_discovery.yml
@@ -5,12 +5,42 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious file and directory discovery activities, which may indicate an attacker's effort to locate sensitive documents and files on a compromised system. This behavior often precedes data exfiltration, as adversaries seek to identify valuable or confidential information for theft. By identifying unusual or unauthorized attempts to browse or enumerate files and directories, this analytic helps security teams detect potential reconnaissance or preparatory actions by an attacker, enabling timely intervention to prevent data breaches or unauthorized access.
+description: The following analytic detects suspicious file and directory discovery
+  activities, which may indicate an attacker's effort to locate sensitive documents
+  and files on a compromised system. This behavior often precedes data exfiltration,
+  as adversaries seek to identify valuable or confidential information for theft.
+  By identifying unusual or unauthorized attempts to browse or enumerate files and
+  directories, this analytic helps security teams detect potential reconnaissance
+  or preparatory actions by an attacker, enabling timely intervention to prevent data
+  breaches or unauthorized access.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.tif%") OR LIKE (process_exec, "%.tiff%") OR LIKE (process_exec, "%.gif%") OR LIKE (process_exec, "%.jpeg%")OR LIKE (process_exec, "%.jpg%")OR LIKE (process_exec, "%.jif%")OR LIKE (process_exec, "%.jfif%")OR LIKE (process_exec, "%.jp2%")OR LIKE (process_exec, "%.jpx%")OR LIKE (process_exec, "%.j2k%")OR LIKE (process_exec, "%.j2c%")OR LIKE (process_exec, "%.fpx%")OR LIKE (process_exec, "%.pcd%")OR LIKE (process_exec, "%.png%")OR LIKE (process_exec, "%.flv%") OR LIKE (process_exec, "%.pdf%")OR LIKE (process_exec, "%.mp4%")OR LIKE (process_exec, "%.mp3%")OR LIKE (process_exec, "%.gifv%")OR LIKE (process_exec, "%.avi%")OR LIKE (process_exec, "%.mov%")OR LIKE (process_exec, "%.mpeg%")OR LIKE (process_exec, "%.wav%")OR LIKE (process_exec, "%.doc%")OR LIKE (process_exec, "%.docx%")OR LIKE (process_exec, "%.xls%")OR LIKE (process_exec, "%.xlsx%")OR LIKE (process_exec, "%.svg%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_file_and_directory_discovery_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%.tif%") OR LIKE (process_exec, "%.tiff%") OR LIKE (process_exec,
+  "%.gif%") OR LIKE (process_exec, "%.jpeg%")OR LIKE (process_exec, "%.jpg%")OR LIKE
+  (process_exec, "%.jif%")OR LIKE (process_exec, "%.jfif%")OR LIKE (process_exec,
+  "%.jp2%")OR LIKE (process_exec, "%.jpx%")OR LIKE (process_exec, "%.j2k%")OR LIKE
+  (process_exec, "%.j2c%")OR LIKE (process_exec, "%.fpx%")OR LIKE (process_exec, "%.pcd%")OR
+  LIKE (process_exec, "%.png%")OR LIKE (process_exec, "%.flv%") OR LIKE (process_exec,
+  "%.pdf%")OR LIKE (process_exec, "%.mp4%")OR LIKE (process_exec, "%.mp3%")OR LIKE
+  (process_exec, "%.gifv%")OR LIKE (process_exec, "%.avi%")OR LIKE (process_exec,
+  "%.mov%")OR LIKE (process_exec, "%.mpeg%")OR LIKE (process_exec, "%.wav%")OR LIKE
+  (process_exec, "%.doc%")OR LIKE (process_exec, "%.docx%")OR LIKE (process_exec,
+  "%.xls%")OR LIKE (process_exec, "%.xlsx%")OR LIKE (process_exec, "%.svg%")) | stats
+  count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_file_and_directory_discovery_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to discover files and directories.
+  message: A [$process_exec$] event occurred on host - [$dest$] to discover files
+    and directories.
   mitre_attack_id:
   - T1083
   observable:
@@ -44,14 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_document/linux_auditd_find_document.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_document/linux_auditd_find_document.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml b/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml
index 3855ba25dc..ca0d983f27 100644
--- a/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml
+++ b/detections/endpoint/linux_auditd_file_permission_modification_via_chmod.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious file permission modifications using the `chmod` command, which may indicate an attacker attempting to alter access controls on critical files or directories. Such modifications can be used to grant unauthorized users elevated privileges or to conceal malicious activities by restricting legitimate access. By monitoring for unusual or unauthorized `chmod` usage, this analytic helps identify potential security breaches, allowing security teams to respond promptly to prevent privilege escalation, data tampering, or other unauthorized actions on the system.
+description: The following analytic detects suspicious file permission modifications
+  using the `chmod` command, which may indicate an attacker attempting to alter access
+  controls on critical files or directories. Such modifications can be used to grant
+  unauthorized users elevated privileges or to conceal malicious activities by restricting
+  legitimate access. By monitoring for unusual or unauthorized `chmod` usage, this
+  analytic helps identify potential security breaches, allowing security teams to
+  respond promptly to prevent privilege escalation, data tampering, or other unauthorized
+  actions on the system.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest  | where LIKE(process_exec, "%chmod%") AND (LIKE(process_exec, "% 777 %") OR LIKE(process_exec, "% 755 %") OR LIKE(process_exec, "%x%") OR LIKE(process_exec, "% 754 %") OR LIKE(process_exec, "% 700 %")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_file_permission_modification_via_chmod_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest  | where LIKE(process_exec, "%chmod%") AND (LIKE(process_exec, "% 777 %")
+  OR LIKE(process_exec, "% 755 %") OR LIKE(process_exec, "%x%") OR LIKE(process_exec,
+  "% 754 %") OR LIKE(process_exec, "% 700 %")) | stats count min(_time) as firstTime
+  max(_time) as lastTime by process_exec proctitle dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_file_permission_modification_via_chmod_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to modify file permissions using the "chmod" command.
+  message: A [$process_exec$] event occurred on host - [$dest$] to modify file permissions
+    using the "chmod" command.
   mitre_attack_id:
   - T1222.002
   - T1222
@@ -44,13 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chmod_exec_attrib/linux_auditd_chmod_exec_attrib.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chmod_exec_attrib/linux_auditd_chmod_exec_attrib.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml b/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml
index 6238e2149b..99d0783557 100644
--- a/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml
+++ b/detections/endpoint/linux_auditd_file_permissions_modification_via_chattr.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious file permissions modifications using the chattr command, which may indicate an attacker attempting to manipulate file attributes to evade detection or prevent alteration. The chattr command can be used to make files immutable or restrict deletion, which can be leveraged to protect malicious files or disrupt system operations. By monitoring for unusual or unauthorized chattr usage, this analytic helps identify potential tampering with critical files, enabling security teams to quickly respond to and mitigate threats associated with unauthorized file attribute changes.
+description: The following analytic detects suspicious file permissions modifications
+  using the chattr command, which may indicate an attacker attempting to manipulate
+  file attributes to evade detection or prevent alteration. The chattr command can
+  be used to make files immutable or restrict deletion, which can be leveraged to
+  protect malicious files or disrupt system operations. By monitoring for unusual
+  or unauthorized chattr usage, this analytic helps identify potential tampering with
+  critical files, enabling security teams to quickly respond to and mitigate threats
+  associated with unauthorized file attribute changes.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest |  where LIKE(process_exec, "%chattr %") AND LIKE(process_exec, "% -i%") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_file_permissions_modification_via_chattr_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest |  where LIKE(process_exec, "%chattr %") AND LIKE(process_exec, "% -i%")
+  | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle
+  normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_file_permissions_modification_via_chattr_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A [$process_exec$] event occurred on host - [$dest$] to modify file permissions using the "chattr" command.
+  message: A [$process_exec$] event occurred on host - [$dest$] to modify file permissions
+    using the "chattr" command.
   mitre_attack_id:
   - T1222.002
   - T1222
@@ -44,14 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chattr_i/linux_auditd_chattr_i.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.002/linux_auditd_chattr_i/linux_auditd_chattr_i.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml b/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml
index 07630cf985..2bd584fe16 100644
--- a/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml
+++ b/detections/endpoint/linux_auditd_find_credentials_from_password_managers.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious attempts to find credentials stored in password managers, which may indicate an attacker's effort to retrieve sensitive login information. Password managers are often targeted by adversaries seeking to access stored passwords for further compromise or lateral movement within a network. By monitoring for unusual or unauthorized access to password manager files or processes, this analytic helps identify potential credential theft attempts, enabling security teams to respond quickly to protect critical accounts and prevent further unauthorized access.
+description: The following analytic detects suspicious attempts to find credentials
+  stored in password managers, which may indicate an attacker's effort to retrieve
+  sensitive login information. Password managers are often targeted by adversaries
+  seeking to access stored passwords for further compromise or lateral movement within
+  a network. By monitoring for unusual or unauthorized access to password manager
+  files or processes, this analytic helps identify potential credential theft attempts,
+  enabling security teams to respond quickly to protect critical accounts and prevent
+  further unauthorized access.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.kdbx%") OR LIKE (process_exec, "%KeePass%") OR LIKE (process_exec, "%KeePass\.enforced%") OR LIKE (process_exec, "%.lpdb%")OR LIKE (process_exec, "%.opvault%")OR LIKE (process_exec, "%.agilekeychain%")OR LIKE (process_exec, "%.dashlane%")OR LIKE (process_exec, "%.rfx%")OR LIKE (process_exec, "%passbolt%")OR LIKE (process_exec, "%.spdb%")OR LIKE (process_exec, "%StickyPassword%")OR LIKE (process_exec, "%.walletx%")OR LIKE (process_exec, "%enpass%")OR LIKE (process_exec, "%vault%")OR LIKE (process_exec, "%.kdb%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_find_credentials_from_password_managers_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%.kdbx%") OR LIKE (process_exec, "%KeePass%") OR LIKE (process_exec,
+  "%KeePass\.enforced%") OR LIKE (process_exec, "%.lpdb%")OR LIKE (process_exec, "%.opvault%")OR
+  LIKE (process_exec, "%.agilekeychain%")OR LIKE (process_exec, "%.dashlane%")OR LIKE
+  (process_exec, "%.rfx%")OR LIKE (process_exec, "%passbolt%")OR LIKE (process_exec,
+  "%.spdb%")OR LIKE (process_exec, "%StickyPassword%")OR LIKE (process_exec, "%.walletx%")OR
+  LIKE (process_exec, "%enpass%")OR LIKE (process_exec, "%vault%")OR LIKE (process_exec,
+  "%.kdb%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc
+  process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_find_credentials_from_password_managers_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A [$process_exec$] event occurred on host - [$dest$] to find credentials stored in password managers.
+  message: A [$process_exec$] event occurred on host - [$dest$] to find credentials
+    stored in password managers.
   mitre_attack_id:
   - T1555.005
   - T1555
@@ -45,14 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_password_db/linux_auditd_find_password_db.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_password_db/linux_auditd_find_password_db.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml b/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml
index 2f996d9e67..8f87f2f717 100644
--- a/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml
+++ b/detections/endpoint/linux_auditd_find_credentials_from_password_stores.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
+description: The following analytic detects suspicious attempts to find credentials
+  stored in password stores, indicating a potential attacker's effort to access sensitive
+  login information. Password stores are critical repositories that contain valuable
+  credentials, and unauthorized access to them can lead to significant security breaches.
+  By monitoring for unusual or unauthorized activities related to password store access,
+  this analytic helps identify potential credential theft attempts, allowing security
+  teams to respond promptly and prevent unauthorized access to critical systems and
+  data.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%password%") OR LIKE (process_exec, "%pass %") OR LIKE (process_exec, "%credential%")OR LIKE (process_exec, "%creds%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_find_credentials_from_password_stores_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%password%") OR LIKE (process_exec, "%pass %") OR LIKE (process_exec,
+  "%credential%")OR LIKE (process_exec, "%creds%")) | stats count min(_time) as firstTime
+  max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_find_credentials_from_password_stores_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A [$process_exec$] event occurred on host - [$dest$] to find credentials stored in password managers.
+  message: A [$process_exec$] event occurred on host - [$dest$] to find credentials
+    stored in password managers.
   mitre_attack_id:
   - T1555.005
   - T1555
@@ -45,14 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_credentials/linux_auditd_find_credentials.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.005/linux_auditd_find_credentials/linux_auditd_find_credentials.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_find_private_keys.yml b/detections/endpoint/linux_auditd_find_private_keys.yml
index a96015abad..091226d039 100644
--- a/detections/endpoint/linux_auditd_find_private_keys.yml
+++ b/detections/endpoint/linux_auditd_find_private_keys.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious attempts to find private keys, which may indicate an attacker's effort to access sensitive cryptographic information. Private keys are crucial for securing encrypted communications and data, and unauthorized access to them can lead to severe security breaches, including data decryption and identity theft. By monitoring for unusual or unauthorized searches for private keys, this analytic helps identify potential threats to cryptographic security, enabling security teams to take swift action to protect the integrity and confidentiality of encrypted information.
+description: The following analytic detects suspicious attempts to find private keys,
+  which may indicate an attacker's effort to access sensitive cryptographic information.
+  Private keys are crucial for securing encrypted communications and data, and unauthorized
+  access to them can lead to severe security breaches, including data decryption and
+  identity theft. By monitoring for unusual or unauthorized searches for private keys,
+  this analytic helps identify potential threats to cryptographic security, enabling
+  security teams to take swift action to protect the integrity and confidentiality
+  of encrypted information.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.pem%") OR LIKE (process_exec, "%.cer%") OR LIKE (process_exec, "%.crt%") OR LIKE (process_exec, "%.pgp%") OR LIKE (process_exec, "%.key%") OR LIKE (process_exec, "%.gpg%")OR LIKE (process_exec, "%.ppk%") OR LIKE (process_exec, "%.p12%")OR LIKE (process_exec, "%.pfx%")OR LIKE (process_exec, "%.p7b%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_find_private_keys_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%.pem%") OR LIKE (process_exec, "%.cer%") OR LIKE (process_exec,
+  "%.crt%") OR LIKE (process_exec, "%.pgp%") OR LIKE (process_exec, "%.key%") OR LIKE
+  (process_exec, "%.gpg%")OR LIKE (process_exec, "%.ppk%") OR LIKE (process_exec,
+  "%.p12%")OR LIKE (process_exec, "%.pfx%")OR LIKE (process_exec, "%.p7b%")) | stats
+  count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_find_private_keys_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,14 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_gpg/linux_auditd_find_gpg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_gpg/linux_auditd_find_gpg.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_find_ssh_private_keys.yml b/detections/endpoint/linux_auditd_find_ssh_private_keys.yml
index 58362d5ec3..4bb6ba5e0d 100644
--- a/detections/endpoint/linux_auditd_find_ssh_private_keys.yml
+++ b/detections/endpoint/linux_auditd_find_ssh_private_keys.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious attempts to find SSH private keys, which may indicate an attacker's effort to compromise secure access to systems. SSH private keys are essential for secure authentication, and unauthorized access to these keys can enable attackers to gain unauthorized access to servers and other critical infrastructure. By monitoring for unusual or unauthorized searches for SSH private keys, this analytic helps identify potential threats to network security, allowing security teams to quickly respond and safeguard against unauthorized access and potential breaches.
+description: The following analytic detects suspicious attempts to find SSH private
+  keys, which may indicate an attacker's effort to compromise secure access to systems.
+  SSH private keys are essential for secure authentication, and unauthorized access
+  to these keys can enable attackers to gain unauthorized access to servers and other
+  critical infrastructure. By monitoring for unusual or unauthorized searches for
+  SSH private keys, this analytic helps identify potential threats to network security,
+  allowing security teams to quickly respond and safeguard against unauthorized access
+  and potential breaches.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%id_rsa%") OR LIKE (process_exec, "%id_dsa%")OR LIKE (process_exec, "%.key%") OR LIKE (process_exec, "%ssh_key%")OR LIKE (process_exec, "%authorized_keys%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_find_ssh_private_keys_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%id_rsa%") OR LIKE (process_exec, "%id_dsa%")OR LIKE (process_exec,
+  "%.key%") OR LIKE (process_exec, "%ssh_key%")OR LIKE (process_exec, "%authorized_keys%"))
+  | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_find_ssh_private_keys_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A [$process_exec$] event occurred on host - [$dest$] to find SSH private keys.
+  message: A [$process_exec$] event occurred on host - [$dest$] to find SSH private
+    keys.
   mitre_attack_id:
   - T1552.004
   - T1552
@@ -45,14 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_ssh_files/linux_auditd_find_ssh_files.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.004/linux_auditd_find_ssh_files/linux_auditd_find_ssh_files.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml b/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml
index 3be456e249..37d471b815 100644
--- a/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml
+++ b/detections/endpoint/linux_auditd_hardware_addition_swapoff.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the "swapoff" command, which disables the swapping of paging devices on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because disabling swap can be a tactic used by malware, such as Awfulshred, to evade detection and hinder forensic analysis. If confirmed malicious, this action could allow an attacker to manipulate system memory management, potentially leading to data corruption, system instability, or evasion of memory-based detection mechanisms.
+description: The following analytic detects the execution of the "swapoff" command,
+  which disables the swapping of paging devices on a Linux system. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs. This activity is significant because disabling swap can be a tactic used by
+  malware, such as Awfulshred, to evade detection and hinder forensic analysis. If
+  confirmed malicious, this action could allow an attacker to manipulate system memory
+  management, potentially leading to data corruption, system instability, or evasion
+  of memory-based detection mechanisms.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where LIKE(process_exec, "%swapoff %") AND LIKE(process_exec, "% -a%") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_hardware_addition_swapoff_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: administrator may disable swapping of devices in a linux host. Filter is needed.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where LIKE(process_exec, "%swapoff %") AND LIKE(process_exec, "% -a%")
+  | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle
+  normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_hardware_addition_swapoff_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: administrator may disable swapping of devices in a linux host.
+  Filter is needed.
 references:
 - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A [$process_exec$] event occurred on host - [$dest$] to disable the swapping of paging devices on a Linux system.
+  message: A [$process_exec$] event occurred on host - [$dest$] to disable the swapping
+    of paging devices on a Linux system.
   mitre_attack_id:
   - T1200
   observable:
@@ -42,14 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/linux_auditd_swapoff/linux_auditd_swapoff.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1200/linux_auditd_swapoff/linux_auditd_swapoff.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml b/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml
index 00f45638c4..ebda6a346e 100644
--- a/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml
+++ b/detections/endpoint/linux_auditd_hidden_files_and_directories_creation.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious creation of hidden files and directories, which may indicate an attacker's attempt to conceal malicious activities or unauthorized data. Hidden files and directories are often used to evade detection by security tools and administrators, providing a stealthy means for storing malware, logs, or sensitive information. By monitoring for unusual or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide malicious operations, enabling security teams to uncover and address hidden threats effectively.
+description: The following analytic detects suspicious creation of hidden files and
+  directories, which may indicate an attacker's attempt to conceal malicious activities
+  or unauthorized data. Hidden files and directories are often used to evade detection
+  by security tools and administrators, providing a stealthy means for storing malware,
+  logs, or sensitive information. By monitoring for unusual or unauthorized creation
+  of hidden files and directories, this analytic helps identify potential attempts
+  to hide or unauthorized creation of hidden files and directories, this analytic
+  helps identify potential attempts to hide malicious operations, enabling security
+  teams to uncover and address hidden threats effectively.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where (LIKE (process_exec,"%touch %") OR LIKE (process_exec,"%mkdir %")OR LIKE (process_exec,"%vim %") OR LIKE (process_exec,"%vi %") OR LIKE (process_exec,"%nano %")) AND (LIKE (process_exec,"% ./.%")  OR LIKE (process_exec," .%")OR LIKE (process_exec," /.%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_hidden_files_and_directories_creation_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where (LIKE (process_exec,"%touch %") OR LIKE (process_exec,"%mkdir %")OR
+  LIKE (process_exec,"%vim %") OR LIKE (process_exec,"%vi %") OR LIKE (process_exec,"%nano
+  %")) AND (LIKE (process_exec,"% ./.%")  OR LIKE (process_exec," .%")OR LIKE (process_exec,"
+  /.%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_hidden_files_and_directories_creation_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,14 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_hidden_file/linux_auditd_hidden_file.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_hidden_file/linux_auditd_hidden_file.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml b/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml
index 71b2f5029f..c11a964f08 100644
--- a/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml
+++ b/detections/endpoint/linux_auditd_insert_kernel_module_using_insmod_utility.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Linux Auditd, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system.
+description: The following analytic detects the insertion of a Linux kernel module
+  using the insmod utility. It leverages data from Linux Auditd, focusing on process
+  execution logs that include process names and command-line details. This activity
+  is significant as it may indicate the installation of a rootkit or malicious kernel
+  module, potentially allowing an attacker to gain elevated privileges and bypass
+  security detections. If confirmed malicious, this could lead to unauthorized code
+  execution, persistent access, and severe compromise of the affected system.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=insmod | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_insert_kernel_module_using_insmod_utility_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm=insmod | rename host as dest | stats count
+  min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid
+  success dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_insert_kernel_module_using_insmod_utility_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/
 - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to insert a Linux kernel module using the insmod utility.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to insert a
+    Linux kernel module using the insmod utility.
   mitre_attack_id:
   - T1547.006
   - T1547
@@ -46,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_insmod/linux_auditd_insmod.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_insmod/linux_auditd_insmod.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml b/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml
index e7f8b99efc..c0d0a86d40 100644
--- a/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml
+++ b/detections/endpoint/linux_auditd_install_kernel_module_using_modprobe_utility.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the installation of a Linux kernel module using the modprobe utility. It leverages data from Linux Auditd, focusing on process names and command-line executions. This activity is significant because installing a kernel module can indicate an attempt to deploy a rootkit or other malicious kernel-level code, potentially leading to elevated privileges and bypassing security detections. If confirmed malicious, this could allow an attacker to gain persistent, high-level access to the system, compromising its integrity and security.
+description: The following analytic detects the installation of a Linux kernel module
+  using the modprobe utility. It leverages data from Linux Auditd, focusing on process
+  names and command-line executions. This activity is significant because installing
+  a kernel module can indicate an attempt to deploy a rootkit or other malicious kernel-level
+  code, potentially leading to elevated privileges and bypassing security detections.
+  If confirmed malicious, this could allow an attacker to gain persistent, high-level
+  access to the system, compromising its integrity and security.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=modprobe | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_install_kernel_module_using_modprobe_utility_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm=modprobe | rename host as dest | stats count
+  min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid
+  success dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_install_kernel_module_using_modprobe_utility_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/
 - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to install a Linux kernel module using the modprobe utility.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to install a
+    Linux kernel module using the modprobe utility.
   mitre_attack_id:
   - T1547.006
   - T1547
@@ -46,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe/linux_auditd_modprobe.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe/linux_auditd_modprobe.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_kernel_module_enumeration.yml b/detections/endpoint/linux_auditd_kernel_module_enumeration.yml
index 0f8a68263d..6ca64c07d9 100644
--- a/detections/endpoint/linux_auditd_kernel_module_enumeration.yml
+++ b/detections/endpoint/linux_auditd_kernel_module_enumeration.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Linux Auditd, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system.
+description: The following analytic identifies the use of the 'kmod' process to list
+  kernel modules on a Linux system. This detection leverages data from Linux Auditd,
+  focusing on process names and command-line executions. While listing kernel modules
+  is not inherently malicious, it can be a precursor to loading unauthorized modules
+  using 'insmod'. If confirmed malicious, this activity could allow an attacker to
+  load kernel modules, potentially leading to privilege escalation, persistence, or
+  other malicious actions within the system.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=lsmod | rename host as dest  | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `linux_auditd_kernel_module_enumeration_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '`linux_auditd` type=SYSCALL comm=lsmod | rename host as dest  | stats count
+  min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid
+  success dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `linux_auditd_kernel_module_enumeration_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://man7.org/linux/man-pages/man8/kmod.8.html
 drilldown_searches:
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to list kernel modules.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to list kernel
+    modules.
   mitre_attack_id:
   - T1082
   - T1014
@@ -42,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/linux_auditd_lsmod/linux_auditd_lsmod.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/linux_auditd_lsmod/linux_auditd_lsmod.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml b/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml
index 22ad724680..b5875a45d9 100644
--- a/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml
+++ b/detections/endpoint/linux_auditd_kernel_module_using_rmmod_utility.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious use of the `rmmod` utility for kernel module removal, which may indicate an attacker attempt to unload critical or security-related kernel modules. The `rmmod` command is used to remove modules from the Linux kernel, and unauthorized use can be a tactic to disable security features, conceal malicious activities, or disrupt system operations. By monitoring for unusual or unauthorized `rmmod` activity, this analytic helps identify potential tampering with kernel modules, enabling security teams to take proactive measures to protect system integrity and security.
+description: The following analytic detects suspicious use of the `rmmod` utility
+  for kernel module removal, which may indicate an attacker attempt to unload critical
+  or security-related kernel modules. The `rmmod` command is used to remove modules
+  from the Linux kernel, and unauthorized use can be a tactic to disable security
+  features, conceal malicious activities, or disrupt system operations. By monitoring
+  for unusual or unauthorized `rmmod` activity, this analytic helps identify potential
+  tampering with kernel modules, enabling security teams to take proactive measures
+  to protect system integrity and security.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=rmmod | rename host as dest  | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid success dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_kernel_module_using_rmmod_utility_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm=rmmod | rename host as dest  | stats count
+  min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid
+  success dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_kernel_module_using_rmmod_utility_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to remove a Linux kernel module using the rmmod utility.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to remove a
+    Linux kernel module using the rmmod utility.
   mitre_attack_id:
   - T1547.006
   - T1547
@@ -44,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_rmmod/linux_auditd_rmmod.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_rmmod/linux_auditd_rmmod.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml b/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml
index 7f096482a7..65a130b5b3 100644
--- a/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml
+++ b/detections/endpoint/linux_auditd_nopasswd_entry_in_sudoers_file.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the addition of NOPASSWD entries to the /etc/sudoers file on Linux systems. It leverages Linux Auditd data to identify command lines containing "NOPASSWD:". This activity is significant because it allows users to execute commands with elevated privileges without requiring a password, which can be exploited by adversaries to maintain persistent, privileged access. If confirmed malicious, this could lead to unauthorized privilege escalation, persistent access, and potential compromise of sensitive data and system integrity.
+description: The following analytic detects the addition of NOPASSWD entries to the
+  /etc/sudoers file on Linux systems. It leverages Linux Auditd data to identify command
+  lines containing "NOPASSWD:". This activity is significant because it allows users
+  to execute commands with elevated privileges without requiring a password, which
+  can be exploited by adversaries to maintain persistent, privileged access. If confirmed
+  malicious, this could lead to unauthorized privilege escalation, persistent access,
+  and potential compromise of sensitive data and system integrity.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where LIKE (process_exec, "%NOPASSWD%") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_nopasswd_entry_in_sudoers_file_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where LIKE (process_exec, "%NOPASSWD%") | stats count min(_time) as firstTime
+  max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter
+  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_nopasswd_entry_in_sudoers_file_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands
 - https://help.ubuntu.com/community/Sudoers
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A [$process_exec$] event occurred on host - [$dest$] to add NOPASSWD entry in sudoers file.
+  message: A [$process_exec$] event occurred on host - [$dest$] to add NOPASSWD entry
+    in sudoers file.
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -44,13 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_nopasswd/linux_auditd_nopasswd.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_nopasswd/linux_auditd_nopasswd.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_osquery_service_stop.yml b/detections/endpoint/linux_auditd_osquery_service_stop.yml
index 515427d49f..b32b89579c 100644
--- a/detections/endpoint/linux_auditd_osquery_service_stop.yml
+++ b/detections/endpoint/linux_auditd_osquery_service_stop.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious stopping of the `osquery` service, which may indicate an attempt to disable monitoring and evade detection. `Osquery` is a powerful tool used for querying system information and detecting anomalies, and stopping its service can be a sign that an attacker is trying to disrupt security monitoring or hide malicious activities. By monitoring for unusual or unauthorized stops of the `osquery` service, this analytic helps identify potential efforts to bypass security controls, enabling security teams to investigate and respond to possible threats effectively.
+description: The following analytic detects suspicious stopping of the `osquery` service,
+  which may indicate an attempt to disable monitoring and evade detection. `Osquery`
+  is a powerful tool used for querying system information and detecting anomalies,
+  and stopping its service can be a sign that an attacker is trying to disrupt security
+  monitoring or hide malicious activities. By monitoring for unusual or unauthorized
+  stops of the `osquery` service, this analytic helps identify potential efforts to
+  bypass security controls, enabling security teams to investigate and respond to
+  possible threats effectively.
 data_source:
 - Linux Auditd Service Stop
-search: '`linux_auditd` type=SERVICE_STOP unit IN ("osqueryd") | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_osquery_service_stop_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SERVICE_STOP unit IN ("osqueryd") | rename host as dest
+  | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm
+  exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_osquery_service_stop_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A service event - [$type$] event occured on host - [$dest$] to stop the osquery service.
+  message: A service event - [$type$] event occured on host - [$dest$] to stop the
+    osquery service.
   mitre_attack_id:
   - T1489
   observable:
@@ -43,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - type
-  - pid
-  - UID
-  - comm
-  - exe
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_osquerd_service_stop/linux_auditd_osquerd_service_stop.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_osquerd_service_stop/linux_auditd_osquerd_service_stop.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml b/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml
index 5789f3ddda..a0bc93d08f 100644
--- a/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml
+++ b/detections/endpoint/linux_auditd_possible_access_or_modification_of_sshd_config_file.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious access or modification of the sshd_config file on Linux systems. It leverages data from Linux Auditd, focusing on command-line executions involving processes like "cat," "nano," "vim," and "vi" accessing the sshd_config file. This activity is significant because unauthorized changes to sshd_config can allow threat actors to redirect port connections or use unauthorized keys, potentially compromising the system. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent backdoor access, posing a severe security risk.
+description: The following analytic detects suspicious access or modification of the
+  sshd_config file on Linux systems. It leverages data from Linux Auditd, focusing
+  on command-line executions involving processes like "cat," "nano," "vim," and "vi"
+  accessing the sshd_config file. This activity is significant because unauthorized
+  changes to sshd_config can allow threat actors to redirect port connections or use
+  unauthorized keys, potentially compromising the system. If confirmed malicious,
+  this could lead to unauthorized access, privilege escalation, or persistent backdoor
+  access, posing a severe security risk.
 data_source:
 - Linux Auditd Path
-search: '`linux_auditd` type=PATH name="/etc/ssh/ssh_config*" | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_possible_access_or_modification_of_sshd_config_file_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=PATH name="/etc/ssh/ssh_config*" | rename host as dest
+  | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID
+  type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_possible_access_or_modification_of_sshd_config_file_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.hackingarticles.in/ssh-penetration-testing-port-22/
 - https://attack.mitre.org/techniques/T1098/004/
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$type$] has been accessed/modified on host - [$dest$] to modify the sshd_config file.
+  message: A [$type$] has been accessed/modified on host - [$dest$] to modify the
+    sshd_config file.
   mitre_attack_id:
   - T1098.004
   - T1098
@@ -45,15 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - name
-  - nametype
-  - OGID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/linux_auditd_nopasswd/linux_auditd_ssh_config.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/linux_auditd_nopasswd/linux_auditd_ssh_config.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml b/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml
index 5cb09880a6..22b9a3d37a 100644
--- a/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml
+++ b/detections/endpoint/linux_auditd_possible_access_to_credential_files.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects attempts to access or dump the contents of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Linux Auditd, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these files. This activity is significant as it may indicate credential dumping, a technique used by adversaries to gain persistence or escalate privileges. If confirmed malicious, privileges. If confirmed malicious, attackers could obtain hashed passwords for offline cracking, leading to unauthorized access and potential system compromise.
+description: The following analytic detects attempts to access or dump the contents
+  of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Linux
+  Auditd, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these
+  files. This activity is significant as it may indicate credential dumping, a technique
+  used by adversaries to gain persistence or escalate privileges. If confirmed malicious,
+  privileges. If confirmed malicious, attackers could obtain hashed passwords for
+  offline cracking, leading to unauthorized access and potential system compromise.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where (LIKE (process_exec, "%shadow%") OR LIKE (process_exec, "%passwd%")) AND (LIKE (process_exec, "%cat %") OR LIKE (process_exec, "%nano %")OR LIKE (process_exec, "%vim %") OR LIKE (process_exec, "%vi %")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_possible_access_to_credential_files_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where (LIKE (process_exec, "%shadow%") OR LIKE (process_exec, "%passwd%"))
+  AND (LIKE (process_exec, "%cat %") OR LIKE (process_exec, "%nano %")OR LIKE (process_exec,
+  "%vim %") OR LIKE (process_exec, "%vi %")) | stats count min(_time) as firstTime
+  max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_possible_access_to_credential_files_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://askubuntu.com/questions/445361/what-is-difference-between-etc-shadow-and-etc-passwd
 - https://attack.mitre.org/techniques/T1003/008/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to access or dump the contents of /etc/passwd and /etc/shadow files.
+  message: A [$process_exec$] event occurred on host - [$dest$] to access or dump
+    the contents of /etc/passwd and /etc/shadow files.
   mitre_attack_id:
   - T1003.008
   - T1003
@@ -44,13 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/linux_auditd_access_credential/linux_auditd_access_credential.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/linux_auditd_access_credential/linux_auditd_access_credential.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml b/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml
index 68a28947c6..1a3b57d4fe 100644
--- a/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml
+++ b/detections/endpoint/linux_auditd_possible_access_to_sudoers_file.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects potential access or modification of the /etc/sudoers file on a Linux system. It leverages data from Linux Auditd, focusing on processes like "cat," "nano," "vim," and "vi" accessing the /etc/sudoers file. This activity is significant because the sudoers file controls user permissions for executing commands with elevated privileges. If confirmed malicious, an attacker could gain persistence or escalate privileges, compromising the security of the targeted host.
+description: The following analytic detects potential access or modification of the
+  /etc/sudoers file on a Linux system. It leverages data from Linux Auditd, focusing
+  on processes like "cat," "nano," "vim," and "vi" accessing the /etc/sudoers file.
+  This activity is significant because the sudoers file controls user permissions
+  for executing commands with elevated privileges. If confirmed malicious, an attacker
+  could gain persistence or escalate privileges, compromising the security of the
+  targeted host.
 data_source:
 - Linux Auditd Path
-search: '`linux_auditd` type=PATH name="/etc/sudoers*" | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_possible_access_to_sudoers_file_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=PATH name="/etc/sudoers*" | rename host as dest | stats
+  count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type
+  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_possible_access_to_sudoers_file_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1548/003/
 - https://web.archive.org/web/20210708035426/https://www.cobaltstrike.com/downloads/csmanual43.pdf
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$type$] has been accessed/modified on host - [$dest$] to access or modify the sudoers file.
+  message: A [$type$] has been accessed/modified on host - [$dest$] to access or modify
+    the sudoers file.
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -44,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - name
-  - nametype
-  - OGID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_sudoers_access/linux_auditd_sudoers_access.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_sudoers_access/linux_auditd_sudoers_access.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml b/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml
index 28f6c34f22..90d044a49e 100644
--- a/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml
+++ b/detections/endpoint/linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file.yml
@@ -5,12 +5,31 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects potential tampering with cronjob files on a Linux system by identifying 'echo' commands that append code to existing cronjob files. It leverages logs from Linux Auditd, focusing on process names, parent processes, and command-line executions. This activity is significant because adversaries often use it for persistence or privilege escalation. If confirmed malicious, this could allow attackers to execute unauthorized code automatically, leading to system compromises and unauthorized data access, thereby impacting business operations and data integrity.
+description: The following analytic detects potential tampering with cronjob files
+  on a Linux system by identifying 'echo' commands that append code to existing cronjob
+  files. It leverages logs from Linux Auditd, focusing on process names, parent processes,
+  and command-line executions. This activity is significant because adversaries often
+  use it for persistence or privilege escalation. If confirmed malicious, this could
+  allow attackers to execute unauthorized code automatically, leading to system compromises
+  and unauthorized data access, thereby impacting business operations and data integrity.
 data_source:
 - Linux Auditd Path
-search: '`linux_auditd` type=PATH name IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: False positives may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. Therefore, it's recommended to adjust filter macros to eliminate such false positives.
+search: '`linux_auditd` type=PATH name IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*")
+  | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime
+  by name nametype OGID  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: False positives may arise from legitimate actions by administrators
+  or network operators who may use these commands for automation purposes. Therefore,
+  it's recommended to adjust filter macros to eliminate such false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/003/
 - https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability
@@ -25,7 +44,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A [$type$] event has occured on host - [$dest$] to append a cronjob entry on an existing cronjob file.
+  message: A [$type$] event has occured on host - [$dest$] to append a cronjob entry
+    on an existing cronjob file.
   mitre_attack_id:
   - T1053.003
   - T1053
@@ -38,15 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - name
-  - nametype
-  - OGID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/linux_auditd_cron_file_audited/linux_auditd_cron_file_audited2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/linux_auditd_cron_file_audited/linux_auditd_cron_file_audited2.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml b/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml
index 461bd7013c..bb6c7e2240 100644
--- a/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml
+++ b/detections/endpoint/linux_auditd_preload_hijack_library_calls.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the LD_PRELOAD environment variable to hijack or hook library functions on a Linux platform. It leverages data from Linux Auditd, focusing on process execution logs that include command-line details. This activity is significant because adversaries, malware authors, and red teamers commonly use this technique to gain elevated privileges and establish persistence on a compromised machine. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, and maintain long-term access to the system.
+description: The following analytic detects the use of the LD_PRELOAD environment
+  variable to hijack or hook library functions on a Linux platform. It leverages data
+  from Linux Auditd, focusing on process execution logs that include command-line
+  details. This activity is significant because adversaries, malware authors, and
+  red teamers commonly use this technique to gain elevated privileges and establish
+  persistence on a compromised machine. If confirmed malicious, this behavior could
+  allow attackers to execute arbitrary code, escalate privileges, and maintain long-term
+  access to the system.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE (process_exec, "%LD_PRELOAD%")| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_preload_hijack_library_calls_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE (process_exec, "%LD_PRELOAD%")| stats count min(_time) as firstTime
+  max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `linux_auditd_preload_hijack_library_calls_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A [$process_exec$] event occurred on host - [$dest$] to hijack or hook library functions using the LD_PRELOAD environment variable.
+  message: A [$process_exec$] event occurred on host - [$dest$] to hijack or hook
+    library functions using the LD_PRELOAD environment variable.
   mitre_attack_id:
   - T1574.006
   - T1574
@@ -43,14 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/linux_auditd_ldpreload/linux_auditd_ldpreload.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/linux_auditd_ldpreload/linux_auditd_ldpreload.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml b/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml
index b52f196f87..284ddab718 100644
--- a/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml
+++ b/detections/endpoint/linux_auditd_preload_hijack_via_preload_file.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious preload hijacking via the  `preload` file, which may indicate an attacker's attempt to intercept or manipulate library loading processes. The `preload` file can be used to force the loading of specific libraries before others, potentially allowing malicious code to execute or alter application behavior. By monitoring for unusual or unauthorized modifications to the `preload` file, this analytic helps identify attempts to hijack preload mechanisms, enabling security teams to investigate and address potential threats to system integrity and security.
+description: The following analytic detects suspicious preload hijacking via the  `preload`
+  file, which may indicate an attacker's attempt to intercept or manipulate library
+  loading processes. The `preload` file can be used to force the loading of specific
+  libraries before others, potentially allowing malicious code to execute or alter
+  application behavior. By monitoring for unusual or unauthorized modifications to
+  the `preload` file, this analytic helps identify attempts to hijack preload mechanisms,
+  enabling security teams to investigate and address potential threats to system integrity
+  and security.
 data_source:
 - Linux Auditd Path
-search: '`linux_auditd` type=PATH name="/etc/ld.so.preload*" | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_preload_hijack_via_preload_file_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=PATH name="/etc/ld.so.preload*" | rename host as dest
+  | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID
+  type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_preload_hijack_via_preload_file_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - name
-  - nametype
-  - OGID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/linux_auditd_preload_file/linux_auditd_preload_file.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/linux_auditd_preload_file/linux_auditd_preload_file.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_service_restarted.yml b/detections/endpoint/linux_auditd_service_restarted.yml
index 91f497caa8..739d7ca922 100644
--- a/detections/endpoint/linux_auditd_service_restarted.yml
+++ b/detections/endpoint/linux_auditd_service_restarted.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the restarting or re-enabling of services on Linux systems using the `systemctl` or `service` commands. It leverages data from Linux Auditd, focusing on process and command-line execution logs. This activity is significant as adversaries may use it to maintain persistence or execute unauthorized actions. If confirmed malicious, this behavior could lead to repeated execution of malicious payloads, unauthorized access, or data destruction. Security analysts should investigate these events to mitigate risks and prevent further compromise.
+description: The following analytic detects the restarting or re-enabling of services
+  on Linux systems using the `systemctl` or `service` commands. It leverages data
+  from Linux Auditd, focusing on process and command-line execution logs. This activity
+  is significant as adversaries may use it to maintain persistence or execute unauthorized
+  actions. If confirmed malicious, this behavior could lead to repeated execution
+  of malicious payloads, unauthorized access, or data destruction. Security analysts
+  should investigate these events to mitigate risks and prevent further compromise.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where (LIKE(process_exec, "%systemctl %") OR LIKE(process_exec, "%service %") ) AND(LIKE(process_exec, "%restart%") OR LIKE(process_exec, "%reenable%") OR LIKE(process_exec, "%reload%")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_service_restarted_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where (LIKE(process_exec, "%systemctl %") OR LIKE(process_exec, "%service
+  %") ) AND(LIKE(process_exec, "%restart%") OR LIKE(process_exec, "%reenable%") OR
+  LIKE(process_exec, "%reload%")) | stats count min(_time) as firstTime max(_time)
+  as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `linux_auditd_service_restarted_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1543/003/
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to restart or re-enable a service.
+  message: A [$process_exec$] event occurred on host - [$dest$] to restart or re-enable
+    a service.
   mitre_attack_id:
   - T1053.006
   - T1053
@@ -48,13 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/linux_services_restart/linux_services_restart.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/linux_services_restart/linux_services_restart.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_service_started.yml b/detections/endpoint/linux_auditd_service_started.yml
index 5c6ffb80ca..af88ce3741 100644
--- a/detections/endpoint/linux_auditd_service_started.yml
+++ b/detections/endpoint/linux_auditd_service_started.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious service started. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
+description: The following analytic detects the suspicious service started. This behavior
+  is critical for a SOC to monitor because it may indicate attempts to gain unauthorized
+  access or maintain control over a system. Such actions could be signs of malicious
+  activity. If confirmed, this could lead to serious consequences, including a compromised
+  system, unauthorized access to sensitive data, or even a wider breach affecting
+  the entire network. Detecting and responding to these signs early is essential to
+  prevent potential security incidents.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where (LIKE(process_exec, "%systemctl %") OR LIKE(process_exec, "%service %") ) AND(LIKE(process_exec, "% start %") OR LIKE(process_exec, "% enable %")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_service_started_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where (LIKE(process_exec, "%systemctl %") OR LIKE(process_exec, "%service
+  %") ) AND(LIKE(process_exec, "% start %") OR LIKE(process_exec, "% enable %")) |
+  stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle
+  normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_service_started_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A [$process_exec$] event occurred on host - [$dest$] to start or enable a service.
+  message: A [$process_exec$] event occurred on host - [$dest$] to start or enable
+    a service.
   mitre_attack_id:
   - T1569.002
   - T1569
@@ -44,13 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/linux_service_start/linux_service_start.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/linux_service_start/linux_service_start.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml b/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml
index ca65058d29..f3768ea7ba 100644
--- a/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml
+++ b/detections/endpoint/linux_auditd_setuid_using_chmod_utility.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the chmod utility to set the SUID or SGID bit on files, which can allow users to temporarily gain root or group-level access. This detection leverages data from Linux Auditd, focusing on process names and command-line arguments related to chmod. This activity is significant as it can indicate an attempt to escalate privileges or maintain persistence on a system. If confirmed malicious, an attacker could gain elevated access, potentially compromising sensitive data or critical system functions.
+description: The following analytic detects the execution of the chmod utility to
+  set the SUID or SGID bit on files, which can allow users to temporarily gain root
+  or group-level access. This detection leverages data from Linux Auditd, focusing
+  on process names and command-line arguments related to chmod. This activity is significant
+  as it can indicate an attempt to escalate privileges or maintain persistence on
+  a system. If confirmed malicious, an attacker could gain elevated access, potentially
+  compromising sensitive data or critical system functions.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where LIKE (process_exec, "%chmod %") AND (LIKE (process_exec, "% u+s %") OR LIKE (process_exec, "% g+s %") OR LIKE (process_exec, "% 4777 %") OR LIKE (process_exec, "% 4577 %")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_setuid_using_chmod_utility_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where LIKE (process_exec, "%chmod %") AND (LIKE (process_exec, "% u+s
+  %") OR LIKE (process_exec, "% g+s %") OR LIKE (process_exec, "% 4777 %") OR LIKE
+  (process_exec, "% 4577 %")) | stats count min(_time) as firstTime max(_time) as
+  lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_auditd_setuid_using_chmod_utility_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A [$process_exec$] event occurred on host - [$dest$] to set the SUID or SGID bit on files using the chmod utility.
+  message: A [$process_exec$] event occurred on host - [$dest$] to set the SUID or
+    SGID bit on files using the chmod utility.
   mitre_attack_id:
   - T1548.001
   - T1548
@@ -44,13 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/linux_auditd_setuid.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/linux_auditd_setuid.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml b/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml
index d9c3d74170..a0e05726f6 100644
--- a/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml
+++ b/detections/endpoint/linux_auditd_setuid_using_setcap_utility.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the 'setcap' utility to enable the SUID bit on Linux systems. It leverages Linux Auditd data, focusing on process names and command-line arguments that indicate the use of 'setcap' with specific capabilities. This activity is significant because setting the SUID bit allows a user to temporarily gain root access, posing a substantial security risk. If confirmed malicious, an attacker could escalate privileges, execute arbitrary commands with elevated permissions, and potentially compromise the entire system.
+description: The following analytic detects the execution of the 'setcap' utility
+  to enable the SUID bit on Linux systems. It leverages Linux Auditd data, focusing
+  on process names and command-line arguments that indicate the use of 'setcap' with
+  specific capabilities. This activity is significant because setting the SUID bit
+  allows a user to temporarily gain root access, posing a substantial security risk.
+  If confirmed malicious, an attacker could escalate privileges, execute arbitrary
+  commands with elevated permissions, and potentially compromise the entire system.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE (process_exec, "%setcap %") AND (LIKE (process_exec, "% cap_setuid+ep %") OR LIKE (process_exec, "% cap_setuid=ep %") OR LIKE (process_exec, "% cap_net_bind_service+p %") OR LIKE (process_exec, "% cap_net_raw+ep %") OR LIKE (process_exec, "% cap_dac_read_search+ep %")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_setuid_using_setcap_utility_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE (process_exec, "%setcap %") AND (LIKE (process_exec, "% cap_setuid+ep
+  %") OR LIKE (process_exec, "% cap_setuid=ep %") OR LIKE (process_exec, "% cap_net_bind_service+p
+  %") OR LIKE (process_exec, "% cap_net_raw+ep %") OR LIKE (process_exec, "% cap_dac_read_search+ep
+  %")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec
+  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_setuid_using_setcap_utility_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A [$process_exec$] event occurred on host - [$dest$] to set the SUID or SGID bit on files using the setcap utility.
+  message: A [$process_exec$] event occurred on host - [$dest$] to set the SUID or
+    SGID bit on files using the setcap utility.
   mitre_attack_id:
   - T1548.001
   - T1548
@@ -43,14 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/linux_auditd_setcap_priv.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_auditd_setuid/linux_auditd_setcap_priv.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_shred_overwrite_command.yml b/detections/endpoint/linux_auditd_shred_overwrite_command.yml
index 970b56e7eb..ca750c89aa 100644
--- a/detections/endpoint/linux_auditd_shred_overwrite_command.yml
+++ b/detections/endpoint/linux_auditd_shred_overwrite_command.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.
+description: The following analytic detects the execution of the 'shred' command on
+  a Linux machine, which is used to overwrite files to make them unrecoverable. It
+  leverages data from Linux Auditd, focusing on process names and command-line arguments.
+  This activity is significant because the 'shred' command can be used in destructive
+  attacks, such as those seen in the Industroyer2 malware targeting energy facilities.
+  If confirmed malicious, this activity could lead to the permanent destruction of
+  critical files, severely impacting system integrity and data availability.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where LIKE (process_exec, "%shred%") AND (LIKE (process_exec, "%-n%") OR LIKE (process_exec, "%-z%") OR LIKE (process_exec, "%-u%") OR LIKE (process_exec, "%-s%")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_shred_overwrite_command_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where LIKE (process_exec, "%shred%") AND (LIKE (process_exec, "%-n%")
+  OR LIKE (process_exec, "%-z%") OR LIKE (process_exec, "%-u%") OR LIKE (process_exec,
+  "%-s%")) | stats count min(_time) as firstTime max(_time) as lastTime by process_exec
+  proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `linux_auditd_shred_overwrite_command_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A [$process_exec$] event occurred on host - [$dest$] to overwrite files using the shred utility.
+  message: A [$process_exec$] event occurred on host - [$dest$] to overwrite files
+    using the shred utility.
   mitre_attack_id:
   - T1485
   observable:
@@ -46,13 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_shred/linux_auditd_shred.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_auditd_shred/linux_auditd_shred.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_stop_services.yml b/detections/endpoint/linux_auditd_stop_services.yml
index 2955d30e79..7a3c4303ac 100644
--- a/detections/endpoint/linux_auditd_stop_services.yml
+++ b/detections/endpoint/linux_auditd_stop_services.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to stop or clear a service on Linux systems. It leverages data from Linux Auditd, focusing on processes like "systemctl," "service," and "svcadm" executing stop commands. This activity is significant as adversaries often terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
+description: The following analytic detects attempts to stop or clear a service on
+  Linux systems. It leverages data from Linux Auditd, focusing on processes like "systemctl,"
+  "service," and "svcadm" executing stop commands. This activity is significant as
+  adversaries often terminate security or critical services to disable defenses or
+  disrupt operations, as seen in malware like Industroyer2. If confirmed malicious,
+  this could lead to the disabling of security mechanisms, allowing attackers to persist,
+  escalate privileges, or deploy destructive payloads, severely impacting system integrity
+  and availability.
 data_source:
 - Linux Auditd Service Stop
-search: '`linux_auditd` type=SERVICE_STOP | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_stop_services_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SERVICE_STOP | rename host as dest | stats count min(_time)
+  as firstTime max(_time) as lastTime by type pid UID comm exe dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `linux_auditd_stop_services_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A service event - [$type$] event occured on host - [$dest$] to stop or disable a service.
+  message: A service event - [$type$] event occured on host - [$dest$] to stop or
+    disable a service.
   mitre_attack_id:
   - T1489
   observable:
@@ -44,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - type
-  - pid
-  - UID
-  - comm
-  - exe
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_service_stop/linux_auditd_service_stop.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_service_stop/linux_auditd_service_stop.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_sudo_or_su_execution.yml b/detections/endpoint/linux_auditd_sudo_or_su_execution.yml
index e4d95c5d66..9fe2f8fd13 100644
--- a/detections/endpoint/linux_auditd_sudo_or_su_execution.yml
+++ b/detections/endpoint/linux_auditd_sudo_or_su_execution.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the "sudo" or "su" command on a Linux operating system. It leverages data from Linux Auditd, focusing on process names and parent process names. This activity is significant because "sudo" and "su" commands are commonly used by adversaries to elevate privileges, potentially leading to unauthorized access or control over the system. If confirmed malicious, this activity could allow attackers to execute commands with root privileges, leading to severe security breaches, data exfiltration, or further system compromise.
+description: The following analytic detects the execution of the "sudo" or "su" command
+  on a Linux operating system. It leverages data from Linux Auditd, focusing on process
+  names and parent process names. This activity is significant because "sudo" and
+  "su" commands are commonly used by adversaries to elevate privileges, potentially
+  leading to unauthorized access or control over the system. If confirmed malicious,
+  this activity could allow attackers to execute commands with root privileges, leading
+  to severe security breaches, data exfiltration, or further system compromise.
 data_source:
 - Linux Auditd Proctitle
-search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host as dest | where LIKE(process_exec, "%sudo %") OR LIKE(process_exec, "%su %") | stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_sudo_or_su_execution_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_proctitle_process` | rename host
+  as dest | where LIKE(process_exec, "%sudo %") OR LIKE(process_exec, "%su %") | stats
+  count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle normalized_proctitle_delimiter
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_sudo_or_su_execution_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1548/003/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to execute the sudo or su command.
+  message: A [$process_exec$] event occurred on host - [$dest$] to execute the sudo
+    or su command.
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -43,13 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - proctitle
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_sudo_su/linux_auditd_sudo_su.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/linux_auditd_sudo_su/linux_auditd_sudo_su.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_sysmon_service_stop.yml b/detections/endpoint/linux_auditd_sysmon_service_stop.yml
index 842b4faab1..12410dff80 100644
--- a/detections/endpoint/linux_auditd_sysmon_service_stop.yml
+++ b/detections/endpoint/linux_auditd_sysmon_service_stop.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious sysmon service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
+description: The following analytic detects the suspicious sysmon service stop. This
+  behavior is critical for a SOC to monitor because it may indicate attempts to gain
+  unauthorized access or maintain control over a system. Such actions could be signs
+  of malicious activity. If confirmed, this could lead to serious consequences, including
+  a compromised system, unauthorized access to sensitive data, or even a wider breach
+  affecting the entire network. Detecting and responding to these signs early is essential
+  to prevent potential security incidents.
 data_source:
 - Linux Auditd Service Stop
-search: '`linux_auditd` type=SERVICE_STOP unit IN ("sysmon") | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_sysmon_service_stop_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SERVICE_STOP unit IN ("sysmon") | rename host as dest
+  | stats count min(_time) as firstTime max(_time) as lastTime by type pid UID comm
+  exe unit dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_sysmon_service_stop_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A service event - [$type$] event occured on host - [$dest$] to stop or disable the sysmon service.
+  message: A service event - [$type$] event occured on host - [$dest$] to stop or
+    disable the sysmon service.
   mitre_attack_id:
   - T1489
   observable:
@@ -43,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - type
-  - pid
-  - UID
-  - comm
-  - exe
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_sysmon_service_stop.log/linux_auditd_sysmon_service_stop.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_auditd_sysmon_service_stop.log/linux_auditd_sysmon_service_stop.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml b/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml
index 27c4fb7194..319ef2b3e8 100644
--- a/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml
+++ b/detections/endpoint/linux_auditd_system_network_configuration_discovery.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious system network configuration discovery activities, which may indicate an adversary's attempt to gather information about the network environment. Such actions typically involve commands or tools used to identify network interfaces, routing tables, and active connections. Detecting these activities is crucial, as they often precede more targeted attacks like lateral movement or data exfiltration. By identifying unusual or unauthorized network discovery efforts, this analytic helps security teams to swiftly detect and respond to potential reconnaissance operations, mitigating the risk of further compromise.
+description: The following analytic detects suspicious system network configuration
+  discovery activities, which may indicate an adversary's attempt to gather information
+  about the network environment. Such actions typically involve commands or tools
+  used to identify network interfaces, routing tables, and active connections. Detecting
+  these activities is crucial, as they often precede more targeted attacks like lateral
+  movement or data exfiltration. By identifying unusual or unauthorized network discovery
+  efforts, this analytic helps security teams to swiftly detect and respond to potential
+  reconnaissance operations, mitigating the risk of further compromise.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", "route") | bucket _time span=15m | rename host as dest | stats dc(comm) as unique_commands, values(comm) as comm, values(exe) as exe, values(SYSCALL) as SYSCALL, values(UID) as UID, values(ppid) as ppid, values(pid) as pid, count, min(_time) as firstTime, max(_time) as lastTime by success dest | where unique_commands >= 4 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_auditd_system_network_configuration_discovery_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm IN ("arp", "ifconfig", "ip", "netstat",
+  "firewall-cmd", "ufw", "iptables", "ss", "route") | bucket _time span=15m | rename
+  host as dest | stats dc(comm) as unique_commands, values(comm) as comm, values(exe)
+  as exe, values(SYSCALL) as SYSCALL, values(UID) as UID, values(ppid) as ppid, values(pid)
+  as pid, count, min(_time) as firstTime, max(_time) as lastTime by success dest |
+  where unique_commands >= 4 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_auditd_system_network_configuration_discovery_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover system network configuration.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover
+    system network configuration.
   mitre_attack_id:
   - T1016
   observable:
@@ -43,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/linux_auditd_net_tool/linux_auditd_net_tool.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/linux_auditd_net_tool/linux_auditd_net_tool.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml b/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml
index 6b9894d7e0..baef7d8c62 100644
--- a/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml
+++ b/detections/endpoint/linux_auditd_unix_shell_configuration_modification.yml
@@ -5,12 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to Unix shell configuration files, which may indicate an attempt to alter system behavior or gain unauthorized access. Unix shell configuration files, such as `.bashrc` or `.profile`, control user environment settings and command execution. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
+description: The following analytic detects suspicious modifications to Unix shell
+  configuration files, which may indicate an attempt to alter system behavior or gain
+  unauthorized access. Unix shell configuration files, such as `.bashrc` or `.profile`,
+  control user environment settings and command execution. Unauthorized changes to
+  these files can be used to execute malicious commands, escalate privileges, or hide
+  malicious activities. By monitoring for unusual or unauthorized modifications to
+  shell configuration files, this analytic helps identify potential security threats,
+  allowing security teams to respond quickly and mitigate risks.
 data_source:
 - Linux Auditd Path
-search: '`linux_auditd` type=PATH name IN ("/etc/profile", "/etc/shells", "/etc/profile.d", "/etc/bash.bashrc", "/etc/bashrc", "/etc/zsh/zprofile", "/etc/zsh/zshrc", "/etc/zsh/zlogin", "/etc/zsh/zlogout", "/etc/csh.cshrc", "/etc/csh.login", "/root/.bashrc", "/root/.bash_profile", "root/.profile", "/root/.zshrc", "/root/.zprofile", "/home/*/.bashrc", "/home/*/.zshrc", "/home/*/.bash_profile", "/home/*/.zprofile", "/home/*/.profile", "/home/*/.bash_login", "/home/*/.bash_logout", "/home/*/.zlogin", "/home/*/.zlogout") | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_unix_shell_configuration_modification_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=PATH name IN ("/etc/profile", "/etc/shells", "/etc/profile.d",
+  "/etc/bash.bashrc", "/etc/bashrc", "/etc/zsh/zprofile", "/etc/zsh/zshrc", "/etc/zsh/zlogin",
+  "/etc/zsh/zlogout", "/etc/csh.cshrc", "/etc/csh.login", "/root/.bashrc", "/root/.bash_profile",
+  "root/.profile", "/root/.zshrc", "/root/.zprofile", "/home/*/.bashrc", "/home/*/.zshrc",
+  "/home/*/.bash_profile", "/home/*/.zprofile", "/home/*/.profile", "/home/*/.bash_login",
+  "/home/*/.bash_logout", "/home/*/.zlogin", "/home/*/.zlogout") | rename host as
+  dest | stats count min(_time) as firstTime max(_time) as lastTime by name nametype
+  OGID type dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_unix_shell_configuration_modification_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A [$type$] event occured on host - [$dest$] to modify the unix shell configuration file.
+  message: A [$type$] event occured on host - [$dest$] to modify the unix shell configuration
+    file.
   mitre_attack_id:
   - T1546.004
   - T1546
@@ -45,15 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - name
-  - nametype
-  - OGID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_auditd_unix_shell_mod_config/linux_auditd_unix_shell_mod_config.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_auditd_unix_shell_mod_config/linux_auditd_unix_shell_mod_config.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml b/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml
index 55122ceed8..6b71acfa57 100644
--- a/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml
+++ b/detections/endpoint/linux_auditd_unload_module_via_modprobe.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious use of the `modprobe` command to unload kernel modules, which may indicate an attempt to disable critical system components or evade detection. The `modprobe` utility manages kernel modules, and unauthorized unloading of modules can disrupt system security features, remove logging capabilities, or conceal malicious activities. By monitoring for unusual or unauthorized `modprobe` operations involving module unloading, this analytic helps identify potential tampering with kernel functionality, enabling security teams to investigate and address possible threats to system integrity.
+description: The following analytic detects suspicious use of the `modprobe` command
+  to unload kernel modules, which may indicate an attempt to disable critical system
+  components or evade detection. The `modprobe` utility manages kernel modules, and
+  unauthorized unloading of modules can disrupt system security features, remove logging
+  capabilities, or conceal malicious activities. By monitoring for unusual or unauthorized
+  `modprobe` operations involving module unloading, this analytic helps identify potential
+  tampering with kernel functionality, enabling security teams to investigate and
+  address possible threats to system integrity.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where LIKE (process_exec, "%modprobe%") AND  LIKE (process_exec, "%-r %") | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `linux_auditd_unload_module_via_modprobe_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where LIKE (process_exec, "%modprobe%") AND  LIKE (process_exec, "%-r %")
+  | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec
+  dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|
+  `linux_auditd_unload_module_via_modprobe_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A [$process_exec$] event occurred on host - [$dest$] to unload a kernel module via the modprobe command.
+  message: A [$process_exec$] event occurred on host - [$dest$] to unload a kernel
+    module via the modprobe command.
   mitre_attack_id:
   - T1547.006
   - T1547
@@ -44,14 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe_unload_module/linux_auditd_modprobe_unload_module.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/linux_auditd_modprobe_unload_module/linux_auditd_modprobe_unload_module.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml b/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml
index 44c7e8b0b2..09a675e42d 100644
--- a/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml
+++ b/detections/endpoint/linux_auditd_virtual_disk_file_and_directory_discovery.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious discovery of virtual disk files and directories, which may indicate an attacker's attempt to locate and access virtualized storage environments. Virtual disks can contain sensitive data or critical system configurations, and unauthorized discovery attempts could signify preparatory actions for data exfiltration or further compromise. By monitoring for unusual or unauthorized searches for virtual disk files and directories, this analytic helps identify potential reconnaissance activities, enabling security teams to respond promptly and safeguard against unauthorized access and data breaches.
+description: The following analytic detects suspicious discovery of virtual disk files
+  and directories, which may indicate an attacker's attempt to locate and access virtualized
+  storage environments. Virtual disks can contain sensitive data or critical system
+  configurations, and unauthorized discovery attempts could signify preparatory actions
+  for data exfiltration or further compromise. By monitoring for unusual or unauthorized
+  searches for virtual disk files and directories, this analytic helps identify potential
+  reconnaissance activities, enabling security teams to respond promptly and safeguard
+  against unauthorized access and data breaches.
 data_source:
 - Linux Auditd Execve
-search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND (LIKE (process_exec, "%.vhd%") OR LIKE (process_exec, "%.vhdx%") OR LIKE (process_exec, "%.vmdk%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_virtual_disk_file_and_directory_discovery_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` `linux_auditd_normalized_execve_process` | rename host as
+  dest | where  (LIKE (process_exec, "%find%") OR LIKE (process_exec, "%grep%")) AND
+  (LIKE (process_exec, "%.vhd%") OR LIKE (process_exec, "%.vhdx%") OR LIKE (process_exec,
+  "%.vmdk%")) | stats count min(_time) as firstTime max(_time) as lastTime by argc
+  process_exec dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_virtual_disk_file_and_directory_discovery_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A [$process_exec$] event occurred on host - [$dest$] to discover virtual disk files and directories.
+  message: A [$process_exec$] event occurred on host - [$dest$] to discover virtual
+    disk files and directories.
   mitre_attack_id:
   - T1083
   observable:
@@ -44,14 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - argc
-  - process_exec
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_virtual_disk/linux_auditd_find_virtual_disk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1083/linux_auditd_find_virtual_disk/linux_auditd_find_virtual_disk.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_auditd_whoami_user_discovery.yml b/detections/endpoint/linux_auditd_whoami_user_discovery.yml
index 8aa1e51e02..b84793f3a0 100644
--- a/detections/endpoint/linux_auditd_whoami_user_discovery.yml
+++ b/detections/endpoint/linux_auditd_whoami_user_discovery.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the suspicious use of the whoami command, which may indicate an attacker trying to gather information about the current user account on a compromised system. The whoami command is commonly used to verify user privileges and identity, especially during initial stages of an attack to assess the level of access. By monitoring for unusual or unauthorized executions of whoami, this analytic helps in identifying potential reconnaissance activities, enabling security teams to take action before the attacker escalates privileges or conducts further malicious operations.
+description: The following analytic detects the suspicious use of the whoami command,
+  which may indicate an attacker trying to gather information about the current user
+  account on a compromised system. The whoami command is commonly used to verify user
+  privileges and identity, especially during initial stages of an attack to assess
+  the level of access. By monitoring for unusual or unauthorized executions of whoami,
+  this analytic helps in identifying potential reconnaissance activities, enabling
+  security teams to take action before the attacker escalates privileges or conducts
+  further malicious operations.
 data_source:
 - Linux Auditd Syscall
-search: '`linux_auditd` type=SYSCALL comm=whoami OR exe= "*/whoami" | rename host as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL UID ppid pid dest success | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_auditd_whoami_user_discovery_filter`'
-how_to_implement: To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names  to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '`linux_auditd` type=SYSCALL comm=whoami OR exe= "*/whoami" | rename host
+  as dest | stats count min(_time) as firstTime max(_time) as lastTime by comm exe  SYSCALL
+  UID ppid pid dest success | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `linux_auditd_whoami_user_discovery_filter`'
+how_to_implement: To implement this detection, the process begins by ingesting auditd
+  data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line
+  executions and process details on Unix/Linux systems. These logs should be ingested
+  and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833),
+  which is essential for correctly parsing and categorizing the data. The next step
+  involves normalizing the field names  to match the field names set by the Splunk
+  Common Information Model (CIM) to ensure consistency across different data sources
+  and enhance the efficiency of data modeling. This approach enables effective monitoring
+  and detection of linux endpoints where auditd is deployed
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html
 - https://github.com/peass-ng/PEASS-ng/tree/master/linPEAS
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover virtual disk files and directories.
+  message: A SYSCALL - [$comm$] event was executed on host - [$dest$] to discover
+    virtual disk files and directories.
   mitre_attack_id:
   - T1033
   observable:
@@ -44,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - comm
-  - exe
-  - SYSCALL
-  - UID
-  - ppid
-  - pid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/linux_auditd_whoami/linux_auditd_whoami.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/linux_auditd_whoami/linux_auditd_whoami.log
     source: /var/log/audit/audit.log
     sourcetype: linux:audit
diff --git a/detections/endpoint/linux_awk_privilege_escalation.yml b/detections/endpoint/linux_awk_privilege_escalation.yml
index c5057286ab..c034c042dc 100644
--- a/detections/endpoint/linux_awk_privilege_escalation.yml
+++ b/detections/endpoint/linux_awk_privilege_escalation.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the AWK command with elevated privileges to execute system commands. It leverages Endpoint Detection and Response (EDR) telemetry, specifically monitoring processes that include "sudo," "awk," and "BEGIN*system" in their command lines. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain root access by executing commands as the root user. If confirmed malicious, this could allow an attacker to fully compromise the system, execute arbitrary commands, and maintain persistent control over the affected endpoint.
+description: The following analytic detects the use of the AWK command with elevated
+  privileges to execute system commands. It leverages Endpoint Detection and Response
+  (EDR) telemetry, specifically monitoring processes that include "sudo," "awk," and
+  "BEGIN*system" in their command lines. This activity is significant because it indicates
+  a potential privilege escalation attempt, where a user could gain root access by
+  executing commands as the root user. If confirmed malicious, this could allow an
+  attacker to fully compromise the system, execute arbitrary commands, and maintain
+  persistent control over the affected endpoint.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*" AND Processes.process="*awk*"  AND Processes.process="*BEGIN*system*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_awk_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*" AND
+  Processes.process="*awk*"  AND Processes.process="*BEGIN*system*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_awk_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://www.hacknos.com/awk-privilege-escalation/
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -50,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/awk/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/awk/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_busybox_privilege_escalation.yml b/detections/endpoint/linux_busybox_privilege_escalation.yml
index 63e487cb9a..c90306cd5a 100644
--- a/detections/endpoint/linux_busybox_privilege_escalation.yml
+++ b/detections/endpoint/linux_busybox_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of BusyBox with sudo privileges, which can lead to privilege escalation on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where BusyBox is executed with both 'sh' and 'sudo' commands. This activity is significant because it indicates a user may be attempting to gain root access, bypassing standard security controls. If confirmed malicious, this could allow an attacker to execute arbitrary commands as root, leading to full system compromise and potential persistence within the environment.
+description: The following analytic detects the execution of BusyBox with sudo privileges,
+  which can lead to privilege escalation on Linux systems. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process creation events
+  where BusyBox is executed with both 'sh' and 'sudo' commands. This activity is significant
+  because it indicates a user may be attempting to gain root access, bypassing standard
+  security controls. If confirmed malicious, this could allow an attacker to execute
+  arbitrary commands as root, leading to full system compromise and potential persistence
+  within the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*busybox*" AND Processes.process="*sh*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_busybox_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*busybox*"
+  AND Processes.process="*sh*" AND Processes.process="*sudo*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_busybox_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/busybox/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/busybox/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/busybox/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_c89_privilege_escalation.yml b/detections/endpoint/linux_c89_privilege_escalation.yml
index 29d8560a83..fe84041c23 100644
--- a/detections/endpoint/linux_c89_privilege_escalation.yml
+++ b/detections/endpoint/linux_c89_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'c89' command with elevated privileges, which can be used to compile and execute C programs as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events that include command-line arguments. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute arbitrary commands as root. If confirmed malicious, this could lead to full system compromise, enabling the attacker to gain root access and execute any command with elevated privileges.
+description: The following analytic detects the execution of the 'c89' command with
+  elevated privileges, which can be used to compile and execute C programs as root.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events that include command-line arguments. This activity
+  is significant because it indicates a potential privilege escalation attempt, allowing
+  a user to execute arbitrary commands as root. If confirmed malicious, this could
+  lead to full system compromise, enabling the attacker to gain root access and execute
+  any command with elevated privileges.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*c89*" AND Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_c89_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*c89*" AND
+  Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_c89_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/c89/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c89/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c89/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_c99_privilege_escalation.yml b/detections/endpoint/linux_c99_privilege_escalation.yml
index 23768ac71d..ebf52b5949 100644
--- a/detections/endpoint/linux_c99_privilege_escalation.yml
+++ b/detections/endpoint/linux_c99_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the c99 utility with sudo privileges, which can lead to privilege escalation on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential misuse of the c99 utility to gain root access, which is critical for maintaining system security. If confirmed malicious, this could allow an attacker to execute commands as root, potentially compromising the entire system and accessing sensitive information.
+description: The following analytic detects the execution of the c99 utility with
+  sudo privileges, which can lead to privilege escalation on Linux systems. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs that include command-line details. This activity is significant because it
+  indicates a potential misuse of the c99 utility to gain root access, which is critical
+  for maintaining system security. If confirmed malicious, this could allow an attacker
+  to execute commands as root, potentially compromising the entire system and accessing
+  sensitive information.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*c99*" AND Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_c99_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*c99*" AND
+  Processes.process="*-wrapper*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_c99_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/c99/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c99/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/c99/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_change_file_owner_to_root.yml b/detections/endpoint/linux_change_file_owner_to_root.yml
index cb16851c37..9e087c077e 100644
--- a/detections/endpoint/linux_change_file_owner_to_root.yml
+++ b/detections/endpoint/linux_change_file_owner_to_root.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the 'chown' command to change a file owner to 'root' on a Linux system. It leverages Endpoint Detection and Response (EDR) telemetry, specifically monitoring command-line executions and process details. This activity is significant as it may indicate an attempt to escalate privileges by adversaries, malware, or red teamers. If confirmed malicious, this action could allow an attacker to gain root-level access, leading to full control over the compromised host and potential persistence within the environment.
+description: The following analytic detects the use of the 'chown' command to change
+  a file owner to 'root' on a Linux system. It leverages Endpoint Detection and Response
+  (EDR) telemetry, specifically monitoring command-line executions and process details.
+  This activity is significant as it may indicate an attempt to escalate privileges
+  by adversaries, malware, or red teamers. If confirmed malicious, this action could
+  allow an attacker to gain root-level access, leading to full control over the compromised
+  host and potential persistence within the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = chown OR Processes.process = "*chown *") AND Processes.process = "* root *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_change_file_owner_to_root_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = chown
+  OR Processes.process = "*chown *") AND Processes.process = "* root *" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_change_file_owner_to_root_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://unix.stackexchange.com/questions/101073/how-to-change-permissions-from-root-user-to-all-users
 - https://askubuntu.com/questions/617850/changing-from-user-to-superuser
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_clipboard_data_copy.yml b/detections/endpoint/linux_clipboard_data_copy.yml
index 8111323e16..b458e6295f 100644
--- a/detections/endpoint/linux_clipboard_data_copy.yml
+++ b/detections/endpoint/linux_clipboard_data_copy.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment.
+description: The following analytic detects the use of the Linux 'xclip' command to
+  copy data from the clipboard. It leverages Endpoint Detection and Response (EDR)
+  telemetry, focusing on process names and command-line arguments related to clipboard
+  operations. This activity is significant because adversaries can exploit clipboard
+  data to capture sensitive information such as passwords or IP addresses. If confirmed
+  malicious, this technique could lead to unauthorized data exfiltration, compromising
+  sensitive information and potentially aiding further attacks within the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=xclip Processes.process IN ("*-o *", "*-sel *", "*-selection *", "*clip *","*clipboard*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_clipboard_data_copy_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present on Linux desktop as it may commonly be used by administrators or end users. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=xclip
+  Processes.process IN ("*-o *", "*-sel *", "*-selection *", "*clip *","*clipboard*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_clipboard_data_copy_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present on Linux desktop as it may commonly
+  be used by administrators or end users. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1115/
 - https://linux.die.net/man/1/xclip
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 40
-  message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ adding or removing content from the clipboard.
+  message: An instance of $process_name$ was identified on endpoint $dest$ by user
+    $user$ adding or removing content from the clipboard.
   mitre_attack_id:
   - T1115
   observable:
@@ -49,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/atomic_red_team/linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1115/atomic_red_team/linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_common_process_for_elevation_control.yml b/detections/endpoint/linux_common_process_for_elevation_control.yml
index 35ab66446a..5626489efc 100644
--- a/detections/endpoint/linux_common_process_for_elevation_control.yml
+++ b/detections/endpoint/linux_common_process_for_elevation_control.yml
@@ -5,12 +5,38 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of common Linux processes used for elevation control, such as `chmod`, `chown`, and `setuid`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because these processes are often abused by adversaries to gain persistence or escalate privileges on compromised hosts. If confirmed malicious, this behavior could allow attackers to modify file attributes, change file ownership, or set user IDs, potentially leading to unauthorized access and control over critical system resources.
+description: The following analytic identifies the execution of common Linux processes
+  used for elevation control, such as `chmod`, `chown`, and `setuid`. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant because these processes
+  are often abused by adversaries to gain persistence or escalate privileges on compromised
+  hosts. If confirmed malicious, this behavior could allow attackers to modify file
+  attributes, change file ownership, or set user IDs, potentially leading to unauthorized
+  access and control over critical system resources.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("chmod", "chown", "fchmod", "fchmodat", "fchown", "fchownat", "fremovexattr", "fsetxattr", "lchown", "lremovexattr", "lsetxattr", "removexattr", "setuid", "setgid", "setreuid", "setregid", "chattr") OR Processes.process IN ("*chmod *", "*chown *", "*fchmod *", "*fchmodat *", "*fchown *", "*fchownat *", "*fremovexattr *", "*fsetxattr *", "*lchown *", "*lremovexattr *", "*lsetxattr *", "*removexattr *", "*setuid *", "*setgid *", "*setreuid *", "*setregid *", "*setcap *", "*chattr *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_common_process_for_elevation_control_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("chmod",
+  "chown", "fchmod", "fchmodat", "fchown", "fchownat", "fremovexattr", "fsetxattr",
+  "lchown", "lremovexattr", "lsetxattr", "removexattr", "setuid", "setgid", "setreuid",
+  "setregid", "chattr") OR Processes.process IN ("*chmod *", "*chown *", "*fchmod
+  *", "*fchmodat *", "*fchown *", "*fchownat *", "*fremovexattr *", "*fsetxattr *",
+  "*lchown *", "*lremovexattr *", "*lsetxattr *", "*removexattr *", "*setuid *", "*setgid
+  *", "*setreuid *", "*setregid *", "*setcap *", "*chattr *") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_common_process_for_elevation_control_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1548/001/
 - https://github.com/Neo23x0/auditd/blob/master/audit.rules#L285-L297
@@ -37,19 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_composer_privilege_escalation.yml b/detections/endpoint/linux_composer_privilege_escalation.yml
index f9781b42a6..d2fbe82192 100644
--- a/detections/endpoint/linux_composer_privilege_escalation.yml
+++ b/detections/endpoint/linux_composer_privilege_escalation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the Composer tool with elevated privileges on a Linux system. It identifies instances where Composer is run with the 'sudo' command, allowing the user to execute system commands as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because it can indicate an attempt to escalate privileges, potentially leading to unauthorized root access. If confirmed malicious, an attacker could gain full control over the system, execute arbitrary commands, and compromise sensitive data.
+description: The following analytic detects the execution of the Composer tool with
+  elevated privileges on a Linux system. It identifies instances where Composer is
+  run with the 'sudo' command, allowing the user to execute system commands as root.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process execution logs and command-line arguments. This activity is
+  significant because it can indicate an attempt to escalate privileges, potentially
+  leading to unauthorized root access. If confirmed malicious, an attacker could gain
+  full control over the system, execute arbitrary commands, and compromise sensitive
+  data.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*composer*" AND Processes.process="*run-script*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_composer_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*composer*"
+  AND Processes.process="*run-script*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_composer_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/composer/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/composer/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/composer/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_cpulimit_privilege_escalation.yml b/detections/endpoint/linux_cpulimit_privilege_escalation.yml
index 62011066c0..6dc74d2250 100644
--- a/detections/endpoint/linux_cpulimit_privilege_escalation.yml
+++ b/detections/endpoint/linux_cpulimit_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the 'cpulimit' command with specific flags ('-l', '-f') executed with 'sudo' privileges. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and execution details. This activity is significant because if 'cpulimit' is granted sudo rights, a user can potentially execute system commands as root, leading to privilege escalation. If confirmed malicious, this could allow an attacker to gain root access, execute arbitrary commands, and fully compromise the affected system.
+description: The following analytic detects the use of the 'cpulimit' command with
+  specific flags ('-l', '-f') executed with 'sudo' privileges. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments
+  and execution details. This activity is significant because if 'cpulimit' is granted
+  sudo rights, a user can potentially execute system commands as root, leading to
+  privilege escalation. If confirmed malicious, this could allow an attacker to gain
+  root access, execute arbitrary commands, and fully compromise the affected system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*cpulimit*" AND Processes.process="*-l*" AND Processes.process="*-f*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_cpulimit_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*cpulimit*"
+  AND Processes.process="*-l*" AND Processes.process="*-f*" AND Processes.process="*sudo*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_cpulimit_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/cpulimit/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 40
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/cpulimit/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/cpulimit/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_csvtool_privilege_escalation.yml b/detections/endpoint/linux_csvtool_privilege_escalation.yml
index 14691b18e4..49b8a5a57b 100644
--- a/detections/endpoint/linux_csvtool_privilege_escalation.yml
+++ b/detections/endpoint/linux_csvtool_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'csvtool' command with 'sudo' privileges, which can allow a user to run system commands as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain unauthorized root access. If confirmed malicious, this could lead to full system compromise, allowing an attacker to execute arbitrary commands, escalate privileges, and maintain persistent access.
+description: The following analytic detects the execution of the 'csvtool' command
+  with 'sudo' privileges, which can allow a user to run system commands as root. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process execution logs that include command-line details. This activity is significant
+  because it indicates a potential privilege escalation attempt, where a user could
+  gain unauthorized root access. If confirmed malicious, this could lead to full system
+  compromise, allowing an attacker to execute arbitrary commands, escalate privileges,
+  and maintain persistent access.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*csvtool*" AND Processes.process="*call*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_csvtool_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*csvtool*"
+  AND Processes.process="*call*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_csvtool_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/csvtool/
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -50,19 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/csvtool/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/csvtool/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_curl_upload_file.yml b/detections/endpoint/linux_curl_upload_file.yml
index c9c095bfcf..384021b192 100644
--- a/detections/endpoint/linux_curl_upload_file.yml
+++ b/detections/endpoint/linux_curl_upload_file.yml
@@ -5,12 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the curl command with specific switches (-F, --form, --upload-file, -T, -d, --data, --data-raw, -I, --head) to upload AWS credentials or configuration files to a remote destination. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to exfiltrate sensitive AWS credentials, a technique known to be used by the TeamTNT group. If confirmed malicious, this could lead to unauthorized access and potential compromise of AWS resources.
+description: The following analytic detects the use of the curl command with specific
+  switches (-F, --form, --upload-file, -T, -d, --data, --data-raw, -I, --head) to
+  upload AWS credentials or configuration files to a remote destination. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions and process details. This activity is significant as it may indicate
+  an attempt to exfiltrate sensitive AWS credentials, a technique known to be used
+  by the TeamTNT group. If confirmed malicious, this could lead to unauthorized access
+  and potential compromise of AWS resources.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process IN ("*-F *", "*--form *","*--upload-file *","*-T *","*-d *","*--data *","*--data-raw *", "*-I *", "*--head *") AND Processes.process IN ("*.aws/credentials*". "*.aws/config*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_curl_upload_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Filtering may be required. In addition to AWS credentials, add other important files and monitor. The inverse would be to look for _all_ -F behavior and tune from there.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl
+  Processes.process IN ("*-F *", "*--form *","*--upload-file *","*-T *","*-d *","*--data
+  *","*--data-raw *", "*-I *", "*--head *") AND Processes.process IN ("*.aws/credentials*".
+  "*.aws/config*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_curl_upload_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Filtering may be required. In addition to AWS credentials,
+  add other important files and monitor. The inverse would be to look for _all_ -F
+  behavior and tune from there.
 references:
 - https://curl.se/docs/manpage.html
 - https://www.cadosecurity.com/team-tnt-the-first-crypto-mining-worm-to-steal-aws-credentials/
@@ -21,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ attempting to upload important files to a remote destination.
+  message: An instance of $process_name$ was identified on endpoint $dest$ by user
+    $user$ attempting to upload important files to a remote destination.
   mitre_attack_id:
   - T1105
   observable:
@@ -52,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_data_destruction_command.yml b/detections/endpoint/linux_data_destruction_command.yml
index ad82180622..9d7e9aa35f 100644
--- a/detections/endpoint/linux_data_destruction_command.yml
+++ b/detections/endpoint/linux_data_destruction_command.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a Unix shell command designed to wipe root directories on a Linux host. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the 'rm' command with force recursive deletion and the '--no-preserve-root' option. This activity is significant as it indicates potential data destruction attempts, often associated with malware like Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, system instability, and compromised integrity of the affected Linux host. Immediate investigation and response are crucial to mitigate potential damage.
+description: The following analytic detects the execution of a Unix shell command
+  designed to wipe root directories on a Linux host. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on the 'rm' command with force recursive
+  deletion and the '--no-preserve-root' option. This activity is significant as it
+  indicates potential data destruction attempts, often associated with malware like
+  Awfulshred. If confirmed malicious, this behavior could lead to severe data loss,
+  system instability, and compromised integrity of the affected Linux host. Immediate
+  investigation and response are crucial to mitigate potential damage.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND Processes.process IN ("* -rf*", "* -fr*") AND Processes.process = "* --no-preserve-root" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_data_destruction_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND
+  Processes.process IN ("* -rf*", "* -fr*") AND Processes.process = "* --no-preserve-root"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_data_destruction_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://cert.gov.ua/article/3718487
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 100
-  message: a $process_name$ execute rm command with --no-preserve-root parmeter that can wipe root files in $dest$
+  message: a $process_name$ execute rm command with --no-preserve-root parmeter that
+    can wipe root files in $dest$
   mitre_attack_id:
   - T1485
   observable:
@@ -46,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_dd_file_overwrite.yml b/detections/endpoint/linux_dd_file_overwrite.yml
index 0dcb0e0b86..052a5dccfe 100644
--- a/detections/endpoint/linux_dd_file_overwrite.yml
+++ b/detections/endpoint/linux_dd_file_overwrite.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions.
+description: The following analytic detects the use of the 'dd' command to overwrite
+  files on a Linux system. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs that include command-line details.
+  This activity is significant because adversaries often use the 'dd' command to destroy
+  or irreversibly overwrite files, disrupting system availability and services. If
+  confirmed malicious, this behavior could lead to data destruction, making recovery
+  difficult and potentially causing significant operational disruptions.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dd" AND Processes.process = "*of=*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_dd_file_overwrite_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dd"
+  AND Processes.process = "*of=*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_dd_file_overwrite_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://gtfobins.github.io/gtfobins/dd/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1485/T1485.md
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_dd_file_overwrite/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/linux_dd_file_overwrite/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_decode_base64_to_shell.yml b/detections/endpoint/linux_decode_base64_to_shell.yml
index 9c07b68f42..68f4a20747 100644
--- a/detections/endpoint/linux_decode_base64_to_shell.yml
+++ b/detections/endpoint/linux_decode_base64_to_shell.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because  it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions.
+description: The following analytic detects the behavior of decoding base64-encoded
+  data and passing it to a Linux shell. Additionally, it mitigates the potential damage
+  and protects the organization's systems and data.The detection is made by searching
+  for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode",
+  within the Endpoint.Processes data model. The analytic also includes a filter for
+  Linux shells. The detection is important because  it indicates the presence of malicious
+  activity since Base64 encoding is commonly used to obfuscate malicious commands
+  or payloads, and decoding it can be a step in running those commands. It suggests
+  that an attacker is attempting to run malicious commands on a Linux system to gain
+  unauthorized access, for data exfiltration, or perform other malicious actions.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process="*|*" `linux_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | rex field=process "base64\s+(?<decode_flag>-{1,2}d\w*)" | where isnotnull(decode_flag) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_decode_base64_to_shell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present based on legitimate software being utilized. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.process="*|*" `linux_shells`
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | rex field=process "base64\s+(?<decode_flag>-{1,2}d\w*)" | where isnotnull(decode_flag)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_decode_base64_to_shell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present based on legitimate software
+  being utilized. Filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1027/T1027.md#atomic-test-1---decode-base64-data-into-script
 - https://redcanary.com/blog/lateral-movement-with-secure-shell/
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64 and passing it to a shell.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ decoding base64 and passing it to a shell.
   mitre_attack_id:
   - T1027
   - T1059.004
@@ -55,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml b/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml
index 826db2c09b..e6dff039f9 100644
--- a/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml
+++ b/detections/endpoint/linux_deleting_critical_directory_using_rm_command.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of critical directories on a Linux machine using the `rm` command with argument rf. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions targeting directories like /boot, /var/log, /etc, and /dev. This activity is significant because deleting these directories can severely disrupt system operations and is often associated with destructive campaigns like Industroyer2. If confirmed malicious, this action could lead to system instability, data loss, and potential downtime, making it crucial for immediate investigation and response.
+description: The following analytic detects the deletion of critical directories on
+  a Linux machine using the `rm` command with argument rf. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  targeting directories like /boot, /var/log, /etc, and /dev. This activity is significant
+  because deleting these directories can severely disrupt system operations and is
+  often associated with destructive campaigns like Industroyer2. If confirmed malicious,
+  this action could lead to system instability, data loss, and potential downtime,
+  making it crucial for immediate investigation and response.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name =rm AND Processes.process= "* -rf *" AND Processes.process IN ("*/boot/*", "*/var/log/*", "*/etc/*", "*/dev/*") by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_deleting_critical_directory_using_rm_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name =rm AND
+  Processes.process= "* -rf *" AND Processes.process IN ("*/boot/*", "*/var/log/*",
+  "*/etc/*", "*/dev/*") by Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_deleting_critical_directory_using_rm_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A deletion in known critical list of folder using rm command $process$ executed on $dest$
+  message: A deletion in known critical list of folder using rm command $process$
+    executed on $dest$
   mitre_attack_id:
   - T1485
   observable:
@@ -43,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_deletion_of_cron_jobs.yml b/detections/endpoint/linux_deletion_of_cron_jobs.yml
index 5286f06f19..6442cc7f5c 100644
--- a/detections/endpoint/linux_deletion_of_cron_jobs.yml
+++ b/detections/endpoint/linux_deletion_of_cron_jobs.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of cron jobs on a Linux machine. It leverages filesystem event logs to identify when files within the "/etc/cron.*" directory are deleted. This activity is significant because attackers or malware may delete cron jobs to disable scheduled security tasks or evade detection mechanisms. If confirmed malicious, this action could allow an attacker to disrupt system operations, evade security measures, or facilitate further malicious activities such as data wiping, as seen with the acidrain malware.
+description: The following analytic detects the deletion of cron jobs on a Linux machine.
+  It leverages filesystem event logs to identify when files within the "/etc/cron.*"
+  directory are deleted. This activity is significant because attackers or malware
+  may delete cron jobs to disable scheduled security tasks or evade detection mechanisms.
+  If confirmed malicious, this action could allow an attacker to disrupt system operations,
+  evade security measures, or facilitate further malicious activities such as data
+  wiping, as seen with the acidrain malware.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path="/etc/cron.*" by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_cron_jobs_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path="/etc/cron.*"
+  by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid
+  Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_deletion_of_cron_jobs_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_deletion_of_init_daemon_script.yml b/detections/endpoint/linux_deletion_of_init_daemon_script.yml
index d3183aa1e6..1c0d371231 100644
--- a/detections/endpoint/linux_deletion_of_init_daemon_script.yml
+++ b/detections/endpoint/linux_deletion_of_init_daemon_script.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of init daemon scripts on a Linux machine. It leverages filesystem event logs to identify when files within the /etc/init.d/ directory are deleted. This activity is significant because init daemon scripts control the start and stop of critical services, and their deletion can indicate an attempt to impair security features or evade defenses. If confirmed malicious, this behavior could allow an attacker to disrupt essential services, execute destructive payloads, or persist undetected in the environment.
+description: The following analytic detects the deletion of init daemon scripts on
+  a Linux machine. It leverages filesystem event logs to identify when files within
+  the /etc/init.d/ directory are deleted. This activity is significant because init
+  daemon scripts control the start and stop of critical services, and their deletion
+  can indicate an attempt to impair security features or evade defenses. If confirmed
+  malicious, this behavior could allow an attacker to disrupt essential services,
+  execute destructive payloads, or persist undetected in the environment.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path IN ( "/etc/init.d/*") by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_init_daemon_script_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path
+  IN ( "/etc/init.d/*") by _time span=1h Filesystem.file_name Filesystem.file_path
+  Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_init_daemon_script_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_deletion_of_services.yml b/detections/endpoint/linux_deletion_of_services.yml
index 671a92507d..ea80fc3353 100644
--- a/detections/endpoint/linux_deletion_of_services.yml
+++ b/detections/endpoint/linux_deletion_of_services.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of services on a Linux machine. It leverages filesystem event logs to identify when service files within system directories (e.g., /etc/systemd/, /lib/systemd/, /run/systemd/) are deleted. This activity is significant because attackers may delete or modify services to disable security features or evade defenses. If confirmed malicious, this behavior could indicate an attempt to impair system functionality or execute a destructive payload, potentially leading to system instability or data loss. Immediate investigation is required to determine the responsible process and user.
+description: The following analytic detects the deletion of services on a Linux machine.
+  It leverages filesystem event logs to identify when service files within system
+  directories (e.g., /etc/systemd/, /lib/systemd/, /run/systemd/) are deleted. This
+  activity is significant because attackers may delete or modify services to disable
+  security features or evade defenses. If confirmed malicious, this behavior could
+  indicate an attempt to impair system functionality or execute a destructive payload,
+  potentially leading to system instability or data loss. Immediate investigation
+  is required to determine the responsible process and user.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path IN ( "/etc/systemd/*", "*/lib/systemd/*", "*/run/systemd/*") Filesystem.file_path = "*.service" by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_services_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path
+  IN ( "/etc/systemd/*", "*/lib/systemd/*", "*/run/systemd/*") Filesystem.file_path
+  = "*.service" by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest
+  Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_services_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/
 - https://unix.stackexchange.com/questions/224992/where-do-i-put-my-systemd-unit-file
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,18 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_deletion_of_ssl_certificate.yml b/detections/endpoint/linux_deletion_of_ssl_certificate.yml
index 0d1de08085..b2b5442086 100644
--- a/detections/endpoint/linux_deletion_of_ssl_certificate.yml
+++ b/detections/endpoint/linux_deletion_of_ssl_certificate.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of SSL certificates on a Linux machine. It leverages filesystem event logs to identify when files with extensions .pem or .crt are deleted from the /etc/ssl/certs/ directory. This activity is significant because attackers may delete or modify SSL certificates to disable security features or evade defenses on a compromised system. If confirmed malicious, this behavior could indicate an attempt to disrupt secure communications, evade detection, or execute a destructive payload, potentially leading to significant security breaches and data loss.
+description: The following analytic detects the deletion of SSL certificates on a
+  Linux machine. It leverages filesystem event logs to identify when files with extensions
+  .pem or .crt are deleted from the /etc/ssl/certs/ directory. This activity is significant
+  because attackers may delete or modify SSL certificates to disable security features
+  or evade defenses on a compromised system. If confirmed malicious, this behavior
+  could indicate an attempt to disrupt secure communications, evade detection, or
+  execute a destructive payload, potentially leading to significant security breaches
+  and data loss.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path = "/etc/ssl/certs/*" Filesystem.file_path IN ("*.pem", "*.crt") by _time span=1h Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `linux_deletion_of_ssl_certificate_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path
+  = "/etc/ssl/certs/*" Filesystem.file_path IN ("*.pem", "*.crt") by _time span=1h
+  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.process_guid
+  Filesystem.action | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `linux_deletion_of_ssl_certificate_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/
 drilldown_searches:
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_disable_services.yml b/detections/endpoint/linux_disable_services.yml
index 172b2377ba..9aa5b085f7 100644
--- a/detections/endpoint/linux_disable_services.yml
+++ b/detections/endpoint/linux_disable_services.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to disable a service on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "systemctl," "service," and "svcadm" with commands containing "disable." This activity is significant as adversaries may disable security or critical services to evade detection and facilitate further malicious actions, such as deploying destructive payloads. If confirmed malicious, this could lead to the termination of essential security services, allowing attackers to persist undetected and potentially cause significant damage to the system.
+description: The following analytic detects attempts to disable a service on a Linux
+  system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on processes like "systemctl," "service," and "svcadm" with commands containing
+  "disable." This activity is significant as adversaries may disable security or critical
+  services to evade detection and facilitate further malicious actions, such as deploying
+  destructive payloads. If confirmed malicious, this could lead to the termination
+  of essential security services, allowing attackers to persist undetected and potentially
+  cause significant damage to the system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", "service", "svcadm")  Processes.process = "* disable*" by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_disable_services_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl",
+  "service", "svcadm")  Processes.process = "* disable*" by Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_disable_services_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to disable services on endpoint $dest$ by $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    attempting to disable services on endpoint $dest$ by $user$.
   mitre_attack_id:
   - T1489
   observable:
@@ -43,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_doas_conf_file_creation.yml b/detections/endpoint/linux_doas_conf_file_creation.yml
index 7dc1586a8e..579c18d564 100644
--- a/detections/endpoint/linux_doas_conf_file_creation.yml
+++ b/detections/endpoint/linux_doas_conf_file_creation.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of the doas.conf file on a Linux host. This file is used by the doas utility to allow standard users to perform tasks as root, similar to sudo. The detection leverages filesystem data from the Endpoint data model, focusing on the creation of the doas.conf file. This activity is significant because it can indicate an attempt to gain elevated privileges, potentially by an adversary. If confirmed malicious, this could allow an attacker to execute commands with root privileges, leading to full system compromise.
+description: The following analytic detects the creation of the doas.conf file on
+  a Linux host. This file is used by the doas utility to allow standard users to perform
+  tasks as root, similar to sudo. The detection leverages filesystem data from the
+  Endpoint data model, focusing on the creation of the doas.conf file. This activity
+  is significant because it can indicate an attempt to gain elevated privileges, potentially
+  by an adversary. If confirmed malicious, this could allow an attacker to execute
+  commands with root privileges, leading to full system compromise.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/doas.conf") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_doas_conf_file_creation_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/doas.conf")
+  by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid
+  Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `linux_doas_conf_file_creation_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://wiki.gentoo.org/wiki/Doas
 - https://www.makeuseof.com/how-to-install-and-use-doas/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_doas_tool_execution.yml b/detections/endpoint/linux_doas_tool_execution.yml
index 8b10e6efe4..8ed06924d7 100644
--- a/detections/endpoint/linux_doas_tool_execution.yml
+++ b/detections/endpoint/linux_doas_tool_execution.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'doas' tool on a Linux host. This tool allows standard users to perform tasks with root privileges, similar to 'sudo'. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as 'doas' can be exploited by adversaries to gain elevated privileges on a compromised host. If confirmed malicious, this could lead to unauthorized administrative access, potentially compromising the entire system.
+description: The following analytic detects the execution of the 'doas' tool on a
+  Linux host. This tool allows standard users to perform tasks with root privileges,
+  similar to 'sudo'. The detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant as 'doas' can be exploited by adversaries to gain elevated privileges
+  on a compromised host. If confirmed malicious, this could lead to unauthorized administrative
+  access, potentially compromising the entire system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "doas" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_doas_tool_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "doas"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_doas_tool_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://wiki.gentoo.org/wiki/Doas
 - https://www.makeuseof.com/how-to-install-and-use-doas/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas_exec/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/doas_exec/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_docker_privilege_escalation.yml b/detections/endpoint/linux_docker_privilege_escalation.yml
index e2b7f3013a..82cff2c7f7 100644
--- a/detections/endpoint/linux_docker_privilege_escalation.yml
+++ b/detections/endpoint/linux_docker_privilege_escalation.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects attempts to escalate privileges on a Linux system using Docker. It identifies processes where Docker commands are used to mount the root directory or execute shell commands within a container. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names, command-line arguments, and parent processes. This activity is significant because it can allow an attacker with Docker privileges to modify critical system files, such as /etc/passwd, to create a superuser. If confirmed malicious, this could lead to full system compromise and persistent unauthorized access.
+description: The following analytic detects attempts to escalate privileges on a Linux
+  system using Docker. It identifies processes where Docker commands are used to mount
+  the root directory or execute shell commands within a container. This detection
+  leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names,
+  command-line arguments, and parent processes. This activity is significant because
+  it can allow an attacker with Docker privileges to modify critical system files,
+  such as /etc/passwd, to create a superuser. If confirmed malicious, this could lead
+  to full system compromise and persistent unauthorized access.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN("*docker*-v*/*:*","*docker*--volume*/*:*") OR Processes.process IN("*docker*exec*sh*","*docker*exec*bash*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_docker_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process IN("*docker*-v*/*:*","*docker*--volume*/*:*")
+  OR Processes.process IN("*docker*exec*sh*","*docker*exec*bash*") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_docker_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/docker/
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 10
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -50,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/docker/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/docker/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_edit_cron_table_parameter.yml b/detections/endpoint/linux_edit_cron_table_parameter.yml
index 311457d9c6..55efdae6c5 100644
--- a/detections/endpoint/linux_edit_cron_table_parameter.yml
+++ b/detections/endpoint/linux_edit_cron_table_parameter.yml
@@ -5,12 +5,32 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the suspicious editing of cron jobs in Linux using the crontab command-line parameter (-e). It identifies this activity by monitoring command-line executions involving 'crontab' and the edit parameter. This behavior is significant for a SOC as cron job manipulations can indicate unauthorized persistence attempts or scheduled malicious actions. If confirmed malicious, this activity could lead to system compromise, unauthorized access, or broader network compromise.
+description: The following analytic detects the suspicious editing of cron jobs in
+  Linux using the crontab command-line parameter (-e). It identifies this activity
+  by monitoring command-line executions involving 'crontab' and the edit parameter.
+  This behavior is significant for a SOC as cron job manipulations can indicate unauthorized
+  persistence attempts or scheduled malicious actions. If confirmed malicious, this
+  activity could lead to system compromise, unauthorized access, or broader network
+  compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = crontab Processes.process = "*crontab *" Processes.process = "* -e*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_edit_cron_table_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = crontab
+  Processes.process = "*crontab *" Processes.process = "* -e*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_edit_cron_table_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/003/
 tags:
@@ -35,19 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_edit_parameter/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/crontab_edit_parameter/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_emacs_privilege_escalation.yml b/detections/endpoint/linux_emacs_privilege_escalation.yml
index 2428618654..5350252dcd 100644
--- a/detections/endpoint/linux_emacs_privilege_escalation.yml
+++ b/detections/endpoint/linux_emacs_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of Emacs with elevated privileges using the `sudo` command and the `--eval` option. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line arguments. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain root access by running Emacs with elevated permissions. If confirmed malicious, this could allow an attacker to execute arbitrary commands as root, leading to full system compromise and unauthorized access to sensitive information.
+description: The following analytic detects the execution of Emacs with elevated privileges
+  using the `sudo` command and the `--eval` option. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs that include
+  command-line arguments. This activity is significant because it indicates a potential
+  privilege escalation attempt, where a user could gain root access by running Emacs
+  with elevated permissions. If confirmed malicious, this could allow an attacker
+  to execute arbitrary commands as root, leading to full system compromise and unauthorized
+  access to sensitive information.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*emacs*" AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_emacs_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*emacs*"
+  AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_emacs_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/emacs/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 40
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/emacs/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/emacs/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml b/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml
index 00490204b7..dafb382acd 100644
--- a/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml
+++ b/detections/endpoint/linux_file_created_in_kernel_driver_directory.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of files in the Linux kernel/driver directory. It leverages filesystem data to identify new files in this critical directory. This activity is significant because the kernel/driver directory is typically reserved for kernel modules, and unauthorized file creation here can indicate a rootkit installation. If confirmed malicious, this could allow an attacker to gain high-level privileges, potentially compromising the entire system by executing code at the kernel level.
+description: The following analytic detects the creation of files in the Linux kernel/driver
+  directory. It leverages filesystem data to identify new files in this critical directory.
+  This activity is significant because the kernel/driver directory is typically reserved
+  for kernel modules, and unauthorized file creation here can indicate a rootkit installation.
+  If confirmed malicious, this could allow an attacker to gain high-level privileges,
+  potentially compromising the entire system by executing code at the kernel level.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/kernel/drivers/*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_created_in_kernel_driver_directory_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can create file in this folders for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/kernel/drivers/*")
+  by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `linux_file_created_in_kernel_driver_directory_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints.
+  If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
+known_false_positives: Administrator or network operator can create file in this folders
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/
 - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_file_creation_in_init_boot_directory.yml b/detections/endpoint/linux_file_creation_in_init_boot_directory.yml
index 5d2b61fc81..37795f1716 100644
--- a/detections/endpoint/linux_file_creation_in_init_boot_directory.yml
+++ b/detections/endpoint/linux_file_creation_in_init_boot_directory.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of files in Linux init boot directories, which are used for automatic execution upon system startup. It leverages file system logs to identify new files in directories such as /etc/init.d/ and /etc/rc.d/. This activity is significant as it is a common persistence technique used by adversaries, malware authors, and red teamers. If confirmed malicious, this could allow an attacker to maintain persistence on the compromised host, potentially leading to further exploitation and unauthorized control over the system.
+description: The following analytic detects the creation of files in Linux init boot
+  directories, which are used for automatic execution upon system startup. It leverages
+  file system logs to identify new files in directories such as /etc/init.d/ and /etc/rc.d/.
+  This activity is significant as it is a common persistence technique used by adversaries,
+  malware authors, and red teamers. If confirmed malicious, this could allow an attacker
+  to maintain persistence on the compromised host, potentially leading to further
+  exploitation and unauthorized control over the system.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/init.d/*", "*/etc/rc.d/*", "*/sbin/init.d/*", "*/etc/rc.local*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_creation_in_init_boot_directory_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase
-known_false_positives: Administrator or network operator can create file in this folders for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/init.d/*",
+  "*/etc/rc.d/*", "*/sbin/init.d/*", "*/etc/rc.local*") by Filesystem.dest Filesystem.file_name
+  Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_creation_in_init_boot_directory_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints.
+  If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase
+known_false_positives: Administrator or network operator can create file in this folders
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_file_creation_in_profile_directory.yml b/detections/endpoint/linux_file_creation_in_profile_directory.yml
index bf0e309b4b..532deb998a 100644
--- a/detections/endpoint/linux_file_creation_in_profile_directory.yml
+++ b/detections/endpoint/linux_file_creation_in_profile_directory.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of files in the /etc/profile.d directory on Linux systems. It leverages filesystem data to identify new files in this directory, which is often used by adversaries for persistence by executing scripts upon system boot. This activity is significant as it may indicate an attempt to maintain long-term access to the compromised host. If confirmed malicious, this could allow attackers to execute arbitrary code with elevated privileges each time the system boots, potentially leading to further compromise and data exfiltration.
+description: The following analytic detects the creation of files in the /etc/profile.d
+  directory on Linux systems. It leverages filesystem data to identify new files in
+  this directory, which is often used by adversaries for persistence by executing
+  scripts upon system boot. This activity is significant as it may indicate an attempt
+  to maintain long-term access to the compromised host. If confirmed malicious, this
+  could allow attackers to execute arbitrary code with elevated privileges each time
+  the system boots, potentially leading to further compromise and data exfiltration.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/profile.d/*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_file_creation_in_profile_directory_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can create file in profile.d folders for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/etc/profile.d/*")
+  by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid
+  Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `linux_file_creation_in_profile_directory_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints.
+  If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
+known_false_positives: Administrator or network operator can create file in profile.d
+  folders for automation purposes. Please update the filter macros to remove false
+  positives.
 references:
 - https://attack.mitre.org/techniques/T1546/004/
 - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_find_privilege_escalation.yml b/detections/endpoint/linux_find_privilege_escalation.yml
index 81ea057487..22d7f16bb6 100644
--- a/detections/endpoint/linux_find_privilege_escalation.yml
+++ b/detections/endpoint/linux_find_privilege_escalation.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the 'find' command with 'sudo' and '-exec' options, which can indicate an attempt to escalate privileges on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line arguments. This activity is significant because it can allow a user to execute system commands as root, potentially leading to a root shell. If confirmed malicious, this could enable an attacker to gain full control over the system, leading to severe security breaches and unauthorized access to sensitive data.
+description: The following analytic detects the use of the 'find' command with 'sudo'
+  and '-exec' options, which can indicate an attempt to escalate privileges on a Linux
+  system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process execution logs that include command-line arguments. This activity is
+  significant because it can allow a user to execute system commands as root, potentially
+  leading to a root shell. If confirmed malicious, this could enable an attacker to
+  gain full control over the system, leading to severe security breaches and unauthorized
+  access to sensitive data.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*find*" AND Processes.process="*-exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_find_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*find*" AND
+  Processes.process="*-exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_find_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/find/
 - https://en.wikipedia.org/wiki/Find_(Unix)
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 10
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/find/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/find/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_gdb_privilege_escalation.yml b/detections/endpoint/linux_gdb_privilege_escalation.yml
index 34b4ce7004..e343e7f7aa 100644
--- a/detections/endpoint/linux_gdb_privilege_escalation.yml
+++ b/detections/endpoint/linux_gdb_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the GNU Debugger (GDB) with specific flags that indicate an attempt to escalate privileges on a Linux system. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes where GDB is run with the `-nx`, `-ex`, and `sudo` flags. This activity is significant because it can allow a user to execute system commands as root, potentially leading to a root shell. If confirmed malicious, this could result in full system compromise, allowing an attacker to gain complete control over the affected endpoint.
+description: The following analytic detects the execution of the GNU Debugger (GDB)
+  with specific flags that indicate an attempt to escalate privileges on a Linux system.
+  It leverages Endpoint Detection and Response (EDR) telemetry to identify processes
+  where GDB is run with the `-nx`, `-ex`, and `sudo` flags. This activity is significant
+  because it can allow a user to execute system commands as root, potentially leading
+  to a root shell. If confirmed malicious, this could result in full system compromise,
+  allowing an attacker to gain complete control over the affected endpoint.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*gdb*" AND Processes.process="*-nx*" AND Processes.process="*-ex*!*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_gdb_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*gdb*" AND
+  Processes.process="*-nx*" AND Processes.process="*-ex*!*" AND Processes.process="*sudo*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_gdb_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/gdb/
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -50,19 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gdb/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gdb/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_gem_privilege_escalation.yml b/detections/endpoint/linux_gem_privilege_escalation.yml
index 17e52239bd..36bc33bde8 100644
--- a/detections/endpoint/linux_gem_privilege_escalation.yml
+++ b/detections/endpoint/linux_gem_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the RubyGems utility with elevated privileges, specifically when it is used to run system commands as root. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include "gem open -e" and "sudo". This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute commands as the root user. If confirmed malicious, this could lead to full system compromise, enabling the attacker to gain root access and execute arbitrary commands with elevated privileges.
+description: The following analytic detects the execution of the RubyGems utility
+  with elevated privileges, specifically when it is used to run system commands as
+  root. This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on command-line executions that include "gem open -e" and "sudo". This
+  activity is significant because it indicates a potential privilege escalation attempt,
+  allowing a user to execute commands as the root user. If confirmed malicious, this
+  could lead to full system compromise, enabling the attacker to gain root access
+  and execute arbitrary commands with elevated privileges.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*gem*open*-e*" AND Processes.process="*-c*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_gem_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*gem*open*-e*"
+  AND Processes.process="*-c*" AND Processes.process="*sudo*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_gem_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/gem/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 20
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gem/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gem/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_gnu_awk_privilege_escalation.yml b/detections/endpoint/linux_gnu_awk_privilege_escalation.yml
index 2e92f13eeb..b64aec0a32 100644
--- a/detections/endpoint/linux_gnu_awk_privilege_escalation.yml
+++ b/detections/endpoint/linux_gnu_awk_privilege_escalation.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'gawk' command with elevated privileges on a Linux system. It leverages Endpoint Detection and Response (EDR) telemetry to identify command-line executions where 'gawk' is used with 'sudo' and 'BEGIN{system' patterns. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute system commands as root. If confirmed malicious, this could lead to full root access, enabling the attacker to control the system, modify critical files, and maintain persistent access.
+description: The following analytic detects the execution of the 'gawk' command with
+  elevated privileges on a Linux system. It leverages Endpoint Detection and Response
+  (EDR) telemetry to identify command-line executions where 'gawk' is used with 'sudo'
+  and 'BEGIN{system' patterns. This activity is significant because it indicates a
+  potential privilege escalation attempt, allowing a user to execute system commands
+  as root. If confirmed malicious, this could lead to full root access, enabling the
+  attacker to control the system, modify critical files, and maintain persistent access.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*gawk*" AND Processes.process="*BEGIN*{system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_gnu_awk_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*gawk*" AND
+  Processes.process="*BEGIN*{system*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `linux_gnu_awk_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/gawk/
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gawk/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/gawk/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_hardware_addition_swapoff.yml b/detections/endpoint/linux_hardware_addition_swapoff.yml
index 6af1beebc8..6acc153e58 100644
--- a/detections/endpoint/linux_hardware_addition_swapoff.yml
+++ b/detections/endpoint/linux_hardware_addition_swapoff.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the "swapoff" command, which disables the swapping of paging devices on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because disabling swap can be a tactic used by malware, such as Awfulshred, to evade detection and hinder forensic analysis. If confirmed malicious, this action could allow an attacker to manipulate system memory management, potentially leading to data corruption, system instability, or evasion of memory-based detection mechanisms.
+description: The following analytic detects the execution of the "swapoff" command,
+  which disables the swapping of paging devices on a Linux system. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs. This activity is significant because disabling swap can be a tactic used by
+  malware, such as Awfulshred, to evade detection and hinder forensic analysis. If
+  confirmed malicious, this action could allow an attacker to manipulate system memory
+  management, potentially leading to data corruption, system instability, or evasion
+  of memory-based detection mechanisms.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "swapoff" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_hardware_addition_swapoff_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: administrator may disable swapping of devices in a linux host. Filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "swapoff"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_hardware_addition_swapoff_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: administrator may disable swapping of devices in a linux host.
+  Filter is needed.
 references:
 - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml b/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml
index 1d09187e21..50df340778 100644
--- a/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml
+++ b/detections/endpoint/linux_high_frequency_of_file_deletion_in_boot_folder.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a high frequency of file deletions in the /boot/ folder on Linux systems. It leverages filesystem event logs to identify when 200 or more files are deleted within an hour by the same process. This behavior is significant as it may indicate the presence of wiper malware, such as Industroyer2, which targets critical system directories. If confirmed malicious, this activity could lead to system instability or failure, hindering the boot process and potentially causing a complete system compromise.
+description: The following analytic detects a high frequency of file deletions in
+  the /boot/ folder on Linux systems. It leverages filesystem event logs to identify
+  when 200 or more files are deleted within an hour by the same process. This behavior
+  is significant as it may indicate the presence of wiper malware, such as Industroyer2,
+  which targets critical system directories. If confirmed malicious, this activity
+  could lead to system instability or failure, hindering the boot process and potentially
+  causing a complete system compromise.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as deletedFileNames values(Filesystem.file_path) as deletedFilePath dc(Filesystem.file_path) as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path = "/boot/*" by _time span=1h  Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` | where  numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_high_frequency_of_file_deletion_in_boot_folder_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: linux package installer/uninstaller may cause this event. Please update you filter macro to remove false positives.
+search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as
+  deletedFileNames values(Filesystem.file_path) as deletedFilePath dc(Filesystem.file_path)
+  as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
+  where Filesystem.action=deleted Filesystem.file_path = "/boot/*" by _time span=1h  Filesystem.dest
+  Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)` |
+  where  numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_high_frequency_of_file_deletion_in_boot_folder_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: linux package installer/uninstaller may cause this event. Please
+  update you filter macro to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_boot_dir/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_boot_dir/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml b/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml
index 7662ff0967..7640a23d15 100644
--- a/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml
+++ b/detections/endpoint/linux_high_frequency_of_file_deletion_in_etc_folder.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a high frequency of file deletions in the /etc/ folder on Linux systems. It leverages the Endpoint.Filesystem data model to identify instances where 200 or more files are deleted within an hour, grouped by process name and process ID. This behavior is significant as it may indicate the presence of wiper malware, such as AcidRain, which aims to delete critical system files. If confirmed malicious, this activity could lead to severe system instability, data loss, and potential disruption of services.
+description: The following analytic detects a high frequency of file deletions in
+  the /etc/ folder on Linux systems. It leverages the Endpoint.Filesystem data model
+  to identify instances where 200 or more files are deleted within an hour, grouped
+  by process name and process ID. This behavior is significant as it may indicate
+  the presence of wiper malware, such as AcidRain, which aims to delete critical system
+  files. If confirmed malicious, this activity could lead to severe system instability,
+  data loss, and potential disruption of services.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as deletedFileNames values(Filesystem.file_path) as deletedFilePath dc(Filesystem.file_path) as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.action=deleted Filesystem.file_path = "/etc/*" by _time span=1h  Filesystem.dest Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)`  | where  numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_high_frequency_of_file_deletion_in_etc_folder_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: linux package installer/uninstaller may cause this event. Please update you filter macro to remove false positives.
+search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) as
+  deletedFileNames values(Filesystem.file_path) as deletedFilePath dc(Filesystem.file_path)
+  as numOfDelFilePath count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
+  where Filesystem.action=deleted Filesystem.file_path = "/etc/*" by _time span=1h  Filesystem.dest
+  Filesystem.process_guid Filesystem.action | `drop_dm_object_name(Filesystem)`  |
+  where  numOfDelFilePath >= 200 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_high_frequency_of_file_deletion_in_etc_folder_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: linux package installer/uninstaller may cause this event. Please
+  update you filter macro to remove false positives.
 references:
 - https://www.sentinelone.com/labs/acidrain-a-modem-wiper-rains-down-on-europe/
 drilldown_searches:
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/acidrain/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_impair_defenses_process_kill.yml b/detections/endpoint/linux_impair_defenses_process_kill.yml
index bb309f162a..2795f38cbf 100644
--- a/detections/endpoint/linux_impair_defenses_process_kill.yml
+++ b/detections/endpoint/linux_impair_defenses_process_kill.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of the 'pkill' command, which is used to terminate processes on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because threat actors often use 'pkill' to disable security defenses or terminate critical processes, facilitating further malicious actions. If confirmed malicious, this behavior could lead to the disruption of security applications, enabling attackers to evade detection and potentially corrupt or destroy files on the targeted system.
+description: The following analytic identifies the execution of the 'pkill' command,
+  which is used to terminate processes on a Linux system. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant because threat actors often use 'pkill'
+  to disable security defenses or terminate critical processes, facilitating further
+  malicious actions. If confirmed malicious, this behavior could lead to the disruption
+  of security applications, enabling attackers to evade detection and potentially
+  corrupt or destroy files on the targeted system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ( "pgrep", "pkill") Processes.process = "*pkill *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_impair_defenses_process_kill_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network admin can terminate a process using this linux command. Filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN (
+  "pgrep", "pkill") Processes.process = "*pkill *" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `linux_impair_defenses_process_kill_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network admin can terminate a process using this linux command.
+  Filter is needed.
 references:
 - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/
 - https://cert.gov.ua/article/3718487
@@ -21,7 +42,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: a $process_name$ tries to execute pkill commandline to terminate process in $dest$
+  message: a $process_name$ tries to execute pkill commandline to terminate process
+    in $dest$
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -38,19 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_indicator_removal_clear_cache.yml b/detections/endpoint/linux_indicator_removal_clear_cache.yml
index 6ec4eae5ef..f5e0346b29 100644
--- a/detections/endpoint/linux_indicator_removal_clear_cache.yml
+++ b/detections/endpoint/linux_indicator_removal_clear_cache.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects processes that clear or free page cache on a Linux system. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line executions involving the kernel system request `drop_caches`. This activity is significant as it may indicate an attempt to delete forensic evidence or the presence of wiper malware like Awfulshred. If confirmed malicious, this behavior could allow an attacker to cover their tracks, making it difficult to investigate other malicious activities or system compromises.
+description: The following analytic detects processes that clear or free page cache
+  on a Linux system. It leverages Endpoint Detection and Response (EDR) data, focusing
+  on specific command-line executions involving the kernel system request `drop_caches`.
+  This activity is significant as it may indicate an attempt to delete forensic evidence
+  or the presence of wiper malware like Awfulshred. If confirmed malicious, this behavior
+  could allow an attacker to cover their tracks, making it difficult to investigate
+  other malicious activities or system compromises.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", "sudo", "bash")  AND Processes.process  IN("* echo 3 &gt; *", "* echo 2 &gt; *","* echo 1 &gt; *") AND Processes.process = "*/proc/sys/vm/drop_caches" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_indicator_removal_clear_cache_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash",
+  "sudo", "bash")  AND Processes.process  IN("* echo 3 &gt; *", "* echo 2 &gt; *","*
+  echo 1 &gt; *") AND Processes.process = "*/proc/sys/vm/drop_caches" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `linux_indicator_removal_clear_cache_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: a $process_name$ clear cache using kernel drop cache system request in $dest$
+  message: a $process_name$ clear cache using kernel drop cache system request in
+    $dest$
   mitre_attack_id:
   - T1070
   observable:
@@ -46,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test3/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test3/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_indicator_removal_service_file_deletion.yml b/detections/endpoint/linux_indicator_removal_service_file_deletion.yml
index e04a23caaa..1b0db3e6d2 100644
--- a/detections/endpoint/linux_indicator_removal_service_file_deletion.yml
+++ b/detections/endpoint/linux_indicator_removal_service_file_deletion.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of Linux service unit configuration files by suspicious processes. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on processes executing the 'rm' command targeting '.service' files. This activity is significant as it may indicate malware attempting to disable critical services or security products, a common defense evasion tactic. If confirmed malicious, this behavior could lead to service disruption, security tool incapacitation, or complete system compromise, severely impacting the integrity and availability of the affected Linux host.
+description: The following analytic detects the deletion of Linux service unit configuration
+  files by suspicious processes. It leverages Endpoint Detection and Response (EDR)
+  telemetry, focusing on processes executing the 'rm' command targeting '.service'
+  files. This activity is significant as it may indicate malware attempting to disable
+  critical services or security products, a common defense evasion tactic. If confirmed
+  malicious, this behavior could lead to service disruption, security tool incapacitation,
+  or complete system compromise, severely impacting the integrity and availability
+  of the affected Linux host.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND Processes.process = "*rm *"  AND Processes.process = "*.service" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_indicator_removal_service_file_deletion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network admin can delete services unit configuration file as part of normal software installation. Filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "rm"  AND
+  Processes.process = "*rm *"  AND Processes.process = "*.service" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `linux_indicator_removal_service_file_deletion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network admin can delete services unit configuration file as
+  part of normal software installation. Filter is needed.
 references:
 - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/overview-of-the-cyber-weapons-used-in-the-ukraine-russia-war/
 - https://cert.gov.ua/article/3718487
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: a $process_name$ has a commandline $process$ to delete service configuration file in $dest$
+  message: a $process_name$ has a commandline $process$ to delete service configuration
+    file in $dest$
   mitre_attack_id:
   - T1070.004
   - T1070
@@ -47,19 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test1/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_ingress_tool_transfer_hunting.yml b/detections/endpoint/linux_ingress_tool_transfer_hunting.yml
index 5ab8743734..3c652d8a12 100644
--- a/detections/endpoint/linux_ingress_tool_transfer_hunting.yml
+++ b/detections/endpoint/linux_ingress_tool_transfer_hunting.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of 'curl' and 'wget' commands within a Linux environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, user information, and command-line executions. This activity is significant as 'curl' and 'wget' are commonly used for downloading files, which can indicate potential ingress of malicious tools. If confirmed malicious, this activity could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Monitoring and tuning this detection helps identify and differentiate between normal and potentially harmful usage.
+description: The following analytic detects the use of 'curl' and 'wget' commands
+  within a Linux environment. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names, user information, and command-line executions.
+  This activity is significant as 'curl' and 'wget' are commonly used for downloading
+  files, which can indicate potential ingress of malicious tools. If confirmed malicious,
+  this activity could lead to unauthorized code execution, data exfiltration, or further
+  compromise of the system. Monitoring and tuning this detection helps identify and
+  differentiate between normal and potentially harmful usage.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=curl OR Processes.process_name=wget) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ingress_tool_transfer_hunting_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present. This query is meant to help tune other curl and wget analytics.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=curl
+  OR Processes.process_name=wget) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_ingress_tool_transfer_hunting_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present. This query is meant to help
+  tune other curl and wget analytics.
 references:
 - https://gtfobins.github.io/gtfobins/curl/
 - https://curl.se/docs/manpage.html#-I
@@ -23,7 +44,8 @@ tags:
   asset_type: Endpoint
   confidence: 10
   impact: 10
-  message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ utilizing curl or wget.
+  message: An instance of $process_name$ was identified on endpoint $dest$ by user
+    $user$ utilizing curl or wget.
   mitre_attack_id:
   - T1105
   observable:
@@ -43,23 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml b/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml
index e92b5c54b8..910b767934 100644
--- a/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml
+++ b/detections/endpoint/linux_ingress_tool_transfer_with_curl.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the curl command with specific switches (-O, -sO, -ksO, --output) commonly used to download remote scripts or binaries. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to download and execute potentially malicious files, often used in initial stages of an attack. If confirmed malicious, this could lead to unauthorized code execution, enabling attackers to compromise the system further.
+description: The following analytic detects the use of the curl command with specific
+  switches (-O, -sO, -ksO, --output) commonly used to download remote scripts or binaries.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line arguments. This activity is significant
+  as it may indicate an attempt to download and execute potentially malicious files,
+  often used in initial stages of an attack. If confirmed malicious, this could lead
+  to unauthorized code execution, enabling attackers to compromise the system further.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process, "(?i)(-O|-sO|-ksO|--output)") | `linux_ingress_tool_transfer_with_curl_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present. Tune and then change type to TTP.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  match(process, "(?i)(-O|-sO|-ksO|--output)") | `linux_ingress_tool_transfer_with_curl_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present. Tune and then change type
+  to TTP.
 references:
 - https://gtfobins.github.io/gtfobins/curl/
 - https://curl.se/docs/manpage.html#-I
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 40
-  message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ to download a remote file. Review activity for further details.
+  message: An instance of $process_name$ was identified on endpoint $dest$ by user
+    $user$ to download a remote file. Review activity for further details.
   mitre_attack_id:
   - T1105
   observable:
@@ -52,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml b/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml
index 206223d53f..2c1ad951bb 100644
--- a/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml
+++ b/detections/endpoint/linux_insert_kernel_module_using_insmod_utility.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the insertion of a Linux kernel module using the insmod utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process names and command-line details. This activity is significant as it may indicate the installation of a rootkit or malicious kernel module, potentially allowing an attacker to gain elevated privileges and bypass security detections. If confirmed malicious, this could lead to unauthorized code execution, persistent access, and severe compromise of the affected system.
+description: The following analytic detects the insertion of a Linux kernel module
+  using the insmod utility. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs that include process names and
+  command-line details. This activity is significant as it may indicate the installation
+  of a rootkit or malicious kernel module, potentially allowing an attacker to gain
+  elevated privileges and bypass security detections. If confirmed malicious, this
+  could lead to unauthorized code execution, persistent access, and severe compromise
+  of the affected system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod", "sudo") AND Processes.process = *insmod* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_insert_kernel_module_using_insmod_utility_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod",
+  "sudo") AND Processes.process = *insmod* by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_insert_kernel_module_using_insmod_utility_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/
 - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml b/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml
index 68d160f91d..aa091d682b 100644
--- a/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml
+++ b/detections/endpoint/linux_install_kernel_module_using_modprobe_utility.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the installation of a Linux kernel module using the modprobe utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because installing a kernel module can indicate an attempt to deploy a rootkit or other malicious kernel-level code, potentially leading to elevated privileges and bypassing security detections. If confirmed malicious, this could allow an attacker to gain persistent, high-level access to the system, compromising its integrity and security.
+description: The following analytic detects the installation of a Linux kernel module
+  using the modprobe utility. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant because installing a kernel module can indicate an attempt to deploy
+  a rootkit or other malicious kernel-level code, potentially leading to elevated
+  privileges and bypassing security detections. If confirmed malicious, this could
+  allow an attacker to gain persistent, high-level access to the system, compromising
+  its integrity and security.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod", "sudo") AND Processes.process = *modprobe* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_install_kernel_module_using_modprobe_utility_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("kmod",
+  "sudo") AND Processes.process = *modprobe* by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_install_kernel_module_using_modprobe_utility_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://docs.fedoraproject.org/en-US/fedora/rawhide/system-administrators-guide/kernel-module-driver-configuration/Working_with_Kernel_Modules/
 - https://security.stackexchange.com/questions/175953/how-to-load-a-malicious-lkm-at-startup
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.006/loading_linux_kernel_module/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_iptables_firewall_modification.yml b/detections/endpoint/linux_iptables_firewall_modification.yml
index 670cefc2cd..3212b71db8 100644
--- a/detections/endpoint/linux_iptables_firewall_modification.yml
+++ b/detections/endpoint/linux_iptables_firewall_modification.yml
@@ -5,12 +5,41 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious command-line activity that modifies the iptables firewall settings on a Linux machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command patterns that alter firewall rules to accept traffic on certain TCP ports. This activity is significant as it can indicate malware, such as CyclopsBlink, modifying firewall settings to allow communication with a Command and Control (C2) server. If confirmed malicious, this could enable attackers to maintain persistent access and exfiltrate data, posing a severe security risk.
+description: The following analytic detects suspicious command-line activity that
+  modifies the iptables firewall settings on a Linux machine. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on specific command patterns
+  that alter firewall rules to accept traffic on certain TCP ports. This activity
+  is significant as it can indicate malware, such as CyclopsBlink, modifying firewall
+  settings to allow communication with a Command and Control (C2) server. If confirmed
+  malicious, this could enable attackers to maintain persistent access and exfiltrate
+  data, posing a severe security risk.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*iptables *" AND Processes.process = "* --dport *" AND Processes.process = "* ACCEPT*" AND Processes.process = "*&amp;&gt;/dev/null*" AND Processes.process = "* tcp *" AND NOT(Processes.parent_process_path IN("/bin/*", "/lib/*", "/usr/bin/*", "/sbin/*")) by Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest _time span=10s Processes.user Processes.parent_process_name Processes.parent_process_path Processes.process_path | rex field=Processes.process "--dport (?<port>3269|636|989|994|995|8443)" | stats values(Processes.process) as processes_exec values(port) as ports values(Processes.process_guid) as guids values(Processes.process_id) as pids dc(port) as port_count count by Processes.process_name Processes.parent_process_name Processes.parent_process_id Processes.dest Processes.user Processes.parent_process_path Processes.process_path | where port_count >=3 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_iptables_firewall_modification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: administrator may do this commandline for auditing and testing purposes. In this scenario filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*iptables
+  *" AND Processes.process = "* --dport *" AND Processes.process = "* ACCEPT*" AND
+  Processes.process = "*&amp;&gt;/dev/null*" AND Processes.process = "* tcp *" AND
+  NOT(Processes.parent_process_path IN("/bin/*", "/lib/*", "/usr/bin/*", "/sbin/*"))
+  by Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid Processes.dest _time span=10s Processes.user Processes.parent_process_name
+  Processes.parent_process_path Processes.process_path | rex field=Processes.process
+  "--dport (?<port>3269|636|989|994|995|8443)" | stats values(Processes.process) as
+  processes_exec values(port) as ports values(Processes.process_guid) as guids values(Processes.process_id)
+  as pids dc(port) as port_count count by Processes.process_name Processes.parent_process_name
+  Processes.parent_process_id Processes.dest Processes.user Processes.parent_process_path
+  Processes.process_path | where port_count >=3 | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_iptables_firewall_modification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: administrator may do this commandline for auditing and testing
+  purposes. In this scenario filter is needed.
 references:
 - https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf
 - https://www.trendmicro.com/en_us/research/22/c/cyclops-blink-sets-sights-on-asus-routers--.html
@@ -20,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_java_spawning_shell.yml b/detections/endpoint/linux_java_spawning_shell.yml
index 162440c5cb..5697af647b 100644
--- a/detections/endpoint/linux_java_spawning_shell.yml
+++ b/detections/endpoint/linux_java_spawning_shell.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances where Java, Apache, or Tomcat processes spawn a Linux shell, which may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names and parent-child process relationships. This activity is significant as it can signify a compromised Java application, potentially leading to unauthorized shell access. If confirmed malicious, attackers could execute arbitrary commands, escalate privileges, or maintain persistent access, posing a severe threat to the environment.
+description: The following analytic detects instances where Java, Apache, or Tomcat
+  processes spawn a Linux shell, which may indicate exploitation attempts, such as
+  those related to CVE-2021-44228 (Log4Shell). This detection leverages Endpoint Detection
+  and Response (EDR) telemetry, focusing on process names and parent-child process
+  relationships. This activity is significant as it can signify a compromised Java
+  application, potentially leading to unauthorized shell access. If confirmed malicious,
+  attackers could execute arbitrary commands, escalate privileges, or maintain persistent
+  access, posing a severe threat to the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java OR Processes.parent_process_name=apache OR Processes.parent_process_name=tomcat `linux_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_java_spawning_shell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on asset type.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java
+  OR Processes.parent_process_name=apache OR Processes.parent_process_name=tomcat
+  `linux_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_java_spawning_shell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Filtering may be required on internal developer build systems
+  or classify assets as web facing and restrict the analytic based on asset type.
 references:
 - https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/
 - https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Linux shell, potentially indicative of exploitation.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ spawning a Linux shell, potentially indicative of exploitation.
   mitre_attack_id:
   - T1190
   - T1133
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java_spawn_shell_nix.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java_spawn_shell_nix.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_kernel_module_enumeration.yml b/detections/endpoint/linux_kernel_module_enumeration.yml
index 4c062603ef..668d40ca16 100644
--- a/detections/endpoint/linux_kernel_module_enumeration.yml
+++ b/detections/endpoint/linux_kernel_module_enumeration.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system.
+description: The following analytic identifies the use of the 'kmod' process to list
+  kernel modules on a Linux system. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line executions.
+  While listing kernel modules is not inherently malicious, it can be a precursor
+  to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity
+  could allow an attacker to load kernel modules, potentially leading to privilege
+  escalation, persistence, or other malicious actions within the system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=kmod Processes.process IN ("*lsmod*", "*list*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_kernel_module_enumeration_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=kmod
+  Processes.process IN ("*lsmod*", "*list*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_kernel_module_enumeration_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://man7.org/linux/man-pages/man8/kmod.8.html
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ enumeration kernel modules.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ enumeration kernel modules.
   mitre_attack_id:
   - T1082
   - T1014
@@ -53,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_kworker_process_in_writable_process_path.yml b/detections/endpoint/linux_kworker_process_in_writable_process_path.yml
index f985f280ab..0114f8e0b3 100644
--- a/detections/endpoint/linux_kworker_process_in_writable_process_path.yml
+++ b/detections/endpoint/linux_kworker_process_in_writable_process_path.yml
@@ -5,11 +5,33 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of a kworker process with a command line in writable directories such as /home/, /var/log, and /tmp on a Linux machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process paths. This activity is significant as kworker processes are typically kernel threads, and their presence in writable directories is unusual and indicative of potential malware, such as CyclopsBlink. If confirmed malicious, this could allow attackers to blend malicious processes with legitimate ones, leading to persistent access and further system compromise.
+description: The following analytic detects the execution of a kworker process with
+  a command line in writable directories such as /home/, /var/log, and /tmp on a Linux
+  machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process and parent process paths. This activity is significant as kworker processes
+  are typically kernel threads, and their presence in writable directories is unusual
+  and indicative of potential malware, such as CyclopsBlink. If confirmed malicious,
+  this could allow attackers to blend malicious processes with legitimate ones, leading
+  to persistent access and further system compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.parent_process = "*[kworker/*" Processes.parent_process_path IN ("/home/*", "/tmp/*", "/var/log/*") Processes.process="*iptables*" by  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_path Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_kworker_process_in_writable_process_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.parent_process =
+  "*[kworker/*" Processes.parent_process_path IN ("/home/*", "/tmp/*", "/var/log/*")
+  Processes.process="*iptables*" by  Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.parent_process_path Processes.process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_kworker_process_in_writable_process_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf
@@ -34,21 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_path
-  - Processes.process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_make_privilege_escalation.yml b/detections/endpoint/linux_make_privilege_escalation.yml
index 3e34778d6f..02289ab8ea 100644
--- a/detections/endpoint/linux_make_privilege_escalation.yml
+++ b/detections/endpoint/linux_make_privilege_escalation.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the 'make' command with elevated privileges to execute system commands as root, potentially leading to a root shell. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include 'make', '--eval', and 'sudo'. This activity is significant because it indicates a possible privilege escalation attempt, allowing a user to gain root access. If confirmed malicious, an attacker could achieve full control over the system, execute arbitrary commands, and compromise the entire environment.
+description: The following analytic detects the use of the 'make' command with elevated
+  privileges to execute system commands as root, potentially leading to a root shell.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  command-line executions that include 'make', '--eval', and 'sudo'. This activity
+  is significant because it indicates a possible privilege escalation attempt, allowing
+  a user to gain root access. If confirmed malicious, an attacker could achieve full
+  control over the system, execute arbitrary commands, and compromise the entire environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*make*-s*" AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_make_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*make*-s*"
+  AND Processes.process="*--eval*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_make_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/make/
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 40
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/make/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/make/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_mysql_privilege_escalation.yml b/detections/endpoint/linux_mysql_privilege_escalation.yml
index d5067ff1fb..ae547d2f7b 100644
--- a/detections/endpoint/linux_mysql_privilege_escalation.yml
+++ b/detections/endpoint/linux_mysql_privilege_escalation.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of MySQL commands with elevated privileges using sudo, which can lead to privilege escalation. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential misuse of MySQL to execute system commands as root, which could allow an attacker to gain root shell access. If confirmed malicious, this could result in full control over the affected system, leading to severe security breaches and unauthorized access to sensitive data.
+description: The following analytic detects the execution of MySQL commands with elevated
+  privileges using sudo, which can lead to privilege escalation. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs that include command-line details. This activity is significant because it
+  indicates a potential misuse of MySQL to execute system commands as root, which
+  could allow an attacker to gain root shell access. If confirmed malicious, this
+  could result in full control over the affected system, leading to severe security
+  breaches and unauthorized access to sensitive data.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*mysql*-e*" AND Processes.process="*\!**" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_mysql_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*mysql*-e*"
+  AND Processes.process="*\!**" AND Processes.process="*sudo*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_mysql_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/mysql/
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -50,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/mysql/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/mysql/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml b/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml
index 022870e82e..84f18490f2 100644
--- a/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml
+++ b/detections/endpoint/linux_ngrok_reverse_proxy_usage.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of Ngrok on a Linux operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments associated with Ngrok. This activity is significant because Ngrok can be used by adversaries to establish reverse proxies, potentially bypassing network defenses. If confirmed malicious, this could allow attackers to create persistent, unauthorized access channels, facilitating data exfiltration or further exploitation of the compromised system.
+description: The following analytic detects the use of Ngrok on a Linux operating
+  system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments associated with Ngrok. This activity
+  is significant because Ngrok can be used by adversaries to establish reverse proxies,
+  potentially bypassing network defenses. If confirmed malicious, this could allow
+  attackers to create persistent, unauthorized access channels, facilitating data
+  exfiltration or further exploitation of the compromised system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", "*tcp*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ngrok_reverse_proxy_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if Ngrok is an authorized utility. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok
+  Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*",
+  "*tcp*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_ngrok_reverse_proxy_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if Ngrok is an authorized utility.
+  Filter as needed.
 references:
 - https://ngrok.com
 - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 50
-  message: A reverse proxy was identified spawning from $parent_process_name$ -  $process_name$ on endpoint $dest$ by user $user$.
+  message: A reverse proxy was identified spawning from $parent_process_name$ -  $process_name$
+    on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1572
   - T1090
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/ngrok_linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/ngrok_linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_node_privilege_escalation.yml b/detections/endpoint/linux_node_privilege_escalation.yml
index ab28e25396..a64dc80811 100644
--- a/detections/endpoint/linux_node_privilege_escalation.yml
+++ b/detections/endpoint/linux_node_privilege_escalation.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the execution of Node.js with elevated privileges using sudo, specifically when spawning child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific Node.js commands. This activity is significant because running Node.js as a superuser without dropping privileges can allow unauthorized access to the file system and potential privilege escalation. If confirmed malicious, this could enable an attacker to maintain privileged access, execute arbitrary code, and compromise sensitive data within the environment.
+description: The following analytic identifies the execution of Node.js with elevated
+  privileges using sudo, specifically when spawning child processes. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions that include specific Node.js commands. This activity is significant
+  because running Node.js as a superuser without dropping privileges can allow unauthorized
+  access to the file system and potential privilege escalation. If confirmed malicious,
+  this could enable an attacker to maintain privileged access, execute arbitrary code,
+  and compromise sensitive data within the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*node*" AND Processes.process="*-e*" AND Processes.process="*child_process.spawn*" AND Processes.process="*stdio*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_node_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*sudo*node*"
+  AND Processes.process="*-e*" AND Processes.process="*child_process.spawn*" AND Processes.process="*stdio*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_node_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/docker/
 - https://en.wikipedia.org/wiki/Node.js
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/node/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/node/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml b/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml
index ac89093391..4c61a6b566 100644
--- a/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml
+++ b/detections/endpoint/linux_nopasswd_entry_in_sudoers_file.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the addition of NOPASSWD entries to the /etc/sudoers file on Linux systems. It leverages Endpoint Detection and Response (EDR) telemetry to identify command lines containing "NOPASSWD:". This activity is significant because it allows users to execute commands with elevated privileges without requiring a password, which can be exploited by adversaries to maintain persistent, privileged access. If confirmed malicious, this could lead to unauthorized privilege escalation, persistent access, and potential compromise of sensitive data and system integrity.
+description: The following analytic detects the addition of NOPASSWD entries to the
+  /etc/sudoers file on Linux systems. It leverages Endpoint Detection and Response
+  (EDR) telemetry to identify command lines containing "NOPASSWD:". This activity
+  is significant because it allows users to execute commands with elevated privileges
+  without requiring a password, which can be exploited by adversaries to maintain
+  persistent, privileged access. If confirmed malicious, this could lead to unauthorized
+  privilege escalation, persistent access, and potential compromise of sensitive data
+  and system integrity.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*NOPASSWD:*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_nopasswd_entry_in_sudoers_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*NOPASSWD:*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_nopasswd_entry_in_sudoers_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands
 - https://help.ubuntu.com/community/Sudoers
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/nopasswd_sudoers/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/nopasswd_sudoers/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml b/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml
index 40604936c5..02d4a53e91 100644
--- a/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml
+++ b/detections/endpoint/linux_obfuscated_files_or_information_base64_decode.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the base64 decode command on Linux systems, which is often used to deobfuscate files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include "base64 -d" or "base64 --decode". This activity is significant as it may indicate an attempt to hide malicious payloads or scripts. If confirmed malicious, an attacker could use this technique to execute hidden code, potentially leading to unauthorized access, data exfiltration, or further system compromise.
+description: The following analytic detects the use of the base64 decode command on
+  Linux systems, which is often used to deobfuscate files. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  that include "base64 -d" or "base64 --decode". This activity is significant as it
+  may indicate an attempt to hide malicious payloads or scripts. If confirmed malicious,
+  an attacker could use this technique to execute hidden code, potentially leading
+  to unauthorized access, data exfiltration, or further system compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path="*/base64" Processes.process="*-d*" by Processes.process Processes.dest Processes.process_current_directory Processes.process_name Processes.process_integrity_level Processes.parent_process_name Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_obfuscated_files_or_information_base64_decode_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and will require some tuning based on processes. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_path="*/base64"
+  Processes.process="*-d*" by Processes.process Processes.dest Processes.process_current_directory
+  Processes.process_name Processes.process_integrity_level Processes.parent_process_name
+  Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id
+  Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_obfuscated_files_or_information_base64_decode_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and will require some tuning
+  based on processes. Filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1027/T1027.md#atomic-test-1---decode-base64-data-into-script
 - https://redcanary.com/blog/lateral-movement-with-secure-shell/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ decoding base64.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ decoding base64.
   mitre_attack_id:
   - T1027
   observable:
@@ -54,23 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_octave_privilege_escalation.yml b/detections/endpoint/linux_octave_privilege_escalation.yml
index cb50b2b25b..d1cc2e1048 100644
--- a/detections/endpoint/linux_octave_privilege_escalation.yml
+++ b/detections/endpoint/linux_octave_privilege_escalation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of GNU Octave with elevated privileges, specifically when it runs system commands via sudo. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments that include "octave-cli," "--eval," "system," and "sudo." This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute commands as root. If confirmed malicious, this could lead to full system compromise, enabling an attacker to gain root access and execute arbitrary commands, severely impacting system security and integrity.
+description: The following analytic detects the execution of GNU Octave with elevated
+  privileges, specifically when it runs system commands via sudo. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process command-line
+  arguments that include "octave-cli," "--eval," "system," and "sudo." This activity
+  is significant because it indicates a potential privilege escalation attempt, allowing
+  a user to execute commands as root. If confirmed malicious, this could lead to full
+  system compromise, enabling an attacker to gain root access and execute arbitrary
+  commands, severely impacting system security and integrity.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*octave-cli*" AND Processes.process="*--eval*" AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_octave_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*octave-cli*"
+  AND Processes.process="*--eval*" AND Processes.process="*system*" AND Processes.process="*sudo*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_octave_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/octave/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 40
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/octave/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/octave/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_openvpn_privilege_escalation.yml b/detections/endpoint/linux_openvpn_privilege_escalation.yml
index 9e60cacee7..4f700220ed 100644
--- a/detections/endpoint/linux_openvpn_privilege_escalation.yml
+++ b/detections/endpoint/linux_openvpn_privilege_escalation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of OpenVPN with elevated privileges, specifically when combined with the `--dev`, `--script-security`, `--up`, and `sudo` options. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and execution details. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute system commands as root. If confirmed malicious, this could lead to full system compromise, enabling an attacker to gain root access and execute arbitrary commands with elevated privileges.
+description: The following analytic detects the execution of OpenVPN with elevated
+  privileges, specifically when combined with the `--dev`, `--script-security`, `--up`,
+  and `sudo` options. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process command-line arguments and execution details.
+  This activity is significant because it indicates a potential privilege escalation
+  attempt, allowing a user to execute system commands as root. If confirmed malicious,
+  this could lead to full system compromise, enabling an attacker to gain root access
+  and execute arbitrary commands with elevated privileges.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*openvpn*" AND Processes.process="*--dev*" AND Processes.process="*--script-security*" AND Processes.process="*--up*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_openvpn_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*openvpn*"
+  AND Processes.process="*--dev*" AND Processes.process="*--script-security*" AND
+  Processes.process="*--up*" AND Processes.process="*sudo*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_openvpn_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/openvpn/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/openvpn/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/openvpn/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml b/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml
index 789727ed49..e45eeb8d30 100644
--- a/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml
+++ b/detections/endpoint/linux_persistence_and_privilege_escalation_risk_behavior.yml
@@ -5,11 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Correlation
-description: The following analytic identifies potential Linux persistence and privilege escalation activities. It leverages risk scores and event counts from various Linux-related data sources, focusing on tactics associated with persistence and privilege escalation. This activity is significant for a SOC because it highlights behaviors that could allow an attacker to maintain access or gain elevated privileges on a Linux system. If confirmed malicious, this activity could enable an attacker to execute code with higher privileges, persist in the environment, and potentially access sensitive information, posing a severe security risk.
+description: The following analytic identifies potential Linux persistence and privilege
+  escalation activities. It leverages risk scores and event counts from various Linux-related
+  data sources, focusing on tactics associated with persistence and privilege escalation.
+  This activity is significant for a SOC because it highlights behaviors that could
+  allow an attacker to maintain access or gain elevated privileges on a Linux system.
+  If confirmed malicious, this activity could enable an attacker to execute code with
+  higher privileges, persist in the environment, and potentially access sensitive
+  information, posing a severe security risk.
 data_source: []
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where (All_Risk.analyticstories IN ("Linux Privilege Escalation", "Linux Persistence Techniques") OR source = "*Linux*") All_Risk.annotations.mitre_attack.mitre_tactic IN ("persistence", "privilege-escalation") All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `linux_persistence_and_privilege_escalation_risk_behavior_filter`'
-how_to_implement: Ensure Linux anomaly and TTP analytics are enabled. TTP may be set to Notables for point detections, anomaly should not be notables but risk generators. The correlation relies on more than x amount of distict detection names generated before generating a notable. Modify the value as needed. Default value is set to 4. This value may need to be increased based on activity in your environment.
-known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where (All_Risk.analyticstories
+  IN ("Linux Privilege Escalation", "Linux Persistence Techniques") OR source = "*Linux*")
+  All_Risk.annotations.mitre_attack.mitre_tactic IN ("persistence", "privilege-escalation")
+  All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type
+  All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 4 | `linux_persistence_and_privilege_escalation_risk_behavior_filter`'
+how_to_implement: Ensure Linux anomaly and TTP analytics are enabled. TTP may be set
+  to Notables for point detections, anomaly should not be notables but risk generators.
+  The correlation relies on more than x amount of distict detection names generated
+  before generating a notable. Modify the value as needed. Default value is set to
+  4. This value may need to be increased based on activity in your environment.
+known_false_positives: False positives will be present based on many factors. Tune
+  the correlation as needed to reduce too many triggers.
 references:
 - https://attack.mitre.org/tactics/TA0004/
 drilldown_searches:
@@ -18,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: Privilege escalation and persistence behaviors have been identified on $risk_object$.
+  message: Privilege escalation and persistence behaviors have been identified on
+    $risk_object$.
   mitre_attack_id:
   - T1548
   observable:
@@ -40,17 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: audit
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/linux_risk/linuxrisk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/linux_risk/linuxrisk.log
     source: linuxrisk
     sourcetype: stash
diff --git a/detections/endpoint/linux_php_privilege_escalation.yml b/detections/endpoint/linux_php_privilege_escalation.yml
index 7da2c173f2..bec700acef 100644
--- a/detections/endpoint/linux_php_privilege_escalation.yml
+++ b/detections/endpoint/linux_php_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of PHP commands with elevated privileges on a Linux system. It identifies instances where PHP is used in conjunction with 'sudo' and 'system' commands, indicating an attempt to run system commands as the root user. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments. This activity is significant because it can indicate an attempt to escalate privileges, potentially leading to full root access. If confirmed malicious, this could allow an attacker to execute arbitrary commands with root privileges, compromising the entire system.
+description: The following analytic detects the execution of PHP commands with elevated
+  privileges on a Linux system. It identifies instances where PHP is used in conjunction
+  with 'sudo' and 'system' commands, indicating an attempt to run system commands
+  as the root user. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process command-line arguments. This activity is significant
+  because it can indicate an attempt to escalate privileges, potentially leading to
+  full root access. If confirmed malicious, this could allow an attacker to execute
+  arbitrary commands with root privileges, compromising the entire system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*php*-r*" AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_php_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*php*-r*"
+  AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_php_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/php/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/php/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/php/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_pkexec_privilege_escalation.yml b/detections/endpoint/linux_pkexec_privilege_escalation.yml
index 7db94dac56..18de6ed354 100644
--- a/detections/endpoint/linux_pkexec_privilege_escalation.yml
+++ b/detections/endpoint/linux_pkexec_privilege_escalation.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `pkexec` without any command-line arguments. This behavior leverages data from Endpoint Detection and Response (EDR) agents, focusing on process telemetry. The significance lies in the fact that this pattern is associated with the exploitation of CVE-2021-4034 (PwnKit), a critical vulnerability in Polkit's pkexec component. If confirmed malicious, this activity could allow an attacker to gain full root privileges on the affected Linux system, leading to complete system compromise and potential unauthorized access to sensitive information.
+description: The following analytic detects the execution of `pkexec` without any
+  command-line arguments. This behavior leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process telemetry. The significance lies in the
+  fact that this pattern is associated with the exploitation of CVE-2021-4034 (PwnKit),
+  a critical vulnerability in Polkit's pkexec component. If confirmed malicious, this
+  activity could allow an attacker to gain full root privileges on the affected Linux
+  system, leading to complete system compromise and potential unauthorized access
+  to sensitive information.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=pkexec by _time Processes.dest Processes.user Processes.process_id Processes.parent_process_name Processes.process_name Processes.process Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(^.{1}$)" | `linux_pkexec_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=pkexec by _time Processes.dest Processes.user Processes.process_id
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_path
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(^.{1}$)" | `linux_pkexec_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://www.reddit.com/r/crowdstrike/comments/sdfeig/20220126_cool_query_friday_hunting_pwnkit_local/
@@ -22,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +58,9 @@ tags:
   cve:
   - CVE-2021-4034
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ related to a local privilege escalation in polkit pkexec.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ related to a local privilege escalation in polkit
+    pkexec.
   mitre_attack_id:
   - T1068
   observable:
@@ -58,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/pkexec/linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/pkexec/linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml b/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml
index 5ea328f5ff..ac62741746 100644
--- a/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml
+++ b/detections/endpoint/linux_possible_access_or_modification_of_sshd_config_file.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious access or modification of the sshd_config file on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving processes like "cat," "nano," "vim," and "vi" accessing the sshd_config file. This activity is significant because unauthorized changes to sshd_config can allow threat actors to redirect port connections or use unauthorized keys, potentially compromising the system. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent backdoor access, posing a severe security risk.
+description: The following analytic detects suspicious access or modification of the
+  sshd_config file on Linux systems. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on command-line executions involving processes like
+  "cat," "nano," "vim," and "vi" accessing the sshd_config file. This activity is
+  significant because unauthorized changes to sshd_config can allow threat actors
+  to redirect port connections or use unauthorized keys, potentially compromising
+  the system. If confirmed malicious, this could lead to unauthorized access, privilege
+  escalation, or persistent backdoor access, posing a severe security risk.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/ssh/sshd_config") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_or_modification_of_sshd_config_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat",
+  "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/ssh/sshd_config") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_or_modification_of_sshd_config_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.hackingarticles.in/ssh-penetration-testing-port-22/
 - https://attack.mitre.org/techniques/T1098/004/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_access_to_credential_files.yml b/detections/endpoint/linux_possible_access_to_credential_files.yml
index fc717a61a8..b521dcb19f 100644
--- a/detections/endpoint/linux_possible_access_to_credential_files.yml
+++ b/detections/endpoint/linux_possible_access_to_credential_files.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects attempts to access or dump the contents of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these files. This activity is significant as it may indicate credential dumping, a technique used by adversaries to gain persistence or escalate privileges. If confirmed malicious, attackers could obtain hashed passwords for offline cracking, leading to unauthorized access and potential system compromise.
+description: The following analytic detects attempts to access or dump the contents
+  of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on processes like 'cat', 'nano', 'vim',
+  and 'vi' accessing these files. This activity is significant as it may indicate
+  credential dumping, a technique used by adversaries to gain persistence or escalate
+  privileges. If confirmed malicious, attackers could obtain hashed passwords for
+  offline cracking, leading to unauthorized access and potential system compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/shadow*", "*/etc/passwd*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_to_credential_files_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat",
+  "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/shadow*", "*/etc/passwd*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_to_credential_files_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://askubuntu.com/questions/445361/what-is-difference-between-etc-shadow-and-etc-passwd
 - https://attack.mitre.org/techniques/T1003/008/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_access_to_sudoers_file.yml b/detections/endpoint/linux_possible_access_to_sudoers_file.yml
index fc405dbcd2..412cff91b5 100644
--- a/detections/endpoint/linux_possible_access_to_sudoers_file.yml
+++ b/detections/endpoint/linux_possible_access_to_sudoers_file.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects potential access or modification of the /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "cat," "nano," "vim," and "vi" accessing the /etc/sudoers file. This activity is significant because the sudoers file controls user permissions for executing commands with elevated privileges. If confirmed malicious, an attacker could gain persistence or escalate privileges, compromising the security of the targeted host.
+description: The following analytic detects potential access or modification of the
+  /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on processes like "cat," "nano," "vim," and "vi"
+  accessing the /etc/sudoers file. This activity is significant because the sudoers
+  file controls user permissions for executing commands with elevated privileges.
+  If confirmed malicious, an attacker could gain persistence or escalate privileges,
+  compromising the security of the targeted host.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat", "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/sudoers*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_to_sudoers_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN("cat",
+  "nano*","vim*", "vi*")  AND Processes.process IN("*/etc/sudoers*") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_access_to_sudoers_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1548/003/
 - https://web.archive.org/web/20210708035426/https://www.cobaltstrike.com/downloads/csmanual43.pdf
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.008/copy_file_stdoutpipe/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml b/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml
index 221556bea8..525361fb35 100644
--- a/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml
+++ b/detections/endpoint/linux_possible_append_command_to_at_allow_config_file.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious command lines that append user entries to /etc/at.allow or /etc/at.deny files. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving these files. This activity is significant because altering these configuration files can allow attackers to schedule tasks with elevated permissions, facilitating persistence on a compromised Linux host. If confirmed malicious, this could enable attackers to execute arbitrary code at scheduled intervals, potentially leading to further system compromise and unauthorized access to sensitive information.
+description: The following analytic detects suspicious command lines that append user
+  entries to /etc/at.allow or /etc/at.deny files. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on command-line executions involving
+  these files. This activity is significant because altering these configuration files
+  can allow attackers to schedule tasks with elevated permissions, facilitating persistence
+  on a compromised Linux host. If confirmed malicious, this could enable attackers
+  to execute arbitrary code at scheduled intervals, potentially leading to further
+  system compromise and unauthorized access to sensitive information.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*/etc/at.allow", "*/etc/at.deny") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_command_to_at_allow_config_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes
+  where Processes.process = "*echo*" AND Processes.process IN("*/etc/at.allow", "*/etc/at.deny")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_possible_append_command_to_at_allow_config_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://linuxize.com/post/at-command-in-linux/
 - https://attack.mitre.org/techniques/T1053/001/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/at_execution/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml b/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml
index d687f3f738..3c9ccc7bfe 100644
--- a/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml
+++ b/detections/endpoint/linux_possible_append_command_to_profile_config_file.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious command-lines that modify user profile files to automatically execute scripts or executables upon system reboot. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving profile files like ~/.bashrc and /etc/profile. This activity is significant as it indicates potential persistence mechanisms used by adversaries to maintain access to compromised hosts. If confirmed malicious, this could allow attackers to execute arbitrary code upon reboot, leading to persistent control over the system and potential further exploitation.
+description: The following analytic detects suspicious command-lines that modify user
+  profile files to automatically execute scripts or executables upon system reboot.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  command-line executions involving profile files like ~/.bashrc and /etc/profile.
+  This activity is significant as it indicates potential persistence mechanisms used
+  by adversaries to maintain access to compromised hosts. If confirmed malicious,
+  this could allow attackers to execute arbitrary code upon reboot, leading to persistent
+  control over the system and potential further exploitation.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*~/.bashrc", "*~/.bash_profile", "*/etc/profile", "~/.bash_login", "*~/.profile", "~/.bash_logout") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_command_to_profile_config_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*echo*"
+  AND Processes.process IN("*~/.bashrc", "*~/.bash_profile", "*/etc/profile", "~/.bash_login",
+  "*~/.profile", "~/.bash_logout") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_possible_append_command_to_profile_config_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://unix.stackexchange.com/questions/129143/what-is-the-purpose-of-bashrc-and-how-does-it-work
 - https://attack.mitre.org/techniques/T1546/004/
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.004/linux_init_profile/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml b/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml
index e8e102e9e1..106fbdd152 100644
--- a/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml
+++ b/detections/endpoint/linux_possible_append_cronjob_entry_on_existing_cronjob_file.yml
@@ -5,12 +5,34 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects potential tampering with cronjob files on a Linux system by identifying 'echo' commands that append code to existing cronjob files. It leverages logs from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because adversaries often use it for persistence or privilege escalation. If confirmed malicious, this could allow attackers to execute unauthorized code automatically, leading to system compromises and unauthorized data access, thereby impacting business operations and data integrity.
+description: The following analytic detects potential tampering with cronjob files
+  on a Linux system by identifying 'echo' commands that append code to existing cronjob
+  files. It leverages logs from Endpoint Detection and Response (EDR) agents, focusing
+  on process names, parent processes, and command-line executions. This activity is
+  significant because adversaries often use it for persistence or privilege escalation.
+  If confirmed malicious, this could allow attackers to execute unauthorized code
+  automatically, leading to system compromises and unauthorized data access, thereby
+  impacting business operations and data integrity.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_append_cronjob_entry_on_existing_cronjob_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. Therefore, it's recommended to adjust filter macros to eliminate such false positives.
+search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes
+  where Processes.process = "*echo*" AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*",
+  "*/etc/anacrontab*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_possible_append_cronjob_entry_on_existing_cronjob_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may arise from legitimate actions by administrators
+  or network operators who may use these commands for automation purposes. Therefore,
+  it's recommended to adjust filter macros to eliminate such false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/003/
 - https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability
@@ -37,19 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml b/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml
index 16d3b7c5ee..b6d6915a32 100644
--- a/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml
+++ b/detections/endpoint/linux_possible_cronjob_modification_with_editor.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects potential unauthorized modifications to Linux cronjobs using text editors like "nano," "vi," or "vim." It identifies this activity by monitoring command-line executions that interact with cronjob configuration paths. This behavior is significant for a SOC as it may indicate attempts at privilege escalation or establishing persistent access. If confirmed malicious, the impact could be severe, allowing attackers to execute damaging actions such as data theft, system sabotage, or further network penetration.
+description: The following analytic detects potential unauthorized modifications to
+  Linux cronjobs using text editors like "nano," "vi," or "vim." It identifies this
+  activity by monitoring command-line executions that interact with cronjob configuration
+  paths. This behavior is significant for a SOC as it may indicate attempts at privilege
+  escalation or establishing persistent access. If confirmed malicious, the impact
+  could be severe, allowing attackers to execute damaging actions such as data theft,
+  system sabotage, or further network penetration.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN("nano","vim.basic") OR Processes.process IN ("*nano *", "*vi *", "*vim *")) AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_possible_cronjob_modification_with_editor_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN("nano","vim.basic")
+  OR Processes.process IN ("*nano *", "*vi *", "*vim *")) AND Processes.process IN("*/etc/cron*",
+  "*/var/spool/cron/*", "*/etc/anacrontab*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_possible_cronjob_modification_with_editor_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1053/003/
 tags:
@@ -35,19 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.003/cronjobs_entry/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_possible_ssh_key_file_creation.yml b/detections/endpoint/linux_possible_ssh_key_file_creation.yml
index ecab7de8b5..d5fbf48dd9 100644
--- a/detections/endpoint/linux_possible_ssh_key_file_creation.yml
+++ b/detections/endpoint/linux_possible_ssh_key_file_creation.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of SSH key files in the ~/.ssh/ directory. It leverages filesystem data to identify new files in this specific path. This activity is significant because threat actors often create SSH keys to gain persistent access and escalate privileges on a compromised host. If confirmed malicious, this could allow attackers to remotely access the machine using the OpenSSH daemon service, leading to potential unauthorized control and data exfiltration.
+description: The following analytic detects the creation of SSH key files in the ~/.ssh/
+  directory. It leverages filesystem data to identify new files in this specific path.
+  This activity is significant because threat actors often create SSH keys to gain
+  persistent access and escalate privileges on a compromised host. If confirmed malicious,
+  this could allow attackers to remotely access the machine using the OpenSSH daemon
+  service, leading to potential unauthorized control and data exfiltration.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/.ssh*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_possible_ssh_key_file_creation_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: Administrator or network operator can create file in ~/.ssh folders for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*/.ssh*")
+  by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `linux_possible_ssh_key_file_creation_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints.
+  If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
+known_false_positives: Administrator or network operator can create file in ~/.ssh
+  folders for automation purposes. Please update the filter macros to remove false
+  positives.
 references:
 - https://www.hackingarticles.in/ssh-penetration-testing-port-22/
 - https://attack.mitre.org/techniques/T1098/004/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_preload_hijack_library_calls.yml b/detections/endpoint/linux_preload_hijack_library_calls.yml
index fb4ca3130b..60fe6f3190 100644
--- a/detections/endpoint/linux_preload_hijack_library_calls.yml
+++ b/detections/endpoint/linux_preload_hijack_library_calls.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the LD_PRELOAD environment variable to hijack or hook library functions on a Linux platform. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because adversaries, malware authors, and red teamers commonly use this technique to gain elevated privileges and establish persistence on a compromised machine. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, and maintain long-term access to the system.
+description: The following analytic detects the use of the LD_PRELOAD environment
+  variable to hijack or hook library functions on a Linux platform. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs that include command-line details. This activity is significant because adversaries,
+  malware authors, and red teamers commonly use this technique to gain elevated privileges
+  and establish persistence on a compromised machine. If confirmed malicious, this
+  behavior could allow attackers to execute arbitrary code, escalate privileges, and
+  maintain long-term access to the system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*LD_PRELOAD*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_preload_hijack_library_calls_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*LD_PRELOAD*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_preload_hijack_library_calls_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/lib_hijack/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.006/lib_hijack/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_proxy_socks_curl.yml b/detections/endpoint/linux_proxy_socks_curl.yml
index bc7560ed70..d139d3e20d 100644
--- a/detections/endpoint/linux_proxy_socks_curl.yml
+++ b/detections/endpoint/linux_proxy_socks_curl.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `curl` command with proxy-related arguments such as `-x`, `socks`, `--preproxy`, and `--proxy`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an adversary attempting to use a proxy to evade network monitoring and obscure their actions. If confirmed malicious, this behavior could allow attackers to bypass security controls, making it difficult to track their activities and potentially leading to unauthorized data access or exfiltration.
+description: The following analytic detects the use of the `curl` command with proxy-related
+  arguments such as `-x`, `socks`, `--preproxy`, and `--proxy`. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions and process details. This activity is significant as it may indicate
+  an adversary attempting to use a proxy to evade network monitoring and obscure their
+  actions. If confirmed malicious, this behavior could allow attackers to bypass security
+  controls, making it difficult to track their activities and potentially leading
+  to unauthorized data access or exfiltration.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process IN ("*-x *", "*socks4a://*", "*socks5h://*", "*socks4://*","*socks5://*", "*--preproxy *", "--proxy*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_proxy_socks_curl_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present based on proxy usage internally. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl
+  Processes.process IN ("*-x *", "*socks4a://*", "*socks5h://*", "*socks4://*","*socks5://*",
+  "*--preproxy *", "--proxy*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_proxy_socks_curl_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present based on proxy usage internally.
+  Filter as needed.
 references:
 - https://www.offensive-security.com/metasploit-unleashed/proxytunnels/
 - https://curl.se/docs/manpage.html
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ utilizing a proxy. Review activity for further details.
+  message: An instance of $process_name$ was identified on endpoint $dest$ by user
+    $user$ utilizing a proxy. Review activity for further details.
   mitre_attack_id:
   - T1090
   - T1095
@@ -55,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/curl-linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_puppet_privilege_escalation.yml b/detections/endpoint/linux_puppet_privilege_escalation.yml
index ee0ffbc609..17f1486475 100644
--- a/detections/endpoint/linux_puppet_privilege_escalation.yml
+++ b/detections/endpoint/linux_puppet_privilege_escalation.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of Puppet commands with elevated privileges, specifically when Puppet is used to apply configurations with sudo rights. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it indicates a potential privilege escalation attempt, where a user could gain root access and execute system commands as the root user. If confirmed malicious, this could allow an attacker to fully compromise the system, execute arbitrary commands, and maintain persistent control.
+description: The following analytic detects the execution of Puppet commands with
+  elevated privileges, specifically when Puppet is used to apply configurations with
+  sudo rights. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs that include command-line details.
+  This activity is significant because it indicates a potential privilege escalation
+  attempt, where a user could gain root access and execute system commands as the
+  root user. If confirmed malicious, this could allow an attacker to fully compromise
+  the system, execute arbitrary commands, and maintain persistent control.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*puppet*" AND Processes.process="*apply*" AND Processes.process="*-e*" AND Processes.process="*exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_puppet_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*puppet*"
+  AND Processes.process="*apply*" AND Processes.process="*-e*" AND Processes.process="*exec*"
+  AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_puppet_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/puppet/
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 10
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/puppet/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/puppet/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_rpm_privilege_escalation.yml b/detections/endpoint/linux_rpm_privilege_escalation.yml
index 04837678a1..c630694265 100644
--- a/detections/endpoint/linux_rpm_privilege_escalation.yml
+++ b/detections/endpoint/linux_rpm_privilege_escalation.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the RPM Package Manager with elevated privileges, specifically when it is used to run system commands as root via the `--eval` and `lua:os.execute` options. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to gain root access. If confirmed malicious, this could lead to full system compromise, unauthorized access to sensitive data, and further exploitation of the environment.
+description: The following analytic detects the execution of the RPM Package Manager
+  with elevated privileges, specifically when it is used to run system commands as
+  root via the `--eval` and `lua:os.execute` options. This detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  and process metadata. This activity is significant because it indicates a potential
+  privilege escalation attempt, allowing a user to gain root access. If confirmed
+  malicious, this could lead to full system compromise, unauthorized access to sensitive
+  data, and further exploitation of the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*rpm*--eval*" AND Processes.process="*lua:os.execute*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_rpm_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*rpm*--eval*"
+  AND Processes.process="*lua:os.execute*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_rpm_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/rpm/
 - https://en.wikipedia.org/wiki/RPM_Package_Manager
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/rpm/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/rpm/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_ruby_privilege_escalation.yml b/detections/endpoint/linux_ruby_privilege_escalation.yml
index 56c0e15ad3..0950d8ec36 100644
--- a/detections/endpoint/linux_ruby_privilege_escalation.yml
+++ b/detections/endpoint/linux_ruby_privilege_escalation.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of Ruby commands with elevated privileges on a Linux system. It identifies processes where Ruby is used with the `-e` flag to execute commands via `sudo`, leveraging Endpoint Detection and Response (EDR) telemetry. This activity is significant because it indicates a potential privilege escalation attempt, allowing a user to execute commands as root. If confirmed malicious, this could lead to full system compromise, enabling an attacker to gain root access, execute arbitrary commands, and maintain persistent control over the affected system.
+description: The following analytic detects the execution of Ruby commands with elevated
+  privileges on a Linux system. It identifies processes where Ruby is used with the
+  `-e` flag to execute commands via `sudo`, leveraging Endpoint Detection and Response
+  (EDR) telemetry. This activity is significant because it indicates a potential privilege
+  escalation attempt, allowing a user to execute commands as root. If confirmed malicious,
+  this could lead to full system compromise, enabling an attacker to gain root access,
+  execute arbitrary commands, and maintain persistent control over the affected system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*ruby*-e*" AND Processes.process="*exec*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ruby_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are present based on automated tooling or system administrative usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*ruby*-e*"
+  AND Processes.process="*exec*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ruby_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are present based on automated tooling or system
+  administrative usage. Filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/ruby/
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -50,19 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/ruby/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/ruby/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_service_file_created_in_systemd_directory.yml b/detections/endpoint/linux_service_file_created_in_systemd_directory.yml
index b55bc3e5c9..04c2130c39 100644
--- a/detections/endpoint/linux_service_file_created_in_systemd_directory.yml
+++ b/detections/endpoint/linux_service_file_created_in_systemd_directory.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of suspicious service files within the systemd directories on Linux platforms. It leverages logs containing file name, file path, and process GUID data from endpoints. This activity is significant for a SOC as it may indicate an adversary attempting to establish persistence on a compromised host. If confirmed malicious, this could lead to system compromise or data exfiltration, allowing attackers to maintain control over the system and execute further malicious activities.
+description: The following analytic detects the creation of suspicious service files
+  within the systemd directories on Linux platforms. It leverages logs containing
+  file name, file path, and process GUID data from endpoints. This activity is significant
+  for a SOC as it may indicate an adversary attempting to establish persistence on
+  a compromised host. If confirmed malicious, this could lead to system compromise
+  or data exfiltration, allowing attackers to maintain control over the system and
+  execute further malicious activities.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name = *.service Filesystem.file_path IN ("*/etc/systemd/system*", "*/lib/systemd/system*", "*/usr/lib/systemd/system*", "*/run/systemd/system*", "*~/.config/systemd/*", "*~/.local/share/systemd/*","*/etc/systemd/user*", "*/lib/systemd/user*", "*/usr/lib/systemd/user*", "*/run/systemd/user*") by Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_service_file_created_in_systemd_directory_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the file name, file path, and process_guid executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: False positives may arise when administrators or network operators create files in systemd directories for legitimate automation tasks. Therefore, it's important to adjust filter macros to account for valid activities. To implement this search successfully, it's crucial to ingest appropriate logs, preferably using the Linux Sysmon Add-on from Splunkbase for those using Sysmon.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name = *.service
+  Filesystem.file_path IN ("*/etc/systemd/system*", "*/lib/systemd/system*", "*/usr/lib/systemd/system*",
+  "*/run/systemd/system*", "*~/.config/systemd/*", "*~/.local/share/systemd/*","*/etc/systemd/user*",
+  "*/lib/systemd/user*", "*/usr/lib/systemd/user*", "*/run/systemd/user*") by Filesystem.dest
+  Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `linux_service_file_created_in_systemd_directory_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the file name, file path, and process_guid executions from your endpoints.
+  If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
+known_false_positives: False positives may arise when administrators or network operators
+  create files in systemd directories for legitimate automation tasks. Therefore,
+  it's important to adjust filter macros to account for valid activities. To implement
+  this search successfully, it's crucial to ingest appropriate logs, preferably using
+  the Linux Sysmon Add-on from Splunkbase for those using Sysmon.
 references:
 - https://attack.mitre.org/techniques/T1053/006/
 - https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/
@@ -22,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_service_restarted.yml b/detections/endpoint/linux_service_restarted.yml
index 00d48984c3..ba2a3d66d9 100644
--- a/detections/endpoint/linux_service_restarted.yml
+++ b/detections/endpoint/linux_service_restarted.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the restarting or re-enabling of services on Linux systems using the `systemctl` or `service` commands. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line execution logs. This activity is significant as adversaries may use it to maintain persistence or execute unauthorized actions. If confirmed malicious, this behavior could lead to repeated execution of malicious payloads, unauthorized access, or data destruction. Security analysts should investigate these events to mitigate risks and prevent further compromise.
+description: The following analytic detects the restarting or re-enabling of services
+  on Linux systems using the `systemctl` or `service` commands. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process and command-line
+  execution logs. This activity is significant as adversaries may use it to maintain
+  persistence or execute unauthorized actions. If confirmed malicious, this behavior
+  could lead to repeated execution of malicious payloads, unauthorized access, or
+  data destruction. Security analysts should investigate these events to mitigate
+  risks and prevent further compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process IN ("*restart*", "*reload*", "*reenable*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_service_restarted_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl",
+  "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process
+  IN ("*restart*", "*reload*", "*reenable*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_service_restarted_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1543/003/
 drilldown_searches:
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,19 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_service_started_or_enabled.yml b/detections/endpoint/linux_service_started_or_enabled.yml
index 67b0ca118e..0887b9e8be 100644
--- a/detections/endpoint/linux_service_started_or_enabled.yml
+++ b/detections/endpoint/linux_service_started_or_enabled.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation or enabling of services on Linux platforms using the systemctl or service tools. It leverages Endpoint Detection and Response (EDR) logs, focusing on process names, parent processes, and command-line executions. This activity is significant as adversaries may create or modify services to maintain persistence or execute malicious payloads. If confirmed malicious, this behavior could lead to persistent access, data theft, ransomware deployment, or other damaging outcomes. Monitoring and investigating such activities are crucial for maintaining the security and integrity of the environment.
+description: The following analytic detects the creation or enabling of services on
+  Linux platforms using the systemctl or service tools. It leverages Endpoint Detection
+  and Response (EDR) logs, focusing on process names, parent processes, and command-line
+  executions. This activity is significant as adversaries may create or modify services
+  to maintain persistence or execute malicious payloads. If confirmed malicious, this
+  behavior could lead to persistent access, data theft, ransomware deployment, or
+  other damaging outcomes. Monitoring and investigating such activities are crucial
+  for maintaining the security and integrity of the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl", "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process IN ("* start *", "* enable *") AND NOT (Processes.os="Microsoft Windows" OR Processes.vendor_product="Microsoft Windows") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_service_started_or_enabled_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN ("systemctl",
+  "service") OR Processes.process IN ("*systemctl *", "*service *")) Processes.process
+  IN ("* start *", "* enable *") AND NOT (Processes.os="Microsoft Windows" OR Processes.vendor_product="Microsoft
+  Windows") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_service_started_or_enabled_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this commandline
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1543/003/
 drilldown_searches:
@@ -19,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.006/service_systemd/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_setuid_using_chmod_utility.yml b/detections/endpoint/linux_setuid_using_chmod_utility.yml
index c7ebd6b6d2..2d2b565819 100644
--- a/detections/endpoint/linux_setuid_using_chmod_utility.yml
+++ b/detections/endpoint/linux_setuid_using_chmod_utility.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the chmod utility to set the SUID or SGID bit on files, which can allow users to temporarily gain root or group-level access. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments related to chmod. This activity is significant as it can indicate an attempt to escalate privileges or maintain persistence on a system. If confirmed malicious, an attacker could gain elevated access, potentially compromising sensitive data or critical system functions.
+description: The following analytic detects the execution of the chmod utility to
+  set the SUID or SGID bit on files, which can allow users to temporarily gain root
+  or group-level access. This detection leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process names and command-line arguments related
+  to chmod. This activity is significant as it can indicate an attempt to escalate
+  privileges or maintain persistence on a system. If confirmed malicious, an attacker
+  could gain elevated access, potentially compromising sensitive data or critical
+  system functions.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes WHERE (Processes.process_name = chmod OR Processes.process = "*chmod *") AND Processes.process IN("* g+s *", "* u+s *", "* 4777 *", "* 4577 *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_setuid_using_chmod_utility_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes WHERE (Processes.process_name = chmod
+  OR Processes.process = "*chmod *") AND Processes.process IN("* g+s *", "* u+s *",
+  "* 4777 *", "* 4577 *") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_setuid_using_chmod_utility_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/
 drilldown_searches:
@@ -19,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/chmod_uid/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_setuid_using_setcap_utility.yml b/detections/endpoint/linux_setuid_using_setcap_utility.yml
index 3eccd73c0a..2d37c9d1c6 100644
--- a/detections/endpoint/linux_setuid_using_setcap_utility.yml
+++ b/detections/endpoint/linux_setuid_using_setcap_utility.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'setcap' utility to enable the SUID bit on Linux systems. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments that indicate the use of 'setcap' with specific capabilities. This activity is significant because setting the SUID bit allows a user to temporarily gain root access, posing a substantial security risk. If confirmed malicious, an attacker could escalate privileges, execute arbitrary commands with elevated permissions, and potentially compromise the entire system.
+description: The following analytic detects the execution of the 'setcap' utility
+  to enable the SUID bit on Linux systems. It leverages Endpoint Detection and Response
+  (EDR) data, focusing on process names and command-line arguments that indicate the
+  use of 'setcap' with specific capabilities. This activity is significant because
+  setting the SUID bit allows a user to temporarily gain root access, posing a substantial
+  security risk. If confirmed malicious, an attacker could escalate privileges, execute
+  arbitrary commands with elevated permissions, and potentially compromise the entire
+  system.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = setcap OR Processes.process = "*setcap *") AND Processes.process IN ("* cap_setuid=ep *", "* cap_setuid+ep *", "* cap_net_bind_service+p *", "* cap_net_raw+ep *", "* cap_dac_read_search+ep *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_setuid_using_setcap_utility_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = setcap
+  OR Processes.process = "*setcap *") AND Processes.process IN ("* cap_setuid=ep *",
+  "* cap_setuid+ep *", "* cap_net_bind_service+p *", "* cap_net_raw+ep *", "* cap_dac_read_search+ep
+  *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_setuid_using_setcap_utility_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.hackingarticles.in/linux-privilege-escalation-using-capabilities/
 drilldown_searches:
@@ -19,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_setcap/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.001/linux_setcap/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_shred_overwrite_command.yml b/detections/endpoint/linux_shred_overwrite_command.yml
index 9d8a8a9326..91d2f07590 100644
--- a/detections/endpoint/linux_shred_overwrite_command.yml
+++ b/detections/endpoint/linux_shred_overwrite_command.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.
+description: The following analytic detects the execution of the 'shred' command on
+  a Linux machine, which is used to overwrite files to make them unrecoverable. It
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names and command-line arguments. This activity is significant because the 'shred'
+  command can be used in destructive attacks, such as those seen in the Industroyer2
+  malware targeting energy facilities. If confirmed malicious, this activity could
+  lead to the permanent destruction of critical files, severely impacting system integrity
+  and data availability.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name =shred AND Processes.process IN ("*-n*", "*-u*", "*-z*", "*-s*") by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_shred_overwrite_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name =shred
+  AND Processes.process IN ("*-n*", "*-u*", "*-z*", "*-s*") by Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_shred_overwrite_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/rm_shred_critical_dir/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_sqlite3_privilege_escalation.yml b/detections/endpoint/linux_sqlite3_privilege_escalation.yml
index 692c5b2133..a211176846 100644
--- a/detections/endpoint/linux_sqlite3_privilege_escalation.yml
+++ b/detections/endpoint/linux_sqlite3_privilege_escalation.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the sqlite3 command with elevated privileges, which can be exploited for privilege escalation. It leverages Endpoint Detection and Response (EDR) telemetry to identify instances where sqlite3 is used in conjunction with shell commands and sudo. This activity is significant because it indicates a potential attempt to gain root access, which could lead to full system compromise. If confirmed malicious, an attacker could execute arbitrary commands as root, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects the execution of the sqlite3 command with
+  elevated privileges, which can be exploited for privilege escalation. It leverages
+  Endpoint Detection and Response (EDR) telemetry to identify instances where sqlite3
+  is used in conjunction with shell commands and sudo. This activity is significant
+  because it indicates a potential attempt to gain root access, which could lead to
+  full system compromise. If confirmed malicious, an attacker could execute arbitrary
+  commands as root, leading to unauthorized access, data exfiltration, or further
+  lateral movement within the network.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*sqlite3*" AND Processes.process="*.shell*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sqlite3_privilege_escalation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*sqlite3*"
+  AND Processes.process="*.shell*" AND Processes.process="*sudo*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sqlite3_privilege_escalation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://gtfobins.github.io/gtfobins/sqlite3/
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$
   mitre_attack_id:
   - T1548.003
   - T1548
@@ -51,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/sqlite3/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/sqlite3/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_ssh_authorized_keys_modification.yml b/detections/endpoint/linux_ssh_authorized_keys_modification.yml
index a52cbb948b..c2d4e2c887 100644
--- a/detections/endpoint/linux_ssh_authorized_keys_modification.yml
+++ b/detections/endpoint/linux_ssh_authorized_keys_modification.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the modification of SSH Authorized Keys on Linux systems. It leverages process execution data from Endpoint Detection and Response (EDR) agents, specifically monitoring commands like "bash" and "cat" interacting with "authorized_keys" files. This activity is significant as adversaries often modify SSH Authorized Keys to establish persistent access to compromised endpoints. If confirmed malicious, this behavior could allow attackers to maintain unauthorized access, bypassing traditional authentication mechanisms and potentially leading to further exploitation or data exfiltration.
+description: The following analytic detects the modification of SSH Authorized Keys
+  on Linux systems. It leverages process execution data from Endpoint Detection and
+  Response (EDR) agents, specifically monitoring commands like "bash" and "cat" interacting
+  with "authorized_keys" files. This activity is significant as adversaries often
+  modify SSH Authorized Keys to establish persistent access to compromised endpoints.
+  If confirmed malicious, this behavior could allow attackers to maintain unauthorized
+  access, bypassing traditional authentication mechanisms and potentially leading
+  to further exploitation or data exfiltration.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process_name IN ("bash","cat") Processes.process IN ("*/authorized_keys*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ssh_authorized_keys_modification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Filtering will be required as system administrators will add and remove. One way to filter query is to add "echo".
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.process_name IN ("bash","cat")
+  Processes.process IN ("*/authorized_keys*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_ssh_authorized_keys_modification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Filtering will be required as system administrators will add
+  and remove. One way to filter query is to add "echo".
 references:
 - https://redcanary.com/blog/lateral-movement-with-secure-shell/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1098.004/T1098.004.md
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ modifying SSH Authorized Keys.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ modifying SSH Authorized Keys.
   mitre_attack_id:
   - T1098.004
   observable:
@@ -53,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/authkey_linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098.004/ssh_authorized_keys/authkey_linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_ssh_remote_services_script_execute.yml b/detections/endpoint/linux_ssh_remote_services_script_execute.yml
index 60865158c7..df5d673b9c 100644
--- a/detections/endpoint/linux_ssh_remote_services_script_execute.yml
+++ b/detections/endpoint/linux_ssh_remote_services_script_execute.yml
@@ -5,11 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of SSH to move laterally and execute a script or file on a remote host. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific SSH command-line parameters and URLs. This activity is significant as it may indicate an attacker attempting to execute remote commands or scripts, potentially leading to unauthorized access or control over additional systems. If confirmed malicious, this could result in lateral movement, privilege escalation, or the execution of malicious payloads, compromising the security of the network.
+description: The following analytic detects the use of SSH to move laterally and execute
+  a script or file on a remote host. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on specific SSH command-line parameters and URLs.
+  This activity is significant as it may indicate an attacker attempting to execute
+  remote commands or scripts, potentially leading to unauthorized access or control
+  over additional systems. If confirmed malicious, this could result in lateral movement,
+  privilege escalation, or the execution of malicious payloads, compromising the security
+  of the network.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process_name=ssh Processes.process IN ("*oStrictHostKeyChecking*", "*oConnectTimeout*", "*oBatchMode*") AND Processes.process IN ("*http:*","*https:*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_ssh_remote_services_script_execute_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.process_name=ssh
+  Processes.process IN ("*oStrictHostKeyChecking*", "*oConnectTimeout*", "*oBatchMode*")
+  AND Processes.process IN ("*http:*","*https:*") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `linux_ssh_remote_services_script_execute_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: This is not a common command to be executed. Filter as needed.
 references:
 - https://redcanary.com/blog/lateral-movement-with-secure-shell/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ by user $user$ attempting to move laterally and download a file.
+  message: An instance of $process_name$ was identified on endpoint $dest$ by user
+    $user$ attempting to move laterally and download a file.
   mitre_attack_id:
   - T1021.004
   observable:
@@ -48,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.004/atomic_red_team/linux-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.004/atomic_red_team/linux-sysmon.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml b/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml
index 2002fcc817..ce0133aa4f 100644
--- a/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml
+++ b/detections/endpoint/linux_stdout_redirection_to_dev_null_file.yml
@@ -5,11 +5,31 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects command-line activities that redirect stdout or stderr to the /dev/null file. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This behavior is significant as it can indicate attempts to hide command outputs, a technique observed in the CyclopsBlink malware to conceal modifications to iptables firewall settings. If confirmed malicious, this activity could allow an attacker to stealthily alter system configurations, potentially leading to unauthorized access or persistent control over the compromised machine.
+description: The following analytic detects command-line activities that redirect
+  stdout or stderr to the /dev/null file. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs. This behavior is
+  significant as it can indicate attempts to hide command outputs, a technique observed
+  in the CyclopsBlink malware to conceal modifications to iptables firewall settings.
+  If confirmed malicious, this activity could allow an attacker to stealthily alter
+  system configurations, potentially leading to unauthorized access or persistent
+  control over the compromised machine.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*&amp;&gt;/dev/null*" by  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_stdout_redirection_to_dev_null_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.process = "*&amp;&gt;/dev/null*"
+  by  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid Processes.dest Processes.user Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_stdout_redirection_to_dev_null_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.ncsc.gov.uk/files/Cyclops-Blink-Malware-Analysis-Report.pdf
@@ -35,19 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/cyclopsblink/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_stop_services.yml b/detections/endpoint/linux_stop_services.yml
index 0ca18e459d..b9626fb241 100644
--- a/detections/endpoint/linux_stop_services.yml
+++ b/detections/endpoint/linux_stop_services.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to stop or clear a service on Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "systemctl," "service," and "svcadm" executing stop commands. This activity is significant as adversaries often terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
+description: The following analytic detects attempts to stop or clear a service on
+  Linux systems. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on processes like "systemctl," "service," and "svcadm" executing stop commands.
+  This activity is significant as adversaries often terminate security or critical
+  services to disable defenses or disrupt operations, as seen in malware like Industroyer2.
+  If confirmed malicious, this could lead to the disabling of security mechanisms,
+  allowing attackers to persist, escalate privileges, or deploy destructive payloads,
+  severely impacting system integrity and availability.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl", "service", "svcadm")  Processes.process ="*stop*" by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_stop_services_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("systemctl",
+  "service", "svcadm")  Processes.process ="*stop*" by Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_stop_services_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can use this application
+  for automation purposes. Please update the filter macros to remove false positives.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 - https://cert.gov.ua/article/39518
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified attempting to stop services on endpoint $dest$ by $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    attempting to stop services on endpoint $dest$ by $user$.
   mitre_attack_id:
   - T1489
   observable:
@@ -43,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1489/linux_service_stop_disable/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_sudo_or_su_execution.yml b/detections/endpoint/linux_sudo_or_su_execution.yml
index ade3917bc0..1b4cda49d2 100644
--- a/detections/endpoint/linux_sudo_or_su_execution.yml
+++ b/detections/endpoint/linux_sudo_or_su_execution.yml
@@ -5,12 +5,33 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the "sudo" or "su" command on a Linux operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names. This activity is significant because "sudo" and "su" commands are commonly used by adversaries to elevate privileges, potentially leading to unauthorized access or control over the system. If confirmed malicious, this activity could allow attackers to execute commands with root privileges, leading to severe security breaches, data exfiltration, or further system compromise.
+description: The following analytic detects the execution of the "sudo" or "su" command
+  on a Linux operating system. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and parent process names. This activity
+  is significant because "sudo" and "su" commands are commonly used by adversaries
+  to elevate privileges, potentially leading to unauthorized access or control over
+  the system. If confirmed malicious, this activity could allow attackers to execute
+  commands with root privileges, leading to severe security breaches, data exfiltration,
+  or further system compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("sudo", "su") OR Processes.parent_process_name IN ("sudo", "su") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sudo_or_su_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("sudo",
+  "su") OR Processes.parent_process_name IN ("sudo", "su") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_sudo_or_su_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://attack.mitre.org/techniques/T1548/003/
 tags:
@@ -33,19 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudo_su/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudo_su/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_sudoers_tmp_file_creation.yml b/detections/endpoint/linux_sudoers_tmp_file_creation.yml
index 7ff50ff50a..7fab99251d 100644
--- a/detections/endpoint/linux_sudoers_tmp_file_creation.yml
+++ b/detections/endpoint/linux_sudoers_tmp_file_creation.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of the "sudoers.tmp" file, which occurs when editing the /etc/sudoers file using visudo or another editor on a Linux platform. This detection leverages filesystem data to identify the presence of "sudoers.tmp" files. Monitoring this activity is crucial as adversaries may exploit it to gain elevated privileges on a compromised host. If confirmed malicious, this activity could allow attackers to modify sudoers configurations, potentially granting them unauthorized access to execute commands as other users, including root, thereby compromising the system's security.
+description: The following analytic detects the creation of the "sudoers.tmp" file,
+  which occurs when editing the /etc/sudoers file using visudo or another editor on
+  a Linux platform. This detection leverages filesystem data to identify the presence
+  of "sudoers.tmp" files. Monitoring this activity is crucial as adversaries may exploit
+  it to gain elevated privileges on a compromised host. If confirmed malicious, this
+  activity could allow attackers to modify sudoers configurations, potentially granting
+  them unauthorized access to execute commands as other users, including root, thereby
+  compromising the system's security.
 data_source:
 - Sysmon for Linux EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*sudoers.tmp*") by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `linux_sudoers_tmp_file_creation_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
-known_false_positives: administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*sudoers.tmp*")
+  by Filesystem.dest Filesystem.file_name Filesystem.process_guid Filesystem.file_path
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `linux_sudoers_tmp_file_creation_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you can use the Add-on for Linux Sysmon from
+  Splunkbase.
+known_false_positives: administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://forum.ubuntuusers.de/topic/sudo-visudo-gibt-etc-sudoers-tmp/
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudoers_temp/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/sudoers_temp/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_system_network_discovery.yml b/detections/endpoint/linux_system_network_discovery.yml
index 845950eda9..e090e4c511 100644
--- a/detections/endpoint/linux_system_network_discovery.yml
+++ b/detections/endpoint/linux_system_network_discovery.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies potential enumeration of local network configuration on Linux systems. It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window. This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks. If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment.
+description: The following analytic identifies potential enumeration of local network
+  configuration on Linux systems. It detects this activity by monitoring processes
+  such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss,"
+  and "route" within a 30-minute window. This behavior is significant as it often
+  indicates reconnaissance efforts by adversaries to gather network information for
+  subsequent attacks. If confirmed malicious, this activity could enable attackers
+  to map the network, identify vulnerabilities, and plan further exploitation or lateral
+  movement within the environment.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name_list values(Processes.process) as process_list values(Processes.process_id) as process_id_list values(Processes.parent_process_id) as parent_process_id_list values(Processes.process_guid) as process_guid_list dc(Processes.process_name) as process_name_count from datamodel=Endpoint.Processes where Processes.process_name IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss", "route") by _time span=30m Processes.dest Processes.user | where process_name_count >=4 | `drop_dm_object_name(Processes)`| `linux_system_network_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name_list values(Processes.process) as process_list values(Processes.process_id)
+  as process_id_list values(Processes.parent_process_id) as parent_process_id_list
+  values(Processes.process_guid) as process_guid_list dc(Processes.process_name) as
+  process_name_count from datamodel=Endpoint.Processes where Processes.process_name
+  IN ("arp", "ifconfig", "ip", "netstat", "firewall-cmd", "ufw", "iptables", "ss",
+  "route") by _time span=30m Processes.dest Processes.user | where process_name_count
+  >=4 | `drop_dm_object_name(Processes)`| `linux_system_network_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1016/T1016.md
 drilldown_searches:
@@ -19,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/atomic_red_team/linux_net_discovery/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/atomic_red_team/linux_net_discovery/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_system_reboot_via_system_request_key.yml b/detections/endpoint/linux_system_reboot_via_system_request_key.yml
index 12e6c70b27..d8e1bdb71d 100644
--- a/detections/endpoint/linux_system_reboot_via_system_request_key.yml
+++ b/detections/endpoint/linux_system_reboot_via_system_request_key.yml
@@ -5,11 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the SysReq hack to reboot a Linux system host. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe 'b' to /proc/sysrq-trigger. This activity is significant as it is an uncommon method to reboot a system and was observed in the Awfulshred malware wiper. If confirmed malicious, this technique could indicate the presence of suspicious processes and potential system compromise, leading to unauthorized reboots and disruption of services.
+description: The following analytic detects the execution of the SysReq hack to reboot
+  a Linux system host. It leverages Endpoint Detection and Response (EDR) data to
+  identify processes executing the command to pipe 'b' to /proc/sysrq-trigger. This
+  activity is significant as it is an uncommon method to reboot a system and was observed
+  in the Awfulshred malware wiper. If confirmed malicious, this technique could indicate
+  the presence of suspicious processes and potential system compromise, leading to
+  unauthorized reboots and disruption of services.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", "sudo", "bash")  Processes.process =  "* echo b &gt; *" Processes.process = "*/proc/sysrq-trigger" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_system_reboot_via_system_request_key_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash",
+  "sudo", "bash")  Processes.process =  "* echo b &gt; *" Processes.process = "*/proc/sysrq-trigger"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_system_reboot_via_system_request_key_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.kernel.org/doc/html/latest/admin-guide/sysrq.html
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,21 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_path
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml b/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml
index 4093bd1b33..c44d0dbdcd 100644
--- a/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml
+++ b/detections/endpoint/linux_unix_shell_enable_all_sysrq_functions.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of a command to enable all SysRq functions on a Linux system, a technique associated with the AwfulShred malware. It leverages Endpoint Detection and Response (EDR) data to identify processes executing the command to pipe bitmask '1' to /proc/sys/kernel/sysrq. This activity is significant as it can indicate an attempt to manipulate kernel system requests, which is uncommon and potentially malicious. If confirmed, this could allow an attacker to reboot the system or perform other critical actions, leading to system instability or further compromise.
+description: The following analytic detects the execution of a command to enable all
+  SysRq functions on a Linux system, a technique associated with the AwfulShred malware.
+  It leverages Endpoint Detection and Response (EDR) data to identify processes executing
+  the command to pipe bitmask '1' to /proc/sys/kernel/sysrq. This activity is significant
+  as it can indicate an attempt to manipulate kernel system requests, which is uncommon
+  and potentially malicious. If confirmed, this could allow an attacker to reboot
+  the system or perform other critical actions, leading to system instability or further
+  compromise.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash", "sudo", "bash")  Processes.process =  "* echo 1 &gt; *" Processes.process = "*/proc/sys/kernel/sysrq" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_unix_shell_enable_all_sysrq_functions_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("dash",
+  "sudo", "bash")  Processes.process =  "* echo 1 &gt; *" Processes.process = "*/proc/sys/kernel/sysrq"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `linux_unix_shell_enable_all_sysrq_functions_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.kernel.org/doc/html/latest/admin-guide/sysrq.html
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: a $process_name$ execute sysrq command $process$ to enable all function of system request in $dest$
+  message: a $process_name$ execute sysrq command $process$ to enable all function
+    of system request in $dest$
   mitre_attack_id:
   - T1059.004
   - T1059
@@ -44,21 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_path
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/awfulshred/test2/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/linux_visudo_utility_execution.yml b/detections/endpoint/linux_visudo_utility_execution.yml
index a1595707ad..c27471afb2 100644
--- a/detections/endpoint/linux_visudo_utility_execution.yml
+++ b/detections/endpoint/linux_visudo_utility_execution.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the 'visudo' utility to modify the /etc/sudoers file on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because unauthorized changes to the /etc/sudoers file can grant elevated privileges to users, potentially allowing adversaries to execute commands as root. If confirmed malicious, this could lead to full system compromise, privilege escalation, and persistent unauthorized access, severely impacting the security posture of the affected host.
+description: The following analytic detects the execution of the 'visudo' utility
+  to modify the /etc/sudoers file on a Linux system. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs. This activity
+  is significant because unauthorized changes to the /etc/sudoers file can grant elevated
+  privileges to users, potentially allowing adversaries to execute commands as root.
+  If confirmed malicious, this could lead to full system compromise, privilege escalation,
+  and persistent unauthorized access, severely impacting the security posture of the
+  affected host.
 data_source:
 - Sysmon for Linux EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = visudo by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_visudo_utility_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = visudo
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `linux_visudo_utility_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator can execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://askubuntu.com/questions/334318/sudoers-file-enable-nopasswd-for-user-all-commands
 drilldown_searches:
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/visudo/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.003/visudo/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/living_off_the_land_detection.yml b/detections/endpoint/living_off_the_land_detection.yml
index 7fa028346d..e89d614efb 100644
--- a/detections/endpoint/living_off_the_land_detection.yml
+++ b/detections/endpoint/living_off_the_land_detection.yml
@@ -5,11 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Correlation
-description: The following correlation identifies multiple risk events associated with the "Living Off The Land" analytic story, indicating potentially suspicious behavior. It leverages the Risk data model to aggregate and correlate events tagged under this story, focusing on systems with a high count of distinct sources. This activity is significant as it often involves the use of legitimate tools for malicious purposes, making detection challenging. If confirmed malicious, this behavior could allow attackers to execute code, escalate privileges, or persist within the environment using trusted system utilities.
+description: The following correlation identifies multiple risk events associated
+  with the "Living Off The Land" analytic story, indicating potentially suspicious
+  behavior. It leverages the Risk data model to aggregate and correlate events tagged
+  under this story, focusing on systems with a high count of distinct sources. This
+  activity is significant as it often involves the use of legitimate tools for malicious
+  purposes, making detection challenging. If confirmed malicious, this behavior could
+  allow attackers to execute code, escalate privileges, or persist within the environment
+  using trusted system utilities.
 data_source: []
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Living Off The Land" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 5 | `living_off_the_land_detection_filter`'
-how_to_implement: To implement this correlation search a user needs to enable all detections in the Living Off The Land Analytic Story and confirm it is generating risk events. A simple search `index=risk analyticstories="Living Off The Land"` should contain events.
-known_false_positives: There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation. Modify the static value distinct_detection_name to a higher value. It is also required to tune analytics that are also tagged to ensure volume is never too much.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Living
+  Off The Land" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type
+  All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 5 | `living_off_the_land_detection_filter`'
+how_to_implement: To implement this correlation search a user needs to enable all
+  detections in the Living Off The Land Analytic Story and confirm it is generating
+  risk events. A simple search `index=risk analyticstories="Living Off The Land"`
+  should contain events.
+known_false_positives: There are no known false positive for this search, but it could
+  contain false positives as multiple detections can trigger and not have successful
+  exploitation. Modify the static value distinct_detection_name to a higher value.
+  It is also required to tune analytics that are also tagged to ensure volume is never
+  too much.
 references:
 - https://www.splunk.com/en_us/blog/security/living-off-the-land-threat-research-february-2022-release.html
 - https://research.splunk.com/stories/living_off_the_land/
@@ -19,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/lolbinrisk.log
+  - data: 
+      https://raw.githubusercontent.com/splunk/attack_data/master/datasets/attack_techniques/T1218/living_off_the_land/lolbinrisk.log
     source: lotl
     sourcetype: stash
diff --git a/detections/endpoint/loading_of_dynwrapx_module.yml b/detections/endpoint/loading_of_dynwrapx_module.yml
index 9a6e0985ed..b1748634d8 100644
--- a/detections/endpoint/loading_of_dynwrapx_module.yml
+++ b/detections/endpoint/loading_of_dynwrapx_module.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the loading of the dynwrapx.dll module, which is associated with the DynamicWrapperX ActiveX component. This detection leverages Sysmon EventCode 7 to identify processes that load or register dynwrapx.dll. This activity is significant because DynamicWrapperX can be used to call Windows API functions in scripts, making it a potential tool for malicious actions. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the host. Immediate investigation of parallel processes and registry modifications is recommended.
+description: The following analytic detects the loading of the dynwrapx.dll module,
+  which is associated with the DynamicWrapperX ActiveX component. This detection leverages
+  Sysmon EventCode 7 to identify processes that load or register dynwrapx.dll. This
+  activity is significant because DynamicWrapperX can be used to call Windows API
+  functions in scripts, making it a potential tool for malicious actions. If confirmed
+  malicious, this could allow an attacker to execute arbitrary code, escalate privileges,
+  or maintain persistence on the host. Immediate investigation of parallel processes
+  and registry modifications is recommended.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 (ImageLoaded = "*\\dynwrapx.dll" OR OriginalFileName = "dynwrapx.dll" OR  Product = "DynamicWrapperX") | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName Product process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `loading_of_dynwrapx_module_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on processes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives should be limited, however it is possible to filter by Processes.process_name and specific processes (ex. wscript.exe). Filter as needed. This may need modification based on EDR telemetry and how it brings in registry data. For example, removal of (Default).
+search: '`sysmon` EventCode=7 (ImageLoaded = "*\\dynwrapx.dll" OR OriginalFileName
+  = "dynwrapx.dll" OR  Product = "DynamicWrapperX") | stats count min(_time) as firstTime
+  max(_time) as lastTime by Image ImageLoaded OriginalFileName Product process_name
+  dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `loading_of_dynwrapx_module_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on processes that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem`
+  node. In addition, confirm the latest CIM App 4.20 or higher is installed and the
+  latest TA for the endpoint product.
+known_false_positives: False positives should be limited, however it is possible to
+  filter by Processes.process_name and specific processes (ex. wscript.exe). Filter
+  as needed. This may need modification based on EDR telemetry and how it brings in
+  registry data. For example, removal of (Default).
 references:
 - https://blog.f-secure.com/hunting-for-koadic-a-com-based-rootkit/
 - https://www.script-coding.com/dynwrapx_eng.html
@@ -24,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,21 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - OriginalFileName
-  - Product
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_dynwrapx/sysmon_dynwraper.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_dynwrapx/sysmon_dynwraper.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/local_account_discovery_with_net.yml b/detections/endpoint/local_account_discovery_with_net.yml
index 9cbf3e3a06..302b0472c1 100644
--- a/detections/endpoint/local_account_discovery_with_net.yml
+++ b/detections/endpoint/local_account_discovery_with_net.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `net.exe` or `net1.exe` with command-line arguments `user` or `users` to query local user accounts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate local users, which is a common step in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further attacks, including privilege escalation and lateral movement within the network.
+description: The following analytic detects the execution of `net.exe` or `net1.exe`
+  with command-line arguments `user` or `users` to query local user accounts. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant as it indicates potential
+  reconnaissance efforts by adversaries to enumerate local users, which is a common
+  step in situational awareness and Active Directory discovery. If confirmed malicious,
+  this behavior could lead to further attacks, including privilege escalation and
+  lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` (Processes.process=*user OR Processes.process=*users) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `local_account_discovery_with_net_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_net` (Processes.process=*user
+  OR Processes.process=*users) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `local_account_discovery_with_net_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/001/
@@ -35,12 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/local_account_discovery_with_wmic.yml b/detections/endpoint/local_account_discovery_with_wmic.yml
index f2033f60c7..ea1129ec17 100644
--- a/detections/endpoint/local_account_discovery_with_wmic.yml
+++ b/detections/endpoint/local_account_discovery_with_wmic.yml
@@ -5,13 +5,32 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `wmic.exe` with command-line arguments used to query local user accounts, specifically the `useraccount` argument. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant as it indicates potential reconnaissance efforts by adversaries to enumerate local users, which is a common step in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further targeted attacks, privilege escalation, or lateral movement within the network.
+description: The following analytic detects the execution of `wmic.exe` with command-line
+  arguments used to query local user accounts, specifically the `useraccount` argument.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process execution logs that include command-line details. This activity is significant
+  as it indicates potential reconnaissance efforts by adversaries to enumerate local
+  users, which is a common step in situational awareness and Active Directory discovery.
+  If confirmed malicious, this behavior could lead to further targeted attacks, privilege
+  escalation, or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*useraccount*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `local_account_discovery_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*useraccount*)
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `local_account_discovery_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1087/001/
@@ -34,12 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.001/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml b/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml
index baa1ce6dc0..c39e9b8aa1 100644
--- a/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml
+++ b/detections/endpoint/log4shell_cve_2021_44228_exploitation.yml
@@ -5,11 +5,35 @@ date: '2024-09-30'
 author: Jose Hernandez, Splunk
 status: production
 type: Correlation
-description: The following analytic identifies potential exploitation of Log4Shell CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK tactics from Log4Shell-related detections. This activity is significant because it indicates a high probability of exploitation if two or more distinct tactics are observed. If confirmed malicious, this activity could lead to initial payload delivery, callback to a malicious server, and post-exploitation activities, potentially resulting in unauthorized access, lateral movement, and further compromise of the affected systems.
+description: The following analytic identifies potential exploitation of Log4Shell
+  CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events.
+  It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK
+  tactics from Log4Shell-related detections. This activity is significant because
+  it indicates a high probability of exploitation if two or more distinct tactics
+  are observed. If confirmed malicious, this activity could lead to initial payload
+  delivery, callback to a malicious server, and post-exploitation activities, potentially
+  resulting in unauthorized access, lateral movement, and further compromise of the
+  affected systems.
 data_source: []
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Log4Shell CVE-2021-44228" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 2 | `log4shell_cve_2021_44228_exploitation_filter`'
-how_to_implement: To implement this correlation search a user needs to enable all detections in the Log4Shell Analytic Story and confirm it is generation risk events. A simple search `index=risk analyticstories="Log4Shell CVE-2021-44228"` should contain events.
-known_false_positives: There are no known false positive for this search, but it could contain false positives as multiple detections can trigger and not have successful exploitation.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Log4Shell
+  CVE-2021-44228" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type
+  All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 2 | `log4shell_cve_2021_44228_exploitation_filter`'
+how_to_implement: To implement this correlation search a user needs to enable all
+  detections in the Log4Shell Analytic Story and confirm it is generation risk events.
+  A simple search `index=risk analyticstories="Log4Shell CVE-2021-44228"` should contain
+  events.
+known_false_positives: There are no known false positive for this search, but it could
+  contain false positives as multiple detections can trigger and not have successful
+  exploitation.
 references:
 - https://research.splunk.com/stories/log4shell_cve-2021-44228/
 - https://www.splunk.com/en_us/blog/security/simulating-detecting-and-responding-to-log4shell-with-splunk.html
@@ -19,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://raw.githubusercontent.com/splunk/attack_data/master/datasets/suspicious_behaviour/log4shell_exploitation/log4shell_correlation.txt
+  - data: 
+      https://raw.githubusercontent.com/splunk/attack_data/master/datasets/suspicious_behaviour/log4shell_exploitation/log4shell_correlation.txt
     source: log4shell
     sourcetype: stash
diff --git a/detections/endpoint/logon_script_event_trigger_execution.yml b/detections/endpoint/logon_script_event_trigger_execution.yml
index 3573385267..8238a9b81d 100644
--- a/detections/endpoint/logon_script_event_trigger_execution.yml
+++ b/detections/endpoint/logon_script_event_trigger_execution.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the modification of the UserInitMprLogonScript registry entry, which is often used by attackers to establish persistence and gain privilege escalation upon system boot. It leverages data from the Endpoint.Registry data model, focusing on changes to the specified registry path. This activity is significant because it is a common technique used by APT groups and malware to ensure their payloads execute automatically when the system starts. If confirmed malicious, this could allow attackers to maintain persistent access and potentially escalate their privileges on the compromised host.
+description: The following analytic detects the modification of the UserInitMprLogonScript
+  registry entry, which is often used by attackers to establish persistence and gain
+  privilege escalation upon system boot. It leverages data from the Endpoint.Registry
+  data model, focusing on changes to the specified registry path. This activity is
+  significant because it is a common technique used by APT groups and malware to ensure
+  their payloads execute automatically when the system starts. If confirmed malicious,
+  this could allow attackers to maintain persistent access and potentially escalate
+  their privileges on the compromised host.
 data_source:
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Environment\\UserInitMprLogonScript") by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `logon_script_event_trigger_execution_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path
+  IN ("*\\Environment\\UserInitMprLogonScript") by Registry.dest  Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `logon_script_event_trigger_execution_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1037/001/
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1037.001/logonscript_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1037.001/logonscript_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/lolbas_with_network_traffic.yml b/detections/endpoint/lolbas_with_network_traffic.yml
index cbdbcc39d0..73fa7611f4 100644
--- a/detections/endpoint/lolbas_with_network_traffic.yml
+++ b/detections/endpoint/lolbas_with_network_traffic.yml
@@ -5,12 +5,41 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies the use of Living Off the Land Binaries and Scripts (LOLBAS) with network traffic. It leverages data from the Network Traffic data model to detect when native Windows binaries, often abused by adversaries, initiate network connections. This activity is significant as LOLBAS are frequently used to download malicious payloads, enabling lateral movement, command-and-control, or data exfiltration. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to organizational security.
+description: The following analytic identifies the use of Living Off the Land Binaries
+  and Scripts (LOLBAS) with network traffic. It leverages data from the Network Traffic
+  data model to detect when native Windows binaries, often abused by adversaries,
+  initiate network connections. This activity is significant as LOLBAS are frequently
+  used to download malicious payloads, enabling lateral movement, command-and-control,
+  or data exfiltration. If confirmed malicious, this behavior could allow attackers
+  to execute arbitrary code, escalate privileges, or maintain persistence within the
+  environment, posing a severe threat to organizational security.
 data_source:
 - Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic where (All_Traffic.app IN ("*Regsvcs.exe", "*\\Ftp.exe", "*OfflineScannerShell.exe", "*Rasautou.exe", "*Schtasks.exe", "*Xwizard.exe", "*Pnputil.exe", "*Atbroker.exe", "*Pcwrun.exe", "*Ttdinject.exe", "*Mshta.exe", "*Bitsadmin.exe", "*Certoc.exe", "*Ieexec.exe", "*Microsoft.Workflow.Compiler.exe", "*Runscripthelper.exe", "*Forfiles.exe", "*Msbuild.exe", "*Register-cimprovider.exe", "*Tttracer.exe", "*Ie4uinit.exe", "*Bash.exe", "*Hh.exe", "*SettingSyncHost.exe", "*Cmstp.exe", "*Stordiag.exe", "*Scriptrunner.exe", "*Odbcconf.exe", "*Extexport.exe", "*Msdt.exe", "*WorkFolders.exe", "*Diskshadow.exe", "*Mavinject.exe", "*Regasm.exe", "*Gpscript.exe", "*Regsvr32.exe", "*Msiexec.exe", "*Wuauclt.exe", "*Presentationhost.exe", "*Wmic.exe", "*Runonce.exe", "*Syncappvpublishingserver.exe", "*Verclsid.exe", "*Infdefaultinstall.exe", "*Installutil.exe", "*Netsh.exe", "*Wab.exe", "*Dnscmd.exe", "*\\At.exe", "*Pcalua.exe", "*Msconfig.exe", "*makecab.exe", "*cscript.exe", "*notepad.exe", "*\\cmd.exe", "*certutil.exe", "*\\powershell.exe", "*powershell_ise.exe")) by All_Traffic.app,All_Traffic.src,All_Traffic.src_ip,All_Traffic.user,All_Traffic.dest,All_Traffic.dest_ip | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rex field=app ".*\\\(?<process_name>.*)$" | rename app as process | `lolbas_with_network_traffic_filter`'
-how_to_implement: To successfully implement this detection you must ingest events into the Network traffic data model that contain the source, destination, and communicating process in the app feild. Relevant processes must also be ingested in the Endpoint data model with matching process_id feild. Sysmon EID1 and EID3 are good examples of this type this data type.
-known_false_positives: Legitmate usage of internal automation or scripting, espically powershell.exe internal to internal or logon scripts. It may be necessary to omit internal IP ranges if extremely noisy. ie NOT dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","170.98.0.0/16","0:0:0:0:0:0:0:1")
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic.All_Traffic where (All_Traffic.app IN
+  ("*Regsvcs.exe", "*\\Ftp.exe", "*OfflineScannerShell.exe", "*Rasautou.exe", "*Schtasks.exe",
+  "*Xwizard.exe", "*Pnputil.exe", "*Atbroker.exe", "*Pcwrun.exe", "*Ttdinject.exe",
+  "*Mshta.exe", "*Bitsadmin.exe", "*Certoc.exe", "*Ieexec.exe", "*Microsoft.Workflow.Compiler.exe",
+  "*Runscripthelper.exe", "*Forfiles.exe", "*Msbuild.exe", "*Register-cimprovider.exe",
+  "*Tttracer.exe", "*Ie4uinit.exe", "*Bash.exe", "*Hh.exe", "*SettingSyncHost.exe",
+  "*Cmstp.exe", "*Stordiag.exe", "*Scriptrunner.exe", "*Odbcconf.exe", "*Extexport.exe",
+  "*Msdt.exe", "*WorkFolders.exe", "*Diskshadow.exe", "*Mavinject.exe", "*Regasm.exe",
+  "*Gpscript.exe", "*Regsvr32.exe", "*Msiexec.exe", "*Wuauclt.exe", "*Presentationhost.exe",
+  "*Wmic.exe", "*Runonce.exe", "*Syncappvpublishingserver.exe", "*Verclsid.exe", "*Infdefaultinstall.exe",
+  "*Installutil.exe", "*Netsh.exe", "*Wab.exe", "*Dnscmd.exe", "*\\At.exe", "*Pcalua.exe",
+  "*Msconfig.exe", "*makecab.exe", "*cscript.exe", "*notepad.exe", "*\\cmd.exe", "*certutil.exe",
+  "*\\powershell.exe", "*powershell_ise.exe")) by All_Traffic.app,All_Traffic.src,All_Traffic.src_ip,All_Traffic.user,All_Traffic.dest,All_Traffic.dest_ip
+  | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | rex field=app ".*\\\(?<process_name>.*)$" | rename app as process | `lolbas_with_network_traffic_filter`'
+how_to_implement: To successfully implement this detection you must ingest events
+  into the Network traffic data model that contain the source, destination, and communicating
+  process in the app feild. Relevant processes must also be ingested in the Endpoint
+  data model with matching process_id feild. Sysmon EID1 and EID3 are good examples
+  of this type this data type.
+known_false_positives: Legitmate usage of internal automation or scripting, espically
+  powershell.exe internal to internal or logon scripts. It may be necessary to omit
+  internal IP ranges if extremely noisy. ie NOT dest_ip IN 
+  ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","170.98.0.0/16","0:0:0:0:0:0:0:1")
 references:
 - https://lolbas-project.github.io/#
 - https://www.sans.org/presentations/lolbin-detection-methods-seven-common-attacks-revealed/
@@ -20,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,27 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.dest
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - All_Traffic.app
-  - All_Traffic.src
-  - All_Traffic.src_ip
-  - All_Traffic.dest
-  - All_Traffic.dest_ip
-  - All_Traffic.process_id
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/lolbas_with_network_traffic/lolbas_with_network_traffic.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/lolbas_with_network_traffic/lolbas_with_network_traffic.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/macos___re_opened_applications.yml b/detections/endpoint/macos___re_opened_applications.yml
index 72709aa6a4..82ff0f3f8e 100644
--- a/detections/endpoint/macos___re_opened_applications.yml
+++ b/detections/endpoint/macos___re_opened_applications.yml
@@ -5,12 +5,38 @@ date: '2024-10-17'
 author: Jamie Windley, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies processes referencing plist files that determine which applications are re-opened when a user reboots their MacOS machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes related to "com.apple.loginwindow." This activity is significant because it can indicate attempts to persist across reboots, a common tactic used by attackers to maintain access. If confirmed malicious, this could allow an attacker to execute code or maintain persistence on the affected system, potentially leading to further compromise.
+description: The following analytic identifies processes referencing plist files that
+  determine which applications are re-opened when a user reboots their MacOS machine.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names and parent processes related to "com.apple.loginwindow." This activity
+  is significant because it can indicate attempts to persist across reboots, a common
+  tactic used by attackers to maintain access. If confirmed malicious, this could
+  allow an attacker to execute code or maintain persistence on the affected system,
+  potentially leading to further compromise.
 data_source:
 - Sysmon EventID 1
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos___re_opened_applications_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*"
+  by Processes.user Processes.process_name Processes.parent_process_name Processes.dest
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `macos___re_opened_applications_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: At this stage, there are no known false positives. During testing,
+  no process events refering the com.apple.loginwindow.plist files were observed during
+  normal operation of re-opening applications on reboot. Therefore, it can be asumed
+  that any occurences of this in the process events would be worth investigating.
+  In the event that the legitimate modification by the system of these files is in
+  fact logged to the process log, then the process_name of that process can be added
+  to an allow list.
 references: []
 tags:
   analytic_story:
@@ -32,12 +58,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
   security_domain: threat
diff --git a/detections/endpoint/macos_lolbin.yml b/detections/endpoint/macos_lolbin.yml
index 99b09a72b9..5eb94b9a67 100644
--- a/detections/endpoint/macos_lolbin.yml
+++ b/detections/endpoint/macos_lolbin.yml
@@ -5,10 +5,26 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects multiple executions of Living off the Land (LOLbin) binaries on macOS within a short period. It leverages osquery to monitor process events and identifies commands such as "find", "crontab", "screencapture", "openssl", "curl", "wget", "killall", and "funzip". This activity is significant as LOLbins are often used by attackers to perform malicious actions while evading detection. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk.
+description: The following analytic detects multiple executions of Living off the
+  Land (LOLbin) binaries on macOS within a short period. It leverages osquery to monitor
+  process events and identifies commands such as "find", "crontab", "screencapture",
+  "openssl", "curl", "wget", "killall", and "funzip". This activity is significant
+  as LOLbins are often used by attackers to perform malicious actions while evading
+  detection. If confirmed malicious, this behavior could allow attackers to execute
+  arbitrary code, escalate privileges, or persist within the environment, posing a
+  significant security risk.
 data_source: []
-search: '`osquery_macro` name=es_process_events columns.cmdline IN ("find*", "crontab*", "screencapture*", "openssl*", "curl*", "wget*", "killall*", "funzip*") | rename columns.* as * | stats  min(_time) as firstTime max(_time) as lastTime values(cmdline) as cmdline, values(pid) as pid, values(parent) as parent, values(path) as path, values(signing_id) as signing_id,  dc(path) as dc_path by username host | rename username as user, cmdline as process, path as process_path, host as dest | where dc_path > 3 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `macos_lolbin_filter`'
-how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery.
+search: '`osquery_macro` name=es_process_events columns.cmdline IN ("find*", "crontab*",
+  "screencapture*", "openssl*", "curl*", "wget*", "killall*", "funzip*") | rename
+  columns.* as * | stats  min(_time) as firstTime max(_time) as lastTime values(cmdline)
+  as cmdline, values(pid) as pid, values(parent) as parent, values(path) as path,
+  values(signing_id) as signing_id,  dc(path) as dc_path by username host | rename
+  username as user, cmdline as process, path as process_path, host as dest | where
+  dc_path > 3 | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `macos_lolbin_filter`'
+how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow
+  the link in references, which describes how to setup process auditing in MacOS with
+  endpoint security and osquery.
 known_false_positives: None identified.
 references:
 - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/
@@ -18,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - columns.cmdline
-  - columns.pid
-  - columns.parent
-  - columns.path
-  - columns.signing_id
-  - columns.username
-  - host
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/macos_lolbin/osquery.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/macos_lolbin/osquery.log
     source: osquery
     sourcetype: osquery:results
diff --git a/detections/endpoint/macos_plutil.yml b/detections/endpoint/macos_plutil.yml
index 92e02083d5..4d39ca6c1b 100644
--- a/detections/endpoint/macos_plutil.yml
+++ b/detections/endpoint/macos_plutil.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the usage of the `plutil` command to modify plist files on macOS systems. It leverages osquery to monitor process events, specifically looking for executions of `/usr/bin/plutil`. This activity is significant because adversaries can use `plutil` to alter plist files, potentially adding malicious binaries or command-line arguments that execute upon user logon or system startup. If confirmed malicious, this could allow attackers to achieve persistence, execute arbitrary code, or escalate privileges, posing a significant threat to the system's security.
+description: The following analytic detects the usage of the `plutil` command to modify
+  plist files on macOS systems. It leverages osquery to monitor process events, specifically
+  looking for executions of `/usr/bin/plutil`. This activity is significant because
+  adversaries can use `plutil` to alter plist files, potentially adding malicious
+  binaries or command-line arguments that execute upon user logon or system startup.
+  If confirmed malicious, this could allow attackers to achieve persistence, execute
+  arbitrary code, or escalate privileges, posing a significant threat to the system's
+  security.
 data_source:
 - osquery
-search: '`osquery_macro` name=es_process_events columns.path=/usr/bin/plutil | rename columns.* as * | stats count  min(_time) as firstTime max(_time) as lastTime by username host cmdline pid path parent signing_id | rename username as user, cmdline as process, path as process_path, host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `macos_plutil_filter`'
-how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery.
+search: '`osquery_macro` name=es_process_events columns.path=/usr/bin/plutil | rename
+  columns.* as * | stats count  min(_time) as firstTime max(_time) as lastTime by
+  username host cmdline pid path parent signing_id | rename username as user, cmdline
+  as process, path as process_path, host as dest | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `macos_plutil_filter`'
+how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow
+  the link in references, which describes how to setup process auditing in MacOS with
+  endpoint security and osquery.
 known_false_positives: Administrators using plutil to change plist files.
 references:
 - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - columns.cmdline
-  - columns.pid
-  - columns.parent
-  - columns.path
-  - columns.signing_id
-  - columns.username
-  - host
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1647/atomic_red_team/osquery.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1647/atomic_red_team/osquery.log
     source: osquery
     sourcetype: osquery:results
diff --git a/detections/endpoint/mailsniper_invoke_functions.yml b/detections/endpoint/mailsniper_invoke_functions.yml
index 8863fe1faa..2c7a3ba7ca 100644
--- a/detections/endpoint/mailsniper_invoke_functions.yml
+++ b/detections/endpoint/mailsniper_invoke_functions.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of known MailSniper PowerShell functions on a machine. It leverages PowerShell logs (EventCode 4104) to identify specific script block text associated with MailSniper activities. This behavior is significant as MailSniper is often used by attackers to harvest sensitive emails from compromised Exchange servers. If confirmed malicious, this activity could lead to unauthorized access to sensitive email data, credential theft, and further compromise of the email infrastructure.
+description: The following analytic detects the execution of known MailSniper PowerShell
+  functions on a machine. It leverages PowerShell logs (EventCode 4104) to identify
+  specific script block text associated with MailSniper activities. This behavior
+  is significant as MailSniper is often used by attackers to harvest sensitive emails
+  from compromised Exchange servers. If confirmed malicious, this activity could lead
+  to unauthorized access to sensitive email data, credential theft, and further compromise
+  of the email infrastructure.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mailsniper_invoke_functions_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*Invoke-GlobalO365MailSearch*",
+  "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*",
+  "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*",
+  "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mailsniper_invoke_functions_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the powershell logs  from your endpoints. make sure you enable needed
+  registry to monitor this event.
 known_false_positives: unknown
 references:
 - https://www.blackhillsinfosec.com/introducing-mailsniper-a-tool-for-searching-every-users-email-for-sensitive-data/
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/malicious_inprocserver32_modification.yml b/detections/endpoint/malicious_inprocserver32_modification.yml
index d0435321f6..d08e4ffc3e 100644
--- a/detections/endpoint/malicious_inprocserver32_modification.yml
+++ b/detections/endpoint/malicious_inprocserver32_modification.yml
@@ -5,13 +5,40 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects a process modifying the registry with a known malicious CLSID under InProcServer32. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications within the HKLM or HKCU Software Classes CLSID paths. This activity is significant as it may indicate an attempt to load a malicious DLL, potentially leading to code execution. If confirmed malicious, this could allow an attacker to persist in the environment, execute arbitrary code, or escalate privileges, posing a severe threat to system integrity and security.
+description: The following analytic detects a process modifying the registry with
+  a known malicious CLSID under InProcServer32. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on registry modifications within the HKLM or
+  HKCU Software Classes CLSID paths. This activity is significant as it may indicate
+  an attempt to load a malicious DLL, potentially leading to code execution. If confirmed
+  malicious, this could allow an attacker to persist in the environment, execute arbitrary
+  code, or escalate privileges, posing a severe threat to system integrity and security.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\CLSID\\{89565275-A714-4a43-912E-978B935EDCCC}\\InProcServer32\\(Default)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest Registry.process_guid Registry.user | `drop_dm_object_name(Registry)` | fields _time dest registry_path registry_key_name registry_value_name process_name process_path process process_guid user] | stats count min(_time) as firstTime max(_time) as lastTime by dest, process_name registry_path registry_key_name registry_value_name user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_inprocserver32_modification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, filter as needed. In our test case, Remcos used regsvr32.exe to modify the registry. It may be required, dependent upon the EDR tool producing registry events, to remove (Default) from the command-line.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid
+  Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats
+  `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path=
+  "*\\CLSID\\{89565275-A714-4a43-912E-978B935EDCCC}\\InProcServer32\\(Default)" by
+  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest
+  Registry.process_guid Registry.user | `drop_dm_object_name(Registry)` | fields _time
+  dest registry_path registry_key_name registry_value_name process_name process_path
+  process process_guid user] | stats count min(_time) as firstTime max(_time) as lastTime
+  by dest, process_name registry_path registry_key_name registry_value_name user |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_inprocserver32_modification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, filter as needed. In our
+  test case, Remcos used regsvr32.exe to modify the registry. It may be required,
+  dependent upon the EDR tool producing registry events, to remove (Default) from
+  the command-line.
 references:
 - https://bohops.com/2018/06/28/abusing-com-registry-structure-clsid-localserver32-inprocserver32/
 - https://tria.ge/210929-ap75vsddan
@@ -22,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: The $process_name$ was identified on endpoint $dest$ modifying the registry with a known malicious clsid under InProcServer32.
+  message: The $process_name$ was identified on endpoint $dest$ modifying the registry
+    with a known malicious clsid under InProcServer32.
   mitre_attack_id:
   - T1218.010
   - T1112
@@ -49,18 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - process_name
-  - registry_path
-  - registry_key_name
-  - registry_value_name
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/malicious_powershell_executed_as_a_service.yml b/detections/endpoint/malicious_powershell_executed_as_a_service.yml
index ae58f4d595..aa00444f65 100644
--- a/detections/endpoint/malicious_powershell_executed_as_a_service.yml
+++ b/detections/endpoint/malicious_powershell_executed_as_a_service.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Ryan Becwar
 status: production
 type: TTP
-description: The following analytic identifies the execution of malicious PowerShell commands or payloads via the Windows SC.exe utility. It detects this activity by analyzing Windows System logs (EventCode 7045) and filtering for specific PowerShell-related patterns in the ImagePath field. This behavior is significant because it indicates potential abuse of the Windows Service Control Manager to run unauthorized or harmful scripts, which could lead to system compromise. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic identifies the execution of malicious PowerShell
+  commands or payloads via the Windows SC.exe utility. It detects this activity by
+  analyzing Windows System logs (EventCode 7045) and filtering for specific PowerShell-related
+  patterns in the ImagePath field. This behavior is significant because it indicates
+  potential abuse of the Windows Service Control Manager to run unauthorized or harmful
+  scripts, which could lead to system compromise. If confirmed malicious, this activity
+  could allow attackers to execute arbitrary code, escalate privileges, or maintain
+  persistence within the environment.
 data_source:
 - Windows Event Log System 7045
-search: '`wineventlog_system` EventCode=7045 | eval l_ImagePath=lower(ImagePath) | regex l_ImagePath="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" | regex l_ImagePath="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath ServiceName StartType ServiceType AccountName UserID dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-known_false_positives: Creating a hidden powershell service is rare and could key off of those instances.
+search: '`wineventlog_system` EventCode=7045 | eval l_ImagePath=lower(ImagePath) |
+  regex l_ImagePath="powershell[.\s]|powershell_ise[.\s]|pwsh[.\s]|psexec[.\s]" |
+  regex l_ImagePath="-nop[rofile\s]+|-w[indowstyle]*\s+hid[den]*|-noe[xit\s]+|-enc[odedcommand\s]+"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath
+  ServiceName StartType ServiceType AccountName UserID dest | rename UserID as user|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_executed_as_a_service_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows System logs with the Service name, Service File Name Service Start type,
+  and Service Type from your endpoints.
+known_false_positives: Creating a hidden powershell service is rare and could key
+  off of those instances.
 references:
 - https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/dosfuscation-report.pdf
 - http://az4n6.blogspot.com/2017/
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: Identifies the abuse the Windows SC.exe to execute malicious powerShell as a service $ImagePath$ by $user$ on $dest$
+  message: Identifies the abuse the Windows SC.exe to execute malicious powerShell
+    as a service $ImagePath$ by $user$ on $dest$
   mitre_attack_id:
   - T1569
   - T1569.002
@@ -48,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - Service_File_Name
-  - Service_Type
-  - _time
-  - Service_Name
-  - Service_Start_Type
-  - Service_Account
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/malicious_powershell_executed_as_a_service/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/malicious_powershell_executed_as_a_service/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/malicious_powershell_process___encoded_command.yml b/detections/endpoint/malicious_powershell_process___encoded_command.yml
index 4c14fcaa66..4e74e8586c 100644
--- a/detections/endpoint/malicious_powershell_process___encoded_command.yml
+++ b/detections/endpoint/malicious_powershell_process___encoded_command.yml
@@ -5,13 +5,34 @@ date: '2024-10-17'
 author: David Dorsey, Michael Haag, Splunk, SirDuckly, GitHub Community
 status: production
 type: Hunting
-description: The following analytic detects the use of the EncodedCommand parameter in PowerShell processes. It leverages Endpoint Detection and Response (EDR) data to identify variations of the EncodedCommand parameter, including shortened forms and different command switch types. This activity is significant because adversaries often use encoded commands to obfuscate malicious scripts, making detection harder. If confirmed malicious, this behavior could allow attackers to execute hidden code, potentially leading to unauthorized access, privilege escalation, or persistent threats within the environment. Review parallel events to determine legitimacy and tune based on known administrative scripts.
+description: The following analytic detects the use of the EncodedCommand parameter
+  in PowerShell processes. It leverages Endpoint Detection and Response (EDR) data
+  to identify variations of the EncodedCommand parameter, including shortened forms
+  and different command switch types. This activity is significant because adversaries
+  often use encoded commands to obfuscate malicious scripts, making detection harder.
+  If confirmed malicious, this behavior could allow attackers to execute hidden code,
+  potentially leading to unauthorized access, privilege escalation, or persistent
+  threats within the environment. Review parallel events to determine legitimacy and
+  tune based on known administrative scripts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,\"(?i)[\\-|\\/|\u2013|\u2014|\u2015][Ee^]{1,2}[NnCcOoDdEeMmAa^]+\\s+[\\\"]?[A-Za-z0-9+/=]{5,}[\\\"]?\") | `malicious_powershell_process___encoded_command_filter`"
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user
+  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name
+  Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/|–|—|―][Ee^]{1,2}[NnCcOoDdEeMmAa^]+\s+[\"]?[A-Za-z0-9+/=]{5,}[\"]?")
+  | `malicious_powershell_process___encoded_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: System administrators may use this option, but it's not common.
 references:
 - https://regexr.com/662ov
@@ -47,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml b/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml
index 5878f13b6a..89b5230618 100644
--- a/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml
+++ b/detections/endpoint/malicious_powershell_process___execution_policy_bypass.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Rico Valdez, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects PowerShell processes initiated with parameters that bypass the local execution policy for scripts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing specific flags like "-ex" or "bypass." This activity is significant because bypassing execution policies is a common tactic used by attackers to run malicious scripts undetected. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to further system compromise, data exfiltration, or persistent access within the environment.
+description: The following analytic detects PowerShell processes initiated with parameters
+  that bypass the local execution policy for scripts. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on command-line executions containing
+  specific flags like "-ex" or "bypass." This activity is significant because bypassing
+  execution policies is a common tactic used by attackers to run malicious scripts
+  undetected. If confirmed malicious, this could allow an attacker to execute arbitrary
+  code, potentially leading to further system compromise, data exfiltration, or persistent
+  access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate.
+search: '| tstats `security_content_summariesonly` values(Processes.process_id) as
+  process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process)
+  as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_powershell` (Processes.process="* -ex*" OR Processes.process="* bypass
+  *") by Processes.process_id, Processes.user, Processes.dest | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `malicious_powershell_process___execution_policy_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: There may be legitimate reasons to bypass the PowerShell execution
+  policy. The PowerShell script being run with this parameter should be validated
+  to ensure that it is legitimate.
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml b/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml
index 735ce8cb41..ba774392f2 100644
--- a/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml
+++ b/detections/endpoint/malicious_powershell_process_with_obfuscation_techniques.yml
@@ -5,12 +5,36 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects PowerShell processes launched with command-line arguments indicative of obfuscation techniques. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and complete command-line executions. This activity is significant because obfuscated PowerShell commands are often used by attackers to evade detection and execute malicious scripts. If confirmed malicious, this activity could lead to unauthorized code execution, privilege escalation, or persistent access within the environment, posing a significant security risk.
+description: The following analytic detects PowerShell processes launched with command-line
+  arguments indicative of obfuscation techniques. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, parent processes,
+  and complete command-line executions. This activity is significant because obfuscated
+  PowerShell commands are often used by attackers to evade detection and execute malicious
+  scripts. If confirmed malicious, this activity could lead to unauthorized code execution,
+  privilege escalation, or persistent access within the environment, posing a significant
+  security risk.
 data_source:
 - Sysmon EventID 1
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "''"))-1) | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation > 10'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: These characters might be legitimately on the command-line, but it is not common.
+search: "| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell`
+  by Processes.user Processes.process_name Processes.original_file_name Processes.parent_process_name
+  Processes.dest Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process,\"\
+  `\"))-1) + (mvcount(split(process, \"^\"))-1) + (mvcount(split(process, \"'\"))-1)
+  | `malicious_powershell_process_with_obfuscation_techniques_filter` | search num_obfuscation
+  > 10"
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: These characters might be legitimately on the command-line,
+  but it is not common.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -18,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml b/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml
index 64e411d509..075b2212d9 100644
--- a/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml
+++ b/detections/endpoint/mimikatz_passtheticket_commandline_parameters.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Mimikatz command line parameters associated with pass-the-ticket attacks. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns related to Kerberos ticket manipulation. This activity is significant because pass-the-ticket attacks allow adversaries to move laterally within an environment using stolen Kerberos tickets, bypassing normal access controls. If confirmed malicious, this could enable attackers to escalate privileges, access sensitive information, and maintain persistence within the network.
+description: The following analytic detects the use of Mimikatz command line parameters
+  associated with pass-the-ticket attacks. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on specific command-line patterns related to
+  Kerberos ticket manipulation. This activity is significant because pass-the-ticket
+  attacks allow adversaries to move laterally within an environment using stolen Kerberos
+  tickets, bypassing normal access controls. If confirmed malicious, this could enable
+  attackers to escalate privileges, access sensitive information, and maintain persistence
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*sekurlsa::tickets /export*" OR Processes.process = "*kerberos::ptt*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mimikatz_passtheticket_commandline_parameters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although highly unlikely, legitimate applications may use the same command line parameters as Mimikatz.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*sekurlsa::tickets
+  /export*" OR Processes.process = "*kerberos::ptt*") by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mimikatz_passtheticket_commandline_parameters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although highly unlikely, legitimate applications may use the
+  same command line parameters as Mimikatz.
 references:
 - https://github.com/gentilkiwi/mimikatz
 - https://attack.mitre.org/techniques/T1550/003/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: Mimikatz command line parameters for pass the ticket attacks were used on $dest$
+  message: Mimikatz command line parameters for pass the ticket attacks were used
+    on $dest$
   mitre_attack_id:
   - T1550
   - T1550.003
@@ -55,20 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/mimikatz/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/mimikatz/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/mmc_lolbas_execution_process_spawn.yml b/detections/endpoint/mmc_lolbas_execution_process_spawn.yml
index 7dc2c2400f..24c1133e50 100644
--- a/detections/endpoint/mmc_lolbas_execution_process_spawn.yml
+++ b/detections/endpoint/mmc_lolbas_execution_process_spawn.yml
@@ -5,14 +5,45 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies `mmc.exe` spawning a LOLBAS execution process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where `mmc.exe` is the parent process. This activity is significant because adversaries can abuse the DCOM protocol and MMC20 COM object to execute malicious code, using Windows native binaries documented by the LOLBAS project. If confirmed malicious, this behavior could indicate lateral movement, allowing attackers to execute code remotely, potentially leading to further compromise and persistence within the environment.
+description: The following analytic identifies `mmc.exe` spawning a LOLBAS execution
+  process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process creation events where `mmc.exe` is the parent process. This activity
+  is significant because adversaries can abuse the DCOM protocol and MMC20 COM object
+  to execute malicious code, using Windows native binaries documented by the LOLBAS
+  project. If confirmed malicious, this behavior could indicate lateral movement,
+  allowing attackers to execute code remotely, potentially leading to further compromise
+  and persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=mmc.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mmc_lolbas_execution_process_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate applications may trigger this behavior, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=mmc.exe)
+  (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe",
+  "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe",
+  "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe",
+  "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe",
+  "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe",
+  "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe",
+  "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe",
+  "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe",
+  "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe",
+  "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe",
+  "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mmc_lolbas_execution_process_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate applications may trigger this behavior, filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1021/003/
 - https://www.cybereason.com/blog/dcom-lateral-movement-techniques
@@ -23,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_lolbas/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement_lolbas/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/modification_of_wallpaper.yml b/detections/endpoint/modification_of_wallpaper.yml
index 2dcc46e787..f549683713 100644
--- a/detections/endpoint/modification_of_wallpaper.yml
+++ b/detections/endpoint/modification_of_wallpaper.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the modification of registry keys related to the desktop wallpaper settings. It leverages Sysmon EventCode 13 to identify changes to the "Control Panel\\Desktop\\Wallpaper" and "Control Panel\\Desktop\\WallpaperStyle" registry keys, especially when the modifying process is not explorer.exe or involves suspicious file paths like temp or public directories. This activity is significant as it can indicate ransomware behavior, such as the REVIL ransomware, which changes the wallpaper to display a ransom note. If confirmed malicious, this could signify a compromised machine and the presence of ransomware, leading to potential data encryption and extortion.
+description: The following analytic detects the modification of registry keys related
+  to the desktop wallpaper settings. It leverages Sysmon EventCode 13 to identify
+  changes to the "Control Panel\\Desktop\\Wallpaper" and "Control Panel\\Desktop\\WallpaperStyle"
+  registry keys, especially when the modifying process is not explorer.exe or involves
+  suspicious file paths like temp or public directories. This activity is significant
+  as it can indicate ransomware behavior, such as the REVIL ransomware, which changes
+  the wallpaper to display a ransom note. If confirmed malicious, this could signify
+  a compromised machine and the presence of ransomware, leading to potential data
+  encryption and extortion.
 data_source:
 - Sysmon EventID 13
-search: '`sysmon` EventCode =13  (TargetObject IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control Panel\\Desktop\\WallpaperStyle") AND Image != "*\\explorer.exe") OR (TargetObject IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control Panel\\Desktop\\WallpaperStyle") AND Details IN ("*\\temp\\*", "*\\users\\public\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode Image TargetObject Details Computer process_guid process_id user_id | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modification_of_wallpaper_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode =13  (TargetObject IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control
+  Panel\\Desktop\\WallpaperStyle") AND Image != "*\\explorer.exe") OR (TargetObject
+  IN ("*\\Control Panel\\Desktop\\Wallpaper","*\\Control Panel\\Desktop\\WallpaperStyle")
+  AND Details IN ("*\\temp\\*", "*\\users\\public\\*")) | stats count min(_time) as
+  firstTime max(_time) as lastTime by EventCode Image TargetObject Details Computer
+  process_guid process_id user_id | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `modification_of_wallpaper_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Image, TargetObject registry key, registry Details from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: 3rd party tool may used to changed the wallpaper of the machine
 references:
 - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,20 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Image
-  - TargetObject
-  - Details
-  - dest
-  - process_guid
-  - process_id
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/modify_acl_permission_to_files_or_folder.yml b/detections/endpoint/modify_acl_permission_to_files_or_folder.yml
index 6684223eb5..5bc2e449a3 100644
--- a/detections/endpoint/modify_acl_permission_to_files_or_folder.yml
+++ b/detections/endpoint/modify_acl_permission_to_files_or_folder.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the modification of ACL permissions to files or folders, making them accessible to everyone. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes like "cacls.exe," "icacls.exe," and "xcacls.exe" with specific command-line arguments. This activity is significant as it may indicate an adversary attempting to evade ACLs or access protected files. If confirmed malicious, this could allow unauthorized access to sensitive data, potentially leading to data breaches or further system compromise.
+description: The following analytic detects the modification of ACL permissions to
+  files or folders, making them accessible to everyone. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on processes like "cacls.exe," "icacls.exe,"
+  and "xcacls.exe" with specific command-line arguments. This activity is significant
+  as it may indicate an adversary attempting to evade ACLs or access protected files.
+  If confirmed malicious, this could allow unauthorized access to sensitive data,
+  potentially leading to data breaches or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe") AND Processes.process = "*/G*" AND (Processes.process = "* everyone:*"  OR Processes.process = "* SYSTEM:*" OR Processes.process = "* S-1-1-0:*") by Processes.parent_process_name Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modify_acl_permission_to_files_or_folder_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "cacls.exe"
+  OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe")
+  AND Processes.process = "*/G*" AND (Processes.process = "* everyone:*"  OR Processes.process
+  = "* SYSTEM:*" OR Processes.process = "* S-1-1-0:*") by Processes.parent_process_name
+  Processes.process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `modify_acl_permission_to_files_or_folder_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: administrators may use this command. Filter as needed.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
-  - Processes.user
-  - Processes.process
-  - Processes.process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/monitor_registry_keys_for_print_monitors.yml b/detections/endpoint/monitor_registry_keys_for_print_monitors.yml
index 1b098b4de6..b8309339d8 100644
--- a/detections/endpoint/monitor_registry_keys_for_print_monitors.yml
+++ b/detections/endpoint/monitor_registry_keys_for_print_monitors.yml
@@ -5,16 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick, Bhavin Patel
 status: production
 type: TTP
-description: The following analytic detects modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. It leverages data from the Endpoint.Registry data model, focusing on events where the registry path is modified. This activity is significant because attackers can exploit this registry key to load arbitrary .dll files, which will execute with elevated SYSTEM permissions and persist after a reboot. If confirmed malicious, this could allow attackers to maintain persistence, execute code with high privileges, and potentially compromise the entire system.
+description: The following analytic detects modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`.
+  It leverages data from the Endpoint.Registry data model, focusing on events where
+  the registry path is modified. This activity is significant because attackers can
+  exploit this registry key to load arbitrary .dll files, which will execute with
+  elevated SYSTEM permissions and persist after a reboot. If confirmed malicious,
+  this could allow attackers to maintain persistence, execute code with high privileges,
+  and potentially compromise the entire system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*")
-  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
-  | `monitor_registry_keys_for_print_monitors_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.action=modified AND
+  Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*") BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `monitor_registry_keys_for_print_monitors_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -28,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/moveit_certificate_store_access_failure.yml b/detections/endpoint/moveit_certificate_store_access_failure.yml
index 9a4f3fcd06..d8f889cd10 100644
--- a/detections/endpoint/moveit_certificate_store_access_failure.yml
+++ b/detections/endpoint/moveit_certificate_store_access_failure.yml
@@ -6,10 +6,21 @@ author: Michael Haag, Splunk
 data_source: []
 type: Hunting
 status: production
-description: This detection identifies potential exploitation attempts of the CVE-2024-5806 vulnerability in Progress MOVEit Transfer. It looks for log entries indicating failures to access the certificate store, which can occur when an attacker attempts to exploit the authentication bypass vulnerability. This behavior is a key indicator of attempts to impersonate valid users without proper credentials. While certificate store access failures can occur during normal operations, an unusual increase in such events, especially from unexpected sources, may indicate malicious activity.
-search: '`moveit_sftp_logs` "IpWorksKeyService: Caught exception of type IPWorksSSHException: The certificate store could not be opened"| stats count by source _raw | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `moveit_certificate_store_access_failure_filter`'
-how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is no TA available for MOVEit. Modify the analytic as needed to match the log format of your environment.
-known_false_positives: False positives may occur, therefore utilize the analytic as a jump off point to identifiy potential certificate store errors.
+description: This detection identifies potential exploitation attempts of the CVE-2024-5806
+  vulnerability in Progress MOVEit Transfer. It looks for log entries indicating failures
+  to access the certificate store, which can occur when an attacker attempts to exploit
+  the authentication bypass vulnerability. This behavior is a key indicator of attempts
+  to impersonate valid users without proper credentials. While certificate store access
+  failures can occur during normal operations, an unusual increase in such events,
+  especially from unexpected sources, may indicate malicious activity.
+search: '`moveit_sftp_logs` "IpWorksKeyService: Caught exception of type IPWorksSSHException:
+  The certificate store could not be opened"| stats count by source _raw | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `moveit_certificate_store_access_failure_filter`'
+how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is
+  no TA available for MOVEit. Modify the analytic as needed to match the log format
+  of your environment.
+known_false_positives: False positives may occur, therefore utilize the analytic as
+  a jump off point to identifiy potential certificate store errors.
 references:
 - https://labs.watchtowr.com/auth-bypass-in-un-limited-scenarios-progress-moveit-transfer-cve-2024-5806/
 tags:
@@ -18,7 +29,8 @@ tags:
   asset_type: Web Server
   confidence: 30
   impact: 30
-  message: Potential exploitation of the CVE-2024-5806 vulnerability in Progress MOVEit Transfer, causing certificate store access failure on $source$.
+  message: Potential exploitation of the CVE-2024-5806 vulnerability in Progress MOVEit
+    Transfer, causing certificate store access failure on $source$.
   mitre_attack_id:
   - T1190
   observable:
@@ -30,15 +42,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - source
-  - _raw
   security_domain: endpoint
   cve:
   - CVE-2024-5806
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log
     sourcetype: sftp_server_logs
     source: sftp_server_logs
diff --git a/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml b/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml
index f706ddbc72..19841980c0 100644
--- a/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml
+++ b/detections/endpoint/moveit_empty_key_fingerprint_authentication_attempt.yml
@@ -6,10 +6,23 @@ author: Michael Haag, Splunk
 data_source: []
 type: Hunting
 status: production
-description: This detection identifies attempts to authenticate with an empty public key fingerprint in Progress MOVEit Transfer, which is a key indicator of potential exploitation of the CVE-2024-5806 vulnerability. Such attempts are characteristic of the authentication bypass technique used in this vulnerability, where attackers try to impersonate valid users without providing proper credentials. While occasional empty key fingerprint authentication attempts might occur due to misconfigurations, a sudden increase or attempts from unexpected sources could signify malicious activity. This analytic helps security teams identify and investigate potential exploitation attempts of the MOVEit Transfer authentication bypass vulnerability.
-search: '`moveit_sftp_logs` "UserAuthRequestHandler: SftpPublicKeyAuthenticator: Attempted to authenticate empty public key fingerprint" | stats count by source _raw | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `moveit_empty_key_fingerprint_authentication_attempt_filter`'
-how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is no TA available for MOVEit. Modify the analytic as needed to match the log format of your environment.
-known_false_positives: False positives may occur, therefore utilize the analytic as a jump off point to identify potential empty key fingerprint authentication attempts.
+description: This detection identifies attempts to authenticate with an empty public
+  key fingerprint in Progress MOVEit Transfer, which is a key indicator of potential
+  exploitation of the CVE-2024-5806 vulnerability. Such attempts are characteristic
+  of the authentication bypass technique used in this vulnerability, where attackers
+  try to impersonate valid users without providing proper credentials. While occasional
+  empty key fingerprint authentication attempts might occur due to misconfigurations,
+  a sudden increase or attempts from unexpected sources could signify malicious activity.
+  This analytic helps security teams identify and investigate potential exploitation
+  attempts of the MOVEit Transfer authentication bypass vulnerability.
+search: '`moveit_sftp_logs` "UserAuthRequestHandler: SftpPublicKeyAuthenticator: Attempted
+  to authenticate empty public key fingerprint" | stats count by source _raw | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `moveit_empty_key_fingerprint_authentication_attempt_filter`'
+how_to_implement: The MOVEit logs must be collected in Splunk. Currently, there is
+  no TA available for MOVEit. Modify the analytic as needed to match the log format
+  of your environment.
+known_false_positives: False positives may occur, therefore utilize the analytic as
+  a jump off point to identify potential empty key fingerprint authentication attempts.
 references:
 - https://labs.watchtowr.com/auth-bypass-in-un-limited-scenarios-progress-moveit-transfer-cve-2024-5806/
 tags:
@@ -18,7 +31,8 @@ tags:
   asset_type: Web Server
   confidence: 30
   impact: 30
-  message: Potential exploitation of the CVE-2024-5806 vulnerability in Progress MOVEit Transfer, causing empty key fingerprint authentication attempts via $source$.
+  message: Potential exploitation of the CVE-2024-5806 vulnerability in Progress MOVEit
+    Transfer, causing empty key fingerprint authentication attempts via $source$.
   mitre_attack_id:
   - T1190
   observable:
@@ -30,15 +44,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - source
-  - _raw
   security_domain: endpoint
   cve:
   - CVE-2024-5806
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/moveit/SftpServer.log
     sourcetype: sftp_server_logs
     source: sftp_server_logs
diff --git a/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml b/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml
index 96a0675045..80a3658d04 100644
--- a/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml
+++ b/detections/endpoint/ms_exchange_mailbox_replication_service_writing_active_server_pages.yml
@@ -5,12 +5,34 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the creation of suspicious .aspx files in specific directories associated with Exchange exploitation by the HAFNIUM group and the ProxyShell vulnerability. It detects this activity by monitoring the MSExchangeMailboxReplication.exe process, which typically does not write .aspx files. This behavior is significant as it may indicate an active exploitation attempt on Exchange servers. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, or maintain persistence within the environment. Immediate investigation and remediation are crucial to prevent further compromise.
+description: The following analytic identifies the creation of suspicious .aspx files
+  in specific directories associated with Exchange exploitation by the HAFNIUM group
+  and the ProxyShell vulnerability. It detects this activity by monitoring the MSExchangeMailboxReplication.exe
+  process, which typically does not write .aspx files. This behavior is significant
+  as it may indicate an active exploitation attempt on Exchange servers. If confirmed
+  malicious, attackers could gain unauthorized access, execute arbitrary code, or
+  maintain persistence within the environment. Immediate investigation and remediation
+  are crucial to prevent further compromise.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=MSExchangeMailboxReplication.exe  by _time span=1h Processes.process_id Processes.process_name Processes.process_guid Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process process_guid] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `ms_exchange_mailbox_replication_service_writing_active_server_pages_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
-known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=MSExchangeMailboxReplication.exe  by _time span=1h
+  Processes.process_id Processes.process_name Processes.process_guid Processes.dest
+  | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly`
+  count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*",
+  "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest
+  Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)`
+  | fields _time dest file_create_time file_name file_path process_name process_path
+  process process_guid] | dedup file_create_time | table dest file_create_time, file_name,
+  file_path, process_name | `ms_exchange_mailbox_replication_service_writing_active_server_pages_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem`
+  node.
+known_false_positives: The query is structured in a way that `action` (read, create)
+  is not defined. Review the results of this query, filter, and tune as necessary.
+  It may be necessary to generate this query specific to your endpoint product.
 references:
 - https://redcanary.com/blog/blackbyte-ransomware/
 tags:
@@ -21,7 +43,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A file - $file_name$ was written to disk that is related to IIS exploitation related to ProxyShell. Review further file modifications on endpoint $dest$ by user $user$.
+  message: A file - $file_name$ was written to disk that is related to IIS exploitation
+    related to ProxyShell. Review further file modifications on endpoint $dest$ by
+    user $user$.
   mitre_attack_id:
   - T1505
   - T1505.003
@@ -44,16 +68,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_hash
-  - Filesystem.user
-  - Filesystem.process_guid
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process_guid
   security_domain: endpoint
diff --git a/detections/endpoint/ms_scripting_process_loading_ldap_module.yml b/detections/endpoint/ms_scripting_process_loading_ldap_module.yml
index 3835ea9d2b..0d034bef00 100644
--- a/detections/endpoint/ms_scripting_process_loading_ldap_module.yml
+++ b/detections/endpoint/ms_scripting_process_loading_ldap_module.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of MS scripting processes (wscript.exe or cscript.exe) loading LDAP-related modules (Wldap32.dll, adsldp.dll, adsldpc.dll). It leverages Sysmon EventCode 7 to identify these specific DLL loads. This activity is significant as it may indicate an attempt to query LDAP for host information, a behavior observed in FIN7 implants. If confirmed malicious, this could allow attackers to gather detailed Active Directory information, potentially leading to further exploitation or data exfiltration.
+description: The following analytic detects the execution of MS scripting processes
+  (wscript.exe or cscript.exe) loading LDAP-related modules (Wldap32.dll, adsldp.dll,
+  adsldpc.dll). It leverages Sysmon EventCode 7 to identify these specific DLL loads.
+  This activity is significant as it may indicate an attempt to query LDAP for host
+  information, a behavior observed in FIN7 implants. If confirmed malicious, this
+  could allow attackers to gather detailed Active Directory information, potentially
+  leading to further exploitation or data exfiltration.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded IN ("*\\Wldap32.dll", "*\\adsldp.dll", "*\\adsldpc.dll") | stats min(_time) as firstTime max(_time) as lastTime count by Image EventCode process_name ProcessId ProcessGuid Computer ImageLoaded | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_ldap_module_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
-known_false_positives: automation scripting language may used by network operator to do ldap query.
+search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded
+  IN ("*\\Wldap32.dll", "*\\adsldp.dll", "*\\adsldpc.dll") | stats min(_time) as firstTime
+  max(_time) as lastTime count by Image EventCode process_name ProcessId ProcessGuid
+  Computer ImageLoaded | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_ldap_module_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
+known_false_positives: automation scripting language may used by network operator
+  to do ldap query.
 references:
 - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation
 - https://attack.mitre.org/groups/G0046/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - EventCode
-  - process_name
-  - ProcessId
-  - ProcessGuid
-  - dest
-  - ImageLoaded
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/ms_scripting_process_loading_wmi_module.yml b/detections/endpoint/ms_scripting_process_loading_wmi_module.yml
index ae8ecc68e7..53122c0268 100644
--- a/detections/endpoint/ms_scripting_process_loading_wmi_module.yml
+++ b/detections/endpoint/ms_scripting_process_loading_wmi_module.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the loading of WMI modules by Microsoft scripting processes like wscript.exe or cscript.exe. It leverages Sysmon EventCode 7 to identify instances where these scripting engines load specific WMI-related DLLs. This activity is significant because it can indicate the presence of malware, such as the FIN7 implant, which uses JavaScript to execute WMI queries for gathering host information to send to a C2 server. If confirmed malicious, this behavior could allow attackers to collect sensitive system information and maintain persistence within the environment.
+description: The following analytic detects the loading of WMI modules by Microsoft
+  scripting processes like wscript.exe or cscript.exe. It leverages Sysmon EventCode
+  7 to identify instances where these scripting engines load specific WMI-related
+  DLLs. This activity is significant because it can indicate the presence of malware,
+  such as the FIN7 implant, which uses JavaScript to execute WMI queries for gathering
+  host information to send to a C2 server. If confirmed malicious, this behavior could
+  allow attackers to collect sensitive system information and maintain persistence
+  within the environment.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded IN ("*\\fastprox.dll", "*\\wbemdisp.dll", "*\\wbemprox.dll", "*\\wbemsvc.dll" , "*\\wmiutils.dll", "*\\wbemcomn.dll") | stats min(_time) as firstTime max(_time) as lastTime count by Image EventCode process_name ProcessId ProcessGuid Computer ImageLoaded | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ms_scripting_process_loading_wmi_module_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
-known_false_positives: automation scripting language may used by network operator to do ldap query.
+search: '`sysmon` EventCode =7 Image IN ("*\\wscript.exe", "*\\cscript.exe") ImageLoaded
+  IN ("*\\fastprox.dll", "*\\wbemdisp.dll", "*\\wbemprox.dll", "*\\wbemsvc.dll" ,
+  "*\\wmiutils.dll", "*\\wbemcomn.dll") | stats min(_time) as firstTime max(_time)
+  as lastTime count by Image EventCode process_name ProcessId ProcessGuid Computer
+  ImageLoaded | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `ms_scripting_process_loading_wmi_module_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
+known_false_positives: automation scripting language may used by network operator
+  to do ldap query.
 references:
 - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation
 - https://attack.mitre.org/groups/G0046/
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - EventCode
-  - process_name
-  - ProcessId
-  - ProcessGuid
-  - dest
-  - ImageLoaded
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_js_2/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml b/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml
index 93068ea471..71b5af5edc 100644
--- a/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml
+++ b/detections/endpoint/msbuild_suspicious_spawned_by_script_process.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious spawning of MSBuild.exe by Windows Script Host processes (cscript.exe or wscript.exe). This behavior is often associated with malware or adversaries executing malicious MSBuild processes via scripts on compromised hosts. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where MSBuild is a child of script hosts. This activity is significant as it may indicate an attempt to execute malicious code. If confirmed malicious, it could lead to unauthorized code execution, potentially compromising the host and allowing further malicious activities.
+description: The following analytic detects the suspicious spawning of MSBuild.exe
+  by Windows Script Host processes (cscript.exe or wscript.exe). This behavior is
+  often associated with malware or adversaries executing malicious MSBuild processes
+  via scripts on compromised hosts. The detection leverages Endpoint Detection and
+  Response (EDR) telemetry, focusing on process creation events where MSBuild is a
+  child of script hosts. This activity is significant as it may indicate an attempt
+  to execute malicious code. If confirmed malicious, it could lead to unauthorized
+  code execution, potentially compromising the host and allowing further malicious
+  activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("wscript.exe", "cscript.exe") AND `process_msbuild` by Processes.dest Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msbuild_suspicious_spawned_by_script_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as developers do not spawn MSBuild via a WSH.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("wscript.exe", "cscript.exe") AND `process_msbuild` by Processes.dest Processes.parent_process
+  Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `msbuild_suspicious_spawned_by_script_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as developers do not spawn
+  MSBuild via a WSH.
 references:
 - https://app.any.run/tasks/dc93ee63-050c-4ff8-b07e-8277af9ab939/
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Msbuild.exe process spawned by $parent_process_name$ on $dest$ executed by $user$
+  message: Msbuild.exe process spawned by $parent_process_name$ on $dest$ executed
+    by $user$
   mitre_attack_id:
   - T1127.001
   - T1127
@@ -47,18 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.original_file_name
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/regsvr32_silent/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/regsvr32_silent/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml b/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml
index 7d35094bfe..37c34f2a8a 100644
--- a/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml
+++ b/detections/endpoint/mshta_spawning_rundll32_or_regsvr32_process.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious mshta.exe process spawning rundll32 or regsvr32 child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUID, process name, and parent process fields. This activity is significant as it is a known technique used by malware like Trickbot to load malicious DLLs and execute payloads. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or download additional malware, posing a severe threat to the environment.
+description: The following analytic detects a suspicious mshta.exe process spawning
+  rundll32 or regsvr32 child processes. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process GUID, process name, and parent process
+  fields. This activity is significant as it is a known technique used by malware
+  like Trickbot to load malicious DLLs and execute payloads. If confirmed malicious,
+  this behavior could allow attackers to execute arbitrary code, escalate privileges,
+  or download additional malware, posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "mshta.exe"  `process_rundll32` OR `process_regsvr32` by  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `mshta_spawning_rundll32_or_regsvr32_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: limitted. this anomaly behavior is not commonly seen in clean host.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  = "mshta.exe"  `process_rundll32` OR `process_regsvr32` by  Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `mshta_spawning_rundll32_or_regsvr32_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: limitted. this anomaly behavior is not commonly seen in clean
+  host.
 references:
 - https://twitter.com/cyb3rops/status/1416050325870587910?s=21
 drilldown_searches:
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: a mshta parent process $parent_process_name$ spawn child process $process_name$ in host $dest$
+  message: a mshta parent process $parent_process_name$ spawn child process $process_name$
+    in host $dest$
   mitre_attack_id:
   - T1218
   - T1218.005
@@ -49,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/mshtml_module_load_in_office_product.yml b/detections/endpoint/mshtml_module_load_in_office_product.yml
index 26de9c153b..8ef8e2a228 100644
--- a/detections/endpoint/mshtml_module_load_in_office_product.yml
+++ b/detections/endpoint/mshtml_module_load_in_office_product.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the loading of the mshtml.dll module into an Office product, which is indicative of CVE-2021-40444 exploitation. It leverages Sysmon EventID 7 to monitor image loads by specific Office processes. This activity is significant because it can indicate an attempt to exploit a vulnerability in the MSHTML component via a malicious document. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further network penetration.
+description: The following analytic detects the loading of the mshtml.dll module into
+  an Office product, which is indicative of CVE-2021-40444 exploitation. It leverages
+  Sysmon EventID 7 to monitor image loads by specific Office processes. This activity
+  is significant because it can indicate an attempt to exploit a vulnerability in
+  the MSHTML component via a malicious document. If confirmed malicious, this could
+  allow an attacker to execute arbitrary code, potentially leading to system compromise,
+  data exfiltration, or further network penetration.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventID=7 process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe","Graph.exe","winproj.exe") loaded_file_path IN ("*\\mshtml.dll", "*\\Microsoft.mshtml.dll","*\\IE.Interop.MSHTML.dll","*\\MshtmlDac.dll","*\\MshtmlDed.dll","*\\MshtmlDer.dll") | stats count min(_time) as firstTime max(_time) as lastTime by user_id, dest, process_name, loaded_file, loaded_file_path, original_file_name, process_guid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `mshtml_module_load_in_office_product_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process names and image loads from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Limited false positives will be present, however, tune as necessary. Some applications may legitimately load mshtml.dll.
+search: '`sysmon` EventID=7 process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe",
+  "msaccess.exe","Graph.exe","winproj.exe") loaded_file_path IN ("*\\mshtml.dll",
+  "*\\Microsoft.mshtml.dll","*\\IE.Interop.MSHTML.dll","*\\MshtmlDac.dll","*\\MshtmlDed.dll","*\\MshtmlDer.dll")
+  | stats count min(_time) as firstTime max(_time) as lastTime by user_id, dest, process_name,
+  loaded_file, loaded_file_path, original_file_name, process_guid | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `mshtml_module_load_in_office_product_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process names and image loads from your endpoints. If you are using
+  Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: Limited false positives will be present, however, tune as necessary.
+  Some applications may legitimately load mshtml.dll.
 references:
 - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/
 - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-40444
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +54,8 @@ tags:
   cve:
   - CVE-2021-40444
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ loading mshtml.dll.
+  message: An instance of $process_name$ was identified on endpoint $dest$ loading
+    mshtml.dll.
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -52,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ImageLoaded
-  - process_name
-  - OriginalFileName
-  - process_id
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_mshtml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_mshtml.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/msi_module_loaded_by_non_system_binary.yml b/detections/endpoint/msi_module_loaded_by_non_system_binary.yml
index 3ef4cbe00e..2d8c749e3b 100644
--- a/detections/endpoint/msi_module_loaded_by_non_system_binary.yml
+++ b/detections/endpoint/msi_module_loaded_by_non_system_binary.yml
@@ -5,12 +5,24 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the loading of `msi.dll` by a binary not located in `system32`, `syswow64`, `winsxs`, or `windows` directories. This is identified using Sysmon EventCode 7, which logs DLL loads, and filters out legitimate system paths. This activity is significant as it may indicate exploitation of CVE-2021-41379 or DLL side-loading attacks, both of which can lead to unauthorized system modifications. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment.
+description: The following analytic detects the loading of `msi.dll` by a binary not
+  located in `system32`, `syswow64`, `winsxs`, or `windows` directories. This is identified
+  using Sysmon EventCode 7, which logs DLL loads, and filters out legitimate system
+  paths. This activity is significant as it may indicate exploitation of CVE-2021-41379
+  or DLL side-loading attacks, both of which can lead to unauthorized system modifications.
+  If confirmed malicious, this could allow an attacker to execute arbitrary code,
+  escalate privileges, or persist within the environment.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 ImageLoaded="*\\msi.dll" NOT (Image IN ("*\\System32\\*","*\\syswow64\\*","*\\windows\\*", "*\\winsxs\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msi_module_loaded_by_non_system_binary_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: It is possible some Administrative utilities will load msi.dll outside of normal system paths, filter as needed.
+search: '`sysmon` EventCode=7 ImageLoaded="*\\msi.dll" NOT (Image IN ("*\\System32\\*","*\\syswow64\\*","*\\windows\\*",
+  "*\\winsxs\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by
+  Image ImageLoaded process_name dest EventCode ProcessId | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `msi_module_loaded_by_non_system_binary_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: It is possible some Administrative utilities will load msi.dll
+  outside of normal system paths, filter as needed.
 references:
 - https://attackerkb.com/topics/7LstI2clmF/cve-2021-41379/rapid7-analysis
 - https://github.com/AlexandrVIvanov/InstallerFileTakeOver
@@ -25,7 +37,8 @@ tags:
   cve:
   - CVE-2021-41379
   impact: 80
-  message: The following module $ImageLoaded$ was loaded by $Image$ outside of the normal system paths on endpoint $dest$, potentally related to DLL side-loading.
+  message: The following module $ImageLoaded$ was loaded by $Image$ outside of the
+    normal system paths on endpoint $dest$, potentally related to DLL side-loading.
   mitre_attack_id:
   - T1574.002
   - T1574
@@ -42,18 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/msi_module_load/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/msi_module_load/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/msmpeng_application_dll_side_loading.yml b/detections/endpoint/msmpeng_application_dll_side_loading.yml
index 7c62405e41..437e5aabec 100644
--- a/detections/endpoint/msmpeng_application_dll_side_loading.yml
+++ b/detections/endpoint/msmpeng_application_dll_side_loading.yml
@@ -5,13 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk, Sanjay Govind
 status: production
 type: TTP
-description: The following analytic detects the suspicious creation of msmpeng.exe or mpsvc.dll in non-default Windows Defender folders. It leverages the Endpoint.Filesystem datamodel to identify instances where these files are created outside their expected directories. This activity is significant because it is associated with the REvil ransomware, which uses DLL side-loading to execute malicious payloads. If confirmed malicious, this could lead to ransomware deployment, resulting in data encryption, system compromise, and potential data loss or extortion.
+description: The following analytic detects the suspicious creation of msmpeng.exe
+  or mpsvc.dll in non-default Windows Defender folders. It leverages the Endpoint.Filesystem
+  datamodel to identify instances where these files are created outside their expected
+  directories. This activity is significant because it is associated with the REvil
+  ransomware, which uses DLL side-loading to execute malicious payloads. If confirmed
+  malicious, this could lead to ransomware deployment, resulting in data encryption,
+  system compromise, and potential data loss or extortion.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '|tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll")  AND NOT (Filesystem.file_path IN ("*\\Program Files\\windows defender\\*","*\\WinSxS\\*defender-service*","*\\WinSxS\\Temp\\*defender-service*")) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+search: '|tstats `security_content_summariesonly` values(Filesystem.file_path) as
+  file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem
+  where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll")  AND
+  NOT (Filesystem.file_path IN ("*\\Program Files\\windows defender\\*","*\\WinSxS\\*defender-service*","*\\WinSxS\\Temp\\*defender-service*"))
+  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user
+  Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `msmpeng_application_dll_side_loading_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
 known_false_positives: quite minimal false positive expected.
 references:
 - https://community.sophos.com/b/security-blog/posts/active-ransomware-attack-on-kaseya-customers
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder on host - $dest$
+  message: Suspicious creation of msmpeng.exe or mpsvc.dll in non default windows
+    defender folder on host - $dest$
   mitre_attack_id:
   - T1574.002
   - T1574
@@ -44,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets//malware/revil/msmpeng_side/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets//malware/revil/msmpeng_side/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/net_localgroup_discovery.yml b/detections/endpoint/net_localgroup_discovery.yml
index 69bc783757..f130ece145 100644
--- a/detections/endpoint/net_localgroup_discovery.yml
+++ b/detections/endpoint/net_localgroup_discovery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `net localgroup` command, which is used to enumerate local group memberships on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it can indicate an attacker is gathering information about local group memberships, potentially to identify privileged accounts. If confirmed malicious, this behavior could lead to further privilege escalation or lateral movement within the network.
+description: The following analytic detects the execution of the `net localgroup`
+  command, which is used to enumerate local group memberships on a system. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs that include command-line details. This activity is significant because it
+  can indicate an attacker is gathering information about local group memberships,
+  potentially to identify privileged accounts. If confirmed malicious, this behavior
+  could lead to further privilege escalation or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=net.exe OR Processes.process_name=net1.exe (Processes.process="*localgroup*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `net_localgroup_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=net.exe
+  OR Processes.process_name=net1.exe (Processes.process="*localgroup*") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.original_file_name Processes.process_id Processes.parent_process_id |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `net_localgroup_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present. Tune as needed.
 references:
 - https://attack.mitre.org/techniques/T1069/001/
@@ -49,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/net_profiler_uac_bypass.yml b/detections/endpoint/net_profiler_uac_bypass.yml
index a2a60dba3d..a99fafbed3 100644
--- a/detections/endpoint/net_profiler_uac_bypass.yml
+++ b/detections/endpoint/net_profiler_uac_bypass.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications to the registry aimed at bypassing the User Account Control (UAC) feature in Windows. It identifies changes to the .NET COR_PROFILER_PATH registry key, which can be exploited to load a malicious DLL via mmc.exe. This detection leverages data from the Endpoint.Registry datamodel, focusing on specific registry paths and values. Monitoring this activity is crucial as it can indicate an attempt to escalate privileges or persist within the environment. If confirmed malicious, this could allow an attacker to execute arbitrary code with elevated privileges, compromising system integrity.
+description: The following analytic detects modifications to the registry aimed at
+  bypassing the User Account Control (UAC) feature in Windows. It identifies changes
+  to the .NET COR_PROFILER_PATH registry key, which can be exploited to load a malicious
+  DLL via mmc.exe. This detection leverages data from the Endpoint.Registry datamodel,
+  focusing on specific registry paths and values. Monitoring this activity is crucial
+  as it can indicate an attempt to escalate privileges or persist within the environment.
+  If confirmed malicious, this could allow an attacker to execute arbitrary code with
+  elevated privileges, compromising system integrity.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_data = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: limited false positive. It may trigger by some windows update that will modify this registry.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH"
+  Registry.registry_value_data = "*.dll" by Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_data Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: limited false positive. It may trigger by some windows update
+  that will modify this registry.
 references:
 - https://offsec.almond.consulting/UAC-bypass-dotnet.html
 drilldown_searches:
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: Suspicious modification of registry $registry_path$ with possible payload path $registry_path$ and key $registry_key_name$ in $dest$
+  message: Suspicious modification of registry $registry_path$ with possible payload
+    path $registry_path$ and key $registry_key_name$ in $dest$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -42,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/network_connection_discovery_with_arp.yml b/detections/endpoint/network_connection_discovery_with_arp.yml
index 393346be15..8a4cd0eb98 100644
--- a/detections/endpoint/network_connection_discovery_with_arp.yml
+++ b/detections/endpoint/network_connection_discovery_with_arp.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `arp.exe` with the `-a` flag, which is used to list network connections on a compromised system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and related telemetry. Monitoring this activity is significant because both Red Teams and adversaries use `arp.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this activity could allow attackers to map the network, identify active devices, and plan further lateral movement or attacks.
+description: The following analytic detects the execution of `arp.exe` with the `-a`
+  flag, which is used to list network connections on a compromised system. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names, command-line executions, and related telemetry. Monitoring this activity
+  is significant because both Red Teams and adversaries use `arp.exe` for situational
+  awareness and Active Directory discovery. If confirmed malicious, this activity
+  could allow attackers to map the network, identify active devices, and plan further
+  lateral movement or attacks.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="arp.exe") (Processes.process=*-a*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_arp_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="arp.exe")
+  (Processes.process=*-a*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `network_connection_discovery_with_arp_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1049/
@@ -40,22 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/network_connection_discovery_with_net.yml b/detections/endpoint/network_connection_discovery_with_net.yml
index c0093ee428..d879324449 100644
--- a/detections/endpoint/network_connection_discovery_with_net.yml
+++ b/detections/endpoint/network_connection_discovery_with_net.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `net.exe` or `net1.exe` with command-line arguments used to list network connections on a compromised system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential network reconnaissance by adversaries or Red Teams, aiming to gather situational awareness and Active Directory information. If confirmed malicious, this behavior could allow attackers to map the network, identify critical assets, and plan further attacks, potentially leading to data exfiltration or lateral movement.
+description: The following analytic identifies the execution of `net.exe` or `net1.exe`
+  with command-line arguments used to list network connections on a compromised system.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names and command-line executions. This activity is significant as it indicates
+  potential network reconnaissance by adversaries or Red Teams, aiming to gather situational
+  awareness and Active Directory information. If confirmed malicious, this behavior
+  could allow attackers to map the network, identify critical assets, and plan further
+  attacks, potentially leading to data exfiltration or lateral movement.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process=*use*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_net_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe") (Processes.process=*use*) by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_net_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1049/
@@ -36,22 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/network_connection_discovery_with_netstat.yml b/detections/endpoint/network_connection_discovery_with_netstat.yml
index df29a2863f..5f6405941e 100644
--- a/detections/endpoint/network_connection_discovery_with_netstat.yml
+++ b/detections/endpoint/network_connection_discovery_with_netstat.yml
@@ -5,13 +5,32 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `netstat.exe` with command-line arguments to list network connections on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line executions, and parent processes. This activity is significant as both Red Teams and adversaries use `netstat.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map network connections, identify critical systems, and plan further lateral movement or data exfiltration.
+description: The following analytic detects the execution of `netstat.exe` with command-line
+  arguments to list network connections on a system. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, command-line executions,
+  and parent processes. This activity is significant as both Red Teams and adversaries
+  use `netstat.exe` for situational awareness and Active Directory discovery. If confirmed
+  malicious, this behavior could allow attackers to map network connections, identify
+  critical systems, and plan further lateral movement or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="netstat.exe") (Processes.process=*-a*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_connection_discovery_with_netstat_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="netstat.exe")
+  (Processes.process=*-a*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `network_connection_discovery_with_netstat_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1049/
@@ -41,22 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1049/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/network_discovery_using_route_windows_app.yml b/detections/endpoint/network_discovery_using_route_windows_app.yml
index d469e17364..79b0aabac0 100644
--- a/detections/endpoint/network_discovery_using_route_windows_app.yml
+++ b/detections/endpoint/network_discovery_using_route_windows_app.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the `route.exe` Windows application, commonly used for network discovery. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. This activity is significant because adversaries often use `route.exe` to map network routes and identify potential targets within a network. If confirmed malicious, this behavior could allow attackers to gain insights into network topology, facilitating lateral movement and further exploitation. Note that false positives may occur due to legitimate administrative tasks or automated scripts.
+description: The following analytic detects the execution of the `route.exe` Windows
+  application, commonly used for network discovery. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process creation events. This activity
+  is significant because adversaries often use `route.exe` to map network routes and
+  identify potential targets within a network. If confirmed malicious, this behavior
+  could allow attackers to gain insights into network topology, facilitating lateral
+  movement and further exploitation. Note that false positives may occur due to legitimate
+  administrative tasks or automated scripts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_route` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_discovery_using_route_windows_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A network operator or systems administrator may utilize an automated host discovery application that may generate false positives or an amazon ec2 script that uses this application. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_route` by Processes.dest
+  Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_discovery_using_route_windows_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A network operator or systems administrator may utilize an
+  automated host discovery application that may generate false positives or an amazon
+  ec2 script that uses this application. Filter as needed.
 references:
 - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/
 tags:
@@ -38,20 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/network_share_discovery_via_dir_command.yml b/detections/endpoint/network_share_discovery_via_dir_command.yml
index 3f741495c5..5094d7715b 100644
--- a/detections/endpoint/network_share_discovery_via_dir_command.yml
+++ b/detections/endpoint/network_share_discovery_via_dir_command.yml
@@ -7,10 +7,25 @@ status: production
 type: Hunting
 data_source:
 - Windows Event Log Security 5140
-description: The following analytic detects access to Windows administrative SMB shares (Admin$, IPC$, C$) using the 'dir' command. It leverages Windows Security Event Logs with EventCode 5140 to identify this activity. This behavior is significant as it is commonly used by tools like PsExec/PaExec for staging binaries before creating and starting services on remote endpoints, a technique often employed by adversaries for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to propagate malware, such as IcedID, across the network, leading to widespread infection and potential data breaches.
-search: '`wineventlog_security` EventCode=5140 ShareName IN("\\\\*\\ADMIN$","\\\\*\\C$","*\\\\*\\IPC$") AccessMask= 0x1 | stats min(_time) as firstTime max(_time) as lastTime count by ShareName IpAddress ObjectType SubjectUserName SubjectDomainName IpPort AccessMask Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `network_share_discovery_via_dir_command_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5140 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.
-known_false_positives: System Administrators may use looks like net.exe or "dir commandline" for troubleshooting or administrations tasks. However, this will typically come only from certain users and certain systems that can be added to an allow list.
+description: The following analytic detects access to Windows administrative SMB shares
+  (Admin$, IPC$, C$) using the 'dir' command. It leverages Windows Security Event
+  Logs with EventCode 5140 to identify this activity. This behavior is significant
+  as it is commonly used by tools like PsExec/PaExec for staging binaries before creating
+  and starting services on remote endpoints, a technique often employed by adversaries
+  for lateral movement and remote code execution. If confirmed malicious, this activity
+  could allow attackers to propagate malware, such as IcedID, across the network,
+  leading to widespread infection and potential data breaches.
+search: '`wineventlog_security` EventCode=5140 ShareName IN("\\\\*\\ADMIN$","\\\\*\\C$","*\\\\*\\IPC$")
+  AccessMask= 0x1 | stats min(_time) as firstTime max(_time) as lastTime count by
+  ShareName IpAddress ObjectType SubjectUserName SubjectDomainName IpPort AccessMask
+  Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `network_share_discovery_via_dir_command_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 5140 EventCode enabled. The Windows TA is also
+  required. Also enable the object Audit access success/failure in your group policy.
+known_false_positives: System Administrators may use looks like net.exe or "dir commandline"
+  for troubleshooting or administrations tasks. However, this will typically come
+  only from certain users and certain systems that can be added to an allow list.
 references:
 - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/
 tags:
@@ -21,7 +36,8 @@ tags:
   - 13daa2cf-195a-43df-a8bd-7dd5ffb607b5
   confidence: 50
   impact: 50
-  message: $user$ list executable files or directory in known sensitive SMB share.  Share name=$ShareName$, Access mask=$AccessMask$
+  message: $user$ list executable files or directory in known sensitive SMB share.  Share
+    name=$ShareName$, Access mask=$AccessMask$
   mitre_attack_id:
   - T1135
   observable:
@@ -33,20 +49,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ShareName
-  - IpAddress
-  - ObjectType
-  - SubjectUserName
-  - SubjectDomainName
-  - IpPort
-  - AccessMask
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share_discovery_via_dir/smb_access_security_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share_discovery_via_dir/smb_access_security_xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml b/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml
index 1af860cbc5..e6c202fc8c 100644
--- a/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml
+++ b/detections/endpoint/network_traffic_to_active_directory_web_services_protocol.yml
@@ -7,10 +7,28 @@ status: production
 type: Hunting
 data_source:
 - Sysmon EventID 3
-description: The following analytic identifies network traffic directed to the Active Directory Web Services Protocol (ADWS) on port 9389. It leverages network traffic logs, focusing on source and destination IP addresses, application names, and destination ports. This activity is significant as ADWS is used to manage Active Directory, and unauthorized access could indicate malicious intent. If confirmed malicious, an attacker could manipulate Active Directory, potentially leading to privilege escalation, unauthorized access, or persistent control over the environment.
-search: '| tstats count from datamodel=Network_Traffic where All_Traffic.dest_port=9389 by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.user, All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `network_traffic_to_active_directory_web_services_protocol_filter`'
-how_to_implement: The detection is based on data that originates from network traffic logs. The logs must contain the source and destination IP addresses, the application name, and the destination port. The logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the network traffic data source. The logs must also be mapped to the `Network_Traffic` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as the destination port is specific to Active Directory Web Services Protocol, however we recommend utilizing this analytic to hunt for non-standard processes querying the ADWS port. Filter by App or dest_ip to AD servers and remove known proceses querying ADWS.
+description: The following analytic identifies network traffic directed to the Active
+  Directory Web Services Protocol (ADWS) on port 9389. It leverages network traffic
+  logs, focusing on source and destination IP addresses, application names, and destination
+  ports. This activity is significant as ADWS is used to manage Active Directory,
+  and unauthorized access could indicate malicious intent. If confirmed malicious,
+  an attacker could manipulate Active Directory, potentially leading to privilege
+  escalation, unauthorized access, or persistent control over the environment.
+search: '| tstats count from datamodel=Network_Traffic where All_Traffic.dest_port=9389
+  by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.user, All_Traffic.dest_port
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`
+  | `network_traffic_to_active_directory_web_services_protocol_filter`'
+how_to_implement: The detection is based on data that originates from network traffic
+  logs. The logs must contain the source and destination IP addresses, the application
+  name, and the destination port. The logs must be processed using the appropriate
+  Splunk Technology Add-ons that are specific to the network traffic data source.
+  The logs must also be mapped to the `Network_Traffic` data model. Use the Splunk
+  Common Information Model (CIM) to normalize the field names and speed up the data
+  modeling process.
+known_false_positives: False positives should be limited as the destination port is
+  specific to Active Directory Web Services Protocol, however we recommend utilizing
+  this analytic to hunt for non-standard processes querying the ADWS port. Filter
+  by App or dest_ip to AD servers and remove known proceses querying ADWS.
 references:
 - https://github.com/FalconForceTeam/SOAPHound
 tags:
@@ -20,7 +38,8 @@ tags:
   atomic_guid: []
   confidence: 50
   impact: 20
-  message: Network traffic to Active Directory Web Services Protocol was identified on $dest_ip$ by $src_ip$.
+  message: Network traffic to Active Directory Web Services Protocol was identified
+    on $dest_ip$ by $src_ip$.
   mitre_attack_id:
   - T1087.002
   - T1069.001
@@ -42,16 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
-  - All_Traffic.app
-  - All_Traffic.user
-  - All_Traffic.dest_port
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/nishang_powershelltcponeline.yml b/detections/endpoint/nishang_powershelltcponeline.yml
index b062a51072..8c5d7d0386 100644
--- a/detections/endpoint/nishang_powershelltcponeline.yml
+++ b/detections/endpoint/nishang_powershelltcponeline.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the Nishang Invoke-PowerShellTCPOneLine utility, which initiates a callback to a remote Command and Control (C2) server. It leverages Endpoint Detection and Response (EDR) data, focusing on PowerShell processes that include specific .NET classes like Net.Sockets.TCPClient and System.Text.ASCIIEncoding. This activity is significant as it indicates potential remote control or data exfiltration attempts by an attacker. If confirmed malicious, this could lead to unauthorized remote access, data theft, or further compromise of the affected system.
+description: The following analytic detects the use of the Nishang Invoke-PowerShellTCPOneLine
+  utility, which initiates a callback to a remote Command and Control (C2) server.
+  It leverages Endpoint Detection and Response (EDR) data, focusing on PowerShell
+  processes that include specific .NET classes like Net.Sockets.TCPClient and System.Text.ASCIIEncoding.
+  This activity is significant as it indicates potential remote control or data exfiltration
+  attempts by an attacker. If confirmed malicious, this could lead to unauthorized
+  remote access, data theft, or further compromise of the affected system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process=*Net.Sockets.TCPClient* AND Processes.process=*System.Text.ASCIIEncoding*) by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `nishang_powershelltcponeline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives may be present. Filter as needed based on initial analysis.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process=*Net.Sockets.TCPClient*
+  AND Processes.process=*System.Text.ASCIIEncoding*) by Processes.dest Processes.user
+  Processes.parent_process Processes.original_file_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `nishang_powershelltcponeline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives may be present. Filter as needed based
+  on initial analysis.
 references:
 - https://github.com/samratashok/nishang/blob/master/Shells/Invoke-PowerShellTcpOneLine.ps1
 - https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/nltest_domain_trust_discovery.yml b/detections/endpoint/nltest_domain_trust_discovery.yml
index c3e6e6f3a1..fe20efe969 100644
--- a/detections/endpoint/nltest_domain_trust_discovery.yml
+++ b/detections/endpoint/nltest_domain_trust_discovery.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of `nltest.exe` with command-line arguments `/domain_trusts` or `/all_trusts` to query Domain Trust information. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it indicates potential reconnaissance efforts by adversaries to understand domain trust relationships, which can inform their lateral movement strategies. If confirmed malicious, this activity could enable attackers to map out trusted domains, facilitating further compromise and pivoting within the network.
+description: The following analytic identifies the execution of `nltest.exe` with
+  command-line arguments `/domain_trusts` or `/all_trusts` to query Domain Trust information.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process execution logs and command-line arguments. This activity is significant
+  as it indicates potential reconnaissance efforts by adversaries to understand domain
+  trust relationships, which can inform their lateral movement strategies. If confirmed
+  malicious, this activity could enable attackers to map out trusted domains, facilitating
+  further compromise and pivoting within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_nltest` (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nltest_domain_trust_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_nltest` (Processes.process=*/domain_trusts*
+  OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `nltest_domain_trust_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may use nltest for troubleshooting purposes,
+  otherwise, rarely used.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md
 - https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104
@@ -27,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,19 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml b/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml
index 75d30b8d73..4531be4828 100644
--- a/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml
+++ b/detections/endpoint/non_chrome_process_accessing_chrome_default_dir.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a non-Chrome process accessing files in the Chrome user default folder. It leverages Windows Security Event logs, specifically event code 4663, to identify unauthorized access attempts. This activity is significant because the Chrome default folder contains sensitive user data such as login credentials, browsing history, and cookies. If confirmed malicious, this behavior could indicate an attempt to exfiltrate sensitive information, often associated with RATs, trojans, and advanced persistent threats like FIN7. Such access could lead to data theft and further compromise of the affected system.
+description: The following analytic detects a non-Chrome process accessing files in
+  the Chrome user default folder. It leverages Windows Security Event logs, specifically
+  event code 4663, to identify unauthorized access attempts. This activity is significant
+  because the Chrome default folder contains sensitive user data such as login credentials,
+  browsing history, and cookies. If confirmed malicious, this behavior could indicate
+  an attempt to exfiltrate sensitive information, often associated with RATs, trojans,
+  and advanced persistent threats like FIN7. Such access could lead to data theft
+  and further compromise of the affected system.
 data_source:
 - Windows Event Log Security 4663
-search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\chrome.exe", "*\\explorer.exe", "*sql*")) ObjectName="*\\Google\\Chrome\\User Data\\Default*" | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_chrome_process_accessing_chrome_default_dir_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: other browser not listed related to chrome may catch by this rule.
+search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\chrome.exe",
+  "*\\explorer.exe", "*sql*")) ObjectName="*\\Google\\Chrome\\User Data\\Default*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType
+  ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `non_chrome_process_accessing_chrome_default_dir_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: other browser not listed related to chrome may catch by this
+  rule.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,21 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Object_Name
-  - Object_Type
-  - process_name
-  - Access_Mask
-  - Accesses
-  - process_id
-  - EventCode
-  - dest
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml b/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml
index 4227c6a396..8495a63e02 100644
--- a/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml
+++ b/detections/endpoint/non_firefox_process_access_firefox_profile_dir.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects non-Firefox processes accessing the Firefox profile directory, which contains sensitive user data such as login credentials, browsing history, and cookies. It leverages Windows Security Event logs, specifically event code 4663, to monitor access attempts. This activity is significant because it may indicate attempts by malware, such as RATs or trojans, to harvest user information. If confirmed malicious, this behavior could lead to data exfiltration, unauthorized access to user accounts, and further compromise of the affected system.
+description: The following analytic detects non-Firefox processes accessing the Firefox
+  profile directory, which contains sensitive user data such as login credentials,
+  browsing history, and cookies. It leverages Windows Security Event logs, specifically
+  event code 4663, to monitor access attempts. This activity is significant because
+  it may indicate attempts by malware, such as RATs or trojans, to harvest user information.
+  If confirmed malicious, this behavior could lead to data exfiltration, unauthorized
+  access to user accounts, and further compromise of the affected system.
 data_source:
 - Windows Event Log Security 4663
-search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\firefox.exe", "*\\explorer.exe", "*sql*")) ObjectName="*\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles*" | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `non_firefox_process_access_firefox_profile_dir_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: other browser not listed related to firefox may catch by this rule.
+search: '`wineventlog_security` EventCode=4663 NOT (ProcessName IN ("*\\firefox.exe",
+  "*\\explorer.exe", "*sql*")) ObjectName="*\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by ObjectName ObjectType
+  ProcessName AccessMask EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `non_firefox_process_access_firefox_profile_dir_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: other browser not listed related to firefox may catch by this
+  rule.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -18,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,21 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Object_Name
-  - Object_Type
-  - process_name
-  - Access_Mask
-  - Accesses
-  - process_id
-  - EventCode
-  - dest
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/non_chrome_process_accessing_chrome_default_dir/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/notepad_with_no_command_line_arguments.yml b/detections/endpoint/notepad_with_no_command_line_arguments.yml
index 778ec90970..8e98e7377d 100644
--- a/detections/endpoint/notepad_with_no_command_line_arguments.yml
+++ b/detections/endpoint/notepad_with_no_command_line_arguments.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies instances where Notepad.exe is launched without any command line arguments, a behavior commonly associated with the SliverC2 framework. This detection leverages process creation events from Endpoint Detection and Response (EDR) agents, focusing on processes initiated by Notepad.exe within a short time frame. This activity is significant as it may indicate an attempt to inject malicious code into Notepad.exe, a known tactic for evading detection. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise and unauthorized access.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=notepad.exe AND Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(notepad\.exe.{0,4}$)" | `notepad_with_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering may need to occur based on organization endpoint behavior.
+description: The following analytic identifies instances where Notepad.exe is launched
+  without any command line arguments, a behavior commonly associated with the SliverC2
+  framework. This detection leverages process creation events from Endpoint Detection
+  and Response (EDR) agents, focusing on processes initiated by Notepad.exe within
+  a short time frame. This activity is significant as it may indicate an attempt to
+  inject malicious code into Notepad.exe, a known tactic for evading detection. If
+  confirmed malicious, this could allow an attacker to execute arbitrary code, potentially
+  leading to system compromise and unauthorized access.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=notepad.exe
+  AND Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name
+  Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | regex process="(?i)(notepad\.exe.{0,4}$)"
+  | `notepad_with_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering may need to occur
+  based on organization endpoint behavior.
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/
 - https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors#Purple-Team-Section
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ with no command line arguments.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ with no command line arguments.
   mitre_attack_id:
   - T1055
   observable:
@@ -51,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/notepad_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/notepad_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/ntdsutil_export_ntds.yml b/detections/endpoint/ntdsutil_export_ntds.yml
index 06d7fab085..0764c07f03 100644
--- a/detections/endpoint/ntdsutil_export_ntds.yml
+++ b/detections/endpoint/ntdsutil_export_ntds.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Ntdsutil to export the Active Directory database (NTDS.dit). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because exporting NTDS.dit can be a precursor to offline password cracking, posing a severe security risk. If confirmed malicious, an attacker could gain access to sensitive credentials, potentially leading to unauthorized access and privilege escalation within the network.
+description: The following analytic detects the use of Ntdsutil to export the Active
+  Directory database (NTDS.dit). It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line arguments. This activity
+  is significant because exporting NTDS.dit can be a precursor to offline password
+  cracking, posing a severe security risk. If confirmed malicious, an attacker could
+  gain access to sensitive credentials, potentially leading to unauthorized access
+  and privilege escalation within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe
+  Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `ntdsutil_export_ntds_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Highly possible Server Administrators will troubleshoot with
+  ntdsutil.exe, generating false positives.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil
 - https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11)
@@ -25,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_application_drop_executable.yml b/detections/endpoint/office_application_drop_executable.yml
index 9789275799..f767f8efb1 100644
--- a/detections/endpoint/office_application_drop_executable.yml
+++ b/detections/endpoint/office_application_drop_executable.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Michael Haag, Splunk, TheLawsOfChaos, Github
 status: production
 type: TTP
-description: The following analytic detects Microsoft Office applications dropping or creating executables or scripts on a Windows OS. It leverages process creation and file system events from the Endpoint data model to identify Office applications like Word or Excel generating files with extensions such as .exe, .dll, or .ps1. This behavior is significant as it is often associated with spear-phishing attacks where malicious files are dropped to compromise the host. If confirmed malicious, this activity could lead to code execution, privilege escalation, or persistent access, posing a severe threat to the environment.
+description: The following analytic detects Microsoft Office applications dropping
+  or creating executables or scripts on a Windows OS. It leverages process creation
+  and file system events from the Endpoint data model to identify Office applications
+  like Word or Excel generating files with extensions such as .exe, .dll, or .ps1.
+  This behavior is significant as it is often associated with spear-phishing attacks
+  where malicious files are dropped to compromise the host. If confirmed malicious,
+  this activity could lead to code execution, privilege escalation, or persistent
+  access, posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe","*.dll","*.pif","*.scr","*.js","*.vbs","*.vbe","*.ps1") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process process_guid] | dedup file_create_time | table dest, process_name, process, file_create_time, file_name, file_path, process_guid | `office_application_drop_executable_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe")
+  by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest
+  Processes.process_guid | `drop_dm_object_name(Processes)` |join process_guid, _time
+  [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe","*.dll","*.pif","*.scr","*.js","*.vbs","*.vbe","*.ps1")
+  by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.process_guid Filesystem.file_path | `drop_dm_object_name(Filesystem)`
+  | fields _time dest file_create_time file_name file_path process_name process_path
+  process process_guid] | dedup file_create_time | table dest, process_name, process,
+  file_create_time, file_name, file_path, process_guid | `office_application_drop_executable_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
 known_false_positives: office macro for automation may do this behavior
 references:
 - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,17 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - file_name
-  - process_guid
-  - dest
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_application_spawn_regsvr32_process.yml b/detections/endpoint/office_application_spawn_regsvr32_process.yml
index 870654d7cd..d730e9c0e2 100644
--- a/detections/endpoint/office_application_spawn_regsvr32_process.yml
+++ b/detections/endpoint/office_application_spawn_regsvr32_process.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where an Office application spawns a Regsvr32 process, which is often indicative of macro execution or malicious code. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is a known Office application. This activity is significant because it is a common technique used by malware, such as IcedID, to initiate infections. If confirmed malicious, this behavior could lead to code execution, allowing attackers to gain control over the affected system and potentially escalate privileges.
+description: The following analytic identifies instances where an Office application
+  spawns a Regsvr32 process, which is often indicative of macro execution or malicious
+  code. This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events where the parent process is a known Office application.
+  This activity is significant because it is a common technique used by malware, such
+  as IcedID, to initiate infections. If confirmed malicious, this behavior could lead
+  to code execution, allowing attackers to gain control over the affected system and
+  potentially escalate privileges.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "outlook.exe" OR Processes.parent_process_name = "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name="msaccess.exe") `process_regsvr32` by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_regsvr32_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name
+  = "powerpnt.exe" OR Processes.parent_process_name = "outlook.exe" OR Processes.parent_process_name
+  = "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name
+  = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name="msaccess.exe")
+  `process_regsvr32` by Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.process_guid
+  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `office_application_spawn_regsvr32_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.joesandbox.com/analysis/380662/0/html
@@ -22,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/phish_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/phish_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_application_spawn_rundll32_process.yml b/detections/endpoint/office_application_spawn_rundll32_process.yml
index 2a3c81fb38..c17f4b5e9d 100644
--- a/detections/endpoint/office_application_spawn_rundll32_process.yml
+++ b/detections/endpoint/office_application_spawn_rundll32_process.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where an Office application spawns a rundll32 process, which is often indicative of macro execution or malicious code. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is a known Office application. This activity is significant because it is a common technique used by malware, such as Trickbot, to initiate infections. If confirmed malicious, this behavior could lead to code execution, further system compromise, and potential data exfiltration.
+description: The following analytic identifies instances where an Office application
+  spawns a rundll32 process, which is often indicative of macro execution or malicious
+  code. This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events where the parent process is a known Office application.
+  This activity is significant because it is a common technique used by malware, such
+  as Trickbot, to initiate infections. If confirmed malicious, this behavior could
+  lead to code execution, further system compromise, and potential data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name= "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name = "msaccess.exe") AND `process_rundll32` by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_rundll32_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name
+  = "powerpnt.exe" OR Processes.parent_process_name= "onenote.exe" OR Processes.parent_process_name
+  = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name
+  = "onenoteim.exe" OR Processes.parent_process_name = "msaccess.exe") AND `process_rundll32`
+  by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid
+  Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_application_spawn_rundll32_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://any.run/malware-trends/trickbot
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,22 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_document_creating_schedule_task.yml b/detections/endpoint/office_document_creating_schedule_task.yml
index 0af0cf1456..24890ed62f 100644
--- a/detections/endpoint/office_document_creating_schedule_task.yml
+++ b/detections/endpoint/office_document_creating_schedule_task.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Office document creating a scheduled task, either through a macro VBA API or by loading `taskschd.dll`. This detection leverages Sysmon EventCode 7 to identify when Office applications load the `taskschd.dll` file. This activity is significant as it is a common technique used by malicious macro malware to establish persistence or initiate beaconing. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, or schedule future malicious activities, posing a significant threat to the environment.
+description: The following analytic detects an Office document creating a scheduled
+  task, either through a macro VBA API or by loading `taskschd.dll`. This detection
+  leverages Sysmon EventCode 7 to identify when Office applications load the `taskschd.dll`
+  file. This activity is significant as it is a common technique used by malicious
+  macro malware to establish persistence or initiate beaconing. If confirmed malicious,
+  this could allow an attacker to maintain persistence, execute arbitrary commands,
+  or schedule future malicious activities, posing a significant threat to the environment.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe") loaded_file_path = "*\\taskschd.dll" | stats min(_time) as firstTime max(_time) as lastTime count by user_id, dest, process_name,loaded_file, loaded_file_path, original_file_name, process_guid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_creating_schedule_task_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
-known_false_positives: False positives may occur if legitimate office documents are creating scheduled tasks. Ensure to investigate the scheduled task and the command to be executed. If the task is benign, add the task name to the exclusion list. Some applications may legitimately load taskschd.dll.
+search: '`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe",
+  "msaccess.exe") loaded_file_path = "*\\taskschd.dll" | stats min(_time) as firstTime
+  max(_time) as lastTime count by user_id, dest, process_name,loaded_file, loaded_file_path,
+  original_file_name, process_guid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `office_document_creating_schedule_task_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+  Also be sure to include those monitored dll to your own sysmon config.
+known_false_positives: False positives may occur if legitimate office documents are
+  creating scheduled tasks. Ensure to investigate the scheduled task and the command
+  to be executed. If the task is benign, add the task name to the exclusion list.
+  Some applications may legitimately load taskschd.dll.
 references:
 - https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
 - https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An Office document was identified creating a scheduled task on $dest$. Investigate further.
+  message: An Office document was identified creating a scheduled task on $dest$.
+    Investigate further.
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -43,20 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - ImageLoaded
-  - AllImageLoaded
-  - dest
-  - EventCode
-  - Image
-  - process_name
-  - ProcessId
-  - ProcessGuid
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_document_executing_macro_code.yml b/detections/endpoint/office_document_executing_macro_code.yml
index 4459497eaa..6ee8d7a90a 100644
--- a/detections/endpoint/office_document_executing_macro_code.yml
+++ b/detections/endpoint/office_document_executing_macro_code.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies office documents executing macro code. It leverages Sysmon EventCode 7 to detect when processes like WINWORD.EXE or EXCEL.EXE load specific DLLs associated with macros (e.g., VBE7.DLL). This activity is significant because macros are a common attack vector for delivering malicious payloads, such as malware. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Disabling macros by default is recommended to mitigate this risk.
+description: The following analytic identifies office documents executing macro code.
+  It leverages Sysmon EventCode 7 to detect when processes like WINWORD.EXE or EXCEL.EXE
+  load specific DLLs associated with macros (e.g., VBE7.DLL). This activity is significant
+  because macros are a common attack vector for delivering malicious payloads, such
+  as malware. If confirmed malicious, this could lead to unauthorized code execution,
+  data exfiltration, or further compromise of the system. Disabling macros by default
+  is recommended to mitigate this risk.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") loaded_file_path IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") | stats min(_time) as firstTime max(_time) as lastTime values(loaded_file) as loaded_file count by dest EventCode process_name process_guid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_executing_macro_code_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
-known_false_positives: False positives may occur if legitimate office documents are executing macro code. Ensure to investigate the macro code and the command to be executed. If the macro code is benign, add the document name to the exclusion list. Some applications may legitimately load VBE7INTL.DLL, VBE7.DLL, or VBEUI.DLL.
+search: '`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe")
+  loaded_file_path IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") | stats min(_time)
+  as firstTime max(_time) as lastTime values(loaded_file) as loaded_file count by
+  dest EventCode process_name process_guid | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `office_document_executing_macro_code_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+  Also be sure to include those monitored dll to your own sysmon config.
+known_false_positives: False positives may occur if legitimate office documents are
+  executing macro code. Ensure to investigate the macro code and the command to be
+  executed. If the macro code is benign, add the document name to the exclusion list.
+  Some applications may legitimately load VBE7INTL.DLL, VBE7.DLL, or VBEUI.DLL.
 references:
 - https://www.joesandbox.com/analysis/386500/0/html
 - https://www.joesandbox.com/analysis/702680/0/html
@@ -24,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,20 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - ImageLoaded
-  - AllImageLoaded
-  - dest
-  - EventCode
-  - Image
-  - process_name
-  - ProcessId
-  - ProcessGuid
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_document_spawned_child_process_to_download.yml b/detections/endpoint/office_document_spawned_child_process_to_download.yml
index b49087e41c..eb32923d06 100644
--- a/detections/endpoint/office_document_spawned_child_process_to_download.yml
+++ b/detections/endpoint/office_document_spawned_child_process_to_download.yml
@@ -5,13 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies Office applications spawning child processes to download content via HTTP/HTTPS. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where Office applications like Word or Excel initiate network connections, excluding common browsers. This activity is significant as it often indicates the use of malicious documents to execute living-off-the-land binaries (LOLBins) for payload delivery. If confirmed malicious, this behavior could lead to unauthorized code execution, data exfiltration, or further malware deployment, posing a severe threat to the organization's security.
+description: The following analytic identifies Office applications spawning child
+  processes to download content via HTTP/HTTPS. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events where Office applications
+  like Word or Excel initiate network connections, excluding common browsers. This
+  activity is significant as it often indicates the use of malicious documents to
+  execute living-off-the-land binaries (LOLBins) for payload delivery. If confirmed
+  malicious, this behavior could lead to unauthorized code execution, data exfiltration,
+  or further malware deployment, posing a severe threat to the organization's security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe", "Graph.exe","winproj.exe") Processes.process IN ("*http:*","*https:*") NOT (Processes.original_file_name IN("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe"))  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `office_document_spawned_child_process_to_download_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe",
+  "Graph.exe","winproj.exe") Processes.process IN ("*http:*","*https:*") NOT (Processes.original_file_name
+  IN("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe"))  by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.original_file_name |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `office_document_spawned_child_process_to_download_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Default browser not in the filter list.
 references:
 - https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/
@@ -22,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawn_cmd_process.yml b/detections/endpoint/office_product_spawn_cmd_process.yml
index 31b03dbe0b..b2f2dc9f12 100644
--- a/detections/endpoint/office_product_spawn_cmd_process.yml
+++ b/detections/endpoint/office_product_spawn_cmd_process.yml
@@ -5,14 +5,40 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Office product spawning a CMD process, which is indicative of a macro executing shell commands to download or run malicious code. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names. This activity is significant as it often signals the execution of malicious payloads, such as those seen in Trickbot spear-phishing campaigns. If confirmed malicious, this behavior could lead to unauthorized code execution, potentially compromising the system and allowing further malicious activities.
+description: The following analytic detects an Office product spawning a CMD process,
+  which is indicative of a macro executing shell commands to download or run malicious
+  code. This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process and parent process names. This activity is significant as it
+  often signals the execution of malicious payloads, such as those seen in Trickbot
+  spear-phishing campaigns. If confirmed malicious, this behavior could lead to unauthorized
+  code execution, potentially compromising the system and allowing further malicious
+  activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name= "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name= "onenote.exe" OR Processes.parent_process_name = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name = "onenoteim.exe" OR Processes.parent_process_name = "msaccess.exe" OR  Processes.parent_process_name="Graph.exe" OR Processes.parent_process_name="winproj.exe") `process_cmd` by  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `office_product_spawn_cmd_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: IT or network admin may create an document automation that will run shell script.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  = "winword.exe" OR Processes.parent_process_name= "excel.exe" OR Processes.parent_process_name
+  = "powerpnt.exe" OR Processes.parent_process_name= "onenote.exe" OR Processes.parent_process_name
+  = "onenotem.exe" OR Processes.parent_process_name = "onenoteviewer.exe" OR Processes.parent_process_name
+  = "onenoteim.exe" OR Processes.parent_process_name = "msaccess.exe" OR  Processes.parent_process_name="Graph.exe"
+  OR Processes.parent_process_name="winproj.exe") `process_cmd` by  Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.process_guid Processes.user Processes.dest Processes.original_file_name
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
+  | `office_product_spawn_cmd_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: IT or network admin may create an document automation that
+  will run shell script.
 references:
 - https://twitter.com/cyb3rops/status/1416050325870587910?s=21
 - https://bazaar.abuse.ch/sample/02cbc1ab80695fc12ff8822b926957c3a600247b9ca412a137f69cb5716c8781/
@@ -24,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,7 +74,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: an office product parent process $parent_process_name$ spawn child process $process_name$ in host $dest$
+  message: an office product parent process $parent_process_name$ spawn child process
+    $process_name$ in host $dest$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -60,23 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawning_bitsadmin.yml b/detections/endpoint/office_product_spawning_bitsadmin.yml
index cab6d7536a..2a328dc2e4 100644
--- a/detections/endpoint/office_product_spawning_bitsadmin.yml
+++ b/detections/endpoint/office_product_spawning_bitsadmin.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects any Windows Office Product spawning `bitsadmin.exe`, a behavior often associated with malware families like TA551 and IcedID. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because `bitsadmin.exe` is commonly used for malicious file transfers, potentially indicating a malware infection. If confirmed malicious, this activity could allow attackers to download additional payloads, escalate privileges, or establish persistence, leading to further compromise of the affected system.
+description: The following analytic detects any Windows Office Product spawning `bitsadmin.exe`,
+  a behavior often associated with malware families like TA551 and IcedID. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  and parent process relationships. This activity is significant because `bitsadmin.exe`
+  is commonly used for malicious file transfers, potentially indicating a malware
+  infection. If confirmed malicious, this activity could allow attackers to download
+  additional payloads, escalate privileges, or establish persistence, leading to further
+  compromise of the affected system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe", "Graph.exe","winproj.exe") `process_bitsadmin` by Processes.dest Processes.user  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_bitsadmin_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe",
+  "msaccess.exe", "Graph.exe","winproj.exe") `process_bitsadmin` by Processes.dest
+  Processes.user  Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `office_product_spawning_bitsadmin_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: No false positives known. Filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ with process id $process_id$ in host $dest$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -49,23 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawning_certutil.yml b/detections/endpoint/office_product_spawning_certutil.yml
index 8f57235f1f..4a280058a7 100644
--- a/detections/endpoint/office_product_spawning_certutil.yml
+++ b/detections/endpoint/office_product_spawning_certutil.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects any Windows Office Product spawning `certutil.exe`, a behavior often associated with malware families like TA551 and IcedID. This detection leverages Endpoint Detection and Response (EDR) data, focusing on process relationships and command-line executions. The significance lies in the fact that `certutil.exe` is frequently used for downloading malicious payloads from remote URLs. If confirmed malicious, this activity could lead to unauthorized code execution, data exfiltration, or further system compromise. Immediate investigation and containment are crucial to prevent potential damage.
+description: The following analytic detects any Windows Office Product spawning `certutil.exe`,
+  a behavior often associated with malware families like TA551 and IcedID. This detection
+  leverages Endpoint Detection and Response (EDR) data, focusing on process relationships
+  and command-line executions. The significance lies in the fact that `certutil.exe`
+  is frequently used for downloading malicious payloads from remote URLs. If confirmed
+  malicious, this activity could lead to unauthorized code execution, data exfiltration,
+  or further system compromise. Immediate investigation and containment are crucial
+  to prevent potential damage.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe", "Graph.exe","winproj.exe") `process_certutil` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_certutil_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe",
+  "Graph.exe","winproj.exe") `process_certutil` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_certutil_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: No false positives known. Filter as needed.
 references:
 - https://redcanary.com/threat-detection-report/threats/TA551/
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ with process id $process_id$ in host $dest$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -52,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawning_mshta.yml b/detections/endpoint/office_product_spawning_mshta.yml
index 73a87a8faa..bed27d857c 100644
--- a/detections/endpoint/office_product_spawning_mshta.yml
+++ b/detections/endpoint/office_product_spawning_mshta.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where a Microsoft Office product spawns `mshta.exe`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is an Office application. This activity is significant because it is a common technique used by malware families like TA551 and IcedID to execute malicious scripts or payloads. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to data exfiltration, system compromise, or further malware deployment.
+description: The following analytic identifies instances where a Microsoft Office
+  product spawns `mshta.exe`. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events where the parent
+  process is an Office application. This activity is significant because it is a common
+  technique used by malware families like TA551 and IcedID to execute malicious scripts
+  or payloads. If confirmed malicious, this behavior could allow attackers to execute
+  arbitrary code, potentially leading to data exfiltration, system compromise, or
+  further malware deployment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe", "onenote.exe","onenotem.exe", "msaccess.exe","Graph.exe","winproj.exe") `process_mshta` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_mshta_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe", "onenote.exe","onenotem.exe",
+  "msaccess.exe","Graph.exe","winproj.exe") `process_mshta` by Processes.dest Processes.user
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_mshta_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: No false positives known. Filter as needed.
 references:
 - https://redcanary.com/threat-detection-report/threats/TA551/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ with process id $process_id$ in host $dest$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -51,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawning_rundll32_with_no_dll.yml b/detections/endpoint/office_product_spawning_rundll32_with_no_dll.yml
index de7bdf5c03..6d1fc111a2 100644
--- a/detections/endpoint/office_product_spawning_rundll32_with_no_dll.yml
+++ b/detections/endpoint/office_product_spawning_rundll32_with_no_dll.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process and parent process relationships. This activity is significant as it is a known tactic of the IcedID malware family, which can lead to unauthorized code execution. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to data exfiltration, system compromise, or further malware deployment. Immediate investigation and containment are recommended.
+description: The following analytic detects any Windows Office Product spawning `rundll32.exe`
+  without a `.dll` file extension. This behavior is identified using Endpoint Detection
+  and Response (EDR) telemetry, focusing on process and parent process relationships.
+  This activity is significant as it is a known tactic of the IcedID malware family,
+  which can lead to unauthorized code execution. If confirmed malicious, this could
+  allow attackers to execute arbitrary code, potentially leading to data exfiltration,
+  system compromise, or further malware deployment. Immediate investigation and containment
+  are recommended.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe", "Graph.exe","winproj.exe") `process_rundll32` (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_rundll32_with_no_dll_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, but if any are present, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe",
+  "msaccess.exe", "Graph.exe","winproj.exe") `process_rundll32` (Processes.process!=*.dll*)
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `office_product_spawning_rundll32_with_no_dll_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, but if any are present,
+  filter as needed.
 references:
 - https://www.joesandbox.com/analysis/395471/0/html
 - https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ and no dll commandline $process$ in host $dest$
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ with process id $process_id$ and no dll commandline $process$
+    in host $dest$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -50,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawning_windows_script_host.yml b/detections/endpoint/office_product_spawning_windows_script_host.yml
index cd2f6a27e4..83275c9780 100644
--- a/detections/endpoint/office_product_spawning_windows_script_host.yml
+++ b/detections/endpoint/office_product_spawning_windows_script_host.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects an Office product spawning WScript.exe or CScript.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where Office applications are the parent processes. This activity is significant because it may indicate the execution of potentially malicious scripts through Office products, a common tactic in phishing attacks and malware delivery. If confirmed malicious, this behavior could lead to unauthorized code execution, data exfiltration, or further system compromise.
+description: The following analytic detects an Office product spawning WScript.exe
+  or CScript.exe. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events where Office applications are the parent processes.
+  This activity is significant because it may indicate the execution of potentially
+  malicious scripts through Office products, a common tactic in phishing attacks and
+  malware delivery. If confirmed malicious, this behavior could lead to unauthorized
+  code execution, data exfiltration, or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe", "msaccess.exe","Graph.exe","winproj.exe") Processes.process_name IN ("wscript.exe", "cscript.exe") by  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_windows_script_host_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present based on macro based approved documents in the organization. Filtering may be needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe",
+  "msaccess.exe","Graph.exe","winproj.exe") Processes.process_name IN ("wscript.exe",
+  "cscript.exe") by  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `office_product_spawning_windows_script_host_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present based on macro based approved
+  documents in the organization. Filtering may be needed.
 references:
 - https://blog.cluster25.duskrise.com/2022/09/23/in-the-footsteps-of-the-fancy-bear-powerpoint-graphite/
 - https://www.fortinet.com/blog/threat-research/latest-remcos-rat-phishing
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ on host $dest$.
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ on host $dest$.
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -55,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_spawning_wmic.yml b/detections/endpoint/office_product_spawning_wmic.yml
index e773b755a3..cf98035a26 100644
--- a/detections/endpoint/office_product_spawning_wmic.yml
+++ b/detections/endpoint/office_product_spawning_wmic.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects any Windows Office Product spawning `wmic.exe`, specifically when the command-line of `wmic.exe` contains `wmic process call create`. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as it is commonly associated with the Ursnif malware family, indicating potential malicious activity. If confirmed malicious, this could allow an attacker to execute arbitrary commands, leading to further system compromise, data exfiltration, or lateral movement within the network.
+description: The following analytic detects any Windows Office Product spawning `wmic.exe`,
+  specifically when the command-line of `wmic.exe` contains `wmic process call create`.
+  This behavior is identified using data from Endpoint Detection and Response (EDR)
+  agents, focusing on process and parent process relationships. This activity is significant
+  as it is commonly associated with the Ursnif malware family, indicating potential
+  malicious activity. If confirmed malicious, this could allow an attacker to execute
+  arbitrary commands, leading to further system compromise, data exfiltration, or
+  lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe", "Graph.exe","winproj.exe") `process_wmic` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe",
+  "Graph.exe","winproj.exe") `process_wmic` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `office_product_spawning_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: No false positives known. Filter as needed.
 references:
 - https://app.any.run/tasks/fb894ab8-a966-4b72-920b-935f41756afd/
@@ -24,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ with process id $process_id$ in host $dest$
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -52,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_product_writing_cab_or_inf.yml b/detections/endpoint/office_product_writing_cab_or_inf.yml
index 47e89a9d62..5308f97b50 100644
--- a/detections/endpoint/office_product_writing_cab_or_inf.yml
+++ b/detections/endpoint/office_product_writing_cab_or_inf.yml
@@ -5,15 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects Office products writing .cab or .inf files, indicative of CVE-2021-40444 exploitation. It leverages the Endpoint.Processes and Endpoint.Filesystem data models to identify Office applications creating these file types. This activity is significant as it may signal an attempt to load malicious ActiveX controls and download remote payloads, a known attack vector. If confirmed malicious, this could lead to remote code execution, allowing attackers to gain control over the affected system and potentially compromise sensitive data.
+description: The following analytic detects Office products writing .cab or .inf files,
+  indicative of CVE-2021-40444 exploitation. It leverages the Endpoint.Processes and
+  Endpoint.Filesystem data models to identify Office applications creating these file
+  types. This activity is significant as it may signal an attempt to load malicious
+  ActiveX controls and download remote payloads, a known attack vector. If confirmed
+  malicious, this could lead to remote code execution, allowing attackers to gain
+  control over the affected system and potentially compromise sensitive data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.inf","*.cab") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] | dedup file_create_time | table dest, process_name, process, file_create_time, file_name, file_path, proc_guid | `office_product_writing_cab_or_inf_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
-known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe")
+  by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest
+  Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as
+  proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count
+  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_name IN ("*.inf","*.cab") by _time span=1h Filesystem.dest
+  Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid
+  | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time
+  dest file_create_time file_name file_path process_name process_path process proc_guid]
+  | dedup file_create_time | table dest, process_name, process, file_create_time,
+  file_name, file_path, proc_guid | `office_product_writing_cab_or_inf_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem`
+  node.
+known_false_positives: The query is structured in a way that `action` (read, create)
+  is not defined. Review the results of this query, filter, and tune as necessary.
+  It may be necessary to generate this query specific to your endpoint product.
 references:
 - https://twitter.com/vxunderground/status/1436326057179860992?s=20
 - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/
@@ -26,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +65,8 @@ tags:
   cve:
   - CVE-2021-40444
   impact: 80
-  message: An instance of $process_name$ was identified on $dest$ writing an inf or cab file to this. This is not typical of $process_name$.
+  message: An instance of $process_name$ was identified on $dest$ writing an inf or
+    cab file to this. This is not typical of $process_name$.
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -55,18 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - process_name
-  - process
-  - file_create_time
-  - file_name
-  - file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_cabinf.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_cabinf.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/office_spawning_control.yml b/detections/endpoint/office_spawning_control.yml
index 78372841bc..3437bb3e4c 100644
--- a/detections/endpoint/office_spawning_control.yml
+++ b/detections/endpoint/office_spawning_control.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where `control.exe` is spawned by a Microsoft Office product. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because it can indicate exploitation attempts related to CVE-2021-40444, where `control.exe` is used to execute malicious .cpl or .inf files. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies instances where `control.exe` is spawned
+  by a Microsoft Office product. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process and parent process relationships. This activity
+  is significant because it can indicate exploitation attempts related to CVE-2021-40444,
+  where `control.exe` is used to execute malicious .cpl or .inf files. If confirmed
+  malicious, this behavior could allow an attacker to execute arbitrary code, potentially
+  leading to system compromise, data exfiltration, or further lateral movement within
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") Processes.process_name=control.exe by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `office_spawning_control_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe","wordpad.exe","wordview.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe")
+  Processes.process_name=control.exe by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| `office_spawning_control_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Limited false positives should be present.
 references:
 - https://strontic.github.io/xcyclopedia/library/control.exe-1F13E714A0FEA8887707DFF49287996F.html
@@ -27,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +65,8 @@ tags:
   cve:
   - CVE-2021-40444
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ clicking a suspicious attachment.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ clicking a suspicious attachment.
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -60,22 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_control.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_control.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml b/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml
index 7fd375e0aa..78c35254c9 100644
--- a/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml
+++ b/detections/endpoint/outbound_network_connection_from_java_using_default_ports.yml
@@ -5,12 +5,37 @@ date: '2024-09-30'
 author: Mauricio Velazco, Lou Stella, Splunk
 status: production
 type: TTP
-description: "The following analytic detects outbound network connections from Java processes to default ports used by LDAP and RMI protocols, which may indicate exploitation of the CVE-2021-44228-Log4j vulnerability. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. Monitoring this activity is crucial as it can signify an attacker\u2019s attempt to perform JNDI lookups and retrieve malicious payloads. If confirmed malicious, this activity could lead to remote code execution and further compromise of the affected server."
+description: The following analytic detects outbound network connections from Java
+  processes to default ports used by LDAP and RMI protocols, which may indicate exploitation
+  of the CVE-2021-44228-Log4j vulnerability. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process and network traffic logs.
+  Monitoring this activity is crucial as it can signify an attacker’s attempt to perform
+  JNDI lookups and retrieve malicious payloads. If confirmed malicious, this activity
+  could lead to remote code execution and further compromise of the affected server.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where (Processes.process_name="java.exe" OR Processes.process_name=javaw.exe OR Processes.process_name=javaw.exe) by _time Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where (All_Traffic.dest_port= 389 OR All_Traffic.dest_port= 636 OR All_Traffic.dest_port = 1389 OR All_Traffic.dest_port = 1099 ) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as connection_to_CNC] | table _time dest parent_process_name process_name process_path process connection_to_CNC dest_port| `outbound_network_connection_from_java_using_default_ports_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate Java applications may use perform outbound connections to these ports. Filter as needed
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where (Processes.process_name="java.exe" OR Processes.process_name=javaw.exe OR
+  Processes.process_name=javaw.exe) by _time Processes.process_id Processes.process_name
+  Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | join process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where (All_Traffic.dest_port= 389 OR All_Traffic.dest_port= 636 OR All_Traffic.dest_port
+  = 1389 OR All_Traffic.dest_port = 1099 ) by All_Traffic.process_id All_Traffic.dest
+  All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as connection_to_CNC]
+  | table _time dest parent_process_name process_name process_path process connection_to_CNC
+  dest_port| `outbound_network_connection_from_java_using_default_ports_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate Java applications may use perform outbound connections
+  to these ports. Filter as needed
 references:
 - https://www.lunasec.io/docs/blog/log4j-zero-day/
 - https://www.govcert.admin.ch/blog/zero-day-exploit-targeting-popular-java-library-log4j/
@@ -20,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +61,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 90
-  message: Java performed outbound connections to default ports of LDAP or RMI on $dest$
+  message: Java performed outbound connections to default ports of LDAP or RMI on
+    $dest$
   mitre_attack_id:
   - T1190
   - T1133
@@ -44,21 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
-  - Processes.process_path
-  - Processes.process
-  - Processes.parent_process_name
-  - All_Traffic.process_id
-  - All_Traffic.dest
-  - All_Traffic.dest_port
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/outbound_java/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/outbound_java/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/overwriting_accessibility_binaries.yml b/detections/endpoint/overwriting_accessibility_binaries.yml
index 9ea9fab3e1..2b3c6708d3 100644
--- a/detections/endpoint/overwriting_accessibility_binaries.yml
+++ b/detections/endpoint/overwriting_accessibility_binaries.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications to Windows accessibility binaries such as sethc.exe, utilman.exe, osk.exe, Magnify.exe, Narrator.exe, DisplaySwitch.exe, and AtBroker.exe. It leverages filesystem activity data from the Endpoint.Filesystem data model to identify changes to these specific files. This activity is significant because adversaries can exploit these binaries to gain unauthorized access or execute commands without logging in. If confirmed malicious, this could allow attackers to bypass authentication mechanisms, potentially leading to unauthorized system access and further compromise of the environment.
+description: The following analytic detects modifications to Windows accessibility
+  binaries such as sethc.exe, utilman.exe, osk.exe, Magnify.exe, Narrator.exe, DisplaySwitch.exe,
+  and AtBroker.exe. It leverages filesystem activity data from the Endpoint.Filesystem
+  data model to identify changes to these specific files. This activity is significant
+  because adversaries can exploit these binaries to gain unauthorized access or execute
+  commands without logging in. If confirmed malicious, this could allow attackers
+  to bypass authentication mechanisms, potentially leading to unauthorized system
+  access and further compromise of the environment.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`'
-how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-known_false_positives: Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path)
+  as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe*
+  OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe*
+  OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe*
+  OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*)
+  by Filesystem.file_name Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `overwriting_accessibility_binaries_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint file-system data model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: Microsoft may provide updates to these binaries. Verify that
+  these changes do not correspond with your normal software update cycle.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -18,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_path
-  - Filesystem.file_name
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml b/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml
index bb0b1d1cf6..32fbf305b0 100644
--- a/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml
+++ b/detections/endpoint/papercut_ng_suspicious_behavior_debug_log.yml
@@ -6,10 +6,25 @@ author: Michael Haag, Splunk
 status: experimental
 type: Hunting
 data_source: []
-description: The following analytic identifies potential exploitation attempts on a PaperCut NG server by analyzing its debug log data. It detects unauthorized or suspicious access attempts from public IP addresses and searches for specific URIs associated with known exploits. The detection leverages regex to parse unstructured log data, focusing on admin login activities. This activity is significant as it can indicate an active exploitation attempt on the server. If confirmed malicious, attackers could gain unauthorized access, potentially leading to data breaches or further compromise of the server.
-search: '`papercutng` (loginType=Admin OR userName=admin) | eval uri_match=if(match(_raw, "(?i)(\/app\?service=page\/SetupCompleted|\/app|\/app\?service=page\/PrinterList|\/app\?service=direct\/1\/PrinterList\/selectPrinter&sp=l1001|\/app\?service=direct\/1\/PrinterDetails\/printerOptionsTab\.tab)"), "URI matches", null()) | eval ip_match=if(match(_raw, "(?i)((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))") AND NOT match(_raw, "(?i)(10\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(172\.(1[6-9]|2[0-9]|3[0-1])\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(192\.168\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))"), "IP matches", null()) | where  (isnotnull(uri_match) OR isnotnull(ip_match)) | stats sparkline, count, values(uri_match) AS uri_match, values(ip_match) AS ip_match latest(_raw) BY host, index, sourcetype | `papercut_ng_suspicious_behavior_debug_log_filter`'
-how_to_implement: Debug logs must be enabled and shipped to Splunk in order to properly identify behavior with this analytic.
-known_false_positives: False positives may be present, as this is based on the admin user accessing the Papercut NG instance from a public IP address. Filter as needed.
+description: The following analytic identifies potential exploitation attempts on
+  a PaperCut NG server by analyzing its debug log data. It detects unauthorized or
+  suspicious access attempts from public IP addresses and searches for specific URIs
+  associated with known exploits. The detection leverages regex to parse unstructured
+  log data, focusing on admin login activities. This activity is significant as it
+  can indicate an active exploitation attempt on the server. If confirmed malicious,
+  attackers could gain unauthorized access, potentially leading to data breaches or
+  further compromise of the server.
+search: '`papercutng` (loginType=Admin OR userName=admin) | eval uri_match=if(match(_raw,
+  "(?i)(\/app\?service=page\/SetupCompleted|\/app|\/app\?service=page\/PrinterList|\/app\?service=direct\/1\/PrinterList\/selectPrinter&sp=l1001|\/app\?service=direct\/1\/PrinterDetails\/printerOptionsTab\.tab)"),
+  "URI matches", null()) | eval ip_match=if(match(_raw, "(?i)((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))")
+  AND NOT match(_raw, "(?i)(10\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(172\.(1[6-9]|2[0-9]|3[0-1])\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))|(192\.168\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?))"),
+  "IP matches", null()) | where  (isnotnull(uri_match) OR isnotnull(ip_match)) | stats
+  sparkline, count, values(uri_match) AS uri_match, values(ip_match) AS ip_match latest(_raw)
+  BY host, index, sourcetype | `papercut_ng_suspicious_behavior_debug_log_filter`'
+how_to_implement: Debug logs must be enabled and shipped to Splunk in order to properly
+  identify behavior with this analytic.
+known_false_positives: False positives may be present, as this is based on the admin
+  user accessing the Papercut NG instance from a public IP address. Filter as needed.
 references:
 - https://www.papercut.com/kb/Main/HowToCollectApplicationServerDebugLogs
 - https://github.com/inodee/threathunting-spl/blob/master/hunt-queries/HAFNIUM.md
@@ -25,7 +40,8 @@ tags:
   atomic_guid: []
   confidence: 80
   impact: 80
-  message: Behavior related to exploitation of PaperCut NG has been identified on $host$.
+  message: Behavior related to exploitation of PaperCut NG has been identified on
+    $host$.
   mitre_attack_id:
   - T1190
   - T1133
@@ -38,16 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - uri_match
-  - ip_match
-  - index
-  - sourcetype
-  - host
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/server.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/server.log
     source: papercutng
     sourcetype: papercutng
diff --git a/detections/endpoint/password_policy_discovery_with_net.yml b/detections/endpoint/password_policy_discovery_with_net.yml
index b6fdf12b12..f38a744ab3 100644
--- a/detections/endpoint/password_policy_discovery_with_net.yml
+++ b/detections/endpoint/password_policy_discovery_with_net.yml
@@ -5,13 +5,35 @@ date: '2024-10-17'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `net.exe` or `net1.exe` with command line arguments aimed at obtaining the domain password policy. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gather information about Active Directory password policies. If confirmed malicious, this behavior could allow attackers to understand password complexity requirements, aiding in brute-force or password-guessing attacks, ultimately compromising user accounts and gaining unauthorized access to the network.
+description: The following analytic identifies the execution of `net.exe` or `net1.exe`
+  with command line arguments aimed at obtaining the domain password policy. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant as it indicates potential
+  reconnaissance efforts by adversaries to gather information about Active Directory
+  password policies. If confirmed malicious, this behavior could allow attackers to
+  understand password complexity requirements, aiding in brute-force or password-guessing
+  attacks, ultimately compromising user accounts and gaining unauthorized access to
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") AND Processes.process = "*accounts*" AND Processes.process = "*/domain*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `password_policy_discovery_with_net_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe") AND Processes.process = "*accounts*" AND Processes.process
+  = "*/domain*" by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `password_policy_discovery_with_net_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
@@ -41,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1201/pwd_policy_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/permission_modification_using_takeown_app.yml b/detections/endpoint/permission_modification_using_takeown_app.yml
index 0ca23aebdf..1ba3bdaae0 100644
--- a/detections/endpoint/permission_modification_using_takeown_app.yml
+++ b/detections/endpoint/permission_modification_using_takeown_app.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the modification of file or directory permissions using the takeown.exe Windows application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process GUID, process name, and command-line details. This activity is significant because it is a common technique used by ransomware to take ownership of files or folders for encryption or deletion. If confirmed malicious, this could lead to unauthorized access, data encryption, or data destruction, severely impacting the integrity and availability of critical data.
+description: The following analytic detects the modification of file or directory
+  permissions using the takeown.exe Windows application. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs that include
+  process GUID, process name, and command-line details. This activity is significant
+  because it is a common technique used by ransomware to take ownership of files or
+  folders for encryption or deletion. If confirmed malicious, this could lead to unauthorized
+  access, data encryption, or data destruction, severely impacting the integrity and
+  availability of critical data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe" Processes.process = "*/f*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `permission_modification_using_takeown_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: takeown.exe is a normal windows application that may used by network operator.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe"
+  Processes.process = "*/f*" by Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id
+  Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `permission_modification_using_takeown_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: takeown.exe is a normal windows application that may used by
+  network operator.
 references:
 - https://research.nccgroup.com/2020/06/23/wastedlocker-a-new-ransomware-variant-developed-by-the-evil-corp-group/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A suspicious of execution of $process_name$ with process id $process_id$ and commandline $process$ to modify permission of directory or files in host $dest$
+  message: A suspicious of execution of $process_name$ with process id $process_id$
+    and commandline $process$ to modify permission of directory or files in host $dest$
   mitre_attack_id:
   - T1222
   observable:
@@ -47,20 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/petitpotam_network_share_access_request.yml b/detections/endpoint/petitpotam_network_share_access_request.yml
index 720589755c..5688c0bc44 100644
--- a/detections/endpoint/petitpotam_network_share_access_request.yml
+++ b/detections/endpoint/petitpotam_network_share_access_request.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects network share access requests indicative of the PetitPotam attack (CVE-2021-36942). It leverages Windows Event Code 5145, which logs attempts to access network share objects. This detection is significant as PetitPotam can coerce authentication from domain controllers, potentially leading to unauthorized access. If confirmed malicious, this activity could allow attackers to escalate privileges or move laterally within the network, posing a severe security risk. Ensure Event Code 5145 is enabled via Group Policy to utilize this analytic effectively.
+description: The following analytic detects network share access requests indicative
+  of the PetitPotam attack (CVE-2021-36942). It leverages Windows Event Code 5145,
+  which logs attempts to access network share objects. This detection is significant
+  as PetitPotam can coerce authentication from domain controllers, potentially leading
+  to unauthorized access. If confirmed malicious, this activity could allow attackers
+  to escalate privileges or move laterally within the network, posing a severe security
+  risk. Ensure Event Code 5145 is enabled via Group Policy to utilize this analytic
+  effectively.
 data_source:
 - Windows Event Log Security 5145
-search: '`wineventlog_security` SubjectUserName="ANONYMOUS LOGON" EventCode=5145 RelativeTargetName=lsarpc | stats count min(_time) as firstTime max(_time) as lastTime by dest, SubjectUserSid, ShareName, src, AccessMask, AccessReason | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `petitpotam_network_share_access_request_filter`'
-how_to_implement: Windows Event Code 5145 is required to utilize this analytic and it may not be enabled in most environments.
-known_false_positives: False positives have been limited when the Anonymous Logon is used for Account Name.
+search: '`wineventlog_security` SubjectUserName="ANONYMOUS LOGON" EventCode=5145 RelativeTargetName=lsarpc
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, SubjectUserSid,
+  ShareName, src, AccessMask, AccessReason | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `petitpotam_network_share_access_request_filter`'
+how_to_implement: Windows Event Code 5145 is required to utilize this analytic and
+  it may not be enabled in most environments.
+known_false_positives: False positives have been limited when the Anonymous Logon
+  is used for Account Name.
 references:
 - https://attack.mitre.org/techniques/T1187/
 - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventid=5145
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +49,8 @@ tags:
   cve:
   - CVE-2021-36942
   impact: 80
-  message: A remote host is enumerating a $dest$ to identify permissions. This is a precursor event to CVE-2021-36942, PetitPotam.
+  message: A remote host is enumerating a $dest$ to identify permissions. This is
+    a precursor event to CVE-2021-36942, PetitPotam.
   mitre_attack_id:
   - T1187
   observable:
@@ -44,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - SubjectUserSid
-  - Share_Name
-  - src
-  - AccessMask
-  - AccessReason
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml b/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml
index f7cb68f857..42a246cfbc 100644
--- a/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml
+++ b/detections/endpoint/petitpotam_suspicious_kerberos_tgt_request.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious Kerberos Ticket Granting Ticket (TGT) request, identified by Event Code 4768. This detection leverages Windows Security Event Logs to identify TGT requests with unusual fields, which may indicate the use of tools like Rubeus following the exploitation of CVE-2021-36942 (PetitPotam). This activity is significant as it can signal an attacker leveraging a compromised certificate to request Kerberos tickets, potentially leading to unauthorized access. If confirmed malicious, this could allow attackers to escalate privileges and persist within the environment, posing a severe security risk.
+description: The following analytic detects a suspicious Kerberos Ticket Granting
+  Ticket (TGT) request, identified by Event Code 4768. This detection leverages Windows
+  Security Event Logs to identify TGT requests with unusual fields, which may indicate
+  the use of tools like Rubeus following the exploitation of CVE-2021-36942 (PetitPotam).
+  This activity is significant as it can signal an attacker leveraging a compromised
+  certificate to request Kerberos tickets, potentially leading to unauthorized access.
+  If confirmed malicious, this could allow attackers to escalate privileges and persist
+  within the environment, posing a severe security risk.
 data_source:
 - Windows Event Log Security 4768
-search: '`wineventlog_security` EventCode=4768 src!="::1" TargetUserName=*$ CertThumbprint!="" | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetUserName, src, action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `petitpotam_suspicious_kerberos_tgt_request_filter`'
-how_to_implement: The following analytic requires Event Code 4768. Ensure that it is logging no Domain Controllers and appearing in Splunk.
-known_false_positives: False positives are possible if the environment is using certificates for authentication.
+search: '`wineventlog_security` EventCode=4768 src!="::1" TargetUserName=*$ CertThumbprint!=""
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetUserName,
+  src, action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `petitpotam_suspicious_kerberos_tgt_request_filter`'
+how_to_implement: The following analytic requires Event Code 4768. Ensure that it
+  is logging no Domain Controllers and appearing in Splunk.
+known_false_positives: False positives are possible if the environment is using certificates
+  for authentication.
 references:
 - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventid=4768
 - https://isc.sans.edu/forums/diary/Active+Directory+Certificate+Services+ADCS+PKI+domain+admin+vulnerability/27668/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +49,8 @@ tags:
   cve:
   - CVE-2021-36942
   impact: 80
-  message: A Kerberos TGT was requested in a non-standard manner against $dest$, potentially related to CVE-2021-36942, PetitPotam.
+  message: A Kerberos TGT was requested in a non-standard manner against $dest$, potentially
+    related to CVE-2021-36942, PetitPotam.
   mitre_attack_id:
   - T1003
   observable:
@@ -44,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Account_Name
-  - Client_Address
-  - action
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml-1.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1187/petitpotam/windows-xml-1.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/ping_sleep_batch_command.yml b/detections/endpoint/ping_sleep_batch_command.yml
index 63df2867de..1ee1160e4f 100644
--- a/detections/endpoint/ping_sleep_batch_command.yml
+++ b/detections/endpoint/ping_sleep_batch_command.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the execution of ping sleep batch commands. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process command-line details. This activity is significant as it indicates an attempt to delay malicious code execution, potentially evading detection or sandbox analysis. If confirmed malicious, this technique allows attackers to bypass security measures, making it harder to detect and analyze their activities, thereby increasing the risk of prolonged unauthorized access and potential data exfiltration.
+description: The following analytic identifies the execution of ping sleep batch commands.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process and parent process command-line details. This activity is significant as
+  it indicates an attempt to delay malicious code execution, potentially evading detection
+  or sandbox analysis. If confirmed malicious, this technique allows attackers to
+  bypass security measures, making it harder to detect and analyze their activities,
+  thereby increasing the risk of prolonged unauthorized access and potential data
+  exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_ping` (Processes.parent_process = "*ping*" Processes.parent_process = *-n* Processes.parent_process="* Nul*"Processes.parent_process="*&gt;*") OR (Processes.process = "*ping*" Processes.process = *-n* Processes.process="* Nul*"Processes.process="*&gt;*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `ping_sleep_batch_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator or network operator may execute this command. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_ping` (Processes.parent_process
+  = "*ping*" Processes.parent_process = *-n* Processes.parent_process="* Nul*"Processes.parent_process="*&gt;*")
+  OR (Processes.process = "*ping*" Processes.process = *-n* Processes.process="* Nul*"Processes.process="*&gt;*")
+  by Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.process_guid
+  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `ping_sleep_batch_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator or network operator may execute this command.
+  Please update the filter macros to remove false positives.
 references:
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/ping_sleep/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/ping_sleep/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/possible_browser_pass_view_parameter.yml b/detections/endpoint/possible_browser_pass_view_parameter.yml
index c088333560..8609b6031b 100644
--- a/detections/endpoint/possible_browser_pass_view_parameter.yml
+++ b/detections/endpoint/possible_browser_pass_view_parameter.yml
@@ -5,13 +5,37 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies processes with command-line parameters associated with web browser credential dumping tools, specifically targeting behaviors used by Remcos RAT malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and specific file paths. This activity is significant as it indicates potential credential theft, a common tactic in broader cyber-espionage campaigns. If confirmed malicious, attackers could gain unauthorized access to sensitive web credentials, leading to further system compromise and data breaches.
+description: The following analytic identifies processes with command-line parameters
+  associated with web browser credential dumping tools, specifically targeting behaviors
+  used by Remcos RAT malware. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on command-line executions and specific file paths. This
+  activity is significant as it indicates potential credential theft, a common tactic
+  in broader cyber-espionage campaigns. If confirmed malicious, attackers could gain
+  unauthorized access to sensitive web credentials, leading to further system compromise
+  and data breaches.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process  IN ("*/stext *", "*/shtml *", "*/LoadPasswordsIE*", "*/LoadPasswordsFirefox*", "*/LoadPasswordsChrome*", "*/LoadPasswordsOpera*", "*/LoadPasswordsSafari*" , "*/UseOperaPasswordFile*", "*/OperaPasswordFile*","*/stab*", "*/scomma*", "*/stabular*", "*/shtml*", "*/sverhtml*", "*/sxml*", "*/skeepass*" ) AND Processes.process IN ("*\\temp\\*", "*\\users\\public\\*", "*\\programdata\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `possible_browser_pass_view_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process  IN ("*/stext
+  *", "*/shtml *", "*/LoadPasswordsIE*", "*/LoadPasswordsFirefox*", "*/LoadPasswordsChrome*",
+  "*/LoadPasswordsOpera*", "*/LoadPasswordsSafari*" , "*/UseOperaPasswordFile*", "*/OperaPasswordFile*","*/stab*",
+  "*/scomma*", "*/stabular*", "*/shtml*", "*/sverhtml*", "*/sxml*", "*/skeepass*"
+  ) AND Processes.process IN ("*\\temp\\*", "*\\users\\public\\*", "*\\programdata\\*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `possible_browser_pass_view_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positive is quite limited. Filter is needed
 references:
 - https://www.nirsoft.net/utils/web_browser_password.html
@@ -39,23 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/web_browser_pass_view/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/web_browser_pass_view/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/possible_lateral_movement_powershell_spawn.yml b/detections/endpoint/possible_lateral_movement_powershell_spawn.yml
index b3193ffda1..7205414684 100644
--- a/detections/endpoint/possible_lateral_movement_powershell_spawn.yml
+++ b/detections/endpoint/possible_lateral_movement_powershell_spawn.yml
@@ -5,14 +5,39 @@ date: '2024-10-17'
 author: Mauricio Velazco, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the spawning of a PowerShell process as a child or grandchild of commonly abused processes like services.exe, wmiprsve.exe, svchost.exe, wsmprovhost.exe, and mmc.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names, as well as command-line executions. This activity is significant as it often indicates lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this behavior could allow attackers to execute code remotely, escalate privileges, or persist within the environment.
+description: The following analytic detects the spawning of a PowerShell process as
+  a child or grandchild of commonly abused processes like services.exe, wmiprsve.exe,
+  svchost.exe, wsmprovhost.exe, and mmc.exe. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process and parent process names, as well
+  as command-line executions. This activity is significant as it often indicates lateral
+  movement or remote code execution attempts by adversaries. If confirmed malicious,
+  this behavior could allow attackers to execute code remotely, escalate privileges,
+  or persist within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe OR Processes.parent_process_name=services.exe OR Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wsmprovhost.exe OR Processes.parent_process_name=mmc.exe) (Processes.process_name=powershell.exe OR (Processes.process_name=cmd.exe AND Processes.process=*powershell.exe*) OR Processes.process_name=pwsh.exe OR (Processes.process_name=cmd.exe AND Processes.process=*pwsh.exe*)) NOT (Processes.process IN ("*c:\\windows\\ccm\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `possible_lateral_movement_powershell_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate applications may spawn PowerShell as a child process of the the identified processes. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe
+  OR Processes.parent_process_name=services.exe OR Processes.parent_process_name=svchost.exe
+  OR Processes.parent_process_name=wsmprovhost.exe OR Processes.parent_process_name=mmc.exe)
+  (Processes.process_name=powershell.exe OR (Processes.process_name=cmd.exe AND Processes.process=*powershell.exe*)
+  OR Processes.process_name=pwsh.exe OR (Processes.process_name=cmd.exe AND Processes.process=*pwsh.exe*))
+  NOT (Processes.process IN ("*c:\\windows\\ccm\\*")) by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `possible_lateral_movement_powershell_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate applications may spawn PowerShell as a child process
+  of the the identified processes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1021/003/
 - https://attack.mitre.org/techniques/T1021/006/
@@ -25,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +69,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 90
-  message: A PowerShell process was spawned as a child process of typically abused processes on $dest$
+  message: A PowerShell process was spawned as a child process of typically abused
+    processes on $dest$
   mitre_attack_id:
   - T1021
   - T1021.003
@@ -58,23 +89,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_powershell/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_powershell/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/potential_password_in_username.yml b/detections/endpoint/potential_password_in_username.yml
index acb6f8c0aa..ce3a187c71 100644
--- a/detections/endpoint/potential_password_in_username.yml
+++ b/detections/endpoint/potential_password_in_username.yml
@@ -5,12 +5,37 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies instances where users may have mistakenly entered their passwords in the username field during authentication attempts. It detects this by analyzing failed authentication events with usernames longer than 7 characters and high Shannon entropy, followed by a successful authentication from the same source to the same destination. This activity is significant as it can indicate potential security risks, such as password exposure. If confirmed malicious, attackers could exploit this to gain unauthorized access, leading to potential data breaches or further compromise of the system.
+description: The following analytic identifies instances where users may have mistakenly
+  entered their passwords in the username field during authentication attempts. It
+  detects this by analyzing failed authentication events with usernames longer than
+  7 characters and high Shannon entropy, followed by a successful authentication from
+  the same source to the same destination. This activity is significant as it can
+  indicate potential security risks, such as password exposure. If confirmed malicious,
+  attackers could exploit this to gain unauthorized access, leading to potential data
+  breaches or further compromise of the system.
 data_source:
 - Linux Secure
-search: '| tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication WHERE nodename=Authentication.Failed_Authentication BY "Authentication.user" | `drop_dm_object_name(Authentication)` | lookup ut_shannon_lookup word AS user | where ut_shannon>3 AND len(user)>=8 AND mvcount(src) == 1 | sort count, - ut_shannon | eval incorrect_cred=user | eval endtime=endtime+1000 | map maxsearches=70 search="| tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication WHERE nodename=Authentication.Successful_Authentication Authentication.src=\"$src$\" Authentication.dest=\"$dest$\" sourcetype IN (\"$sourcetype$\") earliest=\"$starttime$\" latest=\"$endtime$\" BY \"Authentication.user\" | `drop_dm_object_name(\"Authentication\")` | `potential_password_in_username_false_positive_reduction` | eval incorrect_cred=\"$incorrect_cred$\" | eval ut_shannon=\"$ut_shannon$\" | sort count" | where user!=incorrect_cred | outlier action=RM count | `potential_password_in_username_filter`'
-how_to_implement: To successfully implement this search, you need to have relevant authentication logs mapped to the Authentication data model. You also need to have the Splunk TA URL Toolbox (https://splunkbase.splunk.com/app/2734/) installed. The detection must run with a time interval shorter than endtime+1000.
-known_false_positives: Valid usernames with high entropy or source/destination system pairs with multiple authenticating users will make it difficult to identify the real user authenticating.
+search: '| tstats `security_content_summariesonly` earliest(_time) AS starttime latest(_time)
+  AS endtime latest(sourcetype) AS sourcetype values(Authentication.src) AS src values(Authentication.dest)
+  AS dest count FROM datamodel=Authentication WHERE nodename=Authentication.Failed_Authentication
+  BY "Authentication.user" | `drop_dm_object_name(Authentication)` | lookup ut_shannon_lookup
+  word AS user | where ut_shannon>3 AND len(user)>=8 AND mvcount(src) == 1 | sort
+  count, - ut_shannon | eval incorrect_cred=user | eval endtime=endtime+1000 | map
+  maxsearches=70 search="| tstats `security_content_summariesonly` earliest(_time)
+  AS starttime latest(_time) AS endtime latest(sourcetype) AS sourcetype values(Authentication.src)
+  AS src values(Authentication.dest) AS dest count FROM datamodel=Authentication WHERE
+  nodename=Authentication.Successful_Authentication Authentication.src=\"$src$\" Authentication.dest=\"$dest$\"
+  sourcetype IN (\"$sourcetype$\") earliest=\"$starttime$\" latest=\"$endtime$\" BY
+  \"Authentication.user\" | `drop_dm_object_name(\"Authentication\")` | `potential_password_in_username_false_positive_reduction`
+  | eval incorrect_cred=\"$incorrect_cred$\" | eval ut_shannon=\"$ut_shannon$\" |
+  sort count" | where user!=incorrect_cred | outlier action=RM count | `potential_password_in_username_filter`'
+how_to_implement: To successfully implement this search, you need to have relevant
+  authentication logs mapped to the Authentication data model. You also need to have
+  the Splunk TA URL Toolbox (https://splunkbase.splunk.com/app/2734/) installed. The
+  detection must run with a time interval shorter than endtime+1000.
+known_false_positives: Valid usernames with high entropy or source/destination system
+  pairs with multiple authenticating users will make it difficult to identify the
+  real user authenticating.
 references:
 - https://medium.com/@markmotig/search-for-passwords-accidentally-typed-into-the-username-field-975f1a389928
 tags:
@@ -41,15 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Authentication.user
-  - Authentication.src
-  - Authentication.dest
-  - sourcetype
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/password_in_username/linux_secure.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.001/password_in_username/linux_secure.log
     source: /var/log/secure
     sourcetype: linux_secure
diff --git a/detections/endpoint/potentially_malicious_code_on_commandline.yml b/detections/endpoint/potentially_malicious_code_on_commandline.yml
index f51522b023..3345ccc13b 100644
--- a/detections/endpoint/potentially_malicious_code_on_commandline.yml
+++ b/detections/endpoint/potentially_malicious_code_on_commandline.yml
@@ -5,14 +5,40 @@ date: '2024-09-30'
 author: Michael Hart, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects potentially malicious command lines using a pretrained machine learning text classifier. It identifies unusual keyword combinations in command lines, such as "streamreader," "webclient," "mutex," "function," and "computehash," which are often associated with adversarial PowerShell code execution for C2 communication. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command lines longer than 200 characters. This activity is significant as it can indicate an attempt to execute malicious scripts, potentially leading to unauthorized code execution, data exfiltration, or further system compromise.
+description: The following analytic detects potentially malicious command lines using
+  a pretrained machine learning text classifier. It identifies unusual keyword combinations
+  in command lines, such as "streamreader," "webclient," "mutex," "function," and
+  "computehash," which are often associated with adversarial PowerShell code execution
+  for C2 communication. This detection leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on command lines longer than 200 characters. This
+  activity is significant as it can indicate an attempt to execute malicious scripts,
+  potentially leading to unauthorized code execution, data exfiltration, or further
+  system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel="Endpoint.Processes" by Processes.parent_process_name Processes.process_name Processes.process Processes.user Processes.dest  | `drop_dm_object_name(Processes)`  | where len(process) > 200 | `potentially_malicious_code_on_cmdline_tokenize_score` | apply unusual_commandline_detection | eval score=''predicted(unusual_cmdline_logits)'', process=orig_process | fields - unusual_cmdline* predicted(unusual_cmdline_logits) orig_process | where score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `potentially_malicious_code_on_commandline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This model is an anomaly detector that identifies usage of APIs and scripting constructs that are correllated with malicious activity.  These APIs and scripting constructs are part of the programming langauge and advanced scripts may generate false positives.
+search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=\"Endpoint.Processes\" by Processes.parent_process_name
+  Processes.process_name Processes.process Processes.user Processes.dest  | `drop_dm_object_name(Processes)`\
+  \  | where len(process) > 200 | `potentially_malicious_code_on_cmdline_tokenize_score`
+  | apply unusual_commandline_detection | eval score='predicted(unusual_cmdline_logits)',
+  process=orig_process | fields - unusual_cmdline* predicted(unusual_cmdline_logits)
+  orig_process | where score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `potentially_malicious_code_on_commandline_filter`"
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This model is an anomaly detector that identifies usage of
+  APIs and scripting constructs that are correllated with malicious activity.  These
+  APIs and scripting constructs are part of the programming langauge and advanced
+  scripts may generate false positives.
 references:
 - https://attack.mitre.org/techniques/T1059/003/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md
@@ -22,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 20
   impact: 60
-  message: Unusual command-line execution with command line length greater than 200 found on $dest$ with commandline value - [$process$]
+  message: Unusual command-line execution with command line length greater than 200
+    found on $dest$ with commandline value - [$process$]
   mitre_attack_id:
   - T1059.003
   observable:
@@ -47,18 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_4104_hunting.yml b/detections/endpoint/powershell_4104_hunting.yml
index f511a92898..43b83ab8b9 100644
--- a/detections/endpoint/powershell_4104_hunting.yml
+++ b/detections/endpoint/powershell_4104_hunting.yml
@@ -5,11 +5,47 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies suspicious PowerShell execution using Script Block Logging (EventCode 4104). It leverages specific patterns and keywords within the ScriptBlockText field to detect potentially malicious activities. This detection is significant for SOC analysts as PowerShell is commonly used by attackers for various malicious purposes, including code execution, privilege escalation, and persistence. If confirmed malicious, this activity could allow attackers to execute arbitrary commands, exfiltrate data, or maintain long-term access to the compromised system, posing a severe threat to the organization's security.
+description: The following analytic identifies suspicious PowerShell execution using
+  Script Block Logging (EventCode 4104). It leverages specific patterns and keywords
+  within the ScriptBlockText field to detect potentially malicious activities. This
+  detection is significant for SOC analysts as PowerShell is commonly used by attackers
+  for various malicious purposes, including code execution, privilege escalation,
+  and persistence. If confirmed malicious, this activity could allow attackers to
+  execute arbitrary commands, exfiltrate data, or maintain long-term access to the
+  compromised system, posing a severe threat to the organization's security.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 | eval DoIt = if(match(ScriptBlockText,"(?i)(\$doit)"), "4", 0) | eval enccom=if(match(ScriptBlockText,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4}|[A-Za-z0-9+\/]{3}=|[A-Za-z0-9+\/]{2}==)") OR match(ScriptBlockText, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) | eval suspcmdlet=if(match(ScriptBlockText, "(?i)Add-Exfiltration|Add-Persistence|Add-RegBackdoor|Add-ScrnSaveBackdoor|Check-VM|Do-Exfiltration|Enabled-DuplicateToken|Exploit-Jboss|Find-Fruit|Find-GPOLocation|Find-TrustedDocuments|Get-ApplicationHost|Get-ChromeDump|Get-ClipboardContents|Get-FoxDump|Get-GPPPassword|Get-IndexedItem|Get-Keystrokes|LSASecret|Get-PassHash|Get-RegAlwaysInstallElevated|Get-RegAutoLogon|Get-RickAstley|Get-Screenshot|Get-SecurityPackages|Get-ServiceFilePermission|Get-ServicePermission|Get-ServiceUnquoted|Get-SiteListPassword|Get-System|Get-TimedScreenshot|Get-UnattendedInstallFile|Get-Unconstrained|Get-VaultCredential|Get-VulnAutoRun|Get-VulnSchTask|Gupt-Backdoor|HTTP-Login|Install-SSP|Install-ServiceBinary|Invoke-ACLScanner|Invoke-ADSBackdoor|Invoke-ARPScan|Invoke-AllChecks|Invoke-BackdoorLNK|Invoke-BypassUAC|Invoke-CredentialInjection|Invoke-DCSync|Invoke-DllInjection|Invoke-DowngradeAccount|Invoke-EgressCheck|Invoke-Inveigh|Invoke-InveighRelay|Invoke-Mimikittenz|Invoke-NetRipper|Invoke-NinjaCopy|Invoke-PSInject|Invoke-Paranoia|Invoke-PortScan|Invoke-PoshRat|Invoke-PostExfil|Invoke-PowerDump|Invoke-PowerShellTCP|Invoke-PsExec|Invoke-PsUaCme|Invoke-ReflectivePEInjection|Invoke-ReverseDNSLookup|Invoke-RunAs|Invoke-SMBScanner|Invoke-SSHCommand|Invoke-Service|Invoke-Shellcode|Invoke-Tater|Invoke-ThunderStruck|Invoke-Token|Invoke-UserHunter|Invoke-VoiceTroll|Invoke-WScriptBypassUAC|Invoke-WinEnum|MailRaider|New-HoneyHash|Out-Minidump|Port-Scan|PowerBreach|PowerUp|PowerView|Remove-Update|Set-MacAttribute|Set-Wallpaper|Show-TargetScreen|Start-CaptureServer|VolumeShadowCopyTools|NEEEEWWW|(Computer|User)Property|CachedRDPConnection|get-net\S+|invoke-\S+hunter|Install-Service|get-\S+(credent|password)|remoteps|Kerberos.*(policy|ticket)|netfirewall|Uninstall-Windows|Verb\s+Runas|AmsiBypass|nishang|Invoke-Interceptor|EXEonRemote|NetworkRelay|PowerShelludp|PowerShellIcmp|CreateShortcut|copy-vss|invoke-dll|invoke-mass|out-shortcut|Invoke-ShellCommand"),1,0) | eval base64 = if(match(lower(ScriptBlockText),"frombase64"), "4", 0) | eval empire=if(match(lower(ScriptBlockText),"system.net.webclient") AND match(lower(ScriptBlockText), "frombase64string") ,5,0) | eval mimikatz=if(match(lower(ScriptBlockText),"mimikatz") OR match(lower(ScriptBlockText), "-dumpcr") OR match(lower(ScriptBlockText), "SEKURLSA::Pth") OR match(lower(ScriptBlockText), "kerberos::ptt") OR match(lower(ScriptBlockText), "kerberos::golden") ,5,0) | eval iex=if(match(ScriptBlockText, "(?i)iex|invoke-expression"),2,0) | eval webclient=if(match(lower(ScriptBlockText),"http") OR match(lower(ScriptBlockText),"web(client|request)") OR match(lower(ScriptBlockText),"socket") OR match(lower(ScriptBlockText),"download(file|string)") OR match(lower(ScriptBlockText),"bitstransfer") OR match(lower(ScriptBlockText),"internetexplorer.application") OR match(lower(ScriptBlockText),"xmlhttp"),5,0) | eval get = if(match(lower(ScriptBlockText),"get-"), "1", 0) | eval rundll32 = if(match(lower(ScriptBlockText),"rundll32"), "4", 0) | eval suspkeywrd=if(match(ScriptBlockText, "(?i)(bitstransfer|mimik|metasp|AssemblyBuilderAccess|Reflection\.Assembly|shellcode|injection|cnvert|shell\.application|start-process|Rc4ByteStream|System\.Security\.Cryptography|lsass\.exe|localadmin|LastLoggedOn|hijack|BackupPrivilege|ngrok|comsvcs|backdoor|brute.?force|Port.?Scan|Exfiltration|exploit|DisableRealtimeMonitoring|beacon)"),1,0) | eval syswow64 = if(match(lower(ScriptBlockText),"syswow64"), "3", 0) | eval httplocal = if(match(lower(ScriptBlockText),"http://127.0.0.1"), "4", 0) | eval reflection = if(match(lower(ScriptBlockText),"reflection"), "1", 0) | eval invokewmi=if(match(lower(ScriptBlockText), "(?i)(wmiobject|WMIMethod|RemoteWMI|PowerShellWmi|wmicommand)"),5,0) | eval downgrade=if(match(ScriptBlockText, "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(ScriptBlockText),"powershell -version"),3,0) | eval compressed=if(match(ScriptBlockText, "(?i)GZipStream|::Decompress|IO.Compression|write-zip|(expand|compress)-Archive"),5,0) | eval invokecmd = if(match(lower(ScriptBlockText),"invoke-command"), "4", 0) | addtotals fieldname=Score DoIt, enccom, suspcmdlet, suspkeywrd, compressed, downgrade, mimikatz, iex, empire, rundll32, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get | stats values(Score) by UserID, Computer, DoIt, enccom, compressed, downgrade, iex, mimikatz, rundll32, empire, webclient, syswow64, httplocal, reflection, invokewmi, invokecmd, base64, get, suspcmdlet, suspkeywrd | rename Computer as dest, UserID as user | `powershell_4104_hunting_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 | eval DoIt = if(match(ScriptBlockText,"(?i)(\$doit)"),
+  "4", 0) | eval enccom=if(match(ScriptBlockText,"[A-Za-z0-9+\/]{44,}([A-Za-z0-9+\/]{4}|[A-Za-z0-9+\/]{3}=|[A-Za-z0-9+\/]{2}==)")
+  OR match(ScriptBlockText, "(?i)[-]e(nc*o*d*e*d*c*o*m*m*a*n*d*)*\s+[^-]"),4,0) |
+  eval suspcmdlet=if(match(ScriptBlockText, "(?i)Add-Exfiltration|Add-Persistence|Add-RegBackdoor|Add-ScrnSaveBackdoor|Check-VM|Do-Exfiltration|Enabled-DuplicateToken|Exploit-Jboss|Find-Fruit|Find-GPOLocation|Find-TrustedDocuments|Get-ApplicationHost|Get-ChromeDump|Get-ClipboardContents|Get-FoxDump|Get-GPPPassword|Get-IndexedItem|Get-Keystrokes|LSASecret|Get-PassHash|Get-RegAlwaysInstallElevated|Get-RegAutoLogon|Get-RickAstley|Get-Screenshot|Get-SecurityPackages|Get-ServiceFilePermission|Get-ServicePermission|Get-ServiceUnquoted|Get-SiteListPassword|Get-System|Get-TimedScreenshot|Get-UnattendedInstallFile|Get-Unconstrained|Get-VaultCredential|Get-VulnAutoRun|Get-VulnSchTask|Gupt-Backdoor|HTTP-Login|Install-SSP|Install-ServiceBinary|Invoke-ACLScanner|Invoke-ADSBackdoor|Invoke-ARPScan|Invoke-AllChecks|Invoke-BackdoorLNK|Invoke-BypassUAC|Invoke-CredentialInjection|Invoke-DCSync|Invoke-DllInjection|Invoke-DowngradeAccount|Invoke-EgressCheck|Invoke-Inveigh|Invoke-InveighRelay|Invoke-Mimikittenz|Invoke-NetRipper|Invoke-NinjaCopy|Invoke-PSInject|Invoke-Paranoia|Invoke-PortScan|Invoke-PoshRat|Invoke-PostExfil|Invoke-PowerDump|Invoke-PowerShellTCP|Invoke-PsExec|Invoke-PsUaCme|Invoke-ReflectivePEInjection|Invoke-ReverseDNSLookup|Invoke-RunAs|Invoke-SMBScanner|Invoke-SSHCommand|Invoke-Service|Invoke-Shellcode|Invoke-Tater|Invoke-ThunderStruck|Invoke-Token|Invoke-UserHunter|Invoke-VoiceTroll|Invoke-WScriptBypassUAC|Invoke-WinEnum|MailRaider|New-HoneyHash|Out-Minidump|Port-Scan|PowerBreach|PowerUp|PowerView|Remove-Update|Set-MacAttribute|Set-Wallpaper|Show-TargetScreen|Start-CaptureServer|VolumeShadowCopyTools|NEEEEWWW|(Computer|User)Property|CachedRDPConnection|get-net\S+|invoke-\S+hunter|Install-Service|get-\S+(credent|password)|remoteps|Kerberos.*(policy|ticket)|netfirewall|Uninstall-Windows|Verb\s+Runas|AmsiBypass|nishang|Invoke-Interceptor|EXEonRemote|NetworkRelay|PowerShelludp|PowerShellIcmp|CreateShortcut|copy-vss|invoke-dll|invoke-mass|out-shortcut|Invoke-ShellCommand"),1,0)
+  | eval base64 = if(match(lower(ScriptBlockText),"frombase64"), "4", 0) | eval empire=if(match(lower(ScriptBlockText),"system.net.webclient")
+  AND match(lower(ScriptBlockText), "frombase64string") ,5,0) | eval mimikatz=if(match(lower(ScriptBlockText),"mimikatz")
+  OR match(lower(ScriptBlockText), "-dumpcr") OR match(lower(ScriptBlockText), "SEKURLSA::Pth")
+  OR match(lower(ScriptBlockText), "kerberos::ptt") OR match(lower(ScriptBlockText),
+  "kerberos::golden") ,5,0) | eval iex=if(match(ScriptBlockText, "(?i)iex|invoke-expression"),2,0)
+  | eval webclient=if(match(lower(ScriptBlockText),"http") OR match(lower(ScriptBlockText),"web(client|request)")
+  OR match(lower(ScriptBlockText),"socket") OR match(lower(ScriptBlockText),"download(file|string)")
+  OR match(lower(ScriptBlockText),"bitstransfer") OR match(lower(ScriptBlockText),"internetexplorer.application")
+  OR match(lower(ScriptBlockText),"xmlhttp"),5,0) | eval get = if(match(lower(ScriptBlockText),"get-"),
+  "1", 0) | eval rundll32 = if(match(lower(ScriptBlockText),"rundll32"), "4", 0) |
+  eval suspkeywrd=if(match(ScriptBlockText, "(?i)(bitstransfer|mimik|metasp|AssemblyBuilderAccess|Reflection\.Assembly|shellcode|injection|cnvert|shell\.application|start-process|Rc4ByteStream|System\.Security\.Cryptography|lsass\.exe|localadmin|LastLoggedOn|hijack|BackupPrivilege|ngrok|comsvcs|backdoor|brute.?force|Port.?Scan|Exfiltration|exploit|DisableRealtimeMonitoring|beacon)"),1,0)
+  | eval syswow64 = if(match(lower(ScriptBlockText),"syswow64"), "3", 0) | eval httplocal
+  = if(match(lower(ScriptBlockText),"http://127.0.0.1"), "4", 0) | eval reflection
+  = if(match(lower(ScriptBlockText),"reflection"), "1", 0) | eval invokewmi=if(match(lower(ScriptBlockText),
+  "(?i)(wmiobject|WMIMethod|RemoteWMI|PowerShellWmi|wmicommand)"),5,0) | eval downgrade=if(match(ScriptBlockText,
+  "(?i)([-]ve*r*s*i*o*n*\s+2)") OR match(lower(ScriptBlockText),"powershell -version"),3,0)
+  | eval compressed=if(match(ScriptBlockText, "(?i)GZipStream|::Decompress|IO.Compression|write-zip|(expand|compress)-Archive"),5,0)
+  | eval invokecmd = if(match(lower(ScriptBlockText),"invoke-command"), "4", 0) |
+  addtotals fieldname=Score DoIt, enccom, suspcmdlet, suspkeywrd, compressed, downgrade,
+  mimikatz, iex, empire, rundll32, webclient, syswow64, httplocal, reflection, invokewmi,
+  invokecmd, base64, get | stats values(Score) by UserID, Computer, DoIt, enccom,
+  compressed, downgrade, iex, mimikatz, rundll32, empire, webclient, syswow64, httplocal,
+  reflection, invokewmi, invokecmd, base64, get, suspcmdlet, suspkeywrd | rename Computer
+  as dest, UserID as user | `powershell_4104_hunting_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Limited false positives. May filter as needed.
 references:
 - https://github.com/inodee/threathunting-spl/blob/master/hunt-queries/powershell_qualifiers.md
@@ -35,7 +71,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: Powershell was identified on endpoint $host$ by user $user$ executing suspicious commands.
+  message: Powershell was identified on endpoint $host$ by user $user$ executing suspicious
+    commands.
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -52,17 +89,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml b/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml
index c857c39737..efc7a3e1d2 100644
--- a/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml
+++ b/detections/endpoint/powershell___connect_to_internet_with_hidden_window.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: David Dorsey, Michael Haag Splunk
 status: production
 type: Hunting
-description: The following analytic detects PowerShell commands using the WindowStyle parameter to hide the window while connecting to the Internet. This behavior is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions that include variations of the WindowStyle parameter. This activity is significant because it attempts to bypass default PowerShell execution policies and conceal its actions, which is often indicative of malicious intent. If confirmed malicious, this could allow an attacker to execute commands stealthily, potentially leading to unauthorized data exfiltration or further compromise of the endpoint.
+description: The following analytic detects PowerShell commands using the WindowStyle
+  parameter to hide the window while connecting to the Internet. This behavior is
+  identified through Endpoint Detection and Response (EDR) telemetry, focusing on
+  command-line executions that include variations of the WindowStyle parameter. This
+  activity is significant because it attempts to bypass default PowerShell execution
+  policies and conceal its actions, which is often indicative of malicious intent.
+  If confirmed malicious, this could allow an attacker to execute commands stealthily,
+  potentially leading to unauthorized data exfiltration or further compromise of the
+  endpoint.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: "| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,\"(?i)[\\-|\\/|\u2013 |\u2014|\u2015]w(in*d*o*w*s*t*y*l*e*)*\\s+[^-]\") | `powershell___connect_to_internet_with_hidden_window_filter`"
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate process can have this combination of command-line options, but it's not common.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` by Processes.user
+  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name
+  Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/|– |—|―]w(in*d*o*w*s*t*y*l*e*)*\s+[^-]")
+  | `powershell___connect_to_internet_with_hidden_window_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate process can have this combination of command-line
+  options, but it's not common.
 references:
 - https://regexr.com/663rr
 - https://github.com/redcanaryco/AtomicTestHarnesses/blob/master/Windows/TestHarnesses/T1059.001_PowerShell/OutPowerShellCommandLineParameter.ps1
@@ -33,7 +55,9 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 90
-  message: PowerShell processes $process$ started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet on host $dest$ executed by user $user$.
+  message: PowerShell processes $process$ started with parameters to modify the execution
+    policy of the run, run in a hidden window, and connect to the Internet on host
+    $dest$ executed by user $user$.
   mitre_attack_id:
   - T1059.001
   - T1059
@@ -54,17 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_name
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml b/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml
index 6d113eda72..c3dade27e7 100644
--- a/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml
+++ b/detections/endpoint/powershell_com_hijacking_inprocserver32_modification.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to modify or add a Component Object Model (COM) entry to the InProcServer32 path within the registry using PowerShell. It leverages PowerShell ScriptBlock Logging (EventCode 4104) to identify suspicious script blocks that target the InProcServer32 registry path. This activity is significant because modifying COM objects can be used for persistence or privilege escalation by attackers. If confirmed malicious, this could allow an attacker to execute arbitrary code or maintain persistent access to the compromised system, posing a severe security risk.
+description: The following analytic detects attempts to modify or add a Component
+  Object Model (COM) entry to the InProcServer32 path within the registry using PowerShell.
+  It leverages PowerShell ScriptBlock Logging (EventCode 4104) to identify suspicious
+  script blocks that target the InProcServer32 registry path. This activity is significant
+  because modifying COM objects can be used for persistence or privilege escalation
+  by attackers. If confirmed malicious, this could allow an attacker to execute arbitrary
+  code or maintain persistent access to the compromised system, posing a severe security
+  risk.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Software\\Classes\\CLSID\\*\\InProcServer32*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_com_hijacking_inprocserver32_modification_filter`'
-how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the PowerShell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: False positives will be present if any scripts are adding to inprocserver32. Filter as needed.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Software\\Classes\\CLSID\\*\\InProcServer32*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_com_hijacking_inprocserver32_modification_filter`'
+how_to_implement: The following analytic requires PowerShell operational logs to be
+  imported. Modify the PowerShell macro as needed to match the sourcetype or add index.
+  This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: False positives will be present if any scripts are adding to
+  inprocserver32. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1546/015/
 - https://www.splunk.com/en_us/blog/security/hunting-for-malicious-powershell-using-script-block-logging.html
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A PowerShell script has been identified with InProcServer32 within the script code on $Computer$.
+  message: A PowerShell script has been identified with InProcServer32 within the
+    script code on $Computer$.
   mitre_attack_id:
   - T1546.015
   - T1059
@@ -45,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_creating_thread_mutex.yml b/detections/endpoint/powershell_creating_thread_mutex.yml
index d3ba30bb11..bbbc503716 100644
--- a/detections/endpoint/powershell_creating_thread_mutex.yml
+++ b/detections/endpoint/powershell_creating_thread_mutex.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of PowerShell scripts using the `mutex` function via EventCode 4104. This detection leverages PowerShell Script Block Logging to identify scripts that create thread mutexes, a technique often used in obfuscated scripts to ensure only one instance runs on a compromised machine. This activity is significant as it may indicate the presence of sophisticated malware or persistence mechanisms. If confirmed malicious, the attacker could maintain exclusive control over a process, potentially leading to further exploitation or persistence within the environment.
+description: The following analytic detects the execution of PowerShell scripts using
+  the `mutex` function via EventCode 4104. This detection leverages PowerShell Script
+  Block Logging to identify scripts that create thread mutexes, a technique often
+  used in obfuscated scripts to ensure only one instance runs on a compromised machine.
+  This activity is significant as it may indicate the presence of sophisticated malware
+  or persistence mechanisms. If confirmed malicious, the attacker could maintain exclusive
+  control over a process, potentially leading to further exploitation or persistence
+  within the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Threading.Mutex*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_creating_thread_mutex_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: powershell developer may used this function in their script for instance checking too.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Threading.Mutex*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_creating_thread_mutex_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: powershell developer may used this function in their script
+  for instance checking too.
 references:
 - https://isc.sans.edu/forums/diary/Some+Powershell+Malicious+Code/22988/
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,16 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_disable_security_monitoring.yml b/detections/endpoint/powershell_disable_security_monitoring.yml
index 4168019138..b51ecc13ed 100644
--- a/detections/endpoint/powershell_disable_security_monitoring.yml
+++ b/detections/endpoint/powershell_disable_security_monitoring.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies attempts to disable Windows Defender real-time behavior monitoring via PowerShell commands. It detects the use of specific `Set-MpPreference` parameters that disable various security features. This activity is significant as it is commonly used by malware such as RATs, bots, or Trojans to evade detection by disabling antivirus protections. If confirmed malicious, this action could allow an attacker to operate undetected, leading to potential data exfiltration, further system compromise, or persistent access within the environment.
+description: The following analytic identifies attempts to disable Windows Defender
+  real-time behavior monitoring via PowerShell commands. It detects the use of specific
+  `Set-MpPreference` parameters that disable various security features. This activity
+  is significant as it is commonly used by malware such as RATs, bots, or Trojans
+  to evade detection by disabling antivirus protections. If confirmed malicious, this
+  action could allow an attacker to operate undetected, leading to potential data
+  exfiltration, further system compromise, or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="*set-mppreference*" AND Processes.process IN ("*disablerealtimemonitoring*","*disableioavprotection*","*disableintrusionpreventionsystem*","*disablescriptscanning*","*disableblockatfirstseen*","*DisableBehaviorMonitoring*","*drtm *","*dioavp *","*dscrptsc *","*dbaf *","*dbm *") by Processes.dest Processes.user Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_disable_security_monitoring_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives. However, tune based on scripts that may perform this action.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="*set-mppreference*"
+  AND Processes.process IN ("*disablerealtimemonitoring*","*disableioavprotection*","*disableintrusionpreventionsystem*","*disablescriptscanning*","*disableblockatfirstseen*","*DisableBehaviorMonitoring*","*drtm
+  *","*dioavp *","*dscrptsc *","*dbaf *","*dbm *") by Processes.dest Processes.user
+  Processes.parent_process Processes.original_file_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_disable_security_monitoring_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives. However, tune based on scripts that
+  may perform this action.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-15---tamper-with-windows-defender-atp-powershell
 - https://docs.microsoft.com/en-us/powershell/module/defender/set-mppreference?view=windowsserver2022-ps
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/pwh_defender_disabling/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/pwh_defender_disabling/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_domain_enumeration.yml b/detections/endpoint/powershell_domain_enumeration.yml
index 60ccc5b10b..df1e5e865e 100644
--- a/detections/endpoint/powershell_domain_enumeration.yml
+++ b/detections/endpoint/powershell_domain_enumeration.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of PowerShell commands used for domain enumeration, such as `get-netdomaintrust` and `get-adgroupmember`. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command sent to PowerShell. This activity is significant as it often indicates reconnaissance efforts by an attacker to map out the domain structure and identify key users and groups. If confirmed malicious, this behavior could lead to further targeted attacks, privilege escalation, and unauthorized access to sensitive information within the domain.
+description: The following analytic detects the execution of PowerShell commands used
+  for domain enumeration, such as `get-netdomaintrust` and `get-adgroupmember`. It
+  leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze
+  the full command sent to PowerShell. This activity is significant as it often indicates
+  reconnaissance efforts by an attacker to map out the domain structure and identify
+  key users and groups. If confirmed malicious, this behavior could lead to further
+  targeted attacks, privilege escalation, and unauthorized access to sensitive information
+  within the domain.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN (*get-netdomaintrust*, *get-netforesttrust*, *get-addomain*, *get-adgroupmember*, *get-domainuser*) | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_domain_enumeration_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText IN (*get-netdomaintrust*, *get-netforesttrust*,
+  *get-addomain*, *get-adgroupmember*, *get-domainuser*) | stats count min(_time)
+  as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText UserID
+  | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_domain_enumeration_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: It is possible there will be false positives, filter as needed.
 references:
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 60
-  message: A suspicious powershell script contains domain enumeration command in $ScriptBlockText$ with EventCode $EventCode$ in host $dest$
+  message: A suspicious powershell script contains domain enumeration command in $ScriptBlockText$
+    with EventCode $EventCode$ in host $dest$
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -52,17 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/enumeration.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/enumeration.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_enable_powershell_remoting.yml b/detections/endpoint/powershell_enable_powershell_remoting.yml
index 171968304c..a464348a9f 100644
--- a/detections/endpoint/powershell_enable_powershell_remoting.yml
+++ b/detections/endpoint/powershell_enable_powershell_remoting.yml
@@ -7,10 +7,23 @@ type: Anomaly
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the use of the Enable-PSRemoting cmdlet, which allows PowerShell remoting on a local or remote computer. This detection leverages PowerShell Script Block Logging (EventCode 4104) to identify when this cmdlet is executed. Monitoring this activity is crucial as it can indicate an attacker enabling remote command execution capabilities on a compromised system. If confirmed malicious, this activity could allow an attacker to take control of the system remotely, execute commands, and potentially pivot to other systems within the network, leading to further compromise and lateral movement.
-search: '`powershell` EventCode=4104 ScriptBlockText="*Enable-PSRemoting*" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `powershell_enable_powershell_remoting_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Note that false positives may occur due to the use of the Enable-PSRemoting cmdlet by legitimate users, such as system administrators. It is recommended to apply appropriate filters as needed to minimize the number of false positives.
+description: The following analytic detects the use of the Enable-PSRemoting cmdlet,
+  which allows PowerShell remoting on a local or remote computer. This detection leverages
+  PowerShell Script Block Logging (EventCode 4104) to identify when this cmdlet is
+  executed. Monitoring this activity is crucial as it can indicate an attacker enabling
+  remote command execution capabilities on a compromised system. If confirmed malicious,
+  this activity could allow an attacker to take control of the system remotely, execute
+  commands, and potentially pivot to other systems within the network, leading to
+  further compromise and lateral movement.
+search: '`powershell` EventCode=4104 ScriptBlockText="*Enable-PSRemoting*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `powershell_enable_powershell_remoting_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Note that false positives may occur due to the use of the Enable-PSRemoting
+  cmdlet by legitimate users, such as system administrators. It is recommended to
+  apply appropriate filters as needed to minimize the number of false positives.
 references:
 - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.core/enable-psremoting?view=powershell-7.3
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,15 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/4104-psremoting-windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/4104-psremoting-windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_enable_smb1protocol_feature.yml b/detections/endpoint/powershell_enable_smb1protocol_feature.yml
index 680d32a4bf..a4bc3d3083 100644
--- a/detections/endpoint/powershell_enable_smb1protocol_feature.yml
+++ b/detections/endpoint/powershell_enable_smb1protocol_feature.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the enabling of the SMB1 protocol via `powershell.exe`. It leverages PowerShell script block logging (EventCode 4104) to identify the execution of the `Enable-WindowsOptionalFeature` cmdlet with the `SMB1Protocol` parameter. This activity is significant because enabling SMB1 can facilitate lateral movement and file encryption by ransomware, such as RedDot. If confirmed malicious, this action could allow an attacker to propagate through the network, encrypt files, and potentially disrupt business operations.
+description: The following analytic detects the enabling of the SMB1 protocol via
+  `powershell.exe`. It leverages PowerShell script block logging (EventCode 4104)
+  to identify the execution of the `Enable-WindowsOptionalFeature` cmdlet with the
+  `SMB1Protocol` parameter. This activity is significant because enabling SMB1 can
+  facilitate lateral movement and file encryption by ransomware, such as RedDot. If
+  confirmed malicious, this action could allow an attacker to propagate through the
+  network, encrypt files, and potentially disrupt business operations.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Enable-WindowsOptionalFeature*" ScriptBlockText = "*SMB1Protocol*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_enable_smb1protocol_feature_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Enable-WindowsOptionalFeature*"
+  ScriptBlockText = "*SMB1Protocol*" | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_enable_smb1protocol_feature_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the powershell logs  from your endpoints. make sure you enable needed
+  registry to monitor this event.
 known_false_positives: network operator may enable or disable this windows feature.
 references:
 - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,16 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_execute_com_object.yml b/detections/endpoint/powershell_execute_com_object.yml
index 127c6ba3a1..e84ddb340a 100644
--- a/detections/endpoint/powershell_execute_com_object.yml
+++ b/detections/endpoint/powershell_execute_com_object.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a COM CLSID through PowerShell. It leverages EventCode 4104 and searches for specific script block text indicating the creation of a COM object. This activity is significant as it is commonly used by adversaries and malware, such as the Conti ransomware, to execute commands, potentially for privilege escalation or bypassing User Account Control (UAC). If confirmed malicious, this technique could allow attackers to gain elevated privileges or persist within the environment, posing a significant security risk.
+description: The following analytic detects the execution of a COM CLSID through PowerShell.
+  It leverages EventCode 4104 and searches for specific script block text indicating
+  the creation of a COM object. This activity is significant as it is commonly used
+  by adversaries and malware, such as the Conti ransomware, to execute commands, potentially
+  for privilege escalation or bypassing User Account Control (UAC). If confirmed malicious,
+  this technique could allow attackers to gain elevated privileges or persist within
+  the environment, posing a significant security risk.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*CreateInstance([type]::GetTypeFromCLSID*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_execute_com_object_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*CreateInstance([type]::GetTypeFromCLSID*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_execute_com_object_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: network operrator may use this command.
 references:
 - https://threadreaderapp.com/thread/1423361119926816776.html
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,15 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/pwh_com_object/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/pwh_com_object/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml b/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml
index e6854ca451..0fb3eeb09e 100644
--- a/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml
+++ b/detections/endpoint/powershell_fileless_process_injection_via_getprocaddress.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `GetProcAddress` in PowerShell script blocks, leveraging PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, which is then logged in Windows event logs. The presence of `GetProcAddress` is unusual for typical PowerShell scripts and often indicates malicious activity, as many attack toolkits use it to achieve code execution. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, potentially leading to system compromise. Analysts should review parallel processes and the entire logged script block for further investigation.
+description: The following analytic detects the use of `GetProcAddress` in PowerShell
+  script blocks, leveraging PowerShell Script Block Logging (EventCode=4104). This
+  method captures the full command sent to PowerShell, which is then logged in Windows
+  event logs. The presence of `GetProcAddress` is unusual for typical PowerShell scripts
+  and often indicates malicious activity, as many attack toolkits use it to achieve
+  code execution. If confirmed malicious, this activity could allow an attacker to
+  execute arbitrary code, potentially leading to system compromise. Analysts should
+  review parallel processes and the entire logged script block for further investigation.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText=*getprocaddress* | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_fileless_process_injection_via_getprocaddress_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText=*getprocaddress* | stats count
+  min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_fileless_process_injection_via_getprocaddress_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Limited false positives. Filter as needed.
 references:
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -23,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml b/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml
index 1d6d6272dc..b0daa54151 100644
--- a/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml
+++ b/detections/endpoint/powershell_fileless_script_contains_base64_encoded_content.yml
@@ -7,9 +7,21 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of PowerShell scripts containing Base64 encoded content, specifically identifying the use of `FromBase64String`. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command sent to PowerShell. This activity is significant as Base64 encoding is often used by attackers to obfuscate malicious payloads, making it harder to detect. If confirmed malicious, this could lead to code execution, allowing attackers to run arbitrary commands and potentially compromise the system.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*frombase64string*" OR ScriptBlockText = "*gnirtS46esaBmorF*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_fileless_script_contains_base64_encoded_content_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+description: The following analytic detects the execution of PowerShell scripts containing
+  Base64 encoded content, specifically identifying the use of `FromBase64String`.
+  It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze
+  the full command sent to PowerShell. This activity is significant as Base64 encoding
+  is often used by attackers to obfuscate malicious payloads, making it harder to
+  detect. If confirmed malicious, this could lead to code execution, allowing attackers
+  to run arbitrary commands and potentially compromise the system.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*frombase64string*" OR ScriptBlockText
+  = "*gnirtS46esaBmorF*" | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText Computer UserID | rename Computer as dest |rename UserID
+  as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_fileless_script_contains_base64_encoded_content_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives should be limited. Filter as needed.
 references:
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -23,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,18 +68,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
   asset_type: Endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/frombase64string.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/frombase64string.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_get_localgroup_discovery.yml b/detections/endpoint/powershell_get_localgroup_discovery.yml
index b1b1cb0437..5838ae3d08 100644
--- a/detections/endpoint/powershell_get_localgroup_discovery.yml
+++ b/detections/endpoint/powershell_get_localgroup_discovery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the use of the `get-localgroup` command executed via PowerShell or cmd.exe to enumerate local groups on an endpoint. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Monitoring this activity is significant as it may indicate an attacker attempting to gather information about local group memberships, which can be a precursor to privilege escalation. If confirmed malicious, this activity could allow an attacker to identify and target privileged accounts, potentially leading to unauthorized access and control over the system.
+description: The following analytic identifies the use of the `get-localgroup` command
+  executed via PowerShell or cmd.exe to enumerate local groups on an endpoint. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments. Monitoring this activity is significant
+  as it may indicate an attacker attempting to gather information about local group
+  memberships, which can be a precursor to privilege escalation. If confirmed malicious,
+  this activity could allow an attacker to identify and target privileged accounts,
+  potentially leading to unauthorized access and control over the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) (Processes.process="*get-localgroup*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe
+  OR Processes.process_name=cmd.exe) (Processes.process="*get-localgroup*") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present. Tune as needed.
 references:
 - https://attack.mitre.org/techniques/T1069/001/
@@ -39,23 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml b/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml
index d6ad50c9f8..b9b090121d 100644
--- a/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml
+++ b/detections/endpoint/powershell_get_localgroup_discovery_with_script_block_logging.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the PowerShell cmdlet `get-localgroup` using PowerShell Script Block Logging (EventCode=4104). This method captures the full command sent to PowerShell, providing detailed visibility into script execution. Monitoring this activity is significant as it can indicate an attempt to enumerate local groups, which may be a precursor to privilege escalation or lateral movement. If confirmed malicious, an attacker could gain insights into group memberships, potentially leading to unauthorized access or privilege abuse. Review parallel processes and the entire script block for comprehensive analysis.
+description: The following analytic detects the execution of the PowerShell cmdlet
+  `get-localgroup` using PowerShell Script Block Logging (EventCode=4104). This method
+  captures the full command sent to PowerShell, providing detailed visibility into
+  script execution. Monitoring this activity is significant as it can indicate an
+  attempt to enumerate local groups, which may be a precursor to privilege escalation
+  or lateral movement. If confirmed malicious, an attacker could gain insights into
+  group memberships, potentially leading to unauthorized access or privilege abuse.
+  Review parallel processes and the entire script block for comprehensive analysis.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*get-localgroup*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText  | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_with_script_block_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*get-localgroup*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText  | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `powershell_get_localgroup_discovery_with_script_block_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives may be present. Tune as needed.
 references:
 - https://www.splunk.com/en_us/blog/security/powershell-detections-threat-research-release-august-2021.html
@@ -41,17 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getlocalgroup.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/getlocalgroup.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml b/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml
index b6e413bc66..d4f51f6d42 100644
--- a/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml
+++ b/detections/endpoint/powershell_invoke_cimmethod_cimsession.yml
@@ -7,10 +7,23 @@ type: Anomaly
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the creation of a New-CIMSession cmdlet followed by the use of the Invoke-CIMMethod cmdlet within PowerShell. It leverages PowerShell Script Block Logging to identify these specific cmdlets in the ScriptBlockText field. This activity is significant because it mirrors the behavior of the Invoke-WMIMethod cmdlet, often used for remote code execution via NTLMv2 pass-the-hash authentication. If confirmed malicious, this could allow an attacker to execute commands remotely, potentially leading to unauthorized access and control over targeted systems.
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*invoke-CIMMethod*", "*New-CimSession*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_invoke_cimmethod_cimsession_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives may be present based on third-party applications or administrators using CIM. It is recommended to apply appropriate filters as needed to minimize the number of false positives.
+description: The following analytic detects the creation of a New-CIMSession cmdlet
+  followed by the use of the Invoke-CIMMethod cmdlet within PowerShell. It leverages
+  PowerShell Script Block Logging to identify these specific cmdlets in the ScriptBlockText
+  field. This activity is significant because it mirrors the behavior of the Invoke-WMIMethod
+  cmdlet, often used for remote code execution via NTLMv2 pass-the-hash authentication.
+  If confirmed malicious, this could allow an attacker to execute commands remotely,
+  potentially leading to unauthorized access and control over targeted systems.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*invoke-CIMMethod*", "*New-CimSession*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_invoke_cimmethod_cimsession_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives may be present based on third-party applications
+  or administrators using CIM. It is recommended to apply appropriate filters as needed
+  to minimize the number of false positives.
 references:
 - https://learn.microsoft.com/en-us/powershell/module/cimcmdlets/invoke-cimmethod?view=powershell-7.3
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: PowerShell was identified running a Invoke-CIMMethod Invoke-CIMSession on $Computer$.
+  message: PowerShell was identified running a Invoke-CIMMethod Invoke-CIMSession
+    on $Computer$.
   mitre_attack_id:
   - T1047
   observable:
@@ -41,15 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/4104-cimmethod-windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/4104-cimmethod-windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_invoke_wmiexec_usage.yml b/detections/endpoint/powershell_invoke_wmiexec_usage.yml
index 6fa93e58f7..ef33656f8c 100644
--- a/detections/endpoint/powershell_invoke_wmiexec_usage.yml
+++ b/detections/endpoint/powershell_invoke_wmiexec_usage.yml
@@ -7,10 +7,22 @@ type: TTP
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the Invoke-WMIExec utility within PowerShell Script Block Logging (EventCode 4104). This detection leverages PowerShell script block logs to identify instances where the Invoke-WMIExec command is used. Monitoring this activity is crucial as it indicates potential lateral movement using WMI commands with NTLMv2 pass-the-hash authentication. If confirmed malicious, this activity could allow an attacker to execute commands remotely on target systems, potentially leading to further compromise and lateral spread within the network.
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*invoke-wmiexec*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_invoke_wmiexec_usage_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives should be limited as this analytic is designed to detect a specific utility. It is recommended to apply appropriate filters as needed to minimize the number of false positives.
+description: The following analytic detects the execution of the Invoke-WMIExec utility
+  within PowerShell Script Block Logging (EventCode 4104). This detection leverages
+  PowerShell script block logs to identify instances where the Invoke-WMIExec command
+  is used. Monitoring this activity is crucial as it indicates potential lateral movement
+  using WMI commands with NTLMv2 pass-the-hash authentication. If confirmed malicious,
+  this activity could allow an attacker to execute commands remotely on target systems,
+  potentially leading to further compromise and lateral spread within the network.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*invoke-wmiexec*") | stats
+  count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_invoke_wmiexec_usage_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives should be limited as this analytic is designed
+  to detect a specific utility. It is recommended to apply appropriate filters as
+  needed to minimize the number of false positives.
 references:
 - https://github.com/Kevin-Robertson/Invoke-TheHash/blob/master/Invoke-WMIExec.ps1
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,15 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/invokewmiexec_windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/invokewmiexec_windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_load_module_in_meterpreter.yml b/detections/endpoint/powershell_load_module_in_meterpreter.yml
index 85b5a1a28b..cd1ec47274 100644
--- a/detections/endpoint/powershell_load_module_in_meterpreter.yml
+++ b/detections/endpoint/powershell_load_module_in_meterpreter.yml
@@ -5,21 +5,40 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of suspicious PowerShell commands associated with Meterpreter modules, such as "MSF.Powershell" and "MSF.Powershell.Meterpreter". It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command sent to PowerShell. This activity is significant as it indicates potential post-exploitation actions, including credential dumping and persistence mechanisms. If confirmed malicious, an attacker could gain extensive control over the compromised system, escalate privileges, and maintain long-term access, posing a severe threat to the environment.
+description: The following analytic detects the execution of suspicious PowerShell
+  commands associated with Meterpreter modules, such as "MSF.Powershell" and "MSF.Powershell.Meterpreter".
+  It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze
+  the full command sent to PowerShell. This activity is significant as it indicates
+  potential post-exploitation actions, including credential dumping and persistence
+  mechanisms. If confirmed malicious, an attacker could gain extensive control over
+  the compromised system, escalate privileges, and maintain long-term access, posing
+  a severe threat to the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*MSF.Powershell*","*MSF.Powershell.Meterpreter*","*MSF.Powershell.Meterpreter.Kiwi*","*MSF.Powershell.Meterpreter.Transport*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_load_module_in_meterpreter_filter`'
-how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: False positives should be very limited as this is strict to MetaSploit behavior.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*MSF.Powershell*","*MSF.Powershell.Meterpreter*","*MSF.Powershell.Meterpreter.Kiwi*","*MSF.Powershell.Meterpreter.Transport*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_load_module_in_meterpreter_filter`'
+how_to_implement: The following analytic requires PowerShell operational logs to be
+  imported. Modify the powershell macro as needed to match the sourcetype or add index.
+  This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: False positives should be very limited as this is strict to
+  MetaSploit behavior.
 references:
 - https://github.com/OJ/metasploit-payloads/blob/master/powershell/MSF.Powershell/Scripts.cs
 drilldown_searches:
 - name: View the detection results for - "$user_id$" and "$Computer$"
-  search: '%original_detection_search% | search  user_id = "$user_id$" Computer = "$Computer$"'
+  search: '%original_detection_search% | search  user_id = "$user_id$" Computer =
+    "$Computer$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user_id$" and "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$", "$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user_id$",
+    "$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: PowerShell was identified running a script utilized by Meterpreter from MetaSploit on endpoint $Computer$ by user $user_id$.
+  message: PowerShell was identified running a script utilized by Meterpreter from
+    MetaSploit on endpoint $Computer$ by user $user_id$.
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -45,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - User_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/metasploit/msf.powershell.powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/metasploit/msf.powershell.powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml b/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml
index 0ec49343d4..b678c4cfc3 100644
--- a/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml
+++ b/detections/endpoint/powershell_loading_dotnet_into_memory_via_reflection.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the use of PowerShell to load .NET assemblies into memory via reflection, a technique often used in malicious activities such as those by Empire and Cobalt Strike. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze the full command executed. This behavior is significant as it can indicate advanced attack techniques aiming to execute code in memory, bypassing traditional defenses. If confirmed malicious, this activity could lead to unauthorized code execution, privilege escalation, and persistent access within the environment.
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*", "*reflection.assembly*") | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_loading_dotnet_into_memory_via_reflection_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives should be limited as day to day scripts do not use this method.
+description: The following analytic detects the use of PowerShell to load .NET assemblies
+  into memory via reflection, a technique often used in malicious activities such
+  as those by Empire and Cobalt Strike. It leverages PowerShell Script Block Logging
+  (EventCode=4104) to capture and analyze the full command executed. This behavior
+  is significant as it can indicate advanced attack techniques aiming to execute code
+  in memory, bypassing traditional defenses. If confirmed malicious, this activity
+  could lead to unauthorized code execution, privilege escalation, and persistent
+  access within the environment.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*",
+  "*reflection.assembly*") | stats count min(_time) as firstTime max(_time) as lastTime
+  by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_loading_dotnet_into_memory_via_reflection_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives should be limited as day to day scripts do
+  not use this method.
 references:
 - https://docs.microsoft.com/en-us/dotnet/api/system.reflection.assembly?view=net-5.0
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -19,11 +32,17 @@ references:
 - https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$UserID$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" UserID = "$UserID$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" UserID =
+    "$UserID$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$UserID$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A suspicious powershell script contains reflective class assembly command in $ScriptBlockText$ to load .net code in memory with EventCode $EventCode$ in host $Computer$
+  message: A suspicious powershell script contains reflective class assembly command
+    in $ScriptBlockText$ to load .net code in memory with EventCode $EventCode$ in
+    host $Computer$
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -54,17 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reflection.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reflection.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_processing_stream_of_data.yml b/detections/endpoint/powershell_processing_stream_of_data.yml
index 74f3e7dd69..a83267c6b8 100644
--- a/detections/endpoint/powershell_processing_stream_of_data.yml
+++ b/detections/endpoint/powershell_processing_stream_of_data.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious PowerShell script execution involving compressed stream data processing, identified via EventCode 4104. It leverages PowerShell Script Block Logging to flag scripts using `IO.Compression`, `IO.StreamReader`, or decompression methods. This activity is significant as it often indicates obfuscated PowerShell or embedded .NET/binary execution, which are common tactics for evading detection. If confirmed malicious, this behavior could allow attackers to execute hidden code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic detects suspicious PowerShell script execution
+  involving compressed stream data processing, identified via EventCode 4104. It leverages
+  PowerShell Script Block Logging to flag scripts using `IO.Compression`, `IO.StreamReader`,
+  or decompression methods. This activity is significant as it often indicates obfuscated
+  PowerShell or embedded .NET/binary execution, which are common tactics for evading
+  detection. If confirmed malicious, this behavior could allow attackers to execute
+  hidden code, escalate privileges, or maintain persistence within the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*IO.Compression.*" OR ScriptBlockText = "*IO.StreamReader*" OR ScriptBlockText = "*]::Decompress*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_processing_stream_of_data_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*IO.Compression.*" OR ScriptBlockText
+  = "*IO.StreamReader*" OR ScriptBlockText = "*]::Decompress*" | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_processing_stream_of_data_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: powershell may used this function to process compressed data.
 references:
 - https://medium.com/@ahmedjouini99/deobfuscating-emotets-powershell-payload-e39fb116f7b9
@@ -21,11 +32,17 @@ references:
 - https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$UserID$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" UserID = "$UserID$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" UserID =
+    "$UserID$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$UserID$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,7 +57,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 50
-  message: A suspicious powershell script contains stream command in $ScriptBlockText$ commonly for processing compressed or to decompressed binary file with EventCode $EventCode$ in host $Computer$
+  message: A suspicious powershell script contains stream command in $ScriptBlockText$
+    commonly for processing compressed or to decompressed binary file with EventCode
+    $EventCode$ in host $Computer$
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -57,17 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
-  - Score
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/streamreader.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/streamreader.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_remote_services_add_trustedhost.yml b/detections/endpoint/powershell_remote_services_add_trustedhost.yml
index 32ca512881..312445c2c8 100644
--- a/detections/endpoint/powershell_remote_services_add_trustedhost.yml
+++ b/detections/endpoint/powershell_remote_services_add_trustedhost.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of a PowerShell script that modifies the 'TrustedHosts' configuration via EventCode 4104. It leverages PowerShell Script Block Logging to identify commands targeting WSMan settings, specifically those altering or concatenating trusted hosts. This activity is significant as it can indicate attempts to manipulate remote connection settings, potentially allowing unauthorized remote access. If confirmed malicious, this could enable attackers to establish persistent remote connections, bypass security protocols, and gain unauthorized access to sensitive systems and data.
-search: '`powershell` EventCode=4104  ScriptBlockText = "*WSMan:\\localhost\\Client\\TrustedHosts*" ScriptBlockText IN ("* -Value *", "* -Concatenate *") | rename Computer as dest, UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_services_add_trustedhost_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: user and network administrator may used this function to add trusted host.
+description: The following analytic detects the execution of a PowerShell script that
+  modifies the 'TrustedHosts' configuration via EventCode 4104. It leverages PowerShell
+  Script Block Logging to identify commands targeting WSMan settings, specifically
+  those altering or concatenating trusted hosts. This activity is significant as it
+  can indicate attempts to manipulate remote connection settings, potentially allowing
+  unauthorized remote access. If confirmed malicious, this could enable attackers
+  to establish persistent remote connections, bypass security protocols, and gain
+  unauthorized access to sensitive systems and data.
+search: '`powershell` EventCode=4104  ScriptBlockText = "*WSMan:\\localhost\\Client\\TrustedHosts*"
+  ScriptBlockText IN ("* -Value *", "* -Concatenate *") | rename Computer as dest,
+  UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode
+  ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_remote_services_add_trustedhost_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: user and network administrator may used this function to add
+  trusted host.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
-  - Score
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/wsman_trustedhost/wsman_pwh.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/wsman_trustedhost/wsman_pwh.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml b/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml
index f5de4ae222..5aaca61dfe 100644
--- a/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml
+++ b/detections/endpoint/powershell_remote_thread_to_known_windows_process.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious PowerShell processes attempting to inject code into critical Windows processes using CreateRemoteThread. It leverages Sysmon EventCode 8 to identify instances where PowerShell spawns threads in processes like svchost.exe, csrss.exe, and others. This activity is significant as it is commonly used by malware such as TrickBot and offensive tools like Cobalt Strike to execute malicious payloads, establish reverse shells, or download additional malware. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, and persistent access within the environment.
+description: The following analytic detects suspicious PowerShell processes attempting
+  to inject code into critical Windows processes using CreateRemoteThread. It leverages
+  Sysmon EventCode 8 to identify instances where PowerShell spawns threads in processes
+  like svchost.exe, csrss.exe, and others. This activity is significant as it is commonly
+  used by malware such as TrickBot and offensive tools like Cobalt Strike to execute
+  malicious payloads, establish reverse shells, or download additional malware. If
+  confirmed malicious, this behavior could lead to unauthorized code execution, privilege
+  escalation, and persistent access within the environment.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode = 8 parent_process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") | stats  min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress dest EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remote_thread_to_known_windows_process_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, Create Remote thread from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of create remote thread may be used.
+search: '`sysmon` EventCode = 8 parent_process_name IN ("powershell_ise.exe", "powershell.exe")
+  TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe")
+  | stats  min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name
+  SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress
+  dest EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_remote_thread_to_known_windows_process_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, Create Remote thread from your endpoints. If you are
+  using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter
+  known instances of create remote thread may be used.
 known_false_positives: unknown
 references:
 - https://thedfirreport.com/2021/01/11/trickbot-still-alive-and-well/
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A suspicious powershell process $process_name$ that tries to create a remote thread on target process $TargetImage$ with eventcode $EventCode$ in host $dest$
+  message: A suspicious powershell process $process_name$ that tries to create a remote
+    thread on target process $TargetImage$ with eventcode $EventCode$ in host $dest$
   mitre_attack_id:
   - T1055
   observable:
@@ -44,22 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - process_name
-  - SourceProcessId
-  - SourceProcessGuid
-  - TargetImage
-  - TargetProcessId
-  - NewThreadId
-  - StartAddress
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_remove_windows_defender_directory.yml b/detections/endpoint/powershell_remove_windows_defender_directory.yml
index 8e8bf86a3a..fa0e321100 100644
--- a/detections/endpoint/powershell_remove_windows_defender_directory.yml
+++ b/detections/endpoint/powershell_remove_windows_defender_directory.yml
@@ -5,21 +5,38 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious PowerShell command attempting to delete the Windows Defender directory. It leverages PowerShell Script Block Logging to identify commands containing "rmdir" and targeting the Windows Defender path. This activity is significant as it may indicate an attempt to disable or corrupt Windows Defender, a key security component. If confirmed malicious, this action could allow an attacker to bypass endpoint protection, facilitating further malicious activities without detection.
+description: The following analytic detects a suspicious PowerShell command attempting
+  to delete the Windows Defender directory. It leverages PowerShell Script Block Logging
+  to identify commands containing "rmdir" and targeting the Windows Defender path.
+  This activity is significant as it may indicate an attempt to disable or corrupt
+  Windows Defender, a key security component. If confirmed malicious, this action
+  could allow an attacker to bypass endpoint protection, facilitating further malicious
+  activities without detection.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*rmdir *" AND ScriptBlockText = "*\\Microsoft\\Windows Defender*" | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_remove_windows_defender_directory_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*rmdir *" AND ScriptBlockText
+  = "*\\Microsoft\\Windows Defender*" | stats count min(_time) as firstTime max(_time)
+  as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_remove_windows_defender_directory_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: unknown
 references:
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$UserID$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" UserID = "$UserID$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" UserID =
+    "$UserID$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$UserID$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_script_block_with_url_chain.yml b/detections/endpoint/powershell_script_block_with_url_chain.yml
index 96102d3276..bfcd192129 100644
--- a/detections/endpoint/powershell_script_block_with_url_chain.yml
+++ b/detections/endpoint/powershell_script_block_with_url_chain.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that contains multiple URLs within a function or array. It leverages PowerShell operational logs to detect script blocks with embedded URLs, often indicative of obfuscated scripts or those attempting to download secondary payloads. This activity is significant as it may signal an attempt to execute malicious code or download additional malware. If confirmed malicious, this could lead to code execution, further system compromise, or data exfiltration. Review parallel processes and the full script block for additional context and related artifacts.
+description: The following analytic identifies suspicious PowerShell script execution
+  via EventCode 4104 that contains multiple URLs within a function or array. It leverages
+  PowerShell operational logs to detect script blocks with embedded URLs, often indicative
+  of obfuscated scripts or those attempting to download secondary payloads. This activity
+  is significant as it may signal an attempt to execute malicious code or download
+  additional malware. If confirmed malicious, this could lead to code execution, further
+  system compromise, or data exfiltration. Review parallel processes and the full
+  script block for additional context and related artifacts.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*http:*","*https:*") | regex ScriptBlockText="(\"?(https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))\"?(?:,|\))?){2,}" | rex max_match=20 field=ScriptBlockText "(?<url>https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))" | eval Path = case(isnotnull(Path),Path,true(),"unknown") | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Path) as file_name values(UserID) as user values(url) as url dc(url) as url_count by ActivityID, Computer, EventCode | rename Computer as dest, EventCode as signature_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_script_block_with_url_chain_filter`'
-how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*http:*","*https:*") | regex
+  ScriptBlockText="(\"?(https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))\"?(?:,|\))?){2,}"
+  | rex max_match=20 field=ScriptBlockText "(?<url>https?:\/\/(?:www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b(?:[-a-zA-Z0-9()@:%_\+.~#?&\/=]*))"
+  | eval Path = case(isnotnull(Path),Path,true(),"unknown") | stats count min(_time)
+  as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Path)
+  as file_name values(UserID) as user values(url) as url dc(url) as url_count by ActivityID,
+  Computer, EventCode | rename Computer as dest, EventCode as signature_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_script_block_with_url_chain_filter`'
+how_to_implement: The following analytic requires PowerShell operational logs to be
+  imported. Modify the powershell macro as needed to match the sourcetype or add index.
+  This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Unknown, possible custom scripting.
 references:
 - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: A suspicious powershell script used by $user$ on host $dest$ contains $url_count$ URLs in an array, this is commonly used for malware.
+  message: A suspicious powershell script used by $user$ on host $dest$ contains $url_count$
+    URLs in an array, this is commonly used for malware.
   mitre_attack_id:
   - T1059.001
   - T1105
@@ -55,16 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ActivityID
-  - Computer
-  - ScriptBlockText
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_start_bitstransfer.yml b/detections/endpoint/powershell_start_bitstransfer.yml
index d5097bbc2a..7ebe66a749 100644
--- a/detections/endpoint/powershell_start_bitstransfer.yml
+++ b/detections/endpoint/powershell_start_bitstransfer.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the PowerShell command `Start-BitsTransfer`, which can be used for file transfers, including potential data exfiltration. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because `Start-BitsTransfer` can be abused by adversaries to upload sensitive files to remote locations, posing a risk of data loss. If confirmed malicious, this could lead to unauthorized data exfiltration, compromising sensitive information and potentially leading to further exploitation of the network.
+description: The following analytic detects the execution of the PowerShell command
+  `Start-BitsTransfer`, which can be used for file transfers, including potential
+  data exfiltration. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process creation events and command-line arguments. This activity
+  is significant because `Start-BitsTransfer` can be abused by adversaries to upload
+  sensitive files to remote locations, posing a risk of data loss. If confirmed malicious,
+  this could lead to unauthorized data exfiltration, compromising sensitive information
+  and potentially leading to further exploitation of the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*start-bitstransfer* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_start_bitstransfer_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process=*start-bitstransfer*
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_start_bitstransfer_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives. It is possible administrators will
+  utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent
+  process or command-line arguments.
 references:
 - https://isc.sans.edu/diary/Investigating+Microsoft+BITS+Activity/23281
 - https://docs.microsoft.com/en-us/windows/win32/bits/using-windows-powershell-to-create-bits-transfer-jobs
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A suspicious process $process_name$ with commandline $process$ that are related to bittransfer functionality in host $dest$
+  message: A suspicious process $process_name$ with commandline $process$ that are
+    related to bittransfer functionality in host $dest$
   mitre_attack_id:
   - T1197
   observable:
@@ -48,23 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_start_or_stop_service.yml b/detections/endpoint/powershell_start_or_stop_service.yml
index 10f65b75c0..5f58edfc9b 100644
--- a/detections/endpoint/powershell_start_or_stop_service.yml
+++ b/detections/endpoint/powershell_start_or_stop_service.yml
@@ -7,10 +7,25 @@ type: Anomaly
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic identifies the use of PowerShell's Start-Service or Stop-Service cmdlets on an endpoint. It leverages PowerShell Script Block Logging to detect these commands. This activity is significant because attackers can manipulate services to disable or stop critical functions, causing system instability or disrupting business operations. If confirmed malicious, this behavior could allow attackers to disable security services, evade detection, or disrupt essential services, leading to potential system downtime and compromised security.
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*start-service*", "*stop-service*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_start_or_stop_service_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: This behavior may be noisy, as these cmdlets are commonly used by system administrators or other legitimate users to manage services. Therefore, it is recommended not to enable this analytic as a direct notable or TTP. Instead, it should be used as part of a broader set of security controls to detect and investigate potential threats.
+description: The following analytic identifies the use of PowerShell's Start-Service
+  or Stop-Service cmdlets on an endpoint. It leverages PowerShell Script Block Logging
+  to detect these commands. This activity is significant because attackers can manipulate
+  services to disable or stop critical functions, causing system instability or disrupting
+  business operations. If confirmed malicious, this behavior could allow attackers
+  to disable security services, evade detection, or disrupt essential services, leading
+  to potential system downtime and compromised security.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*start-service*", "*stop-service*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_start_or_stop_service_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: This behavior may be noisy, as these cmdlets are commonly used
+  by system administrators or other legitimate users to manage services. Therefore,
+  it is recommended not to enable this analytic as a direct notable or TTP. Instead,
+  it should be used as part of a broader set of security controls to detect and investigate
+  potential threats.
 references:
 - https://learn-powershell.net/2012/01/15/startingstopping-and-restarting-remote-services-with-powershell/
 - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.management/start-service?view=powershell-7.3
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,15 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/start_stop_service_windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/start_stop_service_windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_using_memory_as_backing_store.yml b/detections/endpoint/powershell_using_memory_as_backing_store.yml
index c5fb747b3a..9894170328 100644
--- a/detections/endpoint/powershell_using_memory_as_backing_store.yml
+++ b/detections/endpoint/powershell_using_memory_as_backing_store.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious PowerShell script execution using memory streams as a backing store, identified via EventCode 4104. It leverages PowerShell Script Block Logging to capture scripts that create new objects with memory streams, often used to decompress and execute payloads in memory. This activity is significant as it indicates potential in-memory execution of malicious code, bypassing traditional file-based detection. If confirmed malicious, this technique could allow attackers to execute arbitrary code, maintain persistence, or escalate privileges without leaving a trace on the disk.
+description: The following analytic detects suspicious PowerShell script execution
+  using memory streams as a backing store, identified via EventCode 4104. It leverages
+  PowerShell Script Block Logging to capture scripts that create new objects with
+  memory streams, often used to decompress and execute payloads in memory. This activity
+  is significant as it indicates potential in-memory execution of malicious code,
+  bypassing traditional file-based detection. If confirmed malicious, this technique
+  could allow attackers to execute arbitrary code, maintain persistence, or escalate
+  privileges without leaving a trace on the disk.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = *New-Object* ScriptBlockText = *IO.MemoryStream* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_using_memory_as_backing_store_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: powershell may used this function to store out object into memory.
+search: '`powershell` EventCode=4104 ScriptBlockText = *New-Object* ScriptBlockText
+  = *IO.MemoryStream* | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename
+  UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `powershell_using_memory_as_backing_store_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: powershell may used this function to store out object into
+  memory.
 references:
 - https://web.archive.org/web/20201112031711/https://www.carbonblack.com/blog/decoding-malicious-powershell-streams/
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
@@ -24,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,16 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_webrequest_using_memory_stream.yml b/detections/endpoint/powershell_webrequest_using_memory_stream.yml
index 083252eb62..ed0b855c7d 100644
--- a/detections/endpoint/powershell_webrequest_using_memory_stream.yml
+++ b/detections/endpoint/powershell_webrequest_using_memory_stream.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the use of .NET classes in PowerShell to download a URL payload directly into memory, a common fileless malware staging technique. It leverages PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell commands involving `system.net.webclient`, `system.net.webrequest`, and `IO.MemoryStream`. This activity is significant as it indicates potential fileless malware execution, which is harder to detect and can bypass traditional file-based defenses. If confirmed malicious, this technique could allow attackers to execute code in memory, evade detection, and maintain persistence in the environment.
+description: The following analytic detects the use of .NET classes in PowerShell
+  to download a URL payload directly into memory, a common fileless malware staging
+  technique. It leverages PowerShell Script Block Logging (EventCode=4104) to identify
+  suspicious PowerShell commands involving `system.net.webclient`, `system.net.webrequest`,
+  and `IO.MemoryStream`. This activity is significant as it indicates potential fileless
+  malware execution, which is harder to detect and can bypass traditional file-based
+  defenses. If confirmed malicious, this technique could allow attackers to execute
+  code in memory, evade detection, and maintain persistence in the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104  ScriptBlockText IN ("*system.net.webclient*","*system.net.webrequest*") AND ScriptBlockText="*IO.MemoryStream*" | eval Path = case(isnotnull(Path),Path,true(),"unknown") | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText) as command values(Path) as file_name values(UserID) as user by ActivityID, Computer, EventCode | rename Computer as dest, EventCode as signature_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_webrequest_using_memory_stream_filter`'
-how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104  ScriptBlockText IN ("*system.net.webclient*","*system.net.webrequest*")
+  AND ScriptBlockText="*IO.MemoryStream*" | eval Path = case(isnotnull(Path),Path,true(),"unknown")
+  | stats count min(_time) as firstTime max(_time) as lastTime list(ScriptBlockText)
+  as command values(Path) as file_name values(UserID) as user by ActivityID, Computer,
+  EventCode | rename Computer as dest, EventCode as signature_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_webrequest_using_memory_stream_filter`'
+how_to_implement: The following analytic requires PowerShell operational logs to be
+  imported. Modify the powershell macro as needed to match the sourcetype or add index.
+  This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Unknown, possible custom scripting.
 references:
 - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: Powershell webrequest to memory stream behavior. Possible fileless malware staging on $dest$ by $user$.
+  message: Powershell webrequest to memory stream behavior. Possible fileless malware
+    staging on $dest$ by $user$.
   mitre_attack_id:
   - T1059.001
   - T1105
@@ -53,16 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ActivityID
-  - Computer
-  - ScriptBlockText
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/powershell_windows_defender_exclusion_commands.yml b/detections/endpoint/powershell_windows_defender_exclusion_commands.yml
index eb0baf019a..1956350bb2 100644
--- a/detections/endpoint/powershell_windows_defender_exclusion_commands.yml
+++ b/detections/endpoint/powershell_windows_defender_exclusion_commands.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerShell commands to add or set Windows Defender exclusions. It leverages EventCode 4104 to identify suspicious `Add-MpPreference` or `Set-MpPreference` commands with exclusion parameters. This activity is significant because adversaries often use it to bypass Windows Defender, allowing malicious code to execute without detection. If confirmed malicious, this behavior could enable attackers to evade antivirus defenses, maintain persistence, and execute further malicious activities undetected.
+description: The following analytic detects the use of PowerShell commands to add
+  or set Windows Defender exclusions. It leverages EventCode 4104 to identify suspicious
+  `Add-MpPreference` or `Set-MpPreference` commands with exclusion parameters. This
+  activity is significant because adversaries often use it to bypass Windows Defender,
+  allowing malicious code to execute without detection. If confirmed malicious, this
+  behavior could enable attackers to evade antivirus defenses, maintain persistence,
+  and execute further malicious activities undetected.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Add-MpPreference *" OR ScriptBlockText = "*Set-MpPreference *") AND ScriptBlockText = "*-exclusion*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `powershell_windows_defender_exclusion_commands_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Add-MpPreference *" OR ScriptBlockText
+  = "*Set-MpPreference *") AND ScriptBlockText = "*-exclusion*" | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID
+  | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `powershell_windows_defender_exclusion_commands_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
 known_false_positives: admin or user may choose to use this windows features.
 references:
 - https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,16 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/powershell_windows_defender_exclusion_commands/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/powershell_windows_defender_exclusion_commands/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml b/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml
index 8e73f5d7bc..4b144897ed 100644
--- a/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml
+++ b/detections/endpoint/prevent_automatic_repair_mode_using_bcdedit.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of "bcdedit.exe" with parameters to set the boot status policy to ignore all failures. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it can indicate an attempt by ransomware to prevent a compromised machine from booting into automatic repair mode, thereby hindering recovery efforts. If confirmed malicious, this action could allow attackers to maintain control over the infected system, complicating remediation and potentially leading to further damage.
+description: The following analytic detects the execution of "bcdedit.exe" with parameters
+  to set the boot status policy to ignore all failures. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  arguments. This activity is significant because it can indicate an attempt by ransomware
+  to prevent a compromised machine from booting into automatic repair mode, thereby
+  hindering recovery efforts. If confirmed malicious, this action could allow attackers
+  to maintain control over the infected system, complicating remediation and potentially
+  leading to further damage.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe" Processes.process = "*bootstatuspolicy*"  Processes.process = "*ignoreallfailures*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `prevent_automatic_repair_mode_using_bcdedit_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may modify the boot configuration ignore failure during testing and debugging.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe"
+  Processes.process = "*bootstatuspolicy*"  Processes.process = "*ignoreallfailures*"
+  by Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `prevent_automatic_repair_mode_using_bcdedit_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may modify the boot configuration ignore failure
+  during testing and debugging.
 references:
 - https://jsac.jpcert.or.jp/archive/2020/pdf/JSAC2020_1_tamada-yamazaki-nakatsuru_en.pdf
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A suspicious process $process_name$ with process id $process_id$ contains commandline $process$ to ignore all bcdedit execution failure in host $dest$
+  message: A suspicious process $process_name$ with process id $process_id$ contains
+    commandline $process$ to ignore all bcdedit execution failure in host $dest$
   mitre_attack_id:
   - T1490
   observable:
@@ -47,20 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/print_processor_registry_autostart.yml b/detections/endpoint/print_processor_registry_autostart.yml
index a57e56c9a2..6be69e78c6 100644
--- a/detections/endpoint/print_processor_registry_autostart.yml
+++ b/detections/endpoint/print_processor_registry_autostart.yml
@@ -5,13 +5,30 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects suspicious modifications or new entries in the Print Processor registry path. It leverages registry activity data from the Endpoint data model to identify changes in the specified registry path. This activity is significant because the Print Processor registry is known to be exploited by APT groups like Turla for persistence and privilege escalation. If confirmed malicious, this could allow an attacker to execute a malicious DLL payload by restarting the spoolsv.exe process, leading to potential control over the compromised machine.
+description: The following analytic detects suspicious modifications or new entries
+  in the Print Processor registry path. It leverages registry activity data from the
+  Endpoint data model to identify changes in the specified registry path. This activity
+  is significant because the Print Processor registry is known to be exploited by
+  APT groups like Turla for persistence and privilege escalation. If confirmed malicious,
+  this could allow an attacker to execute a malicious DLL payload by restarting the
+  spoolsv.exe process, leading to potential control over the compromised machine.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\Control\\Print\\Environments\\Windows x64\\Print Processors*" by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `print_processor_registry_autostart_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
-known_false_positives: possible new printer installation may add driver component on this registry.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path
+  ="*\\Control\\Print\\Environments\\Windows x64\\Print Processors*" by Registry.dest  Registry.user
+  Registry.registry_path Registry.registry_key_name Registry.registry_value_name |
+  `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`
+  | `print_processor_registry_autostart_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
+known_false_positives: possible new printer installation may add driver component
+  on this registry.
 references:
 - https://attack.mitre.org/techniques/T1547/012/
 - https://www.welivesecurity.com/2020/05/21/no-game-over-winnti-group/
@@ -41,17 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/1f5b68aa-2037-11ec-898e-acde48001122.txt
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/1f5b68aa-2037-11ec-898e-acde48001122.txt
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/print_spooler_adding_a_printer_driver.yml b/detections/endpoint/print_spooler_adding_a_printer_driver.yml
index d8d1867a6c..c5d9eee014 100644
--- a/detections/endpoint/print_spooler_adding_a_printer_driver.yml
+++ b/detections/endpoint/print_spooler_adding_a_printer_driver.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of new printer drivers by monitoring Windows PrintService operational logs, specifically EventCode 316. This detection leverages log data to identify messages indicating the addition or update of printer drivers, such as "kernelbase.dll" and "UNIDRV.DLL." This activity is significant as it may indicate exploitation attempts related to vulnerabilities like CVE-2021-34527 (PrintNightmare). If confirmed malicious, attackers could gain code execution or escalate privileges, potentially compromising the affected system. Immediate isolation and investigation of the endpoint are recommended.
+description: The following analytic detects the addition of new printer drivers by
+  monitoring Windows PrintService operational logs, specifically EventCode 316. This
+  detection leverages log data to identify messages indicating the addition or update
+  of printer drivers, such as "kernelbase.dll" and "UNIDRV.DLL." This activity is
+  significant as it may indicate exploitation attempts related to vulnerabilities
+  like CVE-2021-34527 (PrintNightmare). If confirmed malicious, attackers could gain
+  code execution or escalate privileges, potentially compromising the affected system.
+  Immediate isolation and investigation of the endpoint are recommended.
 data_source:
 - Windows Event Log Printservice 316
-search: '`printservice` EventCode=316 category = "Adding a printer driver" Message = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" | stats  count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_adding_a_printer_driver_filter`'
-how_to_implement: You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems.
+search: '`printservice` EventCode=316 category = "Adding a printer driver" Message
+  = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" | stats  count
+  min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName
+  Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `print_spooler_adding_a_printer_driver_filter`'
+how_to_implement: You will need to ensure PrintService Admin and Operational logs
+  are being logged to Splunk from critical or all systems.
 known_false_positives: Unknown. This may require filtering.
 references:
 - https://twitter.com/MalwareJake/status/1410421445608476679?s=20
@@ -22,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$ComputerName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OpCode
-  - EventCode
-  - ComputerName
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_operational.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_operational.log
     source: WinEventLog:Microsoft-Windows-PrintService/Operational
     sourcetype: WinEventLog
diff --git a/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml b/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml
index 935f8bdcbf..f3c53ceb2f 100644
--- a/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml
+++ b/detections/endpoint/print_spooler_failed_to_load_a_plug_in.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects driver load errors in the Windows PrintService Admin logs, specifically identifying issues related to CVE-2021-34527 (PrintNightmare). It triggers on error messages indicating the print spooler failed to load a plug-in module, such as "meterpreter.dll," with error code 0x45A. This detection method leverages specific event codes and error messages. This activity is significant as it may indicate an exploitation attempt of a known vulnerability. If confirmed malicious, an attacker could gain unauthorized code execution on the affected system, leading to potential system compromise.
+description: The following analytic detects driver load errors in the Windows PrintService
+  Admin logs, specifically identifying issues related to CVE-2021-34527 (PrintNightmare).
+  It triggers on error messages indicating the print spooler failed to load a plug-in
+  module, such as "meterpreter.dll," with error code 0x45A. This detection method
+  leverages specific event codes and error messages. This activity is significant
+  as it may indicate an exploitation attempt of a known vulnerability. If confirmed
+  malicious, an attacker could gain unauthorized code execution on the affected system,
+  leading to potential system compromise.
 data_source:
 - Windows Event Log Printservice 808
 - Windows Event Log Printservice 4909
-search: '`printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `print_spooler_failed_to_load_a_plug_in_filter`'
-how_to_implement: You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems.
+search: '`printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909"))
+  OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\"))
+  | stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode
+  ComputerName Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `print_spooler_failed_to_load_a_plug_in_filter`'
+how_to_implement: You will need to ensure PrintService Admin and Operational logs
+  are being logged to Splunk from critical or all systems.
 known_false_positives: False positives are unknown and filtering may be required.
 references:
 - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available
@@ -22,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$ComputerName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ComputerName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +51,8 @@ tags:
   - CVE-2021-34527
   - CVE-2021-1675
   impact: 80
-  message: Suspicious printer spooler errors have occured on endpoint $ComputerName$ with EventCode $EventCode$.
+  message: Suspicious printer spooler errors have occured on endpoint $ComputerName$
+    with EventCode $EventCode$.
   mitre_attack_id:
   - T1547.012
   - T1547
@@ -47,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OpCode
-  - EventCode
-  - ComputerName
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_admin.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_admin.log
     source: WinEventLog:Microsoft-Windows-PrintService/Admin
     sourcetype: WinEventLog
diff --git a/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml b/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml
index 5c7ba45d78..f7be5fe3e5 100644
--- a/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml
+++ b/detections/endpoint/process_creating_lnk_file_in_suspicious_location.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Jose Hernandez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects a process creating a `.lnk` file in suspicious locations such as `C:\User*` or `*\Local\Temp\*`. It leverages filesystem and process activity data from the Endpoint data model to identify this behavior. This activity is significant because creating `.lnk` files in these directories is a common tactic used by spear phishing tools to establish persistence or execute malicious payloads. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary code, or further compromise the system.
+description: The following analytic detects a process creating a `.lnk` file in suspicious
+  locations such as `C:\User*` or `*\Local\Temp\*`. It leverages filesystem and process
+  activity data from the Endpoint data model to identify this behavior. This activity
+  is significant because creating `.lnk` files in these directories is a common tactic
+  used by spear phishing tools to establish persistence or execute malicious payloads.
+  If confirmed malicious, this could allow an attacker to maintain persistence, execute
+  arbitrary code, or further compromise the system.
 data_source:
 - Sysmon EventID 11 AND Sysmon EventID 1
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND (Filesystem.file_path="C:\\Users\\*" OR Filesystem.file_path="*\\Temp\\*") by _time span=1h Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename process_guid as lnk_guid | join lnk_guid _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time span=1h Processes.parent_process_name Processes.parent_process_guid Processes.process_name Processes.dest Processes.process Processes.path | `drop_dm_object_name(Processes)` | rename parent_process_guid as lnk_guid] | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_guid, user, dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter`'
-how_to_implement: You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
-known_false_positives: This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk"
+  AND (Filesystem.file_path="C:\\Users\\*" OR Filesystem.file_path="*\\Temp\\*") by
+  _time span=1h Filesystem.process_guid Filesystem.file_name Filesystem.file_path
+  Filesystem.file_hash Filesystem.user | `drop_dm_object_name(Filesystem)` | rename
+  process_guid as lnk_guid | join lnk_guid _time [| tstats `security_content_summariesonly`
+  count FROM datamodel=Endpoint.Processes where Processes.process_name=* by _time
+  span=1h Processes.parent_process_name Processes.parent_process_guid Processes.process_name
+  Processes.dest Processes.process Processes.path | `drop_dm_object_name(Processes)`
+  | rename parent_process_guid as lnk_guid] | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | table firstTime, lastTime, lnk_guid, user,
+  dest, file_name, file_path, process_name, process, process_path, file_hash | `process_creating_lnk_file_in_suspicious_location_filter`'
+how_to_implement: You must be ingesting data that records filesystem and process activity
+  from your hosts to populate the Endpoint data model. This is typically populated
+  via endpoint detection-and-response product, such as Carbon Black, or endpoint data
+  sources, such as Sysmon.
+known_false_positives: This detection should yield little or no false positive results.
+  It is uncommon for LNK files to be executed from temporary or user directories.
 references:
 - https://attack.mitre.org/techniques/T1566/001/
 - https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A process $process_name$ that launching .lnk file in $file_path$ in host $dest$
+  message: A process $process_name$ that launching .lnk file in $file_path$ in host
+    $dest$
   mitre_attack_id:
   - T1566
   - T1566.002
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.file_hash
-  - Filesystem.user
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/process_deleting_its_process_file_path.yml b/detections/endpoint/process_deleting_its_process_file_path.yml
index 1caae395f3..237632ec5b 100644
--- a/detections/endpoint/process_deleting_its_process_file_path.yml
+++ b/detections/endpoint/process_deleting_its_process_file_path.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras
 status: production
 type: TTP
-description: The following analytic identifies a process attempting to delete its own file path, a behavior often associated with defense evasion techniques. This detection leverages Sysmon EventCode 1 logs, focusing on command lines executed via cmd.exe that include deletion commands. This activity is significant as it may indicate malware, such as Clop ransomware, trying to evade detection by removing its executable file if certain conditions are met. If confirmed malicious, this could allow the attacker to persist undetected, complicating incident response and remediation efforts.
+description: The following analytic identifies a process attempting to delete its
+  own file path, a behavior often associated with defense evasion techniques. This
+  detection leverages Sysmon EventCode 1 logs, focusing on command lines executed
+  via cmd.exe that include deletion commands. This activity is significant as it may
+  indicate malware, such as Clop ransomware, trying to evade detection by removing
+  its executable file if certain conditions are met. If confirmed malicious, this
+  could allow the attacker to persist undetected, complicating incident response and
+  remediation efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`sysmon` EventCode=1 CommandLine = "* /c *" CommandLine = "* del*" Image = "*\\cmd.exe" | eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by dest user ParentImage ParentCommandLine Image CommandLine EventCode ProcessID result | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_deleting_its_process_file_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '`sysmon` EventCode=1 CommandLine = "* /c *" CommandLine = "* del*" Image
+  = "*\\cmd.exe" | eval result = if(like(process,"%".parent_process."%"), "Found",
+  "Not Found") | stats min(_time) as firstTime max(_time) as lastTime count by dest
+  user ParentImage ParentCommandLine Image CommandLine EventCode ProcessID result
+  | where result = "Found" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `process_deleting_its_process_file_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 60
-  message: A process $Image$ tries to delete its process path in commandline $CommandLine$ as part of defense evasion in host $dest$ by user $user$
+  message: A process $Image$ tries to delete its process path in commandline $CommandLine$
+    as part of defense evasion in host $dest$ by user $user$
   mitre_attack_id:
   - T1070
   observable:
@@ -51,21 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - dest
-  - user
-  - ParentImage
-  - ParentCommandLine
-  - Image
-  - cmdline
-  - ProcessID
-  - result
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/process_execution_via_wmi.yml b/detections/endpoint/process_execution_via_wmi.yml
index 84685ae2b2..fcb6d9d834 100644
--- a/detections/endpoint/process_execution_via_wmi.yml
+++ b/detections/endpoint/process_execution_via_wmi.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Rico Valdez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a process by `WmiPrvSE.exe`, indicating potential use of WMI (Windows Management Instrumentation) for process creation. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as WMI can be used for lateral movement, remote code execution, or persistence by attackers. If confirmed malicious, this could allow an attacker to execute arbitrary commands or scripts, potentially leading to further compromise of the affected system or network.
+description: The following analytic detects the execution of a process by `WmiPrvSE.exe`,
+  indicating potential use of WMI (Windows Management Instrumentation) for process
+  creation. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process and parent process relationships. This activity is significant
+  as WMI can be used for lateral movement, remote code execution, or persistence by
+  attackers. If confirmed malicious, this could allow an attacker to execute arbitrary
+  commands or scripts, potentially leading to further compromise of the affected system
+  or network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=WmiPrvSE.exe NOT (Processes.process IN ("*\\dismhost.exe*")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_execution_via_wmi_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, administrators may use wmi to execute commands for legitimate purposes.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=WmiPrvSE.exe
+  NOT (Processes.process IN ("*\\dismhost.exe*")) by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `process_execution_via_wmi_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, administrators may use wmi to execute commands
+  for legitimate purposes.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A remote instance execution of wmic.exe by WmiPrvSE.exe detected on host - $dest$
+  message: A remote instance execution of wmic.exe by WmiPrvSE.exe detected on host
+    - $dest$
   mitre_attack_id:
   - T1047
   observable:
@@ -45,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.user
-  - Processes.dest
-  - Processes.process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/process_kill_base_on_file_path.yml b/detections/endpoint/process_kill_base_on_file_path.yml
index 0221b1298c..80b769c558 100644
--- a/detections/endpoint/process_kill_base_on_file_path.yml
+++ b/detections/endpoint/process_kill_base_on_file_path.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `wmic.exe` with the `delete` command to remove an executable path. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because it often indicates the initial stages of an adversary setting up malicious activities, such as cryptocurrency mining, on an endpoint. If confirmed malicious, this behavior could allow an attacker to disable security tools or other critical processes, facilitating further compromise and persistence within the environment.
+description: The following analytic detects the use of `wmic.exe` with the `delete`
+  command to remove an executable path. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, parent processes,
+  and command-line executions. This activity is significant because it often indicates
+  the initial stages of an adversary setting up malicious activities, such as cryptocurrency
+  mining, on an endpoint. If confirmed malicious, this behavior could allow an attacker
+  to disable security tools or other critical processes, facilitating further compromise
+  and persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_wmic` AND Processes.process="*process*" AND Processes.process="*executablepath*" AND Processes.process="*delete*" by  Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_kill_base_on_file_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where `process_wmic` AND Processes.process="*process*"
+  AND Processes.process="*executablepath*" AND Processes.process="*delete*" by  Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `process_kill_base_on_file_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A process $process_name$ attempt to kill process by its file path using commandline $process$ in host $dest$
+  message: A process $process_name$ attempt to kill process by its file path using
+    commandline $process$ in host $dest$
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -47,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/process_writing_dynamicwrapperx.yml b/detections/endpoint/process_writing_dynamicwrapperx.yml
index 35f33cbe7c..04df7023be 100644
--- a/detections/endpoint/process_writing_dynamicwrapperx.yml
+++ b/detections/endpoint/process_writing_dynamicwrapperx.yml
@@ -5,12 +5,36 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects a process writing the dynwrapx.dll file to disk and registering it in the registry. It leverages data from the Endpoint datamodel, specifically monitoring process and filesystem events. This activity is significant because DynamicWrapperX is an ActiveX component often used in scripts to call Windows API functions, and its presence in non-standard locations is highly suspicious. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. Immediate investigation of parallel processes and registry modifications is recommended.
+description: The following analytic detects a process writing the dynwrapx.dll file
+  to disk and registering it in the registry. It leverages data from the Endpoint
+  datamodel, specifically monitoring process and filesystem events. This activity
+  is significant because DynamicWrapperX is an ActiveX component often used in scripts
+  to call Windows API functions, and its presence in non-standard locations is highly
+  suspicious. If confirmed malicious, this could allow an attacker to execute arbitrary
+  code, escalate privileges, or maintain persistence within the environment. Immediate
+  investigation of parallel processes and registry modifications is recommended.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="dynwrapx.dll" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.user | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time) as lastTime by dest process_name process_guid file_name file_path file_create_time user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `process_writing_dynamicwrapperx_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives should be limited, however it is possible to filter by Processes.process_name and specific processes (ex. wscript.exe). Filter as needed. This may need modification based on EDR telemetry and how it brings in registry data. For example, removal of (Default).
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  by _time Processes.process_id Processes.process_name Processes.dest Processes.process_guid
+  Processes.user | `drop_dm_object_name(Processes)` | join process_guid [| tstats
+  `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where
+  Filesystem.file_name="dynwrapx.dll" by _time Filesystem.dest Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.user
+  | `drop_dm_object_name(Filesystem)` | fields _time process_guid file_path file_name
+  file_create_time user dest process_name] | stats count min(_time) as firstTime max(_time)
+  as lastTime by dest process_name process_guid file_name file_path file_create_time
+  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `process_writing_dynamicwrapperx_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem`
+  node. In addition, confirm the latest CIM App 4.20 or higher is installed and the
+  latest TA for the endpoint product.
+known_false_positives: False positives should be limited, however it is possible to
+  filter by Processes.process_name and specific processes (ex. wscript.exe). Filter
+  as needed. This may need modification based on EDR telemetry and how it brings in
+  registry data. For example, removal of (Default).
 references:
 - https://blog.f-secure.com/hunting-for-koadic-a-com-based-rootkit/
 - https://www.script-coding.com/dynwrapx_eng.html
@@ -23,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ downloading the DynamicWrapperX dll.
+  message: An instance of $process_name$ was identified on endpoint $dest$ downloading
+    the DynamicWrapperX dll.
   mitre_attack_id:
   - T1059
   - T1559.001
@@ -44,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - process_name
-  - process_guid
-  - file_name
-  - file_path
-  - file_create_time user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/processes_launching_netsh.yml b/detections/endpoint/processes_launching_netsh.yml
index 4dc01d9f8d..93363dab35 100644
--- a/detections/endpoint/processes_launching_netsh.yml
+++ b/detections/endpoint/processes_launching_netsh.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Josef Kuepker, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies processes launching netsh.exe, a command-line utility used to modify network configurations. It detects this activity by analyzing data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, parent processes, and command-line executions. This behavior is significant because netsh.exe can be exploited to execute malicious helper DLLs, serving as a persistence mechanism. If confirmed malicious, an attacker could gain persistent access, modify network settings, and potentially escalate privileges, posing a severe threat to the network's integrity and security.
+description: The following analytic identifies processes launching netsh.exe, a command-line
+  utility used to modify network configurations. It detects this activity by analyzing
+  data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs,
+  names, parent processes, and command-line executions. This behavior is significant
+  because netsh.exe can be exploited to execute malicious helper DLLs, serving as
+  a persistence mechanism. If confirmed malicious, an attacker could gain persistent
+  access, modify network settings, and potentially escalate privileges, posing a severe
+  threat to the network's integrity and security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` by Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` |`processes_launching_netsh_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_netsh` by Processes.parent_process_name Processes.parent_process
+  Processes.original_file_name Processes.process_name Processes.user Processes.dest
+  |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
+  |`processes_launching_netsh_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some VPN applications are known to launch netsh.exe. Outside
+  of these instances, it is unusual for an executable to launch netsh.exe and run
+  commands.
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 20
-  message: A process $process_name$ has launched netsh with command-line $process$ on $dest$.
+  message: A process $process_name$ has launched netsh with command-line $process$
+    on $dest$.
   mitre_attack_id:
   - T1562.004
   - T1562
@@ -53,18 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/processes_tapping_keyboard_events.yml b/detections/endpoint/processes_tapping_keyboard_events.yml
index b9dae36373..0c82d90dee 100644
--- a/detections/endpoint/processes_tapping_keyboard_events.yml
+++ b/detections/endpoint/processes_tapping_keyboard_events.yml
@@ -5,11 +5,28 @@ date: '2024-10-17'
 author: Jose Hernandez, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects processes on macOS systems that are tapping keyboard events, potentially monitoring all keystrokes made by a user. It leverages data from osquery results within the Alerts data model, focusing on specific process names and command lines. This activity is significant as it is a common technique used by Remote Access Trojans (RATs) to log keystrokes, posing a serious security risk. If confirmed malicious, this could lead to unauthorized access to sensitive information, including passwords and personal data, compromising the integrity and confidentiality of the system.
+description: The following analytic detects processes on macOS systems that are tapping
+  keyboard events, potentially monitoring all keystrokes made by a user. It leverages
+  data from osquery results within the Alerts data model, focusing on specific process
+  names and command lines. This activity is significant as it is a common technique
+  used by Remote Access Trojans (RATs) to log keystrokes, posing a serious security
+  risk. If confirmed malicious, this could lead to unauthorized access to sensitive
+  information, including passwords and personal data, compromising the integrity and
+  confidentiality of the system.
 data_source: []
-search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id| dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`'
-how_to_implement: In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model.
-known_false_positives: There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment.
+search: '| from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps
+  | rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id|
+  dedup host,process_name | table host,process_name, cmd, process_id | `processes_tapping_keyboard_events_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from your osquery deployed agents with the 
+  [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599)
+  pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must
+  be deployed across your indexers and universal forwarders in order to have the osquery
+  data populate the Alerts data model.
+known_false_positives: There might be some false positives as keyboard event taps
+  are used by processes like Siri and Zoom video chat, for some good examples of processes
+  to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161)
+  comment.
 references: []
 tags:
   analytic_story:
@@ -27,12 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - app
-  - name
-  - columns.cmdline
-  - columns.name
-  - columns.pid
-  - host
   security_domain: threat
diff --git a/detections/endpoint/randomly_generated_scheduled_task_name.yml b/detections/endpoint/randomly_generated_scheduled_task_name.yml
index 0295b2d152..639dbed68e 100644
--- a/detections/endpoint/randomly_generated_scheduled_task_name.yml
+++ b/detections/endpoint/randomly_generated_scheduled_task_name.yml
@@ -5,11 +5,22 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects the creation of a Scheduled Task with a high entropy, randomly generated name, leveraging Event ID 4698. It uses the `ut_shannon` function from the URL ToolBox Splunk application to measure the entropy of the Task Name. This activity is significant as adversaries often use randomly named Scheduled Tasks for lateral movement and remote code execution, employing tools like Impacket or CrackMapExec. If confirmed malicious, this could allow attackers to execute arbitrary code remotely, potentially leading to further compromise and persistence within the network.
+description: The following analytic detects the creation of a Scheduled Task with
+  a high entropy, randomly generated name, leveraging Event ID 4698. It uses the `ut_shannon`
+  function from the URL ToolBox Splunk application to measure the entropy of the Task
+  Name. This activity is significant as adversaries often use randomly named Scheduled
+  Tasks for lateral movement and remote code execution, employing tools like Impacket
+  or CrackMapExec. If confirmed malicious, this could allow attackers to execute arbitrary
+  code remotely, potentially leading to further compromise and persistence within
+  the network.
 data_source:
 - Windows Event Log Security 4698
-search: '`wineventlog_security` EventCode=4698 | xmlkv Message | lookup ut_shannon_lookup word as Task_Name | where ut_shannon > 3 | table  _time, dest, Task_Name, ut_shannon, Command, Author, Enabled, Hidden | `randomly_generated_scheduled_task_name_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well as the URL ToolBox application are also required.
+search: '`wineventlog_security` EventCode=4698 | xmlkv Message | lookup ut_shannon_lookup
+  word as Task_Name | where ut_shannon > 3 | table  _time, dest, Task_Name, ut_shannon,
+  Command, Author, Enabled, Hidden | `randomly_generated_scheduled_task_name_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well
+  as the URL ToolBox application are also required.
 known_false_positives: Legitimate applications may use random Scheduled Task names.
 references:
 - https://attack.mitre.org/techniques/T1053/005/
@@ -36,10 +47,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Task_Name
-  - Description
-  - Command
   security_domain: endpoint
diff --git a/detections/endpoint/randomly_generated_windows_service_name.yml b/detections/endpoint/randomly_generated_windows_service_name.yml
index 6172b09670..dd8ad5ed56 100644
--- a/detections/endpoint/randomly_generated_windows_service_name.yml
+++ b/detections/endpoint/randomly_generated_windows_service_name.yml
@@ -5,11 +5,22 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects the installation of a Windows Service with a suspicious, high-entropy name, indicating potential malicious activity. It leverages Event ID 7045 and the `ut_shannon` function from the URL ToolBox Splunk application to identify services with random names. This behavior is significant as adversaries often use randomly named services for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic detects the installation of a Windows Service
+  with a suspicious, high-entropy name, indicating potential malicious activity. It
+  leverages Event ID 7045 and the `ut_shannon` function from the URL ToolBox Splunk
+  application to identify services with random names. This behavior is significant
+  as adversaries often use randomly named services for lateral movement and remote
+  code execution. If confirmed malicious, this activity could allow attackers to execute
+  arbitrary code, escalate privileges, or maintain persistence within the environment.
 data_source:
 - Windows Event Log System 7045
-search: '`wineventlog_system` EventCode=7045 | lookup ut_shannon_lookup word as Service_Name | where ut_shannon > 3 | table EventCode ComputerName Service_Name ut_shannon Service_Start_Type Service_Type Service_File_Name | `randomly_generated_windows_service_name_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints. The Windows TA as well as the URL ToolBox application are also required.
+search: '`wineventlog_system` EventCode=7045 | lookup ut_shannon_lookup word as Service_Name
+  | where ut_shannon > 3 | table EventCode ComputerName Service_Name ut_shannon Service_Start_Type
+  Service_Type Service_File_Name | `randomly_generated_windows_service_name_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints. The Windows TA as well as the URL ToolBox application are also
+  required.
 known_false_positives: Legitimate applications may use random Windows Service names.
 references:
 - https://attack.mitre.org/techniques/T1543/003/
@@ -33,12 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ComputerName
-  - Service_File_Name
-  - Service_Type
-  - Service_Name
-  - Service_Start_Type
   security_domain: endpoint
diff --git a/detections/endpoint/ransomware_notes_bulk_creation.yml b/detections/endpoint/ransomware_notes_bulk_creation.yml
index 5bc3022972..c81d99641f 100644
--- a/detections/endpoint/ransomware_notes_bulk_creation.yml
+++ b/detections/endpoint/ransomware_notes_bulk_creation.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras
 status: production
 type: Anomaly
-description: The following analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame. This activity is significant as it often indicates an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
+description: The following analytic identifies the bulk creation of ransomware notes
+  (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode
+  11 to detect multiple instances of these file types being created within a short
+  time frame. This activity is significant as it often indicates an active ransomware
+  attack, where the attacker is notifying the victim of the encryption. If confirmed
+  malicious, this behavior could lead to widespread data encryption, rendering critical
+  files inaccessible and potentially causing significant operational disruption.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") |bin _time span=10s | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name | rename Computer as dest | where unique_readme_path_count >= 15 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ransomware_notes_bulk_creation_filter`'
-how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
+search: '`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") |bin _time
+  span=10s | stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename)
+  as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer
+  Image file_name | rename Computer as dest | where unique_readme_path_count >= 15
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ransomware_notes_bulk_creation_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint file-system data model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: A high frequency file creation of $file_name$ in different file path in host $dest$
+  message: A high frequency file creation of $file_name$ in different file path in
+    host $dest$
   mitre_attack_id:
   - T1486
   observable:
@@ -46,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - file_name
-  - _time
-  - TargetFilename
-  - dest
-  - Image
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml b/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml
index eda82d93d1..46b901f292 100644
--- a/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml
+++ b/detections/endpoint/recon_avproduct_through_pwh_or_wmi.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious PowerShell script execution via EventCode 4104, specifically targeting checks for installed anti-virus products using WMI or PowerShell commands. This detection leverages PowerShell Script Block Logging to identify scripts containing keywords like "SELECT," "WMIC," "AntiVirusProduct," or "AntiSpywareProduct." This activity is significant as it is commonly used by malware and APT actors to map running security applications or services, potentially aiding in evasion techniques. If confirmed malicious, this could allow attackers to disable or bypass security measures, leading to further compromise of the endpoint.
+description: The following analytic detects suspicious PowerShell script execution
+  via EventCode 4104, specifically targeting checks for installed anti-virus products
+  using WMI or PowerShell commands. This detection leverages PowerShell Script Block
+  Logging to identify scripts containing keywords like "SELECT," "WMIC," "AntiVirusProduct,"
+  or "AntiSpywareProduct." This activity is significant as it is commonly used by
+  malware and APT actors to map running security applications or services, potentially
+  aiding in evasion techniques. If confirmed malicious, this could allow attackers
+  to disable or bypass security measures, leading to further compromise of the endpoint.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*SELECT*" OR ScriptBlockText = "*WMIC*") AND (ScriptBlockText = "*AntiVirusProduct*" OR ScriptBlockText = "*AntiSpywareProduct*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_avproduct_through_pwh_or_wmi_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*SELECT*" OR ScriptBlockText
+  = "*WMIC*") AND (ScriptBlockText = "*AntiVirusProduct*" OR ScriptBlockText = "*AntiSpywareProduct*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `recon_avproduct_through_pwh_or_wmi_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: network administrator may used this command for checking purposes
 references:
 - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/
@@ -24,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,16 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/pwh_av_recon/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/pwh_av_recon/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/recon_using_wmi_class.yml b/detections/endpoint/recon_using_wmi_class.yml
index a7cec9cc6f..a7a3d63cfe 100644
--- a/detections/endpoint/recon_using_wmi_class.yml
+++ b/detections/endpoint/recon_using_wmi_class.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious PowerShell activity via EventCode 4104, where WMI performs event queries to gather information on running processes or services. This detection leverages PowerShell Script Block Logging to identify specific WMI queries targeting system information classes like Win32_Bios and Win32_OperatingSystem. This activity is significant as it often indicates reconnaissance efforts by an adversary to profile the compromised machine. If confirmed malicious, the attacker could gain detailed system information, aiding in further exploitation or lateral movement within the network.
+description: The following analytic detects suspicious PowerShell activity via EventCode
+  4104, where WMI performs event queries to gather information on running processes
+  or services. This detection leverages PowerShell Script Block Logging to identify
+  specific WMI queries targeting system information classes like Win32_Bios and Win32_OperatingSystem.
+  This activity is significant as it often indicates reconnaissance efforts by an
+  adversary to profile the compromised machine. If confirmed malicious, the attacker
+  could gain detailed system information, aiding in further exploitation or lateral
+  movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText= "*SELECT*" OR ScriptBlockText= "*Get-WmiObject*") AND (ScriptBlockText= "*Win32_Bios*" OR ScriptBlockText= "*Win32_OperatingSystem*" OR ScriptBlockText= "*Win32_Processor*" OR ScriptBlockText= "*Win32_ComputerSystem*" OR ScriptBlockText= "*Win32_PnPEntity*" OR ScriptBlockText= "*Win32_ShadowCopy*" OR ScriptBlockText= "*Win32_DiskDrive*" OR ScriptBlockText= "*Win32_PhysicalMemory*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recon_using_wmi_class_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 (ScriptBlockText= "*SELECT*" OR ScriptBlockText=
+  "*Get-WmiObject*") AND (ScriptBlockText= "*Win32_Bios*" OR ScriptBlockText= "*Win32_OperatingSystem*"
+  OR ScriptBlockText= "*Win32_Processor*" OR ScriptBlockText= "*Win32_ComputerSystem*"
+  OR ScriptBlockText= "*Win32_PnPEntity*" OR ScriptBlockText= "*Win32_ShadowCopy*"
+  OR ScriptBlockText= "*Win32_DiskDrive*" OR ScriptBlockText= "*Win32_PhysicalMemory*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `recon_using_wmi_class_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: network administrator may used this command for checking purposes
 references:
 - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/
@@ -26,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 75
-  message: A suspicious powershell script contains host recon commands detected on host $dest$
+  message: A suspicious powershell script contains host recon commands detected on
+    host $dest$
   mitre_attack_id:
   - T1592
   - T1059.001
@@ -59,16 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reconusingwmi.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/reconusingwmi.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml b/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml
index 8206bcedef..46d5d2a3ea 100644
--- a/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml
+++ b/detections/endpoint/recursive_delete_of_directory_in_batch_cmd.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a batch command designed to recursively delete files or directories, a technique often used by ransomware like Reddot to delete files in the recycle bin and prevent recovery. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific flags for recursive and quiet deletions. This activity is significant as it indicates potential ransomware behavior aimed at data destruction. If confirmed malicious, it could lead to significant data loss and hinder recovery efforts, severely impacting business operations.
+description: The following analytic detects the execution of a batch command designed
+  to recursively delete files or directories, a technique often used by ransomware
+  like Reddot to delete files in the recycle bin and prevent recovery. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions that include specific flags for recursive and quiet deletions. This activity
+  is significant as it indicates potential ransomware behavior aimed at data destruction.
+  If confirmed malicious, it could lead to significant data loss and hinder recovery
+  efforts, severely impacting business operations.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*/c*  Processes.process="* rd *"  Processes.process="*/s*" Processes.process="*/q*" by Processes.user Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `recursive_delete_of_directory_in_batch_cmd_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network operator may use this batch command to delete recursively a directory or files within directory
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_cmd` Processes.process=*/c*  Processes.process="*
+  rd *"  Processes.process="*/s*" Processes.process="*/q*" by Processes.user Processes.process_name
+  Processes.parent_process_name Processes.parent_process Processes.process Processes.process_id
+  Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `recursive_delete_of_directory_in_batch_cmd_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network operator may use this batch command to delete recursively
+  a directory or files within directory
 references:
 - https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml b/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml
index 8e888ffc3a..cd68273f48 100644
--- a/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml
+++ b/detections/endpoint/reg_exe_manipulating_windows_services_registry_keys.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of reg.exe to modify registry keys associated with Windows services and their configurations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because unauthorized changes to service registry keys can indicate an attempt to establish persistence or escalate privileges. If confirmed malicious, this could allow an attacker to control service behavior, potentially leading to unauthorized code execution or system compromise.
+description: The following analytic detects the use of reg.exe to modify registry
+  keys associated with Windows services and their configurations. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names, parent
+  processes, and command-line executions. This activity is significant because unauthorized
+  changes to service registry keys can indicate an attempt to establish persistence
+  or escalate privileges. If confirmed malicious, this could allow an attacker to
+  control service behavior, potentially leading to unauthorized code execution or
+  system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `reg_exe_manipulating_windows_services_registry_keys_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name)
+  as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes
+  where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add*
+  Processes.process=*Services* by Processes.process_id Processes.dest Processes.process
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `reg_exe_manipulating_windows_services_registry_keys_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is unusual for a service to be created or modified by directly
+  manipulating the registry. However, there may be legitimate instances of this behavior.
+  It is important to validate and investigate, as appropriate.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.user
-  - Processes.process
-  - Processes.process_id
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/registry_keys_for_creating_shim_databases.yml b/detections/endpoint/registry_keys_for_creating_shim_databases.yml
index dac202bdc6..6b9b02bff1 100644
--- a/detections/endpoint/registry_keys_for_creating_shim_databases.yml
+++ b/detections/endpoint/registry_keys_for_creating_shim_databases.yml
@@ -5,16 +5,24 @@ date: '2024-11-14'
 author: Patrick Bareiss, Teoderick Contreras, Splunk, Steven Dick, Bhavin Patel
 status: production
 type: TTP
-description: The following analytic detects registry activity related to the creation of application compatibility shims. It leverages data from the Endpoint.Registry data model, specifically monitoring registry paths associated with AppCompatFlags. This activity is significant because attackers can use shims to bypass security controls, achieve persistence, or escalate privileges. If confirmed malicious, this could allow an attacker to maintain long-term access, execute arbitrary code, or manipulate application behavior, posing a severe risk to the integrity and security of the affected systems.
+description: The following analytic detects registry activity related to the creation
+  of application compatibility shims. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring registry paths associated with AppCompatFlags.
+  This activity is significant because attackers can use shims to bypass security
+  controls, achieve persistence, or escalate privileges. If confirmed malicious, this
+  could allow an attacker to maintain long-term access, execute arbitrary code, or
+  manipulate application behavior, posing a severe risk to the integrity and security
+  of the affected systems.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB*)
-  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `registry_keys_for_creating_shim_databases_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom*
+  OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB*) BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
+  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `registry_keys_for_creating_shim_databases_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -28,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A registry activity in $registry_path$ related to shim modication in host $dest$
+  message: A registry activity in $registry_path$ related to shim modication in host
+    $dest$
   mitre_attack_id:
   - T1546.011
   - T1546
@@ -56,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/registry_keys_used_for_persistence.yml b/detections/endpoint/registry_keys_used_for_persistence.yml
index 1dbe40774f..e99b65d9a5 100644
--- a/detections/endpoint/registry_keys_used_for_persistence.yml
+++ b/detections/endpoint/registry_keys_used_for_persistence.yml
@@ -5,13 +5,51 @@ date: '2024-09-30'
 author: Jose Hernandez, David Dorsey, Teoderick Contreras, Rod Soto, Splunk
 status: production
 type: TTP
-description: The following analytic identifies modifications to registry keys commonly used for persistence mechanisms. It leverages data from endpoint detection sources like Sysmon or Carbon Black, focusing on specific registry paths known to initiate applications or services during system startup. This activity is significant as unauthorized changes to these keys can indicate attempts to maintain persistence or execute malicious actions upon system boot. If confirmed malicious, this could allow attackers to achieve persistent access, execute arbitrary code, or maintain control over compromised systems, posing a severe threat to system integrity and security.
+description: The following analytic identifies modifications to registry keys commonly
+  used for persistence mechanisms. It leverages data from endpoint detection sources
+  like Sysmon or Carbon Black, focusing on specific registry paths known to initiate
+  applications or services during system startup. This activity is significant as
+  unauthorized changes to these keys can indicate attempts to maintain persistence
+  or execute malicious actions upon system boot. If confirmed malicious, this could
+  allow attackers to achieve persistent access, execute arbitrary code, or maintain
+  control over compromised systems, posing a severe threat to system integrity and
+  security.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\User Shell Folders\\*" OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\*" OR Registry.registry_path=*\\currentversion\\run* OR Registry.registry_path=*\\currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Notify* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*\\currentversion\\policies\\explorer\\run* OR Registry.registry_path=*\\currentversion\\runservices* OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\* OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\Common Startup" OR Registry.registry_path= *\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SharedTaskScheduler OR Registry.registry_path= *\\Classes\\htmlfile\\shell\\open\\command OR (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" AND Registry.registry_key_name=Debugger) OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa" AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa\\OSConfig" AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\Microsoft\\Windows NT\\CurrentVersion\\SilentProcessExit\\*") OR (Registry.registry_path="*currentVersion\\Windows" AND Registry.registry_key_name="Load") OR (Registry.registry_path="*\\CurrentVersion" AND Registry.registry_key_name="Svchost") OR (Registry.registry_path="*\\CurrentControlSet\Control\Session Manager"AND Registry.registry_key_name="BootExecute") OR (Registry.registry_path="*\\Software\\Run" AND Registry.registry_key_name="auto_update")) by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_used_for_persistence_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
-known_false_positives: There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\RunOnce
+  OR Registry.registry_path=*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\StartupApproved\\Run
+  OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\User
+  Shell Folders\\*" OR Registry.registry_path= "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell
+  Folders\\*" OR Registry.registry_path=*\\currentversion\\run* OR Registry.registry_path=*\\currentVersion\\Windows\\Appinit_Dlls*
+  OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Notify*
+  OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*\\CurrentVersion\\Winlogon\\VmApplet*
+  OR Registry.registry_path=*\\currentversion\\policies\\explorer\\run* OR Registry.registry_path=*\\currentversion\\runservices*
+  OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\* OR Registry.registry_path=
+  "*\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders\\Common
+  Startup" OR Registry.registry_path= *\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SharedTaskScheduler
+  OR Registry.registry_path= *\\Classes\\htmlfile\\shell\\open\\command OR (Registry.registry_path="*Microsoft\\Windows
+  NT\\CurrentVersion\\Image File Execution Options*" AND Registry.registry_key_name=Debugger)
+  OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa" AND Registry.registry_key_name="Security
+  Packages") OR (Registry.registry_path="*\\CurrentControlSet\\Control\\Lsa\\OSConfig"
+  AND Registry.registry_key_name="Security Packages") OR (Registry.registry_path="*\\Microsoft\\Windows
+  NT\\CurrentVersion\\SilentProcessExit\\*") OR (Registry.registry_path="*currentVersion\\Windows"
+  AND Registry.registry_key_name="Load") OR (Registry.registry_path="*\\CurrentVersion"
+  AND Registry.registry_key_name="Svchost") OR (Registry.registry_path="*\\CurrentControlSet\Control\Session
+  Manager"AND Registry.registry_key_name="BootExecute") OR (Registry.registry_path="*\\Software\\Run"
+  AND Registry.registry_key_name="auto_update")) by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `registry_keys_used_for_persistence_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
+known_false_positives: There are many legitimate applications that must execute on
+  system startup and will use these registry keys to accomplish that task.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -19,7 +57,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -70,16 +113,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.dest
-  - Registry.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/registry_keys_used_for_privilege_escalation.yml b/detections/endpoint/registry_keys_used_for_privilege_escalation.yml
index 7cbb4bc93d..42b0768707 100644
--- a/detections/endpoint/registry_keys_used_for_privilege_escalation.yml
+++ b/detections/endpoint/registry_keys_used_for_privilege_escalation.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: David Dorsey, Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to registry keys under "Image File Execution Options" that can be used for privilege escalation. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths and values like GlobalFlag and Debugger. This activity is significant because attackers can use these modifications to intercept executable calls and attach malicious binaries to legitimate system binaries. If confirmed malicious, this could allow attackers to execute arbitrary code with elevated privileges, leading to potential system compromise and persistent access.
+description: The following analytic detects modifications to registry keys under "Image
+  File Execution Options" that can be used for privilege escalation. It leverages
+  data from the Endpoint.Registry data model, specifically monitoring changes to registry
+  paths and values like GlobalFlag and Debugger. This activity is significant because
+  attackers can use these modifications to intercept executable calls and attach malicious
+  binaries to legitimate system binaries. If confirmed malicious, this could allow
+  attackers to execute arbitrary code with elevated privileges, leading to potential
+  system compromise and persistent access.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE ((Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File
-  Execution Options*") AND (Registry.registry_value_name=GlobalFlag OR Registry.registry_value_name=Debugger))
-  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `registry_keys_used_for_privilege_escalation_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path="*Microsoft\\Windows
+  NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_value_name=GlobalFlag
+  OR Registry.registry_value_name=Debugger)) BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `registry_keys_used_for_privilege_escalation_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -30,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 95
   impact: 80
-  message: A registry activity in $registry_path$ related to privilege escalation in host $dest$
+  message: A registry activity in $registry_path$ related to privilege escalation
+    in host $dest$
   mitre_attack_id:
   - T1546.012
   - T1546
@@ -61,18 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml b/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml
index 09827bc211..e66a55bb29 100644
--- a/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml
+++ b/detections/endpoint/regsvr32_silent_and_install_param_dll_loading.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the loading of a DLL using the regsvr32 application with the silent parameter and DLLInstall execution. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and parent process details. This activity is significant as it is commonly used by RAT malware like Remcos and njRAT to load malicious DLLs on compromised machines. If confirmed malicious, this technique could allow attackers to execute arbitrary code, maintain persistence, and further compromise the system.
+description: The following analytic detects the loading of a DLL using the regsvr32
+  application with the silent parameter and DLLInstall execution. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process command-line
+  arguments and parent process details. This activity is significant as it is commonly
+  used by RAT malware like Remcos and njRAT to load malicious DLLs on compromised
+  machines. If confirmed malicious, this technique could allow attackers to execute
+  arbitrary code, maintain persistence, and further compromise the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` AND Processes.process="*/i*" by Processes.dest Processes.parent_process Processes.process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_silent_and_install_param_dll_loading_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Other third part application may used this parameter but not so common in base windows environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` AND Processes.process="*/i*"
+  by Processes.dest Processes.parent_process Processes.process Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_silent_and_install_param_dll_loading_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Other third part application may used this parameter but not
+  so common in base windows environment.
 references:
 - https://app.any.run/tasks/dc93ee63-050c-4ff8-b07e-8277af9ab939/
 - https://attack.mitre.org/techniques/T1218/010/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +61,9 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a DLL using the silent and dllinstall parameter.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to load a DLL using the silent and
+    dllinstall parameter.
   mitre_attack_id:
   - T1218
   - T1218.010
@@ -61,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml b/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml
index d92809b831..605c62192e 100644
--- a/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml
+++ b/detections/endpoint/regsvr32_with_known_silent_switch_cmdline.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of Regsvr32.exe with the silent switch to load DLLs. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions containing the `-s` or `/s` switches. This activity is significant as it is commonly used in malware campaigns, such as IcedID, to stealthily load malicious DLLs. If confirmed malicious, this could allow an attacker to execute arbitrary code, download additional payloads, and potentially compromise the system further. Immediate investigation and endpoint isolation are recommended.
+description: The following analytic detects the execution of Regsvr32.exe with the
+  silent switch to load DLLs. This behavior is identified using Endpoint Detection
+  and Response (EDR) telemetry, focusing on command-line executions containing the
+  `-s` or `/s` switches. This activity is significant as it is commonly used in malware
+  campaigns, such as IcedID, to stealthily load malicious DLLs. If confirmed malicious,
+  this could allow an attacker to execute arbitrary code, download additional payloads,
+  and potentially compromise the system further. Immediate investigation and endpoint
+  isolation are recommended.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/][Ss]{1}") | `regsvr32_with_known_silent_switch_cmdline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: minimal. but network operator can use this application to load dll.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` by Processes.user
+  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name
+  Processes.dest Processes.process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | where match(process,"(?i)[\-|\/][Ss]{1}")
+  | `regsvr32_with_known_silent_switch_cmdline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: minimal. but network operator can use this application to load
+  dll.
 references:
 - https://app.any.run/tasks/56680cba-2bbc-4b34-8633-5f7878ddf858/
 - https://regexr.com/699e2
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a DLL using the silent parameter.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to load a DLL using the silent parameter.
   mitre_attack_id:
   - T1218
   - T1218.010
@@ -61,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remcos_client_registry_install_entry.yml b/detections/endpoint/remcos_client_registry_install_entry.yml
index f3005e34c3..9cbf2ce94f 100644
--- a/detections/endpoint/remcos_client_registry_install_entry.yml
+++ b/detections/endpoint/remcos_client_registry_install_entry.yml
@@ -5,15 +5,40 @@ date: '2024-09-30'
 author: Steven Dick, Bhavin Patel, Rod Soto, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the presence of a registry key associated with the Remcos RAT agent on a host. It leverages data from the Endpoint.Processes and Endpoint.Registry data models in Splunk, focusing on instances where the "license" key is found in the "Software\Remcos" path. This behavior is significant as it indicates potential compromise by the Remcos RAT, a remote access Trojan used for unauthorized access and data exfiltration. If confirmed malicious, the attacker could gain control over the system, steal sensitive information, or use the compromised host for further attacks. Immediate investigation and remediation are required.
+description: The following analytic detects the presence of a registry key associated
+  with the Remcos RAT agent on a host. It leverages data from the Endpoint.Processes
+  and Endpoint.Registry data models in Splunk, focusing on instances where the "license"
+  key is found in the "Software\Remcos" path. This behavior is significant as it indicates
+  potential compromise by the Remcos RAT, a remote access Trojan used for unauthorized
+  access and data exfiltration. If confirmed malicious, the attacker could gain control
+  over the system, steal sensitive information, or use the compromised host for further
+  attacks. Immediate investigation and remediation are required.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_key_name=*\\Software\\Remcos*) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`remcos_client_registry_install_entry_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_key_name=*\\Software\\Remcos*) BY _time span=1h Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime
+  dest user parent_process_name parent_process process_name process_path process registry_key_name
+  registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`remcos_client_registry_install_entry_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/software/S0332/
@@ -23,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: A registry entry $registry_path$ with registry keyname $registry_key_name$ related to Remcos RAT in host $dest$
+  message: A registry entry $registry_path$ with registry keyname $registry_key_name$
+    related to Remcos RAT in host $dest$
   mitre_attack_id:
   - T1112
   observable:
@@ -45,27 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml b/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml
index ea3d308fa1..483451a0fd 100644
--- a/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml
+++ b/detections/endpoint/remcos_rat_file_creation_in_remcos_folder.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk, Sanjay Govind
 status: production
 type: TTP
-description: The following analytic detects the creation of files in the Remcos folder within the AppData directory, specifically targeting keylog and clipboard log files. It leverages the Endpoint.Filesystem data model to identify .dat files created in paths containing "remcos." This activity is significant as it indicates the presence of the Remcos RAT, which performs keylogging, clipboard capturing, and audio recording. If confirmed malicious, this could lead to unauthorized data exfiltration and extensive surveillance capabilities for the attacker.
+description: The following analytic detects the creation of files in the Remcos folder
+  within the AppData directory, specifically targeting keylog and clipboard log files.
+  It leverages the Endpoint.Filesystem data model to identify .dat files created in
+  paths containing "remcos." This activity is significant as it indicates the presence
+  of the Remcos RAT, which performs keylogging, clipboard capturing, and audio recording.
+  If confirmed malicious, this could lead to unauthorized data exfiltration and extensive
+  surveillance capabilities for the attacker.
 data_source:
 - Sysmon EventID 11
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.dat") Filesystem.file_path = "*\\remcos\\*" by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remcos_rat_file_creation_in_remcos_folder_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.dat")
+  Filesystem.file_path = "*\\remcos\\*" by _time Filesystem.file_name Filesystem.file_path
+  Filesystem.dest Filesystem.file_create_time | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remcos_rat_file_creation_in_remcos_folder_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: unknown
 references:
 - https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,16 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - file_create_time
-  - file_name
-  - file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_desktop_process_running_on_system.yml b/detections/endpoint/remote_desktop_process_running_on_system.yml
index 4ec29dcaf8..e7749fd390 100644
--- a/detections/endpoint/remote_desktop_process_running_on_system.yml
+++ b/detections/endpoint/remote_desktop_process_running_on_system.yml
@@ -5,13 +5,32 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects the execution of the remote desktop process (mstsc.exe) on systems where it is not typically run. This detection leverages data from Endpoint Detection and Response (EDR) agents, filtering out systems categorized as common RDP sources. This activity is significant because unauthorized use of mstsc.exe can indicate lateral movement or unauthorized remote access attempts. If confirmed malicious, this could allow an attacker to gain remote control of a system, potentially leading to data exfiltration, privilege escalation, or further network compromise.
+description: The following analytic detects the execution of the remote desktop process
+  (mstsc.exe) on systems where it is not typically run. This detection leverages data
+  from Endpoint Detection and Response (EDR) agents, filtering out systems categorized
+  as common RDP sources. This activity is significant because unauthorized use of
+  mstsc.exe can indicate lateral movement or unauthorized remote access attempts.
+  If confirmed malicious, this could allow an attacker to gain remote control of a
+  system, potentially leading to data exfiltration, privilege escalation, or further
+  network compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe
+  AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user
+  Processes.process | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `drop_dm_object_name(Processes)` | `remote_desktop_process_running_on_system_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Remote Desktop may be used legitimately by users on the network.
 references: []
 tags:
@@ -38,10 +57,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.dest_category
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
diff --git a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml
index f6ba1f18bc..801b2b24b1 100644
--- a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml
+++ b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with arguments used to start a process on a remote endpoint by abusing the DCOM protocol, specifically targeting ShellExecute and ExecuteShellCommand. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant as it indicates potential lateral movement and remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code remotely, escalate privileges, and move laterally within the network, posing a severe security risk.
+description: The following analytic detects the execution of `powershell.exe` with
+  arguments used to start a process on a remote endpoint by abusing the DCOM protocol,
+  specifically targeting ShellExecute and ExecuteShellCommand. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process names, parent
+  processes, and command-line executions. This activity is significant as it indicates
+  potential lateral movement and remote code execution attempts by adversaries. If
+  confirmed malicious, this could allow attackers to execute arbitrary code remotely,
+  escalate privileges, and move laterally within the network, posing a severe security
+  risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Document.ActiveView.ExecuteShellCommand*" OR Processes.process="*Document.Application.ShellExecute*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may leverage DCOM to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Document.ActiveView.ExecuteShellCommand*"
+  OR Processes.process="*Document.Application.ShellExecute*") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may leverage DCOM to start a process on remote
+  systems, but this activity is usually limited to a small set of hosts or users.
 references:
 - https://attack.mitre.org/techniques/T1021/003/
 - https://www.cybereason.com/blog/dcom-lateral-movement-techniques
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 90
-  message: A process was started on a remote endpoint from $dest$ by abusing DCOM using PowerShell.exe
+  message: A process was started on a remote endpoint from $dest$ by abusing DCOM
+    using PowerShell.exe
   mitre_attack_id:
   - T1021
   - T1021.003
@@ -44,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.003/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml
index c5371659c9..701408dce7 100644
--- a/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml
+++ b/detections/endpoint/remote_process_instantiation_via_dcom_and_powershell_script_block.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of PowerShell commands that initiate a process on a remote endpoint via the DCOM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to identify the use of ShellExecute and ExecuteShellCommand. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this behavior could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network.
+description: The following analytic detects the execution of PowerShell commands that
+  initiate a process on a remote endpoint via the DCOM protocol. It leverages PowerShell
+  Script Block Logging (EventCode=4104) to identify the use of ShellExecute and ExecuteShellCommand.
+  This activity is significant as it may indicate lateral movement or remote code
+  execution attempts by adversaries. If confirmed malicious, this behavior could allow
+  attackers to execute arbitrary code on remote systems, potentially leading to further
+  compromise and persistence within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText="*Document.Application.ShellExecute*" OR ScriptBlockText="*Document.ActiveView.ExecuteShellCommand*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators may leverage DCOM to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '`powershell` EventCode=4104 (ScriptBlockText="*Document.Application.ShellExecute*"
+  OR ScriptBlockText="*Document.ActiveView.ExecuteShellCommand*") | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_dcom_and_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup instructions
+  can be found 
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators may leverage DCOM to start a process on remote
+  systems, but this activity is usually limited to a small set of hosts or users.
 references:
 - https://attack.mitre.org/techniques/T1021/003/
 - https://www.cybereason.com/blog/dcom-lateral-movement-techniques
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 90
-  message: A process was started on a remote endpoint from $Computer$ by abusing WMI using PowerShell.exe
+  message: A process was started on a remote endpoint from $Computer$ by abusing WMI
+    using PowerShell.exe
   mitre_attack_id:
   - T1021
   - T1021.003
@@ -42,16 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml
index b906903293..be8aefd0c5 100644
--- a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml
+++ b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` with arguments used to start a process on a remote endpoint via the WinRM protocol, specifically targeting the `Invoke-Command` cmdlet. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process telemetry. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and lateral spread within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  arguments used to start a process on a remote endpoint via the WinRM protocol, specifically
+  targeting the `Invoke-Command` cmdlet. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions and process telemetry.
+  This activity is significant as it may indicate lateral movement or remote code
+  execution attempts by adversaries. If confirmed malicious, this could allow attackers
+  to execute arbitrary code on remote systems, potentially leading to further compromise
+  and lateral spread within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-Command*" AND Processes.process="*-ComputerName*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start a process on remote systems for system administration or automation use cases. However, this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-Command*"
+  AND Processes.process="*-ComputerName*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `remote_process_instantiation_via_winrm_and_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start
+  a process on remote systems for system administration or automation use cases. However,
+  this activity is usually limited to a small set of hosts or users.
 references:
 - https://attack.mitre.org/techniques/T1021/006/
 - https://pentestlab.blog/2018/05/15/lateral-movement-winrm/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 90
-  message: A process was started on a remote endpoint from $dest$ by abusing WinRM using PowerShell.exe
+  message: A process was started on a remote endpoint from $dest$ by abusing WinRM
+    using PowerShell.exe
   mitre_attack_id:
   - T1021
   - T1021.006
@@ -44,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml
index 3aa274ddb3..2f532f7d07 100644
--- a/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml
+++ b/detections/endpoint/remote_process_instantiation_via_winrm_and_powershell_script_block.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of PowerShell commands that use the `Invoke-Command` cmdlet to start a process on a remote endpoint via the WinRM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to identify such activities. This behavior is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this activity could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network.
+description: The following analytic detects the execution of PowerShell commands that
+  use the `Invoke-Command` cmdlet to start a process on a remote endpoint via the
+  WinRM protocol. It leverages PowerShell Script Block Logging (EventCode=4104) to
+  identify such activities. This behavior is significant as it may indicate lateral
+  movement or remote code execution attempts by adversaries. If confirmed malicious,
+  this activity could allow attackers to execute arbitrary code on remote systems,
+  potentially leading to further compromise and persistence within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText="*Invoke-Command*" AND ScriptBlockText="*-ComputerName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start a process on remote systems for system administration or automation use cases. This activity is usually limited to a small set of hosts or users. In certain environments, tuning may not be possible.
+search: '`powershell` EventCode=4104 (ScriptBlockText="*Invoke-Command*" AND ScriptBlockText="*-ComputerName*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `remote_process_instantiation_via_winrm_and_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup instructions
+  can be found 
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators may leverage WinRM and `Invoke-Command` to start
+  a process on remote systems for system administration or automation use cases. This
+  activity is usually limited to a small set of hosts or users. In certain environments,
+  tuning may not be possible.
 references:
 - https://attack.mitre.org/techniques/T1021/006/
 - https://pentestlab.blog/2018/05/15/lateral-movement-winrm/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 90
-  message: A process was started on a remote endpoint from $Computer$ by abusing WinRM using PowerShell.exe
+  message: A process was started on a remote endpoint from $Computer$ by abusing WinRM
+    using PowerShell.exe
   mitre_attack_id:
   - T1021
   - T1021.006
@@ -42,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_psh/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml b/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml
index d7f4f42003..f8c8225de3 100644
--- a/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml
+++ b/detections/endpoint/remote_process_instantiation_via_winrm_and_winrs.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `winrs.exe` with command-line arguments used to start a process on a remote endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and lateral spread within the network.
+description: The following analytic detects the execution of `winrs.exe` with command-line
+  arguments used to start a process on a remote endpoint. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions mapped to the `Processes` node of the `Endpoint` data model. This activity
+  is significant as it may indicate lateral movement or remote code execution attempts
+  by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary
+  code on remote systems, potentially leading to further compromise and lateral spread
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=winrs.exe OR Processes.original_file_name=winrs.exe) (Processes.process="*-r:*" OR Processes.process="*-remote:*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_winrs_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may leverage WinRM and WinRs to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=winrs.exe
+  OR Processes.original_file_name=winrs.exe) (Processes.process="*-r:*" OR Processes.process="*-remote:*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_winrm_and_winrs_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may leverage WinRM and WinRs to start a process
+  on remote systems, but this activity is usually limited to a small set of hosts
+  or users.
 references:
 - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/winrs
 - https://attack.mitre.org/techniques/T1021/006/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_wmi.yml b/detections/endpoint/remote_process_instantiation_via_wmi.yml
index 7e1df9942b..39dcade671 100644
--- a/detections/endpoint/remote_process_instantiation_via_wmi.yml
+++ b/detections/endpoint/remote_process_instantiation_via_wmi.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Rico Valdez, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of wmic.exe with parameters to spawn a process on a remote system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process telemetry mapped to the `Processes` node of the `Endpoint` data model. This activity is significant as WMI can be abused for lateral movement and remote code execution, often used by adversaries and Red Teams. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, facilitating further compromise and lateral spread within the network.
+description: The following analytic detects the execution of wmic.exe with parameters
+  to spawn a process on a remote system. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions and process telemetry
+  mapped to the `Processes` node of the `Endpoint` data model. This activity is significant
+  as WMI can be abused for lateral movement and remote code execution, often used
+  by adversaries and Red Teams. If confirmed malicious, this could allow attackers
+  to execute arbitrary code on remote systems, facilitating further compromise and
+  lateral spread within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process="*/node:*" AND Processes.process="*process*" AND Processes.process="*call*" AND  Processes.process="*create*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process="*/node:*"
+  AND Processes.process="*process*" AND Processes.process="*call*" AND  Processes.process="*create*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: The wmic.exe utility is a benign Windows application. It may
+  be used legitimately by Administrators with these parameters for remote system administration,
+  but it's relatively uncommon.
 references:
 - https://attack.mitre.org/techniques/T1047/
 - https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/create-method-in-class-win32-process
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A wmic.exe process $process$ contain process spawn commandline $process$ in host $dest$
+  message: A wmic.exe process $process$ contain process spawn commandline $process$
+    in host $dest$
   mitre_attack_id:
   - T1047
   observable:
@@ -50,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml
index 725db95f9b..2c328fb04f 100644
--- a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml
+++ b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `powershell.exe` using the `Invoke-WmiMethod` cmdlet to start a process on a remote endpoint via WMI. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process telemetry. This activity is significant as it indicates potential lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, leading to further compromise and persistence within the network.
+description: The following analytic detects the execution of `powershell.exe` using
+  the `Invoke-WmiMethod` cmdlet to start a process on a remote endpoint via WMI. It
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions and process telemetry. This activity is significant as it indicates potential
+  lateral movement or remote code execution attempts by adversaries. If confirmed
+  malicious, this could allow attackers to execute arbitrary code on remote systems,
+  leading to further compromise and persistence within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-WmiMethod*" AND Processes.process="*-CN*" AND Processes.process="*-Class Win32_Process*" AND  Processes.process="*-Name create*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may leverage WWMI and powershell.exe to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` (Processes.process="*Invoke-WmiMethod*"
+  AND Processes.process="*-CN*" AND Processes.process="*-Class Win32_Process*" AND  Processes.process="*-Name
+  create*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may leverage WWMI and powershell.exe to start
+  a process on remote systems, but this activity is usually limited to a small set
+  of hosts or users.
 references:
 - https://attack.mitre.org/techniques/T1047/
 - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/invoke-wmimethod?view=powershell-5.1
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 90
-  message: A process was started on a remote endpoint from $dest by abusing WMI using PowerShell.exe
+  message: A process was started on a remote endpoint from $dest by abusing WMI using
+    PowerShell.exe
   mitre_attack_id:
   - T1047
   observable:
@@ -43,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml
index f16c647e35..e9b737cab9 100644
--- a/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml
+++ b/detections/endpoint/remote_process_instantiation_via_wmi_and_powershell_script_block.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Invoke-WmiMethod` commandlet with parameters used to start a process on a remote endpoint via WMI, leveraging PowerShell Script Block Logging (EventCode=4104). This method identifies specific script block text patterns associated with remote process instantiation. This activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network.
+description: The following analytic detects the execution of the `Invoke-WmiMethod`
+  commandlet with parameters used to start a process on a remote endpoint via WMI,
+  leveraging PowerShell Script Block Logging (EventCode=4104). This method identifies
+  specific script block text patterns associated with remote process instantiation.
+  This activity is significant as it may indicate lateral movement or remote code
+  execution attempts by adversaries. If confirmed malicious, this could allow attackers
+  to execute arbitrary code on remote systems, potentially leading to further compromise
+  and persistence within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText="*Invoke-WmiMethod*" AND (ScriptBlockText="*-CN*" OR ScriptBlockText="*-ComputerName*") AND ScriptBlockText="*-Class Win32_Process*" AND ScriptBlockText="*-Name create*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID|  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup instructions can be found https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators may leverage WWMI and powershell.exe to start a process on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '`powershell` EventCode=4104 ScriptBlockText="*Invoke-WmiMethod*" AND (ScriptBlockText="*-CN*"
+  OR ScriptBlockText="*-ComputerName*") AND ScriptBlockText="*-Class Win32_Process*"
+  AND ScriptBlockText="*-Name create*" | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode ScriptBlockText Computer UserID|  `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `remote_process_instantiation_via_wmi_and_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup instructions
+  can be found 
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators may leverage WWMI and powershell.exe to start
+  a process on remote systems, but this activity is usually limited to a small set
+  of hosts or users.
 references:
 - https://attack.mitre.org/techniques/T1047/
 - https://docs.microsoft.com/en-us/powershell/module/microsoft.powershell.management/invoke-wmimethod?view=powershell-5.1
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 90
-  message: A process was started on a remote endpoint from $Computer$ by abusing WMI using PowerShell.exe
+  message: A process was started on a remote endpoint from $Computer$ by abusing WMI
+    using PowerShell.exe
   mitre_attack_id:
   - T1047
   observable:
@@ -41,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/wmi_remote_process_powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement/wmi_remote_process_powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_system_discovery_with_adsisearcher.yml b/detections/endpoint/remote_system_discovery_with_adsisearcher.yml
index 6ff3533f15..e5d5e3b40d 100644
--- a/detections/endpoint/remote_system_discovery_with_adsisearcher.yml
+++ b/detections/endpoint/remote_system_discovery_with_adsisearcher.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell scripts to query Active Directory for domain computers. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks containing `adsisearcher` and `objectcategory=computer` with methods like `findAll()` or `findOne()`. This activity is significant as it may indicate an attempt by adversaries or Red Teams to perform Active Directory discovery and gain situational awareness. If confirmed malicious, this could lead to further reconnaissance and potential lateral movement within the network.
+description: The following analytic detects the use of the `[Adsisearcher]` type accelerator
+  in PowerShell scripts to query Active Directory for domain computers. It leverages
+  PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks
+  containing `adsisearcher` and `objectcategory=computer` with methods like `findAll()`
+  or `findOne()`. This activity is significant as it may indicate an attempt by adversaries
+  or Red Teams to perform Active Directory discovery and gain situational awareness.
+  If confirmed malicious, this could lead to further reconnaissance and potential
+  lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*adsisearcher*" AND ScriptBlockText = "*objectcategory=computer*" AND ScriptBlockText IN ("*findAll()*","*findOne()*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)` | `remote_system_discovery_with_adsisearcher_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*adsisearcher*" AND ScriptBlockText
+  = "*objectcategory=computer*" AND ScriptBlockText IN ("*findAll()*","*findOne()*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest |rename UserID as user | `security_content_ctime(firstTime)`
+  | `remote_system_discovery_with_adsisearcher_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Administrators or power users may use Adsisearcher for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,16 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/adsisearcher-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_system_discovery_with_dsquery.yml b/detections/endpoint/remote_system_discovery_with_dsquery.yml
index 9ac82824ac..38178fb6c2 100644
--- a/detections/endpoint/remote_system_discovery_with_dsquery.yml
+++ b/detections/endpoint/remote_system_discovery_with_dsquery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `dsquery.exe` with the `computer` argument, which is used to discover remote systems within a domain. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. Remote system discovery is significant as it indicates potential reconnaissance activities by adversaries or Red Teams to map out network resources and Active Directory structures. If confirmed malicious, this activity could lead to further exploitation, lateral movement, and unauthorized access to critical systems within the network.
+description: The following analytic detects the execution of `dsquery.exe` with the
+  `computer` argument, which is used to discover remote systems within a domain. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments. Remote system discovery is significant
+  as it indicates potential reconnaissance activities by adversaries or Red Teams
+  to map out network resources and Active Directory structures. If confirmed malicious,
+  this activity could lead to further exploitation, lateral movement, and unauthorized
+  access to critical systems within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe") (Processes.process="*computer*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_dsquery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dsquery.exe")
+  (Processes.process="*computer*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `remote_system_discovery_with_dsquery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -34,22 +54,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_system_discovery_with_net.yml b/detections/endpoint/remote_system_discovery_with_net.yml
index 57bcd3b9ca..bd6eefdf41 100644
--- a/detections/endpoint/remote_system_discovery_with_net.yml
+++ b/detections/endpoint/remote_system_discovery_with_net.yml
@@ -5,13 +5,34 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of `net.exe` or `net1.exe` with command-line arguments used to discover remote systems, such as `domain computers /domain`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to map out networked systems and Active Directory structures. If confirmed malicious, this behavior could lead to further network exploitation, privilege escalation, or lateral movement within the environment.
+description: The following analytic identifies the execution of `net.exe` or `net1.exe`
+  with command-line arguments used to discover remote systems, such as `domain computers
+  /domain`. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line arguments. This activity is significant
+  as it indicates potential reconnaissance efforts by adversaries or Red Teams to
+  map out networked systems and Active Directory structures. If confirmed malicious,
+  this behavior could lead to further network exploitation, privilege escalation,
+  or lateral movement within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") (Processes.process="*domain computers*" AND Processes.process=*/do*) OR (Processes.process="*view*" AND Processes.process=*/do*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_net_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe") (Processes.process="*domain computers*" AND
+  Processes.process=*/do*) OR (Processes.process="*view*" AND Processes.process=*/do*)
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_net_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -35,22 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_system_discovery_with_wmic.yml b/detections/endpoint/remote_system_discovery_with_wmic.yml
index 1c5c1923b0..820e05f91a 100644
--- a/detections/endpoint/remote_system_discovery_with_wmic.yml
+++ b/detections/endpoint/remote_system_discovery_with_wmic.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `wmic.exe` with specific command-line arguments used to discover remote systems within a domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to map out network resources and Active Directory structures. If confirmed malicious, this behavior could allow attackers to gain situational awareness, identify critical systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration.
+description: The following analytic detects the execution of `wmic.exe` with specific
+  command-line arguments used to discover remote systems within a domain. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant as it indicates potential
+  reconnaissance efforts by adversaries to map out network resources and Active Directory
+  structures. If confirmed malicious, this behavior could allow attackers to gain
+  situational awareness, identify critical systems, and plan further attacks, potentially
+  leading to unauthorized access and data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_computer* AND Processes.process="*GET ds_samaccountname*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_system_discovery_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe")
+  (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_computer*
+  AND Processes.process="*GET ds_samaccountname*") by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `remote_system_discovery_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,22 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/remote_wmi_command_attempt.yml b/detections/endpoint/remote_wmi_command_attempt.yml
index 21265d17de..0c3e7e010e 100644
--- a/detections/endpoint/remote_wmi_command_attempt.yml
+++ b/detections/endpoint/remote_wmi_command_attempt.yml
@@ -5,14 +5,33 @@ date: '2024-09-30'
 author: Rico Valdez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `wmic.exe` with the `node` switch, indicating an attempt to spawn a local or remote process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, the attacker could gain remote control over the targeted system, execute arbitrary commands, and potentially escalate privileges or persist within the environment.
+description: The following analytic detects the execution of `wmic.exe` with the `node`
+  switch, indicating an attempt to spawn a local or remote process. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  creation events and command-line arguments. This activity is significant as it may
+  indicate lateral movement or remote code execution attempts by an attacker. If confirmed
+  malicious, the attacker could gain remote control over the targeted system, execute
+  arbitrary commands, and potentially escalate privileges or persist within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process=*node* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may use this legitimately to gather info from remote systems. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process=*node*
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `remote_wmi_command_attempt_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may use this legitimately to gather info from
+  remote systems. Filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.yaml
 - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/
@@ -23,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A wmic.exe process $process$ contain node commandline $process$ in host $dest$
+  message: A wmic.exe process $process$ contain node commandline $process$ in host
+    $dest$
   mitre_attack_id:
   - T1047
   observable:
@@ -53,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.parent_process
-  - Processes.parent_process_id
-  - Processes.process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/resize_shadowstorage_volume.yml b/detections/endpoint/resize_shadowstorage_volume.yml
index 020606f51b..308ebcfe1a 100644
--- a/detections/endpoint/resize_shadowstorage_volume.yml
+++ b/detections/endpoint/resize_shadowstorage_volume.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras
 status: production
 type: TTP
-description: The following analytic identifies the resizing of shadow storage volumes, a technique used by ransomware like CLOP to prevent the recreation of shadow volumes. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "vssadmin.exe" with parameters related to resizing shadow storage. This activity is significant as it indicates an attempt to hinder recovery efforts by manipulating shadow copies. If confirmed malicious, this could lead to successful ransomware deployment, making data recovery difficult and increasing the potential for data loss.
+description: The following analytic identifies the resizing of shadow storage volumes,
+  a technique used by ransomware like CLOP to prevent the recreation of shadow volumes.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on command-line executions involving "vssadmin.exe" with parameters related
+  to resizing shadow storage. This activity is significant as it indicates an attempt
+  to hinder recovery efforts by manipulating shadow copies. If confirmed malicious,
+  this could lead to successful ransomware deployment, making data recovery difficult
+  and increasing the potential for data loss.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name =  "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline
+  values(Processes.parent_process_name) as parent_process values(Processes.process_name)
+  as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name
+  = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name
+  =  "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*"
+  Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.dest
+  Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `resize_shadowstorage_volume_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: network admin can resize the shadowstorage for valid purposes.
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
@@ -25,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 80
-  message: A process $parent_process_name$ attempt to resize shadow copy with commandline $process$ in host $dest$
+  message: A process $parent_process_name$ attempt to resize shadow copy with commandline
+    $process$ in host $dest$
   mitre_attack_id:
   - T1490
   observable:
@@ -51,18 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.process
-  - Processes.parent_process_name
-  - _time
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/revil_common_exec_parameter.yml b/detections/endpoint/revil_common_exec_parameter.yml
index 75059aab21..98ee3294de 100644
--- a/detections/endpoint/revil_common_exec_parameter.yml
+++ b/detections/endpoint/revil_common_exec_parameter.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of command-line parameters commonly associated with REVIL ransomware, such as "-nolan", "-nolocal", "-fast", and "-full". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs mapped to the `Processes` node of the `Endpoint` data model. This activity is significant because these parameters are indicative of ransomware attempting to encrypt files on a compromised machine. If confirmed malicious, this could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
+description: The following analytic detects the execution of command-line parameters
+  commonly associated with REVIL ransomware, such as "-nolan", "-nolocal", "-fast",
+  and "-full". It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process execution logs mapped to the `Processes` node of the `Endpoint`
+  data model. This activity is significant because these parameters are indicative
+  of ransomware attempting to encrypt files on a compromised machine. If confirmed
+  malicious, this could lead to widespread data encryption, rendering critical files
+  inaccessible and potentially causing significant operational disruption.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process = "* -full *" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_common_exec_parameter_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: third party tool may have same command line parameters as revil ransomware.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan
+  *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process
+  = "* -full *" by Processes.process_name Processes.process Processes.parent_process_name
+  Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `revil_common_exec_parameter_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: third party tool may have same command line parameters as revil
+  ransomware.
 references:
 - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/
 - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 60
-  message: A process $process_name$ with commandline $process$ related to revil ransomware in host $dest$
+  message: A process $process_name$ with commandline $process$ related to revil ransomware
+    in host $dest$
   mitre_attack_id:
   - T1204
   observable:
@@ -48,20 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/revil_registry_entry.yml b/detections/endpoint/revil_registry_entry.yml
index 02c29f05ce..9b331c92f3 100644
--- a/detections/endpoint/revil_registry_entry.yml
+++ b/detections/endpoint/revil_registry_entry.yml
@@ -5,15 +5,43 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies suspicious modifications in the registry entry, specifically targeting paths used by malware like REVIL. It detects changes in registry paths such as `SOFTWARE\\WOW6432Node\\Facebook_Assistant` and `SOFTWARE\\WOW6432Node\\BlackLivesMatter`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications linked to process GUIDs. This activity is significant as it indicates potential malware persistence mechanisms, often used by advanced persistent threats (APTs) and ransomware. If confirmed malicious, this could allow attackers to maintain persistence, encrypt files, and store critical ransomware-related information on compromised hosts.
+description: The following analytic identifies suspicious modifications in the registry
+  entry, specifically targeting paths used by malware like REVIL. It detects changes
+  in registry paths such as `SOFTWARE\\WOW6432Node\\Facebook_Assistant` and `SOFTWARE\\WOW6432Node\\BlackLivesMatter`.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on registry modifications linked to process GUIDs. This activity is significant
+  as it indicates potential malware persistence mechanisms, often used by advanced
+  persistent threats (APTs) and ransomware. If confirmed malicious, this could allow
+  attackers to maintain persistence, encrypt files, and store critical ransomware-related
+  information on compromised hosts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `revil_registry_entry_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*"
+  OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") BY _time
+  span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`]
+  | fields firstTime lastTime dest user parent_process_name parent_process process_name
+  process_path process registry_key_name registry_path registry_value_name registry_value_data
+  process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `revil_registry_entry_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/
@@ -24,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +68,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 60
-  message: A registry entry $registry_path$ with registry value $registry_value_name$ and $registry_value_name$ related to revil ransomware in host $dest$
+  message: A registry entry $registry_path$ with registry value $registry_value_name$
+    and $registry_value_name$ related to revil ransomware in host $dest$
   mitre_attack_id:
   - T1112
   observable:
@@ -51,27 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rubeus_command_line_parameters.yml b/detections/endpoint/rubeus_command_line_parameters.yml
index 64957ef2ea..69738f8967 100644
--- a/detections/endpoint/rubeus_command_line_parameters.yml
+++ b/detections/endpoint/rubeus_command_line_parameters.yml
@@ -5,14 +5,40 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Rubeus command line parameters, a toolset for Kerberos attacks within Active Directory environments. It leverages Endpoint Detection and Response (EDR) data to identify specific command-line arguments associated with actions like ticket manipulation, kerberoasting, and password spraying. This activity is significant as Rubeus is commonly used by adversaries to exploit Kerberos for privilege escalation and lateral movement. If confirmed malicious, this could lead to unauthorized access, persistence, and potential compromise of sensitive information within the network.
+description: The following analytic detects the use of Rubeus command line parameters,
+  a toolset for Kerberos attacks within Active Directory environments. It leverages
+  Endpoint Detection and Response (EDR) data to identify specific command-line arguments
+  associated with actions like ticket manipulation, kerberoasting, and password spraying.
+  This activity is significant as Rubeus is commonly used by adversaries to exploit
+  Kerberos for privilege escalation and lateral movement. If confirmed malicious,
+  this could lead to unauthorized access, persistence, and potential compromise of
+  sensitive information within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*ptt /ticket*" OR Processes.process = "* monitor /interval*" OR Processes.process ="* asktgt* /user:*" OR Processes.process ="* asktgs* /service:*" OR Processes.process ="* golden* /user:*" OR Processes.process ="* silver* /service:*" OR Processes.process ="* kerberoast*" OR Processes.process ="* asreproast*" OR Processes.process = "* renew* /ticket:*" OR Processes.process = "* brute* /password:*" OR Processes.process = "* brute* /passwords:*" OR Processes.process ="* harvest*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rubeus_command_line_parameters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, legitimate applications may use the same command line parameters as Rubeus. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*ptt /ticket*"
+  OR Processes.process = "* monitor /interval*" OR Processes.process ="* asktgt* /user:*"
+  OR Processes.process ="* asktgs* /service:*" OR Processes.process ="* golden* /user:*"
+  OR Processes.process ="* silver* /service:*" OR Processes.process ="* kerberoast*"
+  OR Processes.process ="* asreproast*" OR Processes.process = "* renew* /ticket:*"
+  OR Processes.process = "* brute* /password:*" OR Processes.process = "* brute* /passwords:*"
+  OR Processes.process ="* harvest*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `rubeus_command_line_parameters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, legitimate applications may use the same
+  command line parameters as Rubeus. Filter as needed.
 references:
 - https://github.com/GhostPack/Rubeus
 - https://web.archive.org/web/20210725005734/http://www.harmj0y.net/blog/redteaming/from-kekeo-to-rubeus/
@@ -24,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -60,20 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml b/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml
index 45de676b16..7b7ba715eb 100644
--- a/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml
+++ b/detections/endpoint/rubeus_kerberos_ticket_exports_through_winlogon_access.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects a process accessing the winlogon.exe system process, indicative of the Rubeus tool attempting to export Kerberos tickets from memory. This detection leverages Sysmon EventCode 10 logs, focusing on processes obtaining a handle to winlogon.exe with specific access rights. This activity is significant as it often precedes pass-the-ticket attacks, where adversaries use stolen Kerberos tickets to move laterally within an environment. If confirmed malicious, this could allow attackers to bypass normal access controls, escalate privileges, and persist within the network, posing a severe security risk.
+description: The following analytic detects a process accessing the winlogon.exe system
+  process, indicative of the Rubeus tool attempting to export Kerberos tickets from
+  memory. This detection leverages Sysmon EventCode 10 logs, focusing on processes
+  obtaining a handle to winlogon.exe with specific access rights. This activity is
+  significant as it often precedes pass-the-ticket attacks, where adversaries use
+  stolen Kerberos tickets to move laterally within an environment. If confirmed malicious,
+  this could allow attackers to bypass normal access controls, escalate privileges,
+  and persist within the network, posing a severe security risk.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=C:\\Windows\\system32\\winlogon.exe (GrantedAccess=0x1f3fff) (SourceImage!=C:\\Windows\\system32\\svchost.exe AND SourceImage!=C:\\Windows\\system32\\lsass.exe AND SourceImage!=C:\\Windows\\system32\\LogonUI.exe AND SourceImage!=C:\\Windows\\system32\\smss.exe AND SourceImage!=C:\\Windows\\system32\\wbem\\wmiprvse.exe) | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `rubeus_kerberos_ticket_exports_through_winlogon_access_filter`'
-how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment.
-known_false_positives: Legitimate applications may obtain a handle for winlogon.exe. Filter as needed
+search: '`sysmon` EventCode=10 TargetImage=C:\\Windows\\system32\\winlogon.exe (GrantedAccess=0x1f3fff)
+  (SourceImage!=C:\\Windows\\system32\\svchost.exe AND SourceImage!=C:\\Windows\\system32\\lsass.exe
+  AND SourceImage!=C:\\Windows\\system32\\LogonUI.exe AND SourceImage!=C:\\Windows\\system32\\smss.exe
+  AND SourceImage!=C:\\Windows\\system32\\wbem\\wmiprvse.exe) | stats count min(_time)
+  as firstTime max(_time) as lastTime by dest, SourceImage, SourceProcessId, TargetImage,
+  TargetProcessId, EventCode, GrantedAccess  | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `rubeus_kerberos_ticket_exports_through_winlogon_access_filter`'
+how_to_implement: This search needs Sysmon Logs and a sysmon configuration, which
+  includes EventCode 10. This search uses an input macro named `sysmon`. We strongly
+  recommend that you specify your environment-specific configurations (index, source,
+  sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations
+  for your Splunk environment.
+known_false_positives: Legitimate applications may obtain a handle for winlogon.exe.
+  Filter as needed
 references:
 - https://github.com/GhostPack/Rubeus
 - https://web.archive.org/web/20210725005734/http://www.harmj0y.net/blog/redteaming/from-kekeo-to-rubeus/
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetImage
-  - CallTrace
-  - dest
-  - TargetProcessId
-  - SourceImage
-  - SourceProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.003/rubeus/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/runas_execution_in_commandline.yml b/detections/endpoint/runas_execution_in_commandline.yml
index a96c3e160f..801cff0c68 100644
--- a/detections/endpoint/runas_execution_in_commandline.yml
+++ b/detections/endpoint/runas_execution_in_commandline.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the runas.exe process with administrator user options. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to gain elevated privileges, a common tactic in privilege escalation and lateral movement. If confirmed malicious, this could allow an attacker to execute commands with higher privileges, potentially leading to unauthorized access, data exfiltration, or further compromise of the target host.
+description: The following analytic detects the execution of the runas.exe process
+  with administrator user options. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on command-line executions and process details. This activity
+  is significant as it may indicate an attempt to gain elevated privileges, a common
+  tactic in privilege escalation and lateral movement. If confirmed malicious, this
+  could allow an attacker to execute commands with higher privileges, potentially
+  leading to unauthorized access, data exfiltration, or further compromise of the
+  target host.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_runas` AND Processes.process = "*/user:*" AND Processes.process = "*admin*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `runas_execution_in_commandline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A network operator or systems administrator may utilize an automated or manual execute this command that may generate false positives. filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_runas` AND Processes.process
+  = "*/user:*" AND Processes.process = "*admin*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `runas_execution_in_commandline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A network operator or systems administrator may utilize an
+  automated or manual execute this command that may generate false positives. filter
+  is needed.
 references:
 - https://app.any.run/tasks/ad4c3cda-41f2-4401-8dba-56cc2d245488/
 tags:
@@ -36,20 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/vilsel/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_control_rundll_hunt.yml b/detections/endpoint/rundll32_control_rundll_hunt.yml
index ccf163fc5c..ef93c3c1e8 100644
--- a/detections/endpoint/rundll32_control_rundll_hunt.yml
+++ b/detections/endpoint/rundll32_control_rundll_hunt.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies instances of rundll32.exe executing with `Control_RunDLL` in the command line, which is indicative of loading a .cpl or other file types. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as rundll32.exe can be exploited to execute malicious Control Panel Item files, potentially linked to CVE-2021-40444. If confirmed malicious, this could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic identifies instances of rundll32.exe executing
+  with `Control_RunDLL` in the command line, which is indicative of loading a .cpl
+  or other file types. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs and command-line arguments. This
+  activity is significant as rundll32.exe can be exploited to execute malicious Control
+  Panel Item files, potentially linked to CVE-2021-40444. If confirmed malicious,
+  this could allow attackers to execute arbitrary code, escalate privileges, or maintain
+  persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL*  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_control_rundll_hunt_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This is a hunting detection, meant to provide a understanding of how voluminous control_rundll is within the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL*  by
+  Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `rundll32_control_rundll_hunt_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This is a hunting detection, meant to provide a understanding
+  of how voluminous control_rundll is within the environment.
 references:
 - https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html
 - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/
@@ -30,7 +51,8 @@ tags:
   cve:
   - CVE-2021-40444
   impact: 30
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -55,22 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml b/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml
index 3b710634d0..b8d67b5170 100644
--- a/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml
+++ b/detections/endpoint/rundll32_control_rundll_world_writable_directory.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe with the `Control_RunDLL` command, loading files from world-writable directories such as windows\temp, programdata, or appdata. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process command-line data and specific directory paths. This activity is significant as it may indicate an attempt to exploit CVE-2021-40444 or similar vulnerabilities, allowing attackers to execute arbitrary code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or persistent access within the environment.
+description: The following analytic detects the execution of rundll32.exe with the
+  `Control_RunDLL` command, loading files from world-writable directories such as
+  windows\temp, programdata, or appdata. This detection leverages Endpoint Detection
+  and Response (EDR) telemetry, focusing on process command-line data and specific
+  directory paths. This activity is significant as it may indicate an attempt to exploit
+  CVE-2021-40444 or similar vulnerabilities, allowing attackers to execute arbitrary
+  code. If confirmed malicious, this could lead to unauthorized code execution, privilege
+  escalation, or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL* AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*")  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_control_rundll_world_writable_directory_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This may be tuned, or a new one related, by adding .cpl to command-line. However, it's important to look for both. Tune/filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*Control_RunDLL*
+  AND Processes.process IN ("*\\appdata\\*", "*\\windows\\temp\\*", "*\\programdata\\*")  by
+  Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `rundll32_control_rundll_world_writable_directory_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This may be tuned, or a new one related, by adding .cpl to
+  command-line. However, it's important to look for both. Tune/filter as needed.
 references:
 - https://strontic.github.io/xcyclopedia/library/rundll32.exe-111474C61232202B5B588D2B512CBB25.html
 - https://app.any.run/tasks/36c14029-9df8-439c-bba0-45f2643b0c70/
@@ -26,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +66,8 @@ tags:
   cve:
   - CVE-2021-40444
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to load a suspicious file from disk.
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -64,22 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml b/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml
index 0c9410f069..3da5116e56 100644
--- a/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml
+++ b/detections/endpoint/rundll32_create_remote_thread_to_a_process.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a remote thread by rundll32.exe into another process. It leverages Sysmon EventCode 8 logs, specifically monitoring SourceImage and TargetImage fields. This activity is significant as it is a common technique used by malware, such as IcedID, to execute malicious code within legitimate processes, aiding in defense evasion and data theft. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, and exfiltrate sensitive information from the compromised host.
+description: The following analytic detects the creation of a remote thread by rundll32.exe
+  into another process. It leverages Sysmon EventCode 8 logs, specifically monitoring
+  SourceImage and TargetImage fields. This activity is significant as it is a common
+  technique used by malware, such as IcedID, to execute malicious code within legitimate
+  processes, aiding in defense evasion and data theft. If confirmed malicious, this
+  behavior could allow an attacker to execute arbitrary code, escalate privileges,
+  and exfiltrate sensitive information from the compromised host.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_create_remote_thread_to_a_process_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe"
+  | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage
+  TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `rundll32_create_remote_thread_to_a_process_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the SourceImage, TargetImage, and EventCode executions from your endpoints
+  related to create remote thread or injecting codes. If you are using Sysmon, you
+  must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.joesandbox.com/analysis/380662/0/html
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: rundl32 process $SourceImage$ create a remote thread to process $TargetImage$ in host $dest$
+  message: rundl32 process $SourceImage$ create a remote thread to process $TargetImage$
+    in host $dest$
   mitre_attack_id:
   - T1055
   observable:
@@ -45,19 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - TargetProcessId
-  - SourceProcessId
-  - StartAddress
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_createremotethread_in_browser.yml b/detections/endpoint/rundll32_createremotethread_in_browser.yml
index 1cd4b35f32..41e612f9b0 100644
--- a/detections/endpoint/rundll32_createremotethread_in_browser.yml
+++ b/detections/endpoint/rundll32_createremotethread_in_browser.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious creation of a remote thread by rundll32.exe targeting browser processes such as firefox.exe, chrome.exe, iexplore.exe, and microsoftedgecp.exe. This detection leverages Sysmon EventCode 8, focusing on SourceImage and TargetImage fields to identify the behavior. This activity is significant as it is commonly associated with malware like IcedID, which hooks browsers to steal sensitive information such as banking details. If confirmed malicious, this could allow attackers to intercept and exfiltrate sensitive user data, leading to potential financial loss and privacy breaches.
+description: The following analytic detects the suspicious creation of a remote thread
+  by rundll32.exe targeting browser processes such as firefox.exe, chrome.exe, iexplore.exe,
+  and microsoftedgecp.exe. This detection leverages Sysmon EventCode 8, focusing on
+  SourceImage and TargetImage fields to identify the behavior. This activity is significant
+  as it is commonly associated with malware like IcedID, which hooks browsers to steal
+  sensitive information such as banking details. If confirmed malicious, this could
+  allow attackers to intercept and exfiltrate sensitive user data, leading to potential
+  financial loss and privacy breaches.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_createremotethread_in_browser_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe",
+  "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time)
+  as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId
+  StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `rundll32_createremotethread_in_browser_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the SourceImage, TargetImage, and EventCode executions from your endpoints
+  related to create remote thread or injecting codes. If you are using Sysmon, you
+  must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.joesandbox.com/analysis/380662/0/html
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: rundl32 process $SourceImage$ create a remote thread to browser process $TargetImage$ in host $dest$
+  message: rundl32 process $SourceImage$ create a remote thread to browser process
+    $TargetImage$ in host $dest$
   mitre_attack_id:
   - T1055
   observable:
@@ -45,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - TargetProcessId
-  - SourceProcessId
-  - StartAddress
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_dnsquery.yml b/detections/endpoint/rundll32_dnsquery.yml
index fc7cbc9b8f..1024ef1a93 100644
--- a/detections/endpoint/rundll32_dnsquery.yml
+++ b/detections/endpoint/rundll32_dnsquery.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious `rundll32.exe` process making HTTP connections and performing DNS queries to web domains. It leverages Sysmon EventCode 22 logs to identify these activities. This behavior is significant as it is commonly associated with IcedID malware, where `rundll32.exe` checks internet connectivity and communicates with C&C servers to download configurations and other components. If confirmed malicious, this activity could allow attackers to establish persistence, download additional payloads, and exfiltrate sensitive data, posing a severe threat to the network.
+description: The following analytic detects a suspicious `rundll32.exe` process making
+  HTTP connections and performing DNS queries to web domains. It leverages Sysmon
+  EventCode 22 logs to identify these activities. This behavior is significant as
+  it is commonly associated with IcedID malware, where `rundll32.exe` checks internet
+  connectivity and communicates with C&C servers to download configurations and other
+  components. If confirmed malicious, this activity could allow attackers to establish
+  persistence, download additional payloads, and exfiltrate sensitive data, posing
+  a severe threat to the network.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode=22 process_name="rundll32.exe" | stats count min(_time) as firstTime max(_time) as lastTime values(query) as query values(answer) as answer values(QueryResults) as query_results values(QueryStatus) as query_status by process_name process_guid Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_dnsquery_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
+search: '`sysmon` EventCode=22 process_name="rundll32.exe" | stats count min(_time)
+  as firstTime max(_time) as lastTime values(query) as query values(answer) as answer
+  values(QueryResults) as query_results values(QueryStatus) as query_status by process_name
+  process_guid Computer | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `rundll32_dnsquery_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and eventcode = 22 dnsquery executions from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+  Tune and filter known instances where renamed rundll32.exe may be used.
 known_false_positives: unknown
 references:
 - https://any.run/malware-trends/icedid
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: rundll32 process $process_name$ made a DNS query for $query$ from host $dest$
+  message: rundll32 process $process_name$ made a DNS query for $query$ from host
+    $dest$
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -46,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - QueryName
-  - QueryStatus
-  - ProcessId
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_lockworkstation.yml b/detections/endpoint/rundll32_lockworkstation.yml
index af7c00ac63..ab33a4cbb8 100644
--- a/detections/endpoint/rundll32_lockworkstation.yml
+++ b/detections/endpoint/rundll32_lockworkstation.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the rundll32.exe command with the user32.dll,LockWorkStation parameter, which is used to lock the workstation via command line. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it is an uncommon method to lock a screen and has been observed in CONTI ransomware tooling for defense evasion. If confirmed malicious, this technique could indicate an attempt to evade detection and hinder incident response efforts.
+description: The following analytic detects the execution of the rundll32.exe command
+  with the user32.dll,LockWorkStation parameter, which is used to lock the workstation
+  via command line. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant as it is an uncommon method to lock a screen and has been observed
+  in CONTI ransomware tooling for defense evasion. If confirmed malicious, this technique
+  could indicate an attempt to evade detection and hinder incident response efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process= "*user32.dll,LockWorkStation*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_lockworkstation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process= "*user32.dll,LockWorkStation*" by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_lockworkstation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://threadreaderapp.com/thread/1423361119926816776.html
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_process_creating_exe_dll_files.yml b/detections/endpoint/rundll32_process_creating_exe_dll_files.yml
index 6915261804..da80587a14 100644
--- a/detections/endpoint/rundll32_process_creating_exe_dll_files.yml
+++ b/detections/endpoint/rundll32_process_creating_exe_dll_files.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a rundll32 process creating executable (.exe) or dynamic link library (.dll) files. It leverages Sysmon EventCode 11 to identify instances where rundll32.exe generates these file types. This activity is significant because rundll32 is often exploited by malware, such as IcedID, to drop malicious payloads in directories like Temp, AppData, or ProgramData. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, establish persistence, or escalate privileges within the environment.
+description: The following analytic detects a rundll32 process creating executable
+  (.exe) or dynamic link library (.dll) files. It leverages Sysmon EventCode 11 to
+  identify instances where rundll32.exe generates these file types. This activity
+  is significant because rundll32 is often exploited by malware, such as IcedID, to
+  drop malicious payloads in directories like Temp, AppData, or ProgramData. If confirmed
+  malicious, this behavior could allow an attacker to execute arbitrary code, establish
+  persistence, or escalate privileges within the environment.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventCode=11 Image="*rundll32.exe" TargetFilename IN ("*.exe", "*.dll") | stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename Computer | rename Computer as dest | rename TargetFilename as file_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_process_creating_exe_dll_files_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, TargetFilename, and eventcode 11 executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
+search: '`sysmon` EventCode=11 Image="*rundll32.exe" TargetFilename IN ("*.exe", "*.dll")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename
+  Computer | rename Computer as dest | rename TargetFilename as file_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `rundll32_process_creating_exe_dll_files_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, TargetFilename, and eventcode 11 executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
 known_false_positives: unknown
 references:
 - https://any.run/malware-trends/icedid
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - file_name
-  - process_guid
-  - dest
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_shimcache_flush.yml b/detections/endpoint/rundll32_shimcache_flush.yml
index 29f5d72894..bfc3309a1f 100644
--- a/detections/endpoint/rundll32_shimcache_flush.yml
+++ b/detections/endpoint/rundll32_shimcache_flush.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a suspicious rundll32 command line used to clear the shim cache. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because clearing the shim cache is an anti-forensic technique aimed at evading detection and removing forensic artifacts. If confirmed malicious, this action could hinder incident response efforts, allowing an attacker to cover their tracks and maintain persistence on the compromised machine.
+description: The following analytic detects the execution of a suspicious rundll32
+  command line used to clear the shim cache. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs and command-line arguments.
+  This activity is significant because clearing the shim cache is an anti-forensic
+  technique aimed at evading detection and removing forensic artifacts. If confirmed
+  malicious, this action could hinder incident response efforts, allowing an attacker
+  to cover their tracks and maintain persistence on the compromised machine.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_rundll32` AND Processes.process = "*apphelp.dll,ShimFlushCache*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_shimcache_flush_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  `process_rundll32` AND Processes.process
+  = "*apphelp.dll,ShimFlushCache*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `rundll32_shimcache_flush_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://blueteamops.medium.com/shimcache-flush-89daff28d15e
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/shimcache_flush/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/shimcache_flush/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml b/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml
index cd1aa00c7e..ec2c80e234 100644
--- a/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml
+++ b/detections/endpoint/rundll32_with_no_command_line_arguments_with_network.yml
@@ -5,12 +5,37 @@ date: '2024-09-30'
 author: Steven Dick,  Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe without command line arguments, followed by a network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry and network traffic data. It is significant because rundll32.exe typically requires arguments to function, and its absence is often associated with malicious activity, such as Cobalt Strike. If confirmed malicious, this activity could indicate an attempt to establish unauthorized network connections, potentially leading to data exfiltration or further compromise of the system.
+description: The following analytic detects the execution of rundll32.exe without
+  command line arguments, followed by a network connection. This behavior is identified
+  using Endpoint Detection and Response (EDR) telemetry and network traffic data.
+  It is significant because rundll32.exe typically requires arguments to function,
+  and its absence is often associated with malicious activity, such as Cobalt Strike.
+  If confirmed malicious, this activity could indicate an attempt to establish unauthorized
+  network connections, potentially leading to data exfiltration or further compromise
+  of the system.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where `process_rundll32` AND Processes.action!="blocked" by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(rundll32\.exe.{0,4}$)" | rename dest as src | join host process_id [| tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `rundll32_with_no_command_line_arguments_with_network_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where `process_rundll32` AND Processes.action!="blocked"
+  by host _time span=1h Processes.process_id Processes.process_name Processes.dest
+  Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(rundll32\.exe.{0,4}$)" | rename dest as src | join host process_id
+  [| tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest
+  latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port
+  FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by host
+  All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `rundll32_with_no_command_line_arguments_with_network_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of rundll32, triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -22,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +68,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 70
-  message: A rundll32 process $process_name$ with no commandline argument like this process commandline $process$ in host $src$
+  message: A rundll32 process $process_name$ with no commandline argument like this
+    process commandline $process$ in host $src$
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -55,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/rundll_loading_dll_by_ordinal.yml b/detections/endpoint/rundll_loading_dll_by_ordinal.yml
index cd4ba53e90..1bea9fe582 100644
--- a/detections/endpoint/rundll_loading_dll_by_ordinal.yml
+++ b/detections/endpoint/rundll_loading_dll_by_ordinal.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects rundll32.exe loading a DLL export function by ordinal value. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This behavior is significant because adversaries may use rundll32.exe to execute malicious code while evading security tools that do not monitor this process. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to system compromise, privilege escalation, or persistent access within the environment.
+description: The following analytic detects rundll32.exe loading a DLL export function
+  by ordinal value. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process command-line executions. This behavior is significant because
+  adversaries may use rundll32.exe to execute malicious code while evading security
+  tools that do not monitor this process. If confirmed malicious, this activity could
+  allow attackers to execute arbitrary code, potentially leading to system compromise,
+  privilege escalation, or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where match(process,"rundll32.+\#\d+") | `rundll_loading_dll_by_ordinal_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are possible with native utilities and third party applications. Filtering may be needed based on command-line, or add world writeable paths to restrict query.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_rundll32` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | where match(process,"rundll32.+\#\d+") |
+  `rundll_loading_dll_by_ordinal_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are possible with native utilities and third
+  party applications. Filtering may be needed based on command-line, or add world
+  writeable paths to restrict query.
 references:
 - https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/
 - https://twitter.com/M_haggis/status/1491109262428635136
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A rundll32 process $process_name$ with ordinal parameter like this process commandline $process$ on host $dest$.
+  message: A rundll32 process $process_name$ with ordinal parameter like this process
+    commandline $process$ on host $dest$.
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -52,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/ordinal_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/ordinal_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/ryuk_test_files_detected.yml b/detections/endpoint/ryuk_test_files_detected.yml
index a5b36b1d93..015e760d5d 100644
--- a/detections/endpoint/ryuk_test_files_detected.yml
+++ b/detections/endpoint/ryuk_test_files_detected.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Rod Soto, Jose Hernandez, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the presence of files containing the keyword "Ryuk" in any folder on the C drive, indicative of Ryuk ransomware activity. It leverages the Endpoint Filesystem data model to detect file paths matching this pattern. This activity is significant as Ryuk ransomware is known for its destructive impact, encrypting critical files and demanding ransom. If confirmed malicious, this could lead to significant data loss, operational disruption, and financial damage due to ransom payments and recovery efforts. Immediate investigation and response are crucial to mitigate potential damage.
+description: The following analytic identifies the presence of files containing the
+  keyword "Ryuk" in any folder on the C drive, indicative of Ryuk ransomware activity.
+  It leverages the Endpoint Filesystem data model to detect file paths matching this
+  pattern. This activity is significant as Ryuk ransomware is known for its destructive
+  impact, encrypting critical files and demanding ransom. If confirmed malicious,
+  this could lead to significant data loss, operational disruption, and financial
+  damage due to ransom payments and recovery efforts. Immediate investigation and
+  response are crucial to mitigate potential damage.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter`'
-how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-known_false_positives: If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk*
+  BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `ryuk_test_files_detected_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint Filesystem data-model object. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: If there are files with this keywoord as file names it might
+  trigger false possitives, please make use of our filters to tune out potential FPs.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,15 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.dest
-  - Filesystem.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/ryuk_wake_on_lan_command.yml b/detections/endpoint/ryuk_wake_on_lan_command.yml
index ae7083f48a..2626ae0b71 100644
--- a/detections/endpoint/ryuk_wake_on_lan_command.yml
+++ b/detections/endpoint/ryuk_wake_on_lan_command.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Wake-on-LAN commands associated with Ryuk ransomware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process and command-line activities. This behavior is significant as Ryuk ransomware uses Wake-on-LAN to power on devices in a compromised network, increasing its encryption success rate. If confirmed malicious, this activity could lead to widespread ransomware encryption across multiple endpoints, causing significant operational disruption and data loss. Immediate isolation and thorough investigation of the affected endpoints are crucial to mitigate the impact.
+description: The following analytic detects the use of Wake-on-LAN commands associated
+  with Ryuk ransomware. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on specific process and command-line activities. This behavior
+  is significant as Ryuk ransomware uses Wake-on-LAN to power on devices in a compromised
+  network, increasing its encryption success rate. If confirmed malicious, this activity
+  could lead to widespread ransomware encryption across multiple endpoints, causing
+  significant operational disruption and data loss. Immediate isolation and thorough
+  investigation of the affected endpoints are crucial to mitigate the impact.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `ryuk_wake_on_lan_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*"
+  OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `ryuk_wake_on_lan_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Limited to no known false positives.
 references:
 - https://www.bleepingcomputer.com/news/security/ryuk-ransomware-uses-wake-on-lan-to-encrypt-offline-devices/
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A process $process_name$ with wake on LAN commandline $process$ in host $dest$
+  message: A process $process_name$ with wake on LAN commandline $process$ in host
+    $dest$
   mitre_attack_id:
   - T1059
   - T1059.003
@@ -49,20 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/sam_database_file_access_attempt.yml b/detections/endpoint/sam_database_file_access_attempt.yml
index 3c3e162a33..bfcdb46c9f 100644
--- a/detections/endpoint/sam_database_file_access_attempt.yml
+++ b/detections/endpoint/sam_database_file_access_attempt.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Michael Haag, Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects attempts to access the SAM, SYSTEM, or SECURITY database files within the `windows\system32\config` directory using Windows Security EventCode 4663. This detection leverages Windows Security Event logs to identify unauthorized access attempts. Monitoring this activity is crucial as it indicates potential credential access attempts, possibly exploiting vulnerabilities like CVE-2021-36934. If confirmed malicious, an attacker could extract user passwords, leading to unauthorized access, privilege escalation, and further compromise of the system.
+description: The following analytic detects attempts to access the SAM, SYSTEM, or
+  SECURITY database files within the `windows\system32\config` directory using Windows
+  Security EventCode 4663. This detection leverages Windows Security Event logs to
+  identify unauthorized access attempts. Monitoring this activity is crucial as it
+  indicates potential credential access attempts, possibly exploiting vulnerabilities
+  like CVE-2021-36934. If confirmed malicious, an attacker could extract user passwords,
+  leading to unauthorized access, privilege escalation, and further compromise of
+  the system.
 data_source:
 - Windows Event Log Security 4663
-search: '`wineventlog_security` (EventCode=4663)  ProcessName!=*\\dllhost.exe ObjectName IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") | stats values(AccessList) count by ProcessName ObjectName dest src_user | rename ProcessName as process_name | `sam_database_file_access_attempt_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: Natively, `dllhost.exe` will access the files. Every environment will have additional native processes that do as well. Filter by process_name. As an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`.
+search: '`wineventlog_security` (EventCode=4663)  ProcessName!=*\\dllhost.exe ObjectName
+  IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*")
+  | stats values(AccessList) count by ProcessName ObjectName dest src_user | rename
+  ProcessName as process_name | `sam_database_file_access_attempt_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: Natively, `dllhost.exe` will access the files. Every environment
+  will have additional native processes that do as well. Filter by process_name. As
+  an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`.
 references:
 - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4663
 - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663
@@ -28,7 +42,8 @@ tags:
   cve:
   - CVE-2021-36934
   impact: 80
-  message: The following process $process_name$ accessed the object $ObjectName$ attempting to gain access to credentials on $dest$ by user $src_user$.
+  message: The following process $process_name$ accessed the object $ObjectName$ attempting
+    to gain access to credentials on $dest$ by user $src_user$.
   mitre_attack_id:
   - T1003.002
   - T1003
@@ -53,16 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - Object_Name
-  - dest
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/serioussam/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/serioussam/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/samsam_test_file_write.yml b/detections/endpoint/samsam_test_file_write.yml
index 4f0e37c22d..36882931b0 100644
--- a/detections/endpoint/samsam_test_file_write.yml
+++ b/detections/endpoint/samsam_test_file_write.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a file named "test.txt" within the Windows system directory, indicative of Samsam ransomware propagation. It leverages file-system activity data from the Endpoint data model, specifically monitoring file paths within the Windows System32 directory. This activity is significant as it aligns with known Samsam ransomware behavior, which uses such files for propagation and execution. If confirmed malicious, this could lead to ransomware deployment, resulting in data encryption, system disruption, and potential data loss. Immediate investigation and remediation are crucial to prevent further damage.
+description: The following analytic detects the creation of a file named "test.txt"
+  within the Windows system directory, indicative of Samsam ransomware propagation.
+  It leverages file-system activity data from the Endpoint data model, specifically
+  monitoring file paths within the Windows System32 directory. This activity is significant
+  as it aligns with known Samsam ransomware behavior, which uses such files for propagation
+  and execution. If confirmed malicious, this could lead to ransomware deployment,
+  resulting in data encryption, system disruption, and potential data loss. Immediate
+  investigation and remediation are crucial to prevent further damage.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`'
-how_to_implement: You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name)
+  as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt
+  by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `samsam_test_file_write_filter`'
+how_to_implement: You must be ingesting data that records the file-system activity
+  from your hosts to populate the Endpoint file-system data-model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
 known_false_positives: No false positives have been identified.
 references: []
 drilldown_searches:
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.user
-  - Filesystem.dest
-  - Filesystem.file_name
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/sc_exe_manipulating_windows_services.yml b/detections/endpoint/sc_exe_manipulating_windows_services.yml
index 087b15483f..cfe8ac66df 100644
--- a/detections/endpoint/sc_exe_manipulating_windows_services.yml
+++ b/detections/endpoint/sc_exe_manipulating_windows_services.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation or modification of Windows services using the sc.exe command. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because manipulating Windows services can be a method for attackers to establish persistence, escalate privileges, or execute arbitrary code. If confirmed malicious, this behavior could allow an attacker to maintain long-term access, disrupt services, or gain control over critical system functions, posing a severe threat to the environment.
+description: The following analytic detects the creation or modification of Windows
+  services using the sc.exe command. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process names and command-line arguments. This
+  activity is significant because manipulating Windows services can be a method for
+  attackers to establish persistence, escalate privileges, or execute arbitrary code.
+  If confirmed malicious, this behavior could allow an attacker to maintain long-term
+  access, disrupt services, or gain control over critical system functions, posing
+  a severe threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="*
+  config *") by Processes.process_name Processes.parent_process_name Processes.dest
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `sc_exe_manipulating_windows_services_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Using sc.exe to manipulate Windows services is uncommon. However,
+  there may be legitimate instances of this behavior. It is important to validate
+  and investigate as appropriate.
 references:
 - https://www.secureworks.com/blog/drokbk-malware-uses-github-as-dead-drop-resolver
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A sc process $process_name$ with commandline $process$ to create of configure services in host $dest$
+  message: A sc process $process_name$ with commandline $process$ to create of configure
+    services in host $dest$
   mitre_attack_id:
   - T1543.003
   - T1543
@@ -54,17 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml b/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml
index 6f241b5e44..b5bd2f63e2 100644
--- a/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml
+++ b/detections/endpoint/schcache_change_by_app_connect_and_create_adsi_object.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects an application attempting to connect and create an ADSI object to perform an LDAP query. It leverages Sysmon EventCode 11 to identify changes in the Active Directory Schema cache files located in %LOCALAPPDATA%\Microsoft\Windows\SchCache or %systemroot%\SchCache. This activity is significant as it can indicate the presence of suspicious applications, such as ransomware, using ADSI object APIs for LDAP queries. If confirmed malicious, this behavior could allow attackers to gather sensitive directory information, potentially leading to further exploitation or lateral movement within the network.
+description: The following analytic detects an application attempting to connect and
+  create an ADSI object to perform an LDAP query. It leverages Sysmon EventCode 11
+  to identify changes in the Active Directory Schema cache files located in %LOCALAPPDATA%\Microsoft\Windows\SchCache
+  or %systemroot%\SchCache. This activity is significant as it can indicate the presence
+  of suspicious applications, such as ransomware, using ADSI object APIs for LDAP
+  queries. If confirmed malicious, this behavior could allow attackers to gather sensitive
+  directory information, potentially leading to further exploitation or lateral movement
+  within the network.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventCode=11  TargetFilename = "*\\Windows\\SchCache\\*" TargetFilename = "*.sch*" NOT (Image IN ("*\\Windows\\system32\\mmc.exe")) |stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename EventCode process_id  process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schcache_change_by_app_connect_and_create_adsi_object_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: normal application like mmc.exe and other ldap query tool may trigger this detections.
+search: '`sysmon` EventCode=11  TargetFilename = "*\\Windows\\SchCache\\*" TargetFilename
+  = "*.sch*" NOT (Image IN ("*\\Windows\\system32\\mmc.exe")) |stats count min(_time)
+  as firstTime max(_time) as lastTime by Image TargetFilename EventCode process_id  process_name
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `schcache_change_by_app_connect_and_create_adsi_object_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: normal application like mmc.exe and other ldap query tool may
+  trigger this detections.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/adsi/adsi-and-uac
 - https://news.sophos.com/en-us/2021/08/09/blackmatter-ransomware-emerges-from-the-shadow-of-darkside/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - TargetFilename
-  - EventCode
-  - process_id
-  - process_name
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/blackmatter_schcache/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/blackmatter_schcache/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/schedule_task_with_http_command_arguments.yml b/detections/endpoint/schedule_task_with_http_command_arguments.yml
index ad70c6ac6d..c4c77caef8 100644
--- a/detections/endpoint/schedule_task_with_http_command_arguments.yml
+++ b/detections/endpoint/schedule_task_with_http_command_arguments.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of scheduled tasks on Windows systems that include HTTP command arguments, using Windows Security EventCode 4698. It identifies tasks registered via schtasks.exe or TaskService with HTTP in their command arguments. This behavior is significant as it often indicates malware activity or the use of Living off the Land binaries (lolbins) to download additional payloads. If confirmed malicious, this activity could lead to data exfiltration, malware propagation, or unauthorized access to sensitive information, necessitating immediate investigation and mitigation.
+description: The following analytic detects the creation of scheduled tasks on Windows
+  systems that include HTTP command arguments, using Windows Security EventCode 4698.
+  It identifies tasks registered via schtasks.exe or TaskService with HTTP in their
+  command arguments. This behavior is significant as it often indicates malware activity
+  or the use of Living off the Land binaries (lolbins) to download additional payloads.
+  If confirmed malicious, this activity could lead to data exfiltration, malware propagation,
+  or unauthorized access to sensitive information, necessitating immediate investigation
+  and mitigation.
 data_source:
 - Windows Event Log Security 4698
-search: '`wineventlog_security` EventCode=4698 | xmlkv Message| search Arguments IN ("*http*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_http_command_arguments_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
+search: '`wineventlog_security` EventCode=4698 | xmlkv Message| search Arguments IN
+  ("*http*") | stats count min(_time) as firstTime max(_time) as lastTime by dest,
+  Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `schedule_task_with_http_command_arguments_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and
+  filter known instances of Task schedule used in your environment.
 known_false_positives: unknown
 references:
 - https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A schedule task process commandline arguments $Arguments$ with http string on it in host $dest$
+  message: A schedule task process commandline arguments $Arguments$ with http string
+    on it in host $dest$
   mitre_attack_id:
   - T1053
   observable:
@@ -43,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Task_Name
-  - Command
-  - Author
-  - Enabled
-  - Hidden
-  - Arguments
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
diff --git a/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml b/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml
index c168bdaedd..b89bec746e 100644
--- a/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml
+++ b/detections/endpoint/schedule_task_with_rundll32_command_trigger.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of scheduled tasks in Windows that use the rundll32 command. It leverages Windows Security EventCode 4698, which logs the creation of scheduled tasks, and filters for tasks executed via rundll32. This activity is significant as it is a common technique used by malware, such as TrickBot, to persist in an environment or deliver additional payloads. If confirmed malicious, this could lead to data theft, ransomware deployment, or other damaging outcomes. Immediate investigation and mitigation are crucial to prevent further compromise.
+description: The following analytic detects the creation of scheduled tasks in Windows
+  that use the rundll32 command. It leverages Windows Security EventCode 4698, which
+  logs the creation of scheduled tasks, and filters for tasks executed via rundll32.
+  This activity is significant as it is a common technique used by malware, such as
+  TrickBot, to persist in an environment or deliver additional payloads. If confirmed
+  malicious, this could lead to data theft, ransomware deployment, or other damaging
+  outcomes. Immediate investigation and mitigation are crucial to prevent further
+  compromise.
 data_source:
 - Windows Event Log Security 4698
-search: '`wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN ("*rundll32*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schedule_task_with_rundll32_command_trigger_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
+search: '`wineventlog_security` EventCode=4698 | xmlkv Message | search Command IN
+  ("*rundll32*") | stats count min(_time) as firstTime max(_time) as lastTime by dest,
+  Task_Name, Command, Author, Enabled, Hidden, Arguments | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `schedule_task_with_rundll32_command_trigger_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and
+  filter known instances of Task schedule used in your environment.
 known_false_positives: unknown
 references:
 - https://labs.vipre.com/trickbot-and-its-modules/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: A schedule task process commandline rundll32 arguments $Arguments$ in host $dest$
+  message: A schedule task process commandline rundll32 arguments $Arguments$ in host
+    $dest$
   mitre_attack_id:
   - T1053
   observable:
@@ -45,19 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Task_Name
-  - Command
-  - Author
-  - Enabled
-  - Hidden
-  - Arguments
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/tasksched/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/tasksched/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
diff --git a/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml b/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml
index 882d740e5d..e5321ec6f9 100644
--- a/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml
+++ b/detections/endpoint/scheduled_task_creation_on_remote_endpoint_using_at.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of scheduled tasks on remote Windows endpoints using the at.exe command. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process creation events involving at.exe with remote command-line arguments. Identifying this activity is significant for a SOC as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, this activity could lead to unauthorized access, persistence, or execution of malicious code, potentially resulting in data theft or further compromise of the network.
+description: The following analytic detects the creation of scheduled tasks on remote
+  Windows endpoints using the at.exe command. This detection leverages Endpoint Detection
+  and Response (EDR) telemetry, focusing on process creation events involving at.exe
+  with remote command-line arguments. Identifying this activity is significant for
+  a SOC as it may indicate lateral movement or remote code execution attempts by an
+  attacker. If confirmed malicious, this activity could lead to unauthorized access,
+  persistence, or execution of malicious code, potentially resulting in data theft
+  or further compromise of the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=at.exe OR Processes.original_file_name=at.exe) (Processes.process=*\\\\*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_creation_on_remote_endpoint_using_at_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may create scheduled tasks on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=at.exe
+  OR Processes.original_file_name=at.exe) (Processes.process=*\\\\*) by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_creation_on_remote_endpoint_using_at_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may create scheduled tasks on remote systems,
+  but this activity is usually limited to a small set of hosts or users.
 references:
 - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/at
 - https://docs.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob?redirectedfrom=MSDN
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.002/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml b/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml
index 77e3e2c83f..1864d66de6 100644
--- a/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml
+++ b/detections/endpoint/scheduled_task_deleted_or_created_via_cmd.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation or deletion of scheduled tasks using the schtasks.exe utility with the -create or -delete flags. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it can indicate unauthorized system manipulation or malicious intent, often associated with threat actors like Dragonfly and incidents such as the SUNBURST attack. If confirmed malicious, this activity could allow attackers to execute code, escalate privileges, or persist within the environment, posing a significant security risk.
+description: The following analytic identifies the creation or deletion of scheduled
+  tasks using the schtasks.exe utility with the -create or -delete flags. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions. This activity is significant as it can indicate unauthorized
+  system manipulation or malicious intent, often associated with threat actors like
+  Dragonfly and incidents such as the SUNBURST attack. If confirmed malicious, this
+  activity could allow attackers to execute code, escalate privileges, or persist
+  within the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: While it is possible for legitimate scripts or administrators to trigger this behavior, filtering can be applied based on the parent process and application to reduce false positives. Analysts should reference the provided references to understand the context and threat landscape associated with this activity.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe
+  (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name
+  Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_deleted_or_created_via_cmd_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: While it is possible for legitimate scripts or administrators
+  to trigger this behavior, filtering can be applied based on the parent process and
+  application to reduce false positives. Analysts should reference the provided references
+  to understand the context and threat landscape associated with this activity.
 references:
 - https://thedfirreport.com/2022/02/21/qbot-and-zerologon-lead-to-full-domain-compromise/
 - https://www.joesandbox.com/analysis/691823/0/html
@@ -22,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,7 +82,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A schedule task process $process_name$ with create or delete commandline $process$ in host $dest$
+  message: A schedule task process $process_name$ with create or delete commandline
+    $process$ in host $dest$
   mitre_attack_id:
   - T1053.005
   - T1053
@@ -71,18 +100,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml b/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml
index 315cc8f70f..9fa8ded483 100644
--- a/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml
+++ b/detections/endpoint/scheduled_task_initiation_on_remote_endpoint.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk, Badoodish, Github Community
 status: production
 type: TTP
-description: The following analytic detects the use of 'schtasks.exe' to start a Scheduled Task on a remote endpoint. This detection leverages Endpoint Detection and Response (EDR) data, focusing on process details such as process name, parent process, and command-line executions. This activity is significant as adversaries often abuse Task Scheduler for lateral movement and remote code execution. If confirmed malicious, this behavior could allow attackers to execute arbitrary code remotely, potentially leading to further compromise of the network.
+description: The following analytic detects the use of 'schtasks.exe' to start a Scheduled
+  Task on a remote endpoint. This detection leverages Endpoint Detection and Response
+  (EDR) data, focusing on process details such as process name, parent process, and
+  command-line executions. This activity is significant as adversaries often abuse
+  Task Scheduler for lateral movement and remote code execution. If confirmed malicious,
+  this behavior could allow attackers to execute arbitrary code remotely, potentially
+  leading to further compromise of the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=schtasks.exe OR Processes.original_file_name=schtasks.exe) (Processes.process= "* /S *" AND Processes.process=*/run*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_initiation_on_remote_endpoint_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may start scheduled tasks on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=schtasks.exe
+  OR Processes.original_file_name=schtasks.exe) (Processes.process= "* /S *" AND Processes.process=*/run*)
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `scheduled_task_initiation_on_remote_endpoint_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may start scheduled tasks on remote systems,
+  but this activity is usually limited to a small set of hosts or users.
 references:
 - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/schtasks
 - https://attack.mitre.org/techniques/T1053/005/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/schtasks_run_task_on_demand.yml b/detections/endpoint/schtasks_run_task_on_demand.yml
index af22e316fc..47da56a411 100644
--- a/detections/endpoint/schtasks_run_task_on_demand.yml
+++ b/detections/endpoint/schtasks_run_task_on_demand.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a Windows Scheduled Task on demand via the shell or command line. It leverages process-related data, including process name, parent process, and command-line executions, sourced from endpoint logs. The detection focuses on 'schtasks.exe' with an associated 'run' command. This activity is significant as adversaries often use it to force the execution of their created Scheduled Tasks for persistent access or lateral movement within a compromised machine. If confirmed malicious, this could allow attackers to maintain persistence or move laterally within the network, potentially leading to further compromise.
+description: The following analytic detects the execution of a Windows Scheduled Task
+  on demand via the shell or command line. It leverages process-related data, including
+  process name, parent process, and command-line executions, sourced from endpoint
+  logs. The detection focuses on 'schtasks.exe' with an associated 'run' command.
+  This activity is significant as adversaries often use it to force the execution
+  of their created Scheduled Tasks for persistent access or lateral movement within
+  a compromised machine. If confirmed malicious, this could allow attackers to maintain
+  persistence or move laterally within the network, potentially leading to further
+  compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_run_task_on_demand_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Bear in mind, administrators debugging Scheduled Task entries may trigger this analytic, necessitating fine-tuning and filtering to distinguish between legitimate and potentially malicious use of 'schtasks.exe'.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_id) as process_id count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe"
+  Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `schtasks_run_task_on_demand_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Bear in mind, administrators debugging Scheduled Task entries
+  may trigger this analytic, necessitating fine-tuning and filtering to distinguish
+  between legitimate and potentially malicious use of 'schtasks.exe'.
 references:
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 60
-  message: A "on demand" execution of schedule task process $process_name$  using commandline $process$ in host $dest$
+  message: A "on demand" execution of schedule task process $process_name$  using
+    commandline $process$ in host $dest$
   mitre_attack_id:
   - T1053
   observable:
@@ -51,18 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml b/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml
index fe1b5f12b0..2eb5352578 100644
--- a/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml
+++ b/detections/endpoint/schtasks_scheduling_job_on_remote_system.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: David Dorsey, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of 'schtasks.exe' to create a scheduled task on a remote system, indicating potential lateral movement or remote code execution. It leverages process data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line arguments and flags. This activity is significant as it may signify an adversary's attempt to persist or execute code remotely. If confirmed malicious, this could allow attackers to maintain access, execute arbitrary commands, or further infiltrate the network, posing a severe security risk.
+description: The following analytic detects the use of 'schtasks.exe' to create a
+  scheduled task on a remote system, indicating potential lateral movement or remote
+  code execution. It leverages process data from Endpoint Detection and Response (EDR)
+  agents, focusing on specific command-line arguments and flags. This activity is
+  significant as it may signify an adversary's attempt to persist or execute code
+  remotely. If confirmed malicious, this could allow attackers to maintain access,
+  execute arbitrary commands, or further infiltrate the network, posing a severe security
+  risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = schtasks.exe OR Processes.original_file_name=schtasks.exe) (Processes.process="*/create*" AND Processes.process="*/s*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: While it is possible to have false positives, due to legitimate administrative tasks, these are usually limited and should still be validated and investigated as appropriate.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = schtasks.exe
+  OR Processes.original_file_name=schtasks.exe) (Processes.process="*/create*" AND
+  Processes.process="*/s*") by Processes.process_name Processes.process Processes.parent_process_name
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `schtasks_scheduling_job_on_remote_system_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: While it is possible to have false positives, due to legitimate
+  administrative tasks, these are usually limited and should still be validated and
+  investigated as appropriate.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A schedule task process $process_name$ with remote job command-line $process$ in host $dest$ by $user$.
+  message: A schedule task process $process_name$ with remote job command-line $process$
+    in host $dest$ by $user$.
   mitre_attack_id:
   - T1053.005
   - T1053
@@ -56,17 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml b/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml
index 109d5405d2..61c56f603b 100644
--- a/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml
+++ b/detections/endpoint/schtasks_used_for_forcing_a_reboot.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of 'schtasks.exe' to schedule forced system reboots using the 'shutdown' and '/create' flags. It leverages endpoint process data to identify instances where these specific command-line arguments are used. This activity is significant because it may indicate an adversary attempting to disrupt operations or force a reboot to execute further malicious actions. If confirmed malicious, this could lead to system downtime, potential data loss, and provide an attacker with an opportunity to execute additional payloads or evade detection.
+description: The following analytic detects the use of 'schtasks.exe' to schedule
+  forced system reboots using the 'shutdown' and '/create' flags. It leverages endpoint
+  process data to identify instances where these specific command-line arguments are
+  used. This activity is significant because it may indicate an adversary attempting
+  to disrupt operations or force a reboot to execute further malicious actions. If
+  confirmed malicious, this could lead to system downtime, potential data loss, and
+  provide an attacker with an opportunity to execute additional payloads or evade
+  detection.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schtasks_used_for_forcing_a_reboot_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This analytic may also capture legitimate administrative activities such as system updates or maintenance tasks, which can be classified as false positives. Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create
+  *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `schtasks_used_for_forcing_a_reboot_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This analytic may also capture legitimate administrative activities
+  such as system updates or maintenance tasks, which can be classified as false positives.
+  Filter as needed.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A schedule task process $process_name$ with force reboot commandline $process$ in host $dest$
+  message: A schedule task process $process_name$ with force reboot commandline $process$
+    in host $dest$
   mitre_attack_id:
   - T1053.005
   - T1053
@@ -48,17 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/screensaver_event_trigger_execution.yml b/detections/endpoint/screensaver_event_trigger_execution.yml
index b3ddc5bbdc..5b80ea908b 100644
--- a/detections/endpoint/screensaver_event_trigger_execution.yml
+++ b/detections/endpoint/screensaver_event_trigger_execution.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications to the SCRNSAVE.EXE registry entry, indicating potential event trigger execution via screensaver settings for persistence or privilege escalation. It leverages registry activity data from the Endpoint data model to identify changes to the specified registry path. This activity is significant as it is a known technique used by APT groups and malware to maintain persistence or escalate privileges. If confirmed malicious, this could allow an attacker to execute arbitrary code with elevated privileges, leading to further system compromise and persistent access.
+description: The following analytic detects modifications to the SCRNSAVE.EXE registry
+  entry, indicating potential event trigger execution via screensaver settings for
+  persistence or privilege escalation. It leverages registry activity data from the
+  Endpoint data model to identify changes to the specified registry path. This activity
+  is significant as it is a known technique used by APT groups and malware to maintain
+  persistence or escalate privileges. If confirmed malicious, this could allow an
+  attacker to execute arbitrary code with elevated privileges, leading to further
+  system compromise and persistent access.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Control Panel\\Desktop\\SCRNSAVE.EXE*") by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `screensaver_event_trigger_execution_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Control
+  Panel\\Desktop\\SCRNSAVE.EXE*") by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `screensaver_event_trigger_execution_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1546/002/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.002/scrnsave_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.002/scrnsave_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/script_execution_via_wmi.yml b/detections/endpoint/script_execution_via_wmi.yml
index c11a794cb7..1f6cf943d8 100644
--- a/detections/endpoint/script_execution_via_wmi.yml
+++ b/detections/endpoint/script_execution_via_wmi.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Rico Valdez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of scripts via Windows Management Instrumentation (WMI) by monitoring the process 'scrcons.exe'. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. WMI-based script execution is significant because adversaries often use it to perform malicious activities stealthily, such as system compromise, data exfiltration, or establishing persistence. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain long-term access to the environment. Analysts should differentiate between legitimate administrative use and potential threats.
+description: The following analytic detects the execution of scripts via Windows Management
+  Instrumentation (WMI) by monitoring the process 'scrcons.exe'. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process creation
+  events. WMI-based script execution is significant because adversaries often use
+  it to perform malicious activities stealthily, such as system compromise, data exfiltration,
+  or establishing persistence. If confirmed malicious, this activity could allow attackers
+  to execute arbitrary code, escalate privileges, or maintain long-term access to
+  the environment. Analysts should differentiate between legitimate administrative
+  use and potential threats.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=scrcons.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `script_execution_via_wmi_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=scrcons.exe
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `script_execution_via_wmi_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, administrators may use wmi to launch scripts
+  for legitimate purposes. Filter as needed.
 references:
 - https://redcanary.com/blog/child-processes/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,15 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/sdclt_uac_bypass.yml b/detections/endpoint/sdclt_uac_bypass.yml
index 9a5058348d..b7c13efdb2 100644
--- a/detections/endpoint/sdclt_uac_bypass.yml
+++ b/detections/endpoint/sdclt_uac_bypass.yml
@@ -5,15 +5,42 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to the sdclt.exe registry, a technique often used to bypass User Account Control (UAC). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific registry paths and values associated with sdclt.exe. This activity is significant because UAC bypasses can allow attackers to execute payloads with elevated privileges without user consent. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, and potential persistence within the environment, posing a severe security risk.
+description: The following analytic detects suspicious modifications to the sdclt.exe
+  registry, a technique often used to bypass User Account Control (UAC). It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on specific registry
+  paths and values associated with sdclt.exe. This activity is significant because
+  UAC bypasses can allow attackers to execute payloads with elevated privileges without
+  user consent. If confirmed malicious, this could lead to unauthorized code execution,
+  privilege escalation, and potential persistence within the environment, posing a
+  severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "IsolatedCommand")) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdclt_uac_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE ((Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*"
+  OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_value_name
+  = "(Default)" OR Registry.registry_value_name = "IsolatedCommand")) BY _time span=1h
+  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime
+  dest user parent_process_name parent_process process_name process_path process registry_key_name
+  registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdclt_uac_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Limited to no false positives are expected.
 references:
 - https://enigma0x3.net/2017/03/17/fileless-uac-bypass-using-sdclt-exe/
@@ -25,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ in $dest$
+  message: Suspicious modification of registry $registry_path$ with possible payload
+    path $registry_value_name$ in $dest$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -48,27 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/sdelete_application_execution.yml b/detections/endpoint/sdelete_application_execution.yml
index 15c29bd149..dd027a05c9 100644
--- a/detections/endpoint/sdelete_application_execution.yml
+++ b/detections/endpoint/sdelete_application_execution.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the sdelete.exe application, a Sysinternals tool often used by adversaries to securely delete files and remove forensic evidence from a targeted host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial as sdelete.exe is not commonly used in regular operations and its presence may indicate an attempt to cover malicious activities. If confirmed malicious, this could lead to the loss of critical forensic data, hindering incident response and investigation efforts.
+description: The following analytic detects the execution of the sdelete.exe application,
+  a Sysinternals tool often used by adversaries to securely delete files and remove
+  forensic evidence from a targeted host. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs. Monitoring
+  this activity is crucial as sdelete.exe is not commonly used in regular operations
+  and its presence may indicate an attempt to cover malicious activities. If confirmed
+  malicious, this could lead to the loss of critical forensic data, hindering incident
+  response and investigation efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_sdelete` by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sdelete_application_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.parent_process) as parent_process values(Processes.process_id)
+  as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_sdelete` by  Processes.process_name Processes.original_file_name
+  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `sdelete_application_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: user may execute and use this application
 references:
 - https://app.any.run/tasks/956f50be-2c13-465a-ac00-6224c14c5f89/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/sdelete/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/sdelete/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml b/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml
index 54b7b23443..9d88d654b2 100644
--- a/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml
+++ b/detections/endpoint/searchprotocolhost_with_no_command_line_with_network.yml
@@ -5,12 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances of searchprotocolhost.exe running without command line arguments but with an active network connection. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution and network traffic data. It is significant because searchprotocolhost.exe typically runs with specific command line arguments, and deviations from this norm can indicate malicious activity, such as Cobalt Strike usage. If confirmed malicious, this activity could allow attackers to establish network connections for command and control, potentially leading to data exfiltration or further system compromise.
+description: The following analytic detects instances of searchprotocolhost.exe running
+  without command line arguments but with an active network connection. This behavior
+  is identified using Endpoint Detection and Response (EDR) telemetry, focusing on
+  process execution and network traffic data. It is significant because searchprotocolhost.exe
+  typically runs with specific command line arguments, and deviations from this norm
+  can indicate malicious activity, such as Cobalt Strike usage. If confirmed malicious,
+  this activity could allow attackers to establish network connections for command
+  and control, potentially leading to data exfiltration or further system compromise.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time dest parent_process_name process_name process_path process process_id dest_port C2 | `searchprotocolhost_with_no_command_line_with_network_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id
+  Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" | join  process_id [| tstats
+  `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port
+  | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time dest parent_process_name
+  process_name process_path process process_id dest_port C2 | `searchprotocolhost_with_no_command_line_with_network_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives may be present in small environments.
+  Tuning may be required based on parent process.
 references:
 - https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc
 drilldown_searches:
@@ -19,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: A searchprotocolhost.exe process $process_name$ with no commandline in host $dest$
+  message: A searchprotocolhost.exe process $process_name$ with no commandline in
+    host $dest$
   mitre_attack_id:
   - T1055
   observable:
@@ -46,17 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - process_id
-  - parent_process_name
-  - dest_port
-  - process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml b/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml
index e4b5cb6aa6..98e5fdfa92 100644
--- a/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml
+++ b/detections/endpoint/secretdumps_offline_ntds_dumping_tool.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the potential use of the secretsdump.py tool to dump NTLM hashes from a copy of ntds.dit and the SAM, SYSTEM, and SECURITY registry hives. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and process names associated with secretsdump.py. This activity is significant because it indicates an attempt to extract sensitive credential information offline, which is a common post-exploitation technique. If confirmed malicious, this could allow an attacker to obtain NTLM hashes, facilitating further lateral movement and potential privilege escalation within the network.
+description: The following analytic detects the potential use of the secretsdump.py
+  tool to dump NTLM hashes from a copy of ntds.dit and the SAM, SYSTEM, and SECURITY
+  registry hives. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on specific command-line patterns and process names associated with secretsdump.py.
+  This activity is significant because it indicates an attempt to extract sensitive
+  credential information offline, which is a common post-exploitation technique. If
+  confirmed malicious, this could allow an attacker to obtain NTLM hashes, facilitating
+  further lateral movement and potential privilege escalation within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `secretdumps_offline_ntds_dumping_tool_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe"
+  Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*"
+  OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process
+  = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name
+  Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `secretdumps_offline_ntds_dumping_tool_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://github.com/SecureAuthCorp/impacket/blob/master/examples/secretsdump.py
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: A secretdump process $process_name$ with secretdump commandline $process$ to dump credentials in host $dest$
+  message: A secretdump process $process_name$ with secretdump commandline $process$
+    to dump credentials in host $dest$
   mitre_attack_id:
   - T1003.003
   - T1003
@@ -49,20 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml b/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml
index cf103c1bc2..185b78fc85 100644
--- a/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml
+++ b/detections/endpoint/serviceprincipalnames_discovery_with_powershell.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `powershell.exe` to query the domain for Service Principal Names (SPNs) using Script Block Logging EventCode 4104. It identifies the use of the KerberosRequestorSecurityToken class within the script block, which is equivalent to using setspn.exe. This activity is significant as it often precedes kerberoasting or silver ticket attacks, which can lead to credential theft. If confirmed malicious, attackers could leverage this information to escalate privileges or persist within the environment.
+description: The following analytic detects the use of `powershell.exe` to query the
+  domain for Service Principal Names (SPNs) using Script Block Logging EventCode 4104.
+  It identifies the use of the KerberosRequestorSecurityToken class within the script
+  block, which is equivalent to using setspn.exe. This activity is significant as
+  it often precedes kerberoasting or silver ticket attacks, which can lead to credential
+  theft. If confirmed malicious, attackers could leverage this information to escalate
+  privileges or persist within the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText="*KerberosRequestorSecurityToken*" | stats count min(_time) as firstTime max(_time) as lastTime by ScriptBlockText Opcode Computer UserID EventCode | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `serviceprincipalnames_discovery_with_powershell_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText="*KerberosRequestorSecurityToken*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by ScriptBlockText
+  Opcode Computer UserID EventCode | rename Computer as dest | rename UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `serviceprincipalnames_discovery_with_powershell_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives should be limited, however filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/ad/service-principal-names
@@ -32,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of attempting to identify service principle detected on $dest$ names.
+  message: An instance of attempting to identify service principle detected on $dest$
+    names.
   mitre_attack_id:
   - T1558.003
   observable:
@@ -60,17 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/sbl_xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml b/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml
index 2ed8235964..da85e00038 100644
--- a/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml
+++ b/detections/endpoint/serviceprincipalnames_discovery_with_setspn.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `setspn.exe` to query the domain for Service Principal Names (SPNs). This detection leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line arguments associated with `setspn.exe`. Monitoring this activity is crucial as it often precedes Kerberoasting or Silver Ticket attacks, which can lead to credential theft. If confirmed malicious, an attacker could use the gathered SPNs to escalate privileges or persist within the environment, posing a significant security risk.
+description: The following analytic detects the use of `setspn.exe` to query the domain
+  for Service Principal Names (SPNs). This detection leverages Endpoint Detection
+  and Response (EDR) data, focusing on specific command-line arguments associated
+  with `setspn.exe`. Monitoring this activity is crucial as it often precedes Kerberoasting
+  or Silver Ticket attacks, which can lead to credential theft. If confirmed malicious,
+  an attacker could use the gathered SPNs to escalate privileges or persist within
+  the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_setspn` (Processes.process="*-t*" AND Processes.process="*-f*") OR (Processes.process="*-q*" AND Processes.process="**/**") OR (Processes.process="*-q*") OR (Processes.process="*-s*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `serviceprincipalnames_discovery_with_setspn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be caused by Administrators resetting SPNs or querying for SPNs. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_setspn` (Processes.process="*-t*"
+  AND Processes.process="*-f*") OR (Processes.process="*-q*" AND Processes.process="**/**")
+  OR (Processes.process="*-q*") OR (Processes.process="*-s*") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `serviceprincipalnames_discovery_with_setspn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be caused by Administrators resetting SPNs
+  or querying for SPNs. Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/ad/service-principal-names
 - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/t1208-kerberoasting
@@ -29,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to identify service principle names.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to identify service principle names.
   mitre_attack_id:
   - T1558.003
   observable:
@@ -64,23 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-sysmon_setspn.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-sysmon_setspn.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/services_escalate_exe.yml b/detections/endpoint/services_escalate_exe.yml
index 3c605f1555..d6ef7b1283 100644
--- a/detections/endpoint/services_escalate_exe.yml
+++ b/detections/endpoint/services_escalate_exe.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of a randomly named binary via `services.exe`, indicative of privilege escalation using Cobalt Strike's `svc-exe`. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process lineage and command-line executions. This activity is significant as it often follows initial access, allowing adversaries to escalate privileges and establish persistence. If confirmed malicious, this behavior could enable attackers to execute arbitrary code, maintain long-term access, and potentially move laterally within the network, posing a severe threat to the organization's security.
+description: The following analytic identifies the execution of a randomly named binary
+  via `services.exe`, indicative of privilege escalation using Cobalt Strike's `svc-exe`.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process lineage and command-line executions. This activity is significant
+  as it often follows initial access, allowing adversaries to escalate privileges
+  and establish persistence. If confirmed malicious, this behavior could enable attackers
+  to execute arbitrary code, maintain long-term access, and potentially move laterally
+  within the network, posing a severe threat to the organization's security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe Processes.process_path=*admin$* by Processes.process_path Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_escalate_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as `services.exe` should never spawn a process from `ADMIN$`. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe
+  Processes.process_path=*admin$* by Processes.process_path Processes.dest Processes.user
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_escalate_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as `services.exe` should
+  never spawn a process from `ADMIN$`. Filter as needed.
 references:
 - https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/
 - https://attack.mitre.org/techniques/T1548/
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 95
   impact: 80
-  message: A service process $parent_process_name$ with process path $process_path$ in host $dest$
+  message: A service process $parent_process_name$ with process path $process_path$
+    in host $dest$
   mitre_attack_id:
   - T1548
   observable:
@@ -51,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/services_lolbas_execution_process_spawn.yml b/detections/endpoint/services_lolbas_execution_process_spawn.yml
index a233754e44..7b424f5d09 100644
--- a/detections/endpoint/services_lolbas_execution_process_spawn.yml
+++ b/detections/endpoint/services_lolbas_execution_process_spawn.yml
@@ -5,14 +5,45 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies `services.exe` spawning a LOLBAS (Living Off the Land Binaries and Scripts) execution process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where `services.exe` is the parent process. This activity is significant because adversaries often abuse the Service Control Manager to execute malicious code via native Windows binaries, facilitating lateral movement. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe security risk.
+description: The following analytic identifies `services.exe` spawning a LOLBAS (Living
+  Off the Land Binaries and Scripts) execution process. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process creation events where `services.exe`
+  is the parent process. This activity is significant because adversaries often abuse
+  the Service Control Manager to execute malicious code via native Windows binaries,
+  facilitating lateral movement. If confirmed malicious, this behavior could allow
+  attackers to execute arbitrary code, escalate privileges, or maintain persistence
+  within the environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=services.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_lolbas_execution_process_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate applications may trigger this behavior, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=services.exe)
+  (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe",
+  "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe",
+  "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe",
+  "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe",
+  "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe",
+  "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe",
+  "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe",
+  "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe",
+  "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe",
+  "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe",
+  "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `services_lolbas_execution_process_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate applications may trigger this behavior, filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1543/003/
 - https://pentestlab.blog/2020/07/21/lateral-movement-services/
@@ -23,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_lolbas/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement_lolbas/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml b/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml
index 1afa6c3807..81d0d5c471 100644
--- a/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml
+++ b/detections/endpoint/set_default_powershell_execution_policy_to_unrestricted_or_bypass.yml
@@ -5,16 +5,45 @@ date: '2024-09-30'
 author: Steven Dick, Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic detects changes to the PowerShell ExecutionPolicy in the registry to "Unrestricted" or "Bypass." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications under the path *Software\Microsoft\Powershell\1\ShellIds\Microsoft.PowerShell*. This activity is significant because setting the ExecutionPolicy to these values can allow the execution of potentially malicious scripts without restriction. If confirmed malicious, this could enable an attacker to execute arbitrary code, leading to further compromise of the system and potential escalation of privileges.
+description: The following analytic detects changes to the PowerShell ExecutionPolicy
+  in the registry to "Unrestricted" or "Bypass." It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on registry modifications under the path *Software\Microsoft\Powershell\1\ShellIds\Microsoft.PowerShell*.
+  This activity is significant because setting the ExecutionPolicy to these values
+  can allow the execution of potentially malicious scripts without restriction. If
+  confirmed malicious, this could enable an attacker to execute arbitrary code, leading
+  to further compromise of the system and potential escalation of privileges.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_value_name=ExecutionPolicy (Registry.registry_value_data=Unrestricted OR Registry.registry_value_data=Bypass)) BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell*
+  Registry.registry_value_name=ExecutionPolicy (Registry.registry_value_data=Unrestricted
+  OR Registry.registry_value_data=Bypass)) BY _time span=1h Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime
+  dest user parent_process_name parent_process process_name process_path process registry_key_name
+  registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may attempt to change the default execution
+  policy on a system for a variety of reasons. However, setting the policy to "unrestricted"
+  or "bypass" as this search is designed to identify, would be unusual. Hits should
+  be reviewed and investigated as appropriate.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -22,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +70,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 60
-  message: A registry modification in $registry_path$ with reg key $registry_key_name$ and reg value $registry_value_name$ in host $dest$
+  message: A registry modification in $registry_path$ with reg key $registry_key_name$
+    and reg value $registry_value_name$ in host $dest$
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -53,27 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/shim_database_file_creation.yml b/detections/endpoint/shim_database_file_creation.yml
index 40da5d44b2..049f8a9c89 100644
--- a/detections/endpoint/shim_database_file_creation.yml
+++ b/detections/endpoint/shim_database_file_creation.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of shim database files (.sdb) in default directories using the sdbinst.exe application. It leverages filesystem activity data from the Endpoint.Filesystem data model to identify file writes to the Windows\AppPatch\Custom directory. This activity is significant because shims can intercept and alter API calls, potentially allowing attackers to bypass security controls or execute malicious code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or persistent access within the environment.
+description: The following analytic detects the creation of shim database files (.sdb)
+  in default directories using the sdbinst.exe application. It leverages filesystem
+  activity data from the Endpoint.Filesystem data model to identify file writes to
+  the Windows\AppPatch\Custom directory. This activity is significant because shims
+  can intercept and alter API calls, potentially allowing attackers to bypass security
+  controls or execute malicious code. If confirmed malicious, this could lead to unauthorized
+  code execution, privilege escalation, or persistent access within the environment.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`'
-how_to_implement: You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-known_false_positives: Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.
+search: '| tstats `security_content_summariesonly` count values(Filesystem.action)
+  values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path  min(_time)
+  as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom*
+  by Filesystem.file_name Filesystem.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  |`drop_dm_object_name(Filesystem)` | `shim_database_file_creation_filter`'
+how_to_implement: You must be ingesting data that records the filesystem activity
+  from your hosts to populate the Endpoint file-system data model node. If you are
+  using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which
+  you want to collect data.
+known_false_positives: Because legitimate shim files are created and used all the
+  time, this event, in itself, is not suspicious. However, if there are other correlating
+  events, it may warrant further investigation.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_hash
-  - Filesystem.file_path
-  - Filesystem.file_name
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml b/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml
index 56fe163b87..cb4bfa451c 100644
--- a/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml
+++ b/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of sdbinst.exe with parameters indicative of silently creating a shim database. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line arguments. This activity is significant because shim databases can be used to intercept and manipulate API calls, potentially allowing attackers to bypass security controls or achieve persistence. If confirmed malicious, this could enable unauthorized code execution, privilege escalation, or persistent access to the compromised system.
+description: The following analytic detects the execution of sdbinst.exe with parameters
+  indicative of silently creating a shim database. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, parent processes,
+  and command-line arguments. This activity is significant because shim databases
+  can be used to intercept and manipulate API calls, potentially allowing attackers
+  to bypass security controls or achieve persistence. If confirmed malicious, this
+  could enable unauthorized code execution, privilege escalation, or persistent access
+  to the compromised system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: None identified
 references: []
 drilldown_searches:
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A process $process_name$ that possible create a shim db silently in host $dest$
+  message: A process $process_name$ that possible create a shim db silently in host
+    $dest$
   mitre_attack_id:
   - T1546.011
   - T1546
@@ -46,16 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/short_lived_scheduled_task.yml b/detections/endpoint/short_lived_scheduled_task.yml
index fdc61b38f9..8e807fb5ce 100644
--- a/detections/endpoint/short_lived_scheduled_task.yml
+++ b/detections/endpoint/short_lived_scheduled_task.yml
@@ -5,13 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation and deletion of scheduled tasks within a short time frame (less than 30 seconds) using Windows Security EventCodes 4698 and 4699. This behavior is identified by analyzing Windows Security Event Logs and leveraging the Windows TA for parsing. Such activity is significant as it may indicate lateral movement or remote code execution attempts by adversaries. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or execution of malicious payloads, necessitating prompt investigation and response by security analysts.
+description: The following analytic detects the creation and deletion of scheduled
+  tasks within a short time frame (less than 30 seconds) using Windows Security EventCodes
+  4698 and 4699. This behavior is identified by analyzing Windows Security Event Logs
+  and leveraging the Windows TA for parsing. Such activity is significant as it may
+  indicate lateral movement or remote code execution attempts by adversaries. If confirmed
+  malicious, this could lead to unauthorized access, data exfiltration, or execution
+  of malicious payloads, necessitating prompt investigation and response by security
+  analysts.
 data_source:
 - Windows Event Log Security 4698
 - Windows Event Log Security 4699
-search: '`wineventlog_security` EventCode=4698 OR EventCode=4699 | xmlkv Message | transaction Task_Name  startswith=(EventCode=4698) endswith=(EventCode=4699) | eval short_lived=case((duration<30),"TRUE") | search  short_lived = TRUE | rename ComputerName as dest| table _time, dest, Account_Name, Command, Task_Name, short_lived | `short_lived_scheduled_task_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
-known_false_positives: Although uncommon, legitimate applications may create and delete a Scheduled Task within 30 seconds. Filter as needed.
+search: '`wineventlog_security` EventCode=4698 OR EventCode=4699 | xmlkv Message |
+  transaction Task_Name  startswith=(EventCode=4698) endswith=(EventCode=4699) | eval
+  short_lived=case((duration<30),"TRUE") | search  short_lived = TRUE | rename ComputerName
+  as dest| table _time, dest, Account_Name, Command, Task_Name, short_lived | `short_lived_scheduled_task_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: Although uncommon, legitimate applications may create and delete
+  a Scheduled Task within 30 seconds. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1053/005/
 - https://docs.microsoft.com/en-us/windows/win32/taskschd/about-the-task-scheduler
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - ComputerName
-  - Account_Name
-  - Task_Name
-  - Description
-  - Command
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/lateral_movement/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
diff --git a/detections/endpoint/short_lived_windows_accounts.yml b/detections/endpoint/short_lived_windows_accounts.yml
index d949a5aa28..36f00dd3b7 100644
--- a/detections/endpoint/short_lived_windows_accounts.yml
+++ b/detections/endpoint/short_lived_windows_accounts.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects the rapid creation and deletion of Windows accounts within a short time frame. It leverages the "Change" data model in Splunk, specifically monitoring events with result IDs 4720 (account creation) and 4726 (account deletion). This behavior is significant as it may indicate an attacker attempting to create and remove accounts quickly to evade detection or gain unauthorized access. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or further malicious actions within the environment. Immediate investigation of flagged events is crucial to mitigate potential damage.
+description: The following analytic detects the rapid creation and deletion of Windows
+  accounts within a short time frame. It leverages the "Change" data model in Splunk,
+  specifically monitoring events with result IDs 4720 (account creation) and 4726
+  (account deletion). This behavior is significant as it may indicate an attacker
+  attempting to create and remove accounts quickly to evade detection or gain unauthorized
+  access. If confirmed malicious, this activity could lead to unauthorized access,
+  privilege escalation, or further malicious actions within the environment. Immediate
+  investigation of flagged events is crucial to mitigate potential damage.
 data_source:
 - Windows Event Log System 4720
 - Windows Event Log System 4726
-search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726 | transaction user connected=false maxspan=240m | table firstTime lastTime count user dest result_id | `short_lived_windows_accounts_filter`'
-how_to_implement: 'This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/'
-known_false_positives: It is possible that an administrator created and deleted an account in a short time period.  Verifying activity with an administrator is advised.
+search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as
+  result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change
+  where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h
+  All_Changes.user All_Changes.dest | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `drop_dm_object_name("All_Changes")` | search result_id = 4720 result_id=4726
+  | transaction user connected=false maxspan=240m | table firstTime lastTime count
+  user dest result_id | `short_lived_windows_accounts_filter`'
+how_to_implement: 'This search requires you to have enabled your Group Management
+  Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More
+  information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/'
+known_false_positives: It is possible that an administrator created and deleted an
+  account in a short time period.  Verifying activity with an administrator is advised.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,21 +66,19 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.result_id
-  - All_Changes.user
-  - All_Changes.dest
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log
     source: WinEventLog:System
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/silentcleanup_uac_bypass.yml b/detections/endpoint/silentcleanup_uac_bypass.yml
index 8521586aee..89ef0b1794 100644
--- a/detections/endpoint/silentcleanup_uac_bypass.yml
+++ b/detections/endpoint/silentcleanup_uac_bypass.yml
@@ -5,15 +5,41 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to the registry that may indicate a UAC (User Account Control) bypass attempt via the SilentCleanup task. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry changes in the path "*\\Environment\\windir" with executable values. This activity is significant as it can allow an attacker to gain high-privilege execution without user consent, bypassing UAC protections. If confirmed malicious, this could lead to unauthorized administrative access, enabling further system compromise and persistence.
+description: The following analytic detects suspicious modifications to the registry
+  that may indicate a UAC (User Account Control) bypass attempt via the SilentCleanup
+  task. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on registry changes in the path "*\\Environment\\windir" with executable values.
+  This activity is significant as it can allow an attacker to gain high-privilege
+  execution without user consent, bypassing UAC protections. If confirmed malicious,
+  this could lead to unauthorized administrative access, enabling further system compromise
+  and persistence.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_data = "*.exe*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_data
+  = "*.exe*") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`]
+  | fields firstTime lastTime dest user parent_process_name parent_process process_name
+  process_path process registry_key_name registry_path registry_value_name registry_value_data
+  process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `silentcleanup_uac_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://github.com/hfiref0x/UACME
@@ -24,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ in $dest$
+  message: Suspicious modification of registry $registry_path$ with possible payload
+    path $registry_value_name$ in $dest$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -48,27 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/single_letter_process_on_endpoint.yml b/detections/endpoint/single_letter_process_on_endpoint.yml
index ab0c13e5b8..c52899df30 100644
--- a/detections/endpoint/single_letter_process_on_endpoint.yml
+++ b/detections/endpoint/single_letter_process_on_endpoint.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects processes with names consisting of a single letter, which is often indicative of malware or an attacker attempting to evade detection. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because attackers use such techniques to obscure their presence and carry out malicious activities like data theft or ransomware attacks. If confirmed malicious, this behavior could lead to unauthorized access, data exfiltration, or system compromise. Immediate investigation is required to determine the legitimacy of the process.
+description: The following analytic detects processes with names consisting of a single
+  letter, which is often indicative of malware or an attacker attempting to evade
+  detection. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line executions. This activity is
+  significant because attackers use such techniques to obscure their presence and
+  carry out malicious activities like data theft or ransomware attacks. If confirmed
+  malicious, this behavior could lead to unauthorized access, data exfiltration, or
+  system compromise. Immediate investigation is required to determine the legitimacy
+  of the process.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name | `single_letter_process_on_endpoint_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user,
+  Processes.process, Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | eval process_name_length = len(process_name),
+  endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5
+  AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name
+  | `single_letter_process_on_endpoint_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Single-letter executables are not always malicious. Investigate
+  this activity with your normal incident-response process.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,16 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.process
-  - Processes.process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/slui_runas_elevated.yml b/detections/endpoint/slui_runas_elevated.yml
index fb5813e876..5caeb27484 100644
--- a/detections/endpoint/slui_runas_elevated.yml
+++ b/detections/endpoint/slui_runas_elevated.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the Microsoft Software Licensing User Interface Tool (`slui.exe`) with elevated privileges using the `-verb runas` function. This activity is identified through logs from Endpoint Detection and Response (EDR) agents, focusing on specific registry keys and command-line parameters. This behavior is significant as it indicates a potential privilege escalation attempt, which could allow an attacker to gain elevated access and execute malicious actions with higher privileges. If confirmed malicious, this could lead to unauthorized system changes, data exfiltration, or further compromise of the affected endpoint.
+description: The following analytic detects the execution of the Microsoft Software
+  Licensing User Interface Tool (`slui.exe`) with elevated privileges using the `-verb
+  runas` function. This activity is identified through logs from Endpoint Detection
+  and Response (EDR) agents, focusing on specific registry keys and command-line parameters.
+  This behavior is significant as it indicates a potential privilege escalation attempt,
+  which could allow an attacker to gain elevated access and execute malicious actions
+  with higher privileges. If confirmed malicious, this could lead to unauthorized
+  system changes, data exfiltration, or further compromise of the affected endpoint.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=slui.exe (Processes.process=*-verb* Processes.process=*runas*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `slui_runas_elevated_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives should be present as this is not commonly used by legitimate applications.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=slui.exe
+  (Processes.process=*-verb* Processes.process=*runas*) by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `slui_runas_elevated_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives should be present as this is not commonly
+  used by legitimate applications.
 references:
 - https://www.exploit-db.com/exploits/46998
 - https://mattharr0ey.medium.com/privilege-escalation-uac-bypass-in-changepk-c40b92818d1b
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A slui process $process_name$ with elevated commandline $process$ in host $dest$
+  message: A slui process $process_name$ with elevated commandline $process$ in host
+    $dest$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -52,19 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/slui_spawning_a_process.yml b/detections/endpoint/slui_spawning_a_process.yml
index 31255d3c41..aebda96146 100644
--- a/detections/endpoint/slui_spawning_a_process.yml
+++ b/detections/endpoint/slui_spawning_a_process.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the Microsoft Software Licensing User Interface Tool (`slui.exe`) spawning a child process. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where `slui.exe` is the parent process. This activity is significant because `slui.exe` should not typically spawn child processes, and doing so may indicate a UAC bypass attempt, leading to elevated privileges. If confirmed malicious, an attacker could leverage this to execute code with elevated privileges, potentially compromising the system's security and gaining unauthorized access.
+description: The following analytic detects the Microsoft Software Licensing User
+  Interface Tool (`slui.exe`) spawning a child process. This behavior is identified
+  using Endpoint Detection and Response (EDR) telemetry, focusing on process creation
+  events where `slui.exe` is the parent process. This activity is significant because
+  `slui.exe` should not typically spawn child processes, and doing so may indicate
+  a UAC bypass attempt, leading to elevated privileges. If confirmed malicious, an
+  attacker could leverage this to execute code with elevated privileges, potentially
+  compromising the system's security and gaining unauthorized access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=slui.exe by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `slui_spawning_a_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Certain applications may spawn from `slui.exe` that are legitimate. Filtering will be needed to ensure proper monitoring.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=slui.exe
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `slui_spawning_a_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Certain applications may spawn from `slui.exe` that are legitimate.
+  Filtering will be needed to ensure proper monitoring.
 references:
 - https://www.exploit-db.com/exploits/46998
 - https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A slui process $parent_process_name$ spawning child process $process_name$ in host $dest$
+  message: A slui process $parent_process_name$ spawning child process $process_name$
+    in host $dest$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -50,19 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/spike_in_file_writes.yml b/detections/endpoint/spike_in_file_writes.yml
index 22394f3fd5..82d77ad321 100644
--- a/detections/endpoint/spike_in_file_writes.yml
+++ b/detections/endpoint/spike_in_file_writes.yml
@@ -5,12 +5,30 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects a sharp increase in the number of files written to a specific host. It leverages the Endpoint.Filesystem data model, focusing on 'created' actions and comparing current file write counts against historical averages and standard deviations. This activity is significant as a sudden spike in file writes can indicate malicious activities such as ransomware encryption or data exfiltration. If confirmed malicious, this behavior could lead to significant data loss, system compromise, or further propagation of malware within the network.
+description: The following analytic detects a sharp increase in the number of files
+  written to a specific host. It leverages the Endpoint.Filesystem data model, focusing
+  on 'created' actions and comparing current file write counts against historical
+  averages and standard deviations. This activity is significant as a sudden spike
+  in file writes can indicate malicious activities such as ransomware encryption or
+  data exfiltration. If confirmed malicious, this behavior could lead to significant
+  data loss, system compromise, or further propagation of malware within the network.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime, "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"), count, null))) as stdev by "dest" | eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter`'
-how_to_implement: In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system.
-known_false_positives: It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem
+  where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)`
+  | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time
+  >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime,
+  "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"),
+  count, null))) as stdev by "dest" | eval upperBound=(avg+stdev*4), isOutlier=if((count
+  > upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 | `spike_in_file_writes_filter`'
+how_to_implement: In order to implement this search, you must populate the Endpoint
+  file-system data model node. This is typically populated via endpoint detection
+  and response product, such as Carbon Black or endpoint data sources such as Sysmon.
+  The data used for this search is typically generated via logs that report reads
+  and writes to the file system.
+known_false_positives: It is important to understand that if you happen to install
+  any new applications on your hosts or are copying a large number of files, you can
+  expect to see a large increase of file modifications.
 references: []
 tags:
   analytic_story:
@@ -31,8 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.action
-  - Filesystem.dest
   security_domain: endpoint
diff --git a/detections/endpoint/spoolsv_spawning_rundll32.yml b/detections/endpoint/spoolsv_spawning_rundll32.yml
index f2f15aa9d2..32a21e4c84 100644
--- a/detections/endpoint/spoolsv_spawning_rundll32.yml
+++ b/detections/endpoint/spoolsv_spawning_rundll32.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the spawning of `rundll32.exe` without command-line arguments by `spoolsv.exe`, which is unusual and potentially indicative of exploitation attempts like CVE-2021-34527 (PrintNightmare). This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where `spoolsv.exe` is the parent process. This activity is significant as `spoolsv.exe` typically does not spawn other processes, and such behavior could indicate an active exploitation attempt. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised endpoint.
+description: The following analytic detects the spawning of `rundll32.exe` without
+  command-line arguments by `spoolsv.exe`, which is unusual and potentially indicative
+  of exploitation attempts like CVE-2021-34527 (PrintNightmare). This detection leverages
+  Endpoint Detection and Response (EDR) telemetry, focusing on process creation events
+  where `spoolsv.exe` is the parent process. This activity is significant as `spoolsv.exe`
+  typically does not spawn other processes, and such behavior could indicate an active
+  exploitation attempt. If confirmed malicious, this could allow an attacker to execute
+  arbitrary code, escalate privileges, or maintain persistence on the compromised
+  endpoint.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe `process_rundll32` by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_spawning_rundll32_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives have been identified. There are limited instances where `rundll32.exe` may be spawned by a legitimate print driver.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe
+  `process_rundll32` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.original_file_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `spoolsv_spawning_rundll32_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives have been identified. There are limited
+  instances where `rundll32.exe` may be spawned by a legitimate print driver.
 references:
 - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available
 - https://www.truesec.com/hub/blog/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 80
-  message: $parent_process_name$ has spawned $process_name$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
+  message: $parent_process_name$ has spawned $process_name$ on endpoint $dest$. This
+    behavior is suspicious and related to PrintNightmare.
   mitre_attack_id:
   - T1547.012
   - T1547
@@ -51,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/spoolsv_suspicious_loaded_modules.yml b/detections/endpoint/spoolsv_suspicious_loaded_modules.yml
index d4cc628847..c042653681 100644
--- a/detections/endpoint/spoolsv_suspicious_loaded_modules.yml
+++ b/detections/endpoint/spoolsv_suspicious_loaded_modules.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the suspicious loading of DLLs by spoolsv.exe, potentially indicating PrintNightmare exploitation. It leverages Sysmon EventCode 7 to identify instances where spoolsv.exe loads multiple DLLs from the Windows System32 spool drivers x64 directory. This activity is significant as it may signify an attacker exploiting the PrintNightmare vulnerability to execute arbitrary code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, and persistent access within the environment, posing a severe security risk.
+description: The following analytic detects the suspicious loading of DLLs by spoolsv.exe,
+  potentially indicating PrintNightmare exploitation. It leverages Sysmon EventCode
+  7 to identify instances where spoolsv.exe loads multiple DLLs from the Windows System32
+  spool drivers x64 directory. This activity is significant as it may signify an attacker
+  exploiting the PrintNightmare vulnerability to execute arbitrary code. If confirmed
+  malicious, this could lead to unauthorized code execution, privilege escalation,
+  and persistent access within the environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) as ImageLoaded count min(_time) as firstTime max(_time) as lastTime by Image Computer ProcessId EventCode | rename Computer as dest | where countImgloaded >= 3 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*"
+  ImageLoaded = "*.dll" | stats dc(ImageLoaded) as countImgloaded values(ImageLoaded)
+  as ImageLoaded count min(_time) as firstTime max(_time) as lastTime by Image Computer
+  ProcessId EventCode | rename Computer as dest | where countImgloaded >= 3 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `spoolsv_suspicious_loaded_modules_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://raw.githubusercontent.com/hieuttmmo/sigma/dceb13fe3f1821b119ae495b41e24438bd97e3d0/rules/windows/image_load/sysmon_cve_2021_1675_print_nightmare.yml
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 80
-  message: $Image$ with process id $ProcessId$ has loaded a driver from $ImageLoaded$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
+  message: $Image$ with process id $ProcessId$ has loaded a driver from $ImageLoaded$
+    on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
   mitre_attack_id:
   - T1547.012
   - T1547
@@ -43,16 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - dest
-  - EventCode
-  - ImageLoaded
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/spoolsv_suspicious_process_access.yml b/detections/endpoint/spoolsv_suspicious_process_access.yml
index e7bfc3ebd2..777a19f974 100644
--- a/detections/endpoint/spoolsv_suspicious_process_access.yml
+++ b/detections/endpoint/spoolsv_suspicious_process_access.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious process access by spoolsv.exe, potentially indicating exploitation of the PrintNightmare vulnerability (CVE-2021-34527). It leverages Sysmon EventCode 10 to identify when spoolsv.exe accesses critical system files or processes like rundll32.exe with elevated privileges. This activity is significant as it may signal an attempt to gain unauthorized privilege escalation on a vulnerable machine. If confirmed malicious, an attacker could achieve elevated privileges, leading to further system compromise, persistent access, or unauthorized control over the affected environment.
+description: The following analytic detects suspicious process access by spoolsv.exe,
+  potentially indicating exploitation of the PrintNightmare vulnerability (CVE-2021-34527).
+  It leverages Sysmon EventCode 10 to identify when spoolsv.exe accesses critical
+  system files or processes like rundll32.exe with elevated privileges. This activity
+  is significant as it may signal an attempt to gain unauthorized privilege escalation
+  on a vulnerable machine. If confirmed malicious, an attacker could achieve elevated
+  privileges, leading to further system compromise, persistent access, or unauthorized
+  control over the affected environment.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff | stats  count min(_time) as firstTime max(_time) as lastTime by dest SourceImage TargetImage GrantedAccess CallTrace  EventCode ProcessID| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_suspicious_process_access_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with process access event where SourceImage, TargetImage, GrantedAccess and CallTrace executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of spoolsv.exe.
+search: '`sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*"
+  TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff |
+  stats  count min(_time) as firstTime max(_time) as lastTime by dest SourceImage
+  TargetImage GrantedAccess CallTrace  EventCode ProcessID| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `spoolsv_suspicious_process_access_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with process access event where SourceImage, TargetImage, GrantedAccess and
+  CallTrace executions from your endpoints. If you are using Sysmon, you must have
+  at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of spoolsv.exe.
 known_false_positives: Unknown. Filter as needed.
 references:
 - https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +52,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 80
-  message: $SourceImage$ was GrantedAccess open access to $TargetImage$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
+  message: $SourceImage$ was GrantedAccess open access to $TargetImage$ on endpoint
+    $dest$. This behavior is suspicious and related to PrintNightmare.
   mitre_attack_id:
   - T1068
   observable:
@@ -53,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - GrantedAccess
-  - CallTrace
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/spoolsv_writing_a_dll.yml b/detections/endpoint/spoolsv_writing_a_dll.yml
index 1102b0041c..60c1383390 100644
--- a/detections/endpoint/spoolsv_writing_a_dll.yml
+++ b/detections/endpoint/spoolsv_writing_a_dll.yml
@@ -5,14 +5,33 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages the Endpoint datamodel, specifically monitoring process and filesystem events to identify `.dll` file creation within the `\spool\drivers\x64\` path. This activity is significant as it may signify an attacker attempting to execute malicious code via the Print Spooler service. If confirmed malicious, this could lead to unauthorized code execution and potential system compromise. Immediate endpoint isolation and further investigation are recommended.
+description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which
+  is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527
+  (PrintNightmare). This detection leverages the Endpoint datamodel, specifically
+  monitoring process and filesystem events to identify `.dll` file creation within
+  the `\spool\drivers\x64\` path. This activity is significant as it may signify an
+  attacker attempting to execute malicious code via the Print Spooler service. If
+  confirmed malicious, this could lead to unauthorized code execution and potential
+  system compromise. Immediate endpoint isolation and further investigation are recommended.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=spoolsv.exe by _time Processes.process_guid Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.process_guid Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process_guid process] | dedup file_create_time | table  dest file_create_time, file_name, file_path, process_name process_guid | `spoolsv_writing_a_dll_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=spoolsv.exe by _time Processes.process_guid Processes.process_name
+  Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [|
+  tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*"
+  Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.process_guid Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` |
+  fields _time dest file_create_time file_name file_path process_name process_path
+  process_guid process] | dedup file_create_time | table  dest file_create_time, file_name,
+  file_path, process_name process_guid | `spoolsv_writing_a_dll_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem`
+  node.
 known_false_positives: Unknown.
 references:
 - https://www.truesec.com/hub/blog/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available
@@ -24,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +59,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 80
-  message: $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
+  message: $process_name$ has been identified writing dll's to $file_path$ on endpoint
+    $dest$. This behavior is suspicious and related to PrintNightmare.
   mitre_attack_id:
   - T1547.012
   - T1547
@@ -52,20 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml b/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml
index 450b0ba03f..15ab5e429e 100644
--- a/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml
+++ b/detections/endpoint/spoolsv_writing_a_dll___sysmon.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Mauricio Velazco, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation events in the `\spool\drivers\x64\` directory. This activity is significant because `spoolsv.exe` typically does not write DLL files, and such behavior could signify an ongoing attack. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised system.
+description: The following analytic detects `spoolsv.exe` writing a `.dll` file, which
+  is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527
+  (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation
+  events in the `\spool\drivers\x64\` directory. This activity is significant because
+  `spoolsv.exe` typically does not write DLL files, and such behavior could signify
+  an ongoing attack. If confirmed malicious, this could allow an attacker to execute
+  arbitrary code, escalate privileges, or maintain persistence on the compromised
+  system.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by dest, UserID, process_name, file_path, file_name, TargetFilename, process_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
+search: '`sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*"
+  file_name=*.dll | stats count min(_time) as firstTime max(_time) as lastTime by
+  dest, UserID, process_name, file_path, file_name, TargetFilename, process_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `spoolsv_writing_a_dll___sysmon_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
 known_false_positives: Limited false positives. Filter as needed.
 references:
 - https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +51,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 80
-  message: $process_name$ has been identified writing dll's to $file_path$ on endpoint $dest$. This behavior is suspicious and related to PrintNightmare.
+  message: $process_name$ has been identified writing dll's to $file_path$ on endpoint
+    $dest$. This behavior is suspicious and related to PrintNightmare.
   mitre_attack_id:
   - T1547.012
   - T1547
@@ -54,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - UserID
-  - process_name
-  - file_path
-  - file_name
-  - TargetFilename
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/sqlite_module_in_temp_folder.yml b/detections/endpoint/sqlite_module_in_temp_folder.yml
index b4ca7759f4..4fdb47602a 100644
--- a/detections/endpoint/sqlite_module_in_temp_folder.yml
+++ b/detections/endpoint/sqlite_module_in_temp_folder.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of sqlite3.dll files in the %temp% folder. It leverages Sysmon EventCode 11 to identify when these files are written to the temporary directory. This activity is significant because it is associated with IcedID malware, which uses the sqlite3 module to parse browser databases and steal sensitive information such as banking details, credit card information, and credentials. If confirmed malicious, this behavior could lead to significant data theft and compromise of user accounts.
+description: The following analytic detects the creation of sqlite3.dll files in the
+  %temp% folder. It leverages Sysmon EventCode 11 to identify when these files are
+  written to the temporary directory. This activity is significant because it is associated
+  with IcedID malware, which uses the sqlite3 module to parse browser databases and
+  steal sensitive information such as banking details, credit card information, and
+  credentials. If confirmed malicious, this behavior could lead to significant data
+  theft and compromise of user accounts.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id process_name file_name file_path action process_guid| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `sqlite_module_in_temp_folder_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename
+  = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") | stats count min(_time) as
+  firstTime max(_time) as lastTime by dest signature signature_id process_name file_name
+  file_path action process_guid| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `sqlite_module_in_temp_folder_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.cisecurity.org/insights/white-papers/security-primer-icedid
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - TargetFilename
-  - EventCode
-  - ProcessId
-  - Image
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml b/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml
index 0cedefbf41..1853527362 100644
--- a/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml
+++ b/detections/endpoint/steal_or_forge_authentication_certificates_behavior_identified.yml
@@ -6,10 +6,31 @@ author: Michael Haag, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies potential threats related to the theft or forgery of authentication certificates. It detects when five or more analytics from the Windows Certificate Services story trigger within a specified timeframe. This detection leverages aggregated risk scores and event counts from the Risk data model. This activity is significant as it may indicate an ongoing attack aimed at compromising authentication mechanisms. If confirmed malicious, attackers could gain unauthorized access to sensitive systems and data, potentially leading to severe security breaches.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Windows Certificate Services" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 5 | `steal_or_forge_authentication_certificates_behavior_identified_filter`'
-how_to_implement: The Windows Certificate Services analytic story must have 5 or more analytics enabled. In addition, ensure data is being logged that is required. Modify the correlation as needed based on volume of noise related to the other analytics.
-known_false_positives: False positives may be present based on automated tooling or system administrators. Filter as needed.
+description: The following analytic identifies potential threats related to the theft
+  or forgery of authentication certificates. It detects when five or more analytics
+  from the Windows Certificate Services story trigger within a specified timeframe.
+  This detection leverages aggregated risk scores and event counts from the Risk data
+  model. This activity is significant as it may indicate an ongoing attack aimed at
+  compromising authentication mechanisms. If confirmed malicious, attackers could
+  gain unauthorized access to sensitive systems and data, potentially leading to severe
+  security breaches.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Windows
+  Certificate Services" All_Risk.risk_object_type="system" by All_Risk.risk_object
+  All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 5 | `steal_or_forge_authentication_certificates_behavior_identified_filter`'
+how_to_implement: The Windows Certificate Services analytic story must have 5 or more
+  analytics enabled. In addition, ensure data is being logged that is required. Modify
+  the correlation as needed based on volume of noise related to the other analytics.
+known_false_positives: False positives may be present based on automated tooling or
+  system administrators. Filter as needed.
 references:
 - https://research.splunk.com/stories/windows_certificate_services/
 - https://attack.mitre.org/techniques/T1649/
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/risk_certificate_services.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/risk_certificate_services.log
     source: certs
     sourcetype: stash
diff --git a/detections/endpoint/sunburst_correlation_dll_and_network_event.yml b/detections/endpoint/sunburst_correlation_dll_and_network_event.yml
index c6b632b71a..de599327f7 100644
--- a/detections/endpoint/sunburst_correlation_dll_and_network_event.yml
+++ b/detections/endpoint/sunburst_correlation_dll_and_network_event.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Patrick Bareiss, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the loading of the malicious SolarWinds.Orion.Core.BusinessLayer.dll by SolarWinds.BusinessLayerHost.exe and subsequent DNS queries to avsvmcloud.com. It uses Sysmon EventID 7 for DLL loading and Event ID 22 for DNS queries, correlating these events within a 12-14 day period. This activity is significant as it indicates potential Sunburst malware infection, a known supply chain attack. If confirmed malicious, this could lead to unauthorized network access, data exfiltration, and further compromise of the affected systems.
+description: The following analytic identifies the loading of the malicious SolarWinds.Orion.Core.BusinessLayer.dll
+  by SolarWinds.BusinessLayerHost.exe and subsequent DNS queries to avsvmcloud.com.
+  It uses Sysmon EventID 7 for DLL loading and Event ID 22 for DNS queries, correlating
+  these events within a 12-14 day period. This activity is significant as it indicates
+  potential Sunburst malware infection, a known supply chain attack. If confirmed
+  malicious, this could lead to unauthorized network access, data exfiltration, and
+  further compromise of the affected systems.
 data_source:
 - Sysmon EventID 7
 - Sysmon EventID 22
-search: (`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode) AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `sunburst_correlation_dll_and_network_event_filter`
-how_to_implement: This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days.
+search: (`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll)
+  OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) | eventstats dc(EventCode)
+  AS dc_events | where dc_events=2 | stats min(_time) as firstTime max(_time) as lastTime
+  values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host | rename
+  host as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `sunburst_correlation_dll_and_network_event_filter`
+how_to_implement: This detection relies on sysmon logs with the Event ID 7, Driver
+  loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll
+  is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query.
+  We suggest to run this detection at least once a day over the last 14 days.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor
@@ -32,9 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ImageLoaded
-  - QueryName
   security_domain: endpoint
diff --git a/detections/endpoint/suspicious_computer_account_name_change.yml b/detections/endpoint/suspicious_computer_account_name_change.yml
index d4a11bcf22..9c04df9026 100644
--- a/detections/endpoint/suspicious_computer_account_name_change.yml
+++ b/detections/endpoint/suspicious_computer_account_name_change.yml
@@ -5,23 +5,39 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious computer account name change in Active Directory. It leverages Event ID 4781, which logs account name changes, to identify instances where a computer account name is changed to one that does not end with a `$`. This behavior is significant as it may indicate an attempt to exploit CVE-2021-42278 and CVE-2021-42287, which can lead to domain controller impersonation and privilege escalation. If confirmed malicious, this activity could allow an attacker to gain elevated privileges and potentially control the domain.
+description: The following analytic detects a suspicious computer account name change
+  in Active Directory. It leverages Event ID 4781, which logs account name changes,
+  to identify instances where a computer account name is changed to one that does
+  not end with a `$`. This behavior is significant as it may indicate an attempt to
+  exploit CVE-2021-42278 and CVE-2021-42287, which can lead to domain controller impersonation
+  and privilege escalation. If confirmed malicious, this activity could allow an attacker
+  to gain elevated privileges and potentially control the domain.
 data_source:
 - Windows Event Log Security 4781
-search: '`wineventlog_security` EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$" | table _time, Computer, Caller_User_Name, OldTargetUserName, NewTargetUserName | rename Computer as dest | `suspicious_computer_account_name_change_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
-known_false_positives: Renaming a computer account name to a name that not end with '$' is highly unsual and may not have any legitimate scenarios.
+search: '`wineventlog_security` EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$"
+  | table _time, Computer, Caller_User_Name, OldTargetUserName, NewTargetUserName
+  | rename Computer as dest | `suspicious_computer_account_name_change_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
+known_false_positives: Renaming a computer account name to a name that not end with
+  '$' is highly unsual and may not have any legitimate scenarios.
 references:
 - https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html
 - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278
 - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42287
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$OldTargetUserName$"
-  search: '%original_detection_search% | search  dest = "$dest$" OldTargetUserName = "$OldTargetUserName$"'
+  search: '%original_detection_search% | search  dest = "$dest$" OldTargetUserName
+    = "$OldTargetUserName$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$OldTargetUserName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$OldTargetUserName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$OldTargetUserName$") starthoursago=168  | stats count min(_time) as firstTime
+    max(_time) as lastTime values(search_name) as "Search Name" values(risk_message)
+    as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +50,8 @@ tags:
   - CVE-2021-42287
   - CVE-2021-42278
   impact: 100
-  message: A computer account $OldTargetUserName$ was renamed with a suspicious computer name on $dest$
+  message: A computer account $OldTargetUserName$ was renamed with a suspicious computer
+    name on $dest$
   mitre_attack_id:
   - T1078
   - T1078.002
@@ -51,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Computer
-  - Caller_User_Name
-  - OldTargetUserName
-  - NewTargetUserName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_computer_account_name_change/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_computer_account_name_change/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_copy_on_system32.yml b/detections/endpoint/suspicious_copy_on_system32.yml
index e28d40570f..f2e4a9ad04 100644
--- a/detections/endpoint/suspicious_copy_on_system32.yml
+++ b/detections/endpoint/suspicious_copy_on_system32.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious file copy operations from the System32 or SysWow64 directories, often indicative of malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes initiated by command-line tools like cmd.exe or PowerShell. This behavior is significant as it may indicate an attempt to execute malicious code using legitimate system tools (LOLBIN). If confirmed malicious, this activity could allow an attacker to execute arbitrary code, potentially leading to system compromise or further lateral movement within the network.
+description: The following analytic detects suspicious file copy operations from the
+  System32 or SysWow64 directories, often indicative of malicious activity. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on processes initiated
+  by command-line tools like cmd.exe or PowerShell. This behavior is significant as
+  it may indicate an attempt to execute malicious code using legitimate system tools
+  (LOLBIN). If confirmed malicious, this activity could allow an attacker to execute
+  arbitrary code, potentially leading to system compromise or further lateral movement
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN("cmd.exe", "powershell*","pwsh.exe", "sqlps.exe", "sqltoolsps.exe", "powershell_ise.exe") AND `process_copy` AND Processes.process IN("*\\Windows\\System32\\*", "*\\Windows\\SysWow64\\*") AND Processes.process = "*copy*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id temp | `drop_dm_object_name(Processes)` | eval splitted_commandline=split(process," ") | eval first_cmdline=lower(mvindex(splitted_commandline,0)) | where NOT LIKE(first_cmdline,"%\\windows\\system32\\%") AND NOT LIKE(first_cmdline,"%\\windows\\syswow64\\%") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |`suspicious_copy_on_system32_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN("cmd.exe", "powershell*","pwsh.exe", "sqlps.exe", "sqltoolsps.exe", "powershell_ise.exe")
+  AND `process_copy` AND Processes.process IN("*\\Windows\\System32\\*", "*\\Windows\\SysWow64\\*")
+  AND Processes.process = "*copy*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  temp | `drop_dm_object_name(Processes)` | eval splitted_commandline=split(process,"
+  ") | eval first_cmdline=lower(mvindex(splitted_commandline,0)) | where NOT LIKE(first_cmdline,"%\\windows\\system32\\%")
+  AND NOT LIKE(first_cmdline,"%\\windows\\syswow64\\%") | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` |`suspicious_copy_on_system32_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: every user may do this event but very un-ussual.
 references:
 - https://www.hybrid-analysis.com/sample/8da5b75b6380a41eee3a399c43dfe0d99eeefaa1fd21027a07b1ecaa4cd96fdd?environmentId=120
@@ -22,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/copy_sysmon/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/copy_sysmon/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_curl_network_connection.yml b/detections/endpoint/suspicious_curl_network_connection.yml
index 7e49da5fa7..3d649d37cd 100644
--- a/detections/endpoint/suspicious_curl_network_connection.yml
+++ b/detections/endpoint/suspicious_curl_network_connection.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the use of the curl command contacting suspicious remote domains, such as s3.amazonaws.com, which is indicative of Command and Control (C2) activity or downloading further implants. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate the presence of MacOS adware or other malicious software attempting to establish persistence or exfiltrate data. If confirmed malicious, this could allow attackers to maintain control over the compromised system and deploy additional payloads.
+description: The following analytic detects the use of the curl command contacting
+  suspicious remote domains, such as s3.amazonaws.com, which is indicative of Command
+  and Control (C2) activity or downloading further implants. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs and command-line arguments. This activity is significant as it may indicate
+  the presence of MacOS adware or other malicious software attempting to establish
+  persistence or exfiltrate data. If confirmed malicious, this could allow attackers
+  to maintain control over the compromised system and deploy additional payloads.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process=s3.amazonaws.com by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_curl_network_connection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl
+  Processes.process=s3.amazonaws.com by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `suspicious_curl_network_connection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown. Filter as needed.
 references:
 - https://redcanary.com/blog/clipping-silver-sparrows-wings/
@@ -40,13 +60,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml b/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml
index 555a418cee..b056c92541 100644
--- a/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml
+++ b/detections/endpoint/suspicious_dllhost_no_command_line_arguments.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances of DLLHost.exe executing without command line arguments. This behavior is unusual and often associated with malicious activities, such as those performed by Cobalt Strike. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because DLLHost.exe typically requires arguments to function correctly, and its absence may indicate an attempt to evade detection. If confirmed malicious, this could lead to unauthorized actions like credential dumping or file manipulation, posing a severe threat to the environment.
+description: The following analytic detects instances of DLLHost.exe executing without
+  command line arguments. This behavior is unusual and often associated with malicious
+  activities, such as those performed by Cobalt Strike. The detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs. This activity is significant because DLLHost.exe typically requires arguments
+  to function correctly, and its absence may indicate an attempt to evade detection.
+  If confirmed malicious, this could lead to unauthorized actions like credential
+  dumping or file manipulation, posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_dllhost` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(dllhost\.exe.{0,4}$)" | `suspicious_dllhost_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_dllhost` by _time span=1h  Processes.process_id Processes.process_name
+  Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(dllhost\.exe.{0,4}$)" | `suspicious_dllhost_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives may be present in small environments.
+  Tuning may be required based on parent process.
 references:
 - https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile
 - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Suspicious dllhost.exe process with no command line arguments executed on $dest$ by $user$
+  message: Suspicious dllhost.exe process with no command line arguments executed
+    on $dest$ by $user$
   mitre_attack_id:
   - T1055
   observable:
@@ -49,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_driver_loaded_path.yml b/detections/endpoint/suspicious_driver_loaded_path.yml
index 1bff27ee4f..4059a15e00 100644
--- a/detections/endpoint/suspicious_driver_loaded_path.yml
+++ b/detections/endpoint/suspicious_driver_loaded_path.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the loading of drivers from suspicious paths, which is a technique often used by malicious software such as coin miners (e.g., xmrig). It leverages Sysmon EventCode 6 to identify drivers loaded from non-standard directories. This activity is significant because legitimate drivers typically reside in specific system directories, and deviations may indicate malicious activity. If confirmed malicious, this could allow an attacker to execute code at the kernel level, potentially leading to privilege escalation, persistence, or further system compromise.
+description: The following analytic detects the loading of drivers from suspicious
+  paths, which is a technique often used by malicious software such as coin miners
+  (e.g., xmrig). It leverages Sysmon EventCode 6 to identify drivers loaded from non-standard
+  directories. This activity is significant because legitimate drivers typically reside
+  in specific system directories, and deviations may indicate malicious activity.
+  If confirmed malicious, this could allow an attacker to execute code at the kernel
+  level, potentially leading to privilege escalation, persistence, or further system
+  compromise.
 data_source:
 - Sysmon EventID 6
-search: '`sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*", "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) |  stats  min(_time) as firstTime max(_time) as lastTime count by dest ImageLoaded Hashes IMPHASH Signature Signed| rename ImageLoaded as file_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_driver_loaded_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Limited false positives will be present. Some applications do load drivers
+search: '`sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*",
+  "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) |  stats  min(_time) as
+  firstTime max(_time) as lastTime count by dest ImageLoaded Hashes IMPHASH Signature
+  Signed| rename ImageLoaded as file_name | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `suspicious_driver_loaded_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the driver loaded and Signature from your endpoints. If you are using
+  Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: Limited false positives will be present. Some applications
+  do load drivers
 references:
 - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/
 - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - ImageLoaded
-  - Hashes
-  - IMPHASH
-  - Signature
-  - Signed
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_event_log_service_behavior.yml b/detections/endpoint/suspicious_event_log_service_behavior.yml
index a70853a248..30ec87dff8 100644
--- a/detections/endpoint/suspicious_event_log_service_behavior.yml
+++ b/detections/endpoint/suspicious_event_log_service_behavior.yml
@@ -5,12 +5,22 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the shutdown of the Windows Event Log service using Windows Event ID 1100. This event is logged every time the service stops, including during normal system shutdowns. Monitoring this activity is crucial as it can indicate attempts to cover tracks or disable logging. If confirmed malicious, an attacker could hide their activities, making it difficult to trace their actions and investigate further incidents. Analysts should verify if the shutdown was planned and review other alerts and data sources for additional suspicious behavior.
+description: The following analytic detects the shutdown of the Windows Event Log
+  service using Windows Event ID 1100. This event is logged every time the service
+  stops, including during normal system shutdowns. Monitoring this activity is crucial
+  as it can indicate attempts to cover tracks or disable logging. If confirmed malicious,
+  an attacker could hide their activities, making it difficult to trace their actions
+  and investigate further incidents. Analysts should verify if the shutdown was planned
+  and review other alerts and data sources for additional suspicious behavior.
 data_source:
 - Windows Event Log Security 1100
-search: (`wineventlog_security` EventCode=1100) | stats count min(_time) as firstTime max(_time) as lastTime by dest name EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_event_log_service_behavior_filter`
-how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
-known_false_positives: It is possible the Event Logging service gets shut down due to system errors or legitimately administration tasks. Filter as needed.
+search: (`wineventlog_security` EventCode=1100) | stats count min(_time) as firstTime
+  max(_time) as lastTime by dest name EventCode | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `suspicious_event_log_service_behavior_filter`
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
+known_false_positives: It is possible the Event Logging service gets shut down due
+  to system errors or legitimately administration tasks. Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1100
 - https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads
@@ -37,14 +47,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_event_log_service_behavior/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_event_log_service_behavior/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml b/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml
index f7a2ee1c89..94414ebaf6 100644
--- a/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml
+++ b/detections/endpoint/suspicious_gpupdate_no_command_line_arguments.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of gpupdate.exe without any command line arguments. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. It is significant because gpupdate.exe typically runs with specific arguments, and its execution without them is often associated with malicious activities, such as those performed by Cobalt Strike. If confirmed malicious, this activity could indicate an attempt to execute unauthorized commands or scripts, potentially leading to further system compromise or lateral movement within the network.
+description: The following analytic detects the execution of gpupdate.exe without
+  any command line arguments. This behavior is identified using data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs. It is significant
+  because gpupdate.exe typically runs with specific arguments, and its execution without
+  them is often associated with malicious activities, such as those performed by Cobalt
+  Strike. If confirmed malicious, this activity could indicate an attempt to execute
+  unauthorized commands or scripts, potentially leading to further system compromise
+  or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_gpupdate` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(gpupdate\.exe.{0,4}$)" | `suspicious_gpupdate_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_gpupdate` by _time span=1h  Processes.process_id Processes.process_name
+  Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(gpupdate\.exe.{0,4}$)" | `suspicious_gpupdate_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives may be present in small environments.
+  Tuning may be required based on parent process.
 references:
 - https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile
 - https://www.cobaltstrike.com/blog/learn-pipe-fitting-for-all-of-your-offense-projects/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Suspicious gpupdate.exe process with no command line arguments executed on $dest$ by $user$
+  message: Suspicious gpupdate.exe process with no command line arguments executed
+    on $dest$ by $user$
   mitre_attack_id:
   - T1055
   observable:
@@ -49,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml b/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml
index 155b803f95..8137f8dbe1 100644
--- a/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml
+++ b/detections/endpoint/suspicious_icedid_rundll32_cmdline.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious `rundll32.exe` command line used to execute a DLL file, a technique associated with IcedID malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing the pattern `*/i:*`. This activity is significant as it indicates potential malware attempting to load an encrypted DLL payload, often named `license.dat`. If confirmed malicious, this could allow attackers to execute arbitrary code, leading to further system compromise and potential data exfiltration.
+description: The following analytic detects a suspicious `rundll32.exe` command line
+  used to execute a DLL file, a technique associated with IcedID malware. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions containing the pattern `*/i:*`. This activity is significant as it indicates
+  potential malware attempting to load an encrypted DLL payload, often named `license.dat`.
+  If confirmed malicious, this could allow attackers to execute arbitrary code, leading
+  to further system compromise and potential data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*/i:* by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_icedid_rundll32_cmdline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: limitted. this parameter is not commonly used by windows application but can be used by the network operator.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*/i:*
+  by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process
+  Processes.process_id Processes.parent_process_id Processes.dest Processes.user |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_icedid_rundll32_cmdline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: limitted. this parameter is not commonly used by windows application
+  but can be used by the network operator.
 references:
 - https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/
 drilldown_searches:
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml b/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml
index 785ebf6728..a9b704404e 100644
--- a/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml
+++ b/detections/endpoint/suspicious_image_creation_in_appdata_folder.yml
@@ -5,11 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of image files in the AppData folder by processes that also have a file reference in the same folder. It leverages data from the Endpoint.Processes and Endpoint.Filesystem datamodels to identify this behavior. This activity is significant because it is commonly associated with malware, such as the Remcos RAT, which captures screenshots and stores them in the AppData folder before exfiltrating them to a command-and-control server. If confirmed malicious, this activity could indicate unauthorized data capture and exfiltration, compromising sensitive information and user privacy.
+description: The following analytic detects the creation of image files in the AppData
+  folder by processes that also have a file reference in the same folder. It leverages
+  data from the Endpoint.Processes and Endpoint.Filesystem datamodels to identify
+  this behavior. This activity is significant because it is commonly associated with
+  malware, such as the Remcos RAT, which captures screenshots and stores them in the
+  AppData folder before exfiltrating them to a command-and-control server. If confirmed
+  malicious, this activity could indicate unauthorized data capture and exfiltration,
+  compromising sensitive information and user privacy.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid |join proc_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.png","*.jpg","*.bmp","*.gif","*.tiff") Filesystem.file_path= "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields _time dest file_create_time file_name file_path process_name process_path process proc_guid] | `suspicious_image_creation_in_appdata_folder_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*"
+  by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest
+  Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as
+  proc_guid |join proc_guid, _time [| tstats `security_content_summariesonly` count
+  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_name IN ("*.png","*.jpg","*.bmp","*.gif","*.tiff") Filesystem.file_path=
+  "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)`
+  |rename process_guid as proc_guid | fields _time dest file_create_time file_name
+  file_path process_name process_path process proc_guid] | `suspicious_image_creation_in_appdata_folder_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: unknown
 references:
 - https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - file_create_time
-  - file_name
-  - file_path
-  - process_name
-  - process_path
-  - process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_kerberos_service_ticket_request.yml b/detections/endpoint/suspicious_kerberos_service_ticket_request.yml
index f8cc88379e..ea4c274d27 100644
--- a/detections/endpoint/suspicious_kerberos_service_ticket_request.yml
+++ b/detections/endpoint/suspicious_kerberos_service_ticket_request.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious Kerberos Service Ticket (TGS) requests where the requesting account name matches the service name, potentially indicating an exploitation attempt of CVE-2021-42278 and CVE-2021-42287. This detection leverages Event ID 4769 from Domain Controller and Kerberos events. Such activity is significant as it may represent an adversary attempting to escalate privileges by impersonating a domain controller. If confirmed malicious, this could allow an attacker to take control of the domain controller, leading to complete domain compromise and unauthorized access to sensitive information.
+description: The following analytic detects suspicious Kerberos Service Ticket (TGS)
+  requests where the requesting account name matches the service name, potentially
+  indicating an exploitation attempt of CVE-2021-42278 and CVE-2021-42287. This detection
+  leverages Event ID 4769 from Domain Controller and Kerberos events. Such activity
+  is significant as it may represent an adversary attempting to escalate privileges
+  by impersonating a domain controller. If confirmed malicious, this could allow an
+  attacker to take control of the domain controller, leading to complete domain compromise
+  and unauthorized access to sensitive information.
 data_source:
 - Windows Event Log Security 4769
-search: '`wineventlog_security` EventCode=4769 | eval isSuspicious = if(lower(ServiceName) = lower(mvindex(split(TargetUserName,"@"),0)),1,0) | where isSuspicious = 1 | rename Computer as dest| rename TargetUserName as user | table _time, dest, src_ip, user, ServiceName, Error_Code, isSuspicious | `suspicious_kerberos_service_ticket_request_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: We have tested this detection logic with ~2 million 4769 events and did not identify false positives. However, they may be possible in certain environments. Filter as needed.
+search: '`wineventlog_security` EventCode=4769 | eval isSuspicious = if(lower(ServiceName)
+  = lower(mvindex(split(TargetUserName,"@"),0)),1,0) | where isSuspicious = 1 | rename
+  Computer as dest| rename TargetUserName as user | table _time, dest, src_ip, user,
+  ServiceName, Error_Code, isSuspicious | `suspicious_kerberos_service_ticket_request_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: We have tested this detection logic with ~2 million 4769 events
+  and did not identify false positives. However, they may be possible in certain environments.
+  Filter as needed.
 references:
 - https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html
 - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Service_Name
-  - Account_Name
-  - Client_Address
-  - Failure_Code
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_kerberos_service_ticket_request/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/suspicious_kerberos_service_ticket_request/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_linux_discovery_commands.yml b/detections/endpoint/suspicious_linux_discovery_commands.yml
index add30cc8fb..ad3545ce16 100644
--- a/detections/endpoint/suspicious_linux_discovery_commands.yml
+++ b/detections/endpoint/suspicious_linux_discovery_commands.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of suspicious bash commands commonly used in scripts like AutoSUID, LinEnum, and LinPeas for system discovery on a Linux host. It leverages Endpoint Detection and Response (EDR) data, specifically looking for a high number of distinct commands executed within a short time frame. This activity is significant as it often precedes privilege escalation or other malicious actions. If confirmed malicious, an attacker could gain detailed system information, identify vulnerabilities, and potentially escalate privileges, posing a severe threat to the environment.
+description: The following analytic detects the execution of suspicious bash commands
+  commonly used in scripts like AutoSUID, LinEnum, and LinPeas for system discovery
+  on a Linux host. It leverages Endpoint Detection and Response (EDR) data, specifically
+  looking for a high number of distinct commands executed within a short time frame.
+  This activity is significant as it often precedes privilege escalation or other
+  malicious actions. If confirmed malicious, an attacker could gain detailed system
+  information, identify vulnerabilities, and potentially escalate privileges, posing
+  a severe threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) values(Processes.process_name) values(Processes.parent_process_name) dc(Processes.process) as distinct_commands dc(Processes.process_name) as distinct_process_names min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where [|inputlookup linux_tool_discovery_process.csv | rename process as Processes.process |table Processes.process] by _time span=5m Processes.user Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| where distinct_commands > 40 AND distinct_process_names > 3| `suspicious_linux_discovery_commands_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unless an administrator is using these commands to troubleshoot or audit a system, the execution of these commands should be monitored.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  values(Processes.process_name) values(Processes.parent_process_name) dc(Processes.process)
+  as distinct_commands dc(Processes.process_name) as distinct_process_names min(_time)
+  as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where [|inputlookup
+  linux_tool_discovery_process.csv | rename process as Processes.process |table Processes.process]
+  by _time span=5m Processes.user Processes.dest | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| where
+  distinct_commands > 40 AND distinct_process_names > 3| `suspicious_linux_discovery_commands_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unless an administrator is using these commands to troubleshoot
+  or audit a system, the execution of these commands should be monitored.
 references:
 - https://attack.mitre.org/matrices/enterprise/linux/
 - https://attack.mitre.org/techniques/T1059/004/
@@ -25,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,16 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.user
-  - Processes.process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/linux_discovery_tools/sysmon_linux.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.004/linux_discovery_tools/sysmon_linux.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml
index 2a51839e52..a6588bce19 100644
--- a/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml
+++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_rename.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the renaming of microsoft.workflow.compiler.exe, a rarely used executable typically located in C:\Windows\Microsoft.NET\Framework64\v4.0.30319. This detection leverages Endpoint Detection and Response (EDR) data, focusing on process names and original file names. This activity is significant because renaming this executable can indicate an attempt to evade security controls. If confirmed malicious, an attacker could use this renamed executable to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment.
+description: The following analytic detects the renaming of microsoft.workflow.compiler.exe,
+  a rarely used executable typically located in C:\Windows\Microsoft.NET\Framework64\v4.0.30319.
+  This detection leverages Endpoint Detection and Response (EDR) data, focusing on
+  process names and original file names. This activity is significant because renaming
+  this executable can indicate an attempt to evade security controls. If confirmed
+  malicious, an attacker could use this renamed executable to execute arbitrary code,
+  potentially leading to privilege escalation or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=microsoft.workflow.compiler.exe AND Processes.original_file_name=Microsoft.Workflow.Compiler.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_rename_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=microsoft.workflow.compiler.exe
+  AND Processes.original_file_name=Microsoft.Workflow.Compiler.exe by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.original_file_name |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_microsoft_workflow_compiler_rename_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of microsoft.workflow.compiler.exe, triggering a false positive.
 references:
 - https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
@@ -27,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: Suspicious renamed microsoft.workflow.compiler.exe binary ran on $dest$ by $user$
+  message: Suspicious renamed microsoft.workflow.compiler.exe binary ran on $dest$
+    by $user$
   mitre_attack_id:
   - T1036
   - T1127
@@ -45,23 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml b/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml
index d16bacb452..9de3c60ab0 100644
--- a/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml
+++ b/detections/endpoint/suspicious_microsoft_workflow_compiler_usage.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the usage of microsoft.workflow.compiler.exe, a rarely utilized executable typically found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution telemetry. The significance of this activity lies in its uncommon usage, which may indicate malicious intent such as code execution or persistence mechanisms. If confirmed malicious, an attacker could leverage this process to execute arbitrary code, potentially leading to unauthorized access or further compromise of the system.
+description: The following analytic identifies the usage of microsoft.workflow.compiler.exe,
+  a rarely utilized executable typically found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process execution telemetry. The significance of this activity lies
+  in its uncommon usage, which may indicate malicious intent such as code execution
+  or persistence mechanisms. If confirmed malicious, an attacker could leverage this
+  process to execute arbitrary code, potentially leading to unauthorized access or
+  further compromise of the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_microsoftworkflowcompiler` by Processes.dest Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_microsoftworkflowcompiler`
+  by Processes.dest Processes.parent_process Processes.process_name Processes.original_file_name
+  Processes.process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_microsoft_workflow_compiler_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, limited instances have been identified coming
+  from native Microsoft utilities similar to SCCM.
 references:
 - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_msbuild_path.yml b/detections/endpoint/suspicious_msbuild_path.yml
index 7879e3d866..2be735e6b7 100644
--- a/detections/endpoint/suspicious_msbuild_path.yml
+++ b/detections/endpoint/suspicious_msbuild_path.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of msbuild.exe from a non-standard path. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that deviate from typical msbuild.exe locations. This activity is significant because msbuild.exe is commonly abused by attackers to execute malicious code, and running it from an unusual path can indicate an attempt to evade detection. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise and further malicious activities.
+description: The following analytic detects the execution of msbuild.exe from a non-standard
+  path. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process execution logs that deviate from typical msbuild.exe locations. This
+  activity is significant because msbuild.exe is commonly abused by attackers to execute
+  malicious code, and running it from an unusual path can indicate an attempt to evade
+  detection. If confirmed malicious, this behavior could allow an attacker to execute
+  arbitrary code, potentially leading to system compromise and further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msbuild` AND (Processes.process_path!=*\\framework*\\v*\\*) by Processes.dest Processes.original_file_name Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `suspicious_msbuild_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_msbuild` AND (Processes.process_path!=*\\framework*\\v*\\*)
+  by Processes.dest Processes.original_file_name Processes.parent_process Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `suspicious_msbuild_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legitimate applications may use a moved copy of msbuild.exe,
+  triggering a false positive. Baselining of MSBuild.exe usage is recommended to better
+  understand it's path usage. Visual Studio runs an instance out of a path that will
+  need to be filtered on.
 references:
 - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_msbuild_rename.yml b/detections/endpoint/suspicious_msbuild_rename.yml
index 0b39a1841a..6d64569e58 100644
--- a/detections/endpoint/suspicious_msbuild_rename.yml
+++ b/detections/endpoint/suspicious_msbuild_rename.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of renamed instances of msbuild.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names within the Endpoint data model. This activity is significant because msbuild.exe is a legitimate tool often abused by attackers to execute malicious code while evading detection. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects the execution of renamed instances of
+  msbuild.exe. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and original file names within the Endpoint data model.
+  This activity is significant because msbuild.exe is a legitimate tool often abused
+  by attackers to execute malicious code while evading detection. If confirmed malicious,
+  this behavior could allow an attacker to execute arbitrary code, potentially leading
+  to system compromise, data exfiltration, or further lateral movement within the
+  network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=msbuild.exe AND Processes.original_file_name=MSBuild.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_rename_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name!=msbuild.exe
+  AND Processes.original_file_name=MSBuild.exe by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_msbuild_rename_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of msbuild, triggering a false positive.
 references:
 - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
@@ -47,23 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_msbuild_spawn.yml b/detections/endpoint/suspicious_msbuild_spawn.yml
index 642728cc81..e0ed5b2e84 100644
--- a/detections/endpoint/suspicious_msbuild_spawn.yml
+++ b/detections/endpoint/suspicious_msbuild_spawn.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where wmiprvse.exe spawns msbuild.exe, which is unusual and indicative of potential misuse of a COM object. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process relationships and command-line executions. This activity is significant because msbuild.exe is typically spawned by devenv.exe during legitimate Visual Studio use, not by wmiprvse.exe. If confirmed malicious, this behavior could indicate an attacker executing arbitrary code or scripts, potentially leading to system compromise or further malicious activities.
+description: The following analytic identifies instances where wmiprvse.exe spawns
+  msbuild.exe, which is unusual and indicative of potential misuse of a COM object.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process relationships and command-line executions. This activity is
+  significant because msbuild.exe is typically spawned by devenv.exe during legitimate
+  Visual Studio use, not by wmiprvse.exe. If confirmed malicious, this behavior could
+  indicate an attacker executing arbitrary code or scripts, potentially leading to
+  system compromise or further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND `process_msbuild` by Processes.dest Processes.parent_process Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe
+  AND `process_msbuild` by Processes.dest Processes.parent_process Processes.original_file_name
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_msbuild_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
 references:
 - https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_mshta_child_process.yml b/detections/endpoint/suspicious_mshta_child_process.yml
index d69f3aa7e9..e13a959b85 100644
--- a/detections/endpoint/suspicious_mshta_child_process.yml
+++ b/detections/endpoint/suspicious_mshta_child_process.yml
@@ -5,14 +5,39 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies child processes spawned from "mshta.exe". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific child processes like "powershell.exe" and "cmd.exe". This activity is significant because "mshta.exe" is often exploited by attackers to execute malicious scripts or commands. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment. Monitoring this activity helps in early detection of potential threats leveraging "mshta.exe" for malicious purposes.
+description: The following analytic identifies child processes spawned from "mshta.exe".
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  specific child processes like "powershell.exe" and "cmd.exe". This activity is significant
+  because "mshta.exe" is often exploited by attackers to execute malicious scripts
+  or commands. If confirmed malicious, this behavior could allow an attacker to execute
+  arbitrary code, escalate privileges, or maintain persistence within the environment.
+  Monitoring this activity helps in early detection of potential threats leveraging
+  "mshta.exe" for malicious purposes.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_child_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe
+  AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe
+  OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe
+  OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe
+  OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe
+  OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_mshta_child_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
 references:
 - https://github.com/redcanaryco/AtomicTestHarnesses
 - https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -22,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.parent_process
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_mshta_spawn.yml b/detections/endpoint/suspicious_mshta_spawn.yml
index 33c616fa81..3c415350c2 100644
--- a/detections/endpoint/suspicious_mshta_spawn.yml
+++ b/detections/endpoint/suspicious_mshta_spawn.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the spawning of mshta.exe by wmiprvse.exe or svchost.exe. This behavior is identified using Endpoint Detection and Response (EDR) data, focusing on process creation events where the parent process is either wmiprvse.exe or svchost.exe. This activity is significant as it may indicate the use of a DCOM object to execute malicious scripts via mshta.exe, a common tactic in sophisticated attacks. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise and further malicious activities.
+description: The following analytic detects the spawning of mshta.exe by wmiprvse.exe
+  or svchost.exe. This behavior is identified using Endpoint Detection and Response
+  (EDR) data, focusing on process creation events where the parent process is either
+  wmiprvse.exe or svchost.exe. This activity is significant as it may indicate the
+  use of a DCOM object to execute malicious scripts via mshta.exe, a common tactic
+  in sophisticated attacks. If confirmed malicious, this could allow an attacker to
+  execute arbitrary code, potentially leading to system compromise and further malicious
+  activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND `process_mshta` by Processes.dest Processes.parent_process Processes.user Processes.original_file_name| `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe
+  OR Processes.parent_process_name=wmiprvse.exe) AND `process_mshta` by Processes.dest
+  Processes.parent_process Processes.user Processes.original_file_name| `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_mshta_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may exhibit
+  this behavior, triggering a false positive.
 references:
 - https://codewhitesec.blogspot.com/2018/07/lethalhta.html
 - https://github.com/redcanaryco/AtomicTestHarnesses
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_plistbuddy_usage.yml b/detections/endpoint/suspicious_plistbuddy_usage.yml
index ac67564bd0..7ab06d72a4 100644
--- a/detections/endpoint/suspicious_plistbuddy_usage.yml
+++ b/detections/endpoint/suspicious_plistbuddy_usage.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the use of the native macOS utility, PlistBuddy, to create or modify property list (.plist) files. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions involving PlistBuddy. This activity is significant because PlistBuddy can be used to establish persistence by modifying LaunchAgents, as seen in the Silver Sparrow malware. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, and potentially escalate privileges on the compromised macOS system.
+description: The following analytic identifies the use of the native macOS utility,
+  PlistBuddy, to create or modify property list (.plist) files. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and command-line executions involving PlistBuddy. This activity is significant because
+  PlistBuddy can be used to establish persistence by modifying LaunchAgents, as seen
+  in the Silver Sparrow malware. If confirmed malicious, this could allow an attacker
+  to maintain persistence, execute arbitrary commands, and potentially escalate privileges
+  on the compromised macOS system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=PlistBuddy (Processes.process=*LaunchAgents* OR Processes.process=*RunAtLoad* OR Processes.process=*true*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |  `suspicious_plistbuddy_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=PlistBuddy
+  (Processes.process=*LaunchAgents* OR Processes.process=*RunAtLoad* OR Processes.process=*true*)
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |  `suspicious_plistbuddy_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legitimate applications may use PlistBuddy to create or
+  modify property lists and possibly generate false positives. Review the property
+  list being modified or created to confirm.
 references:
 - https://www.marcosantadev.com/manage-plist-files-plistbuddy/
 tags:
@@ -38,13 +60,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml b/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml
index d4a3ed2add..6727775e33 100644
--- a/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml
+++ b/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the use of the PlistBuddy utility on macOS to create or modify property list (.plist) files. It leverages OSQuery to monitor process events, specifically looking for commands that interact with LaunchAgents and set properties like RunAtLoad. This activity is significant because PlistBuddy can be used to establish persistence mechanisms, as seen in malware like Silver Sparrow. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, and potentially escalate privileges on the compromised system.
+description: The following analytic detects the use of the PlistBuddy utility on macOS
+  to create or modify property list (.plist) files. It leverages OSQuery to monitor
+  process events, specifically looking for commands that interact with LaunchAgents
+  and set properties like RunAtLoad. This activity is significant because PlistBuddy
+  can be used to establish persistence mechanisms, as seen in malware like Silver
+  Sparrow. If confirmed malicious, this could allow an attacker to maintain persistence,
+  execute arbitrary commands, and potentially escalate privileges on the compromised
+  system.
 data_source: []
-search: '`osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" |  `suspicious_plistbuddy_usage_via_osquery_filter`'
-how_to_implement: OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct.
-known_false_positives: Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.
+search: '`osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*"
+  OR "columns.cmdline"="*true*" |  `suspicious_plistbuddy_usage_via_osquery_filter`'
+how_to_implement: OSQuery must be installed and configured to pick up process events
+  (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402.
+  Modify the macro and validate fields are correct.
+known_false_positives: Some legitimate applications may use PlistBuddy to create or
+  modify property lists and possibly generate false positives. Review the property
+  list being modified or created to confirm.
 references:
 - https://www.marcosantadev.com/manage-plist-files-plistbuddy/
 tags:
@@ -31,7 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - columns.cmdline
   security_domain: endpoint
diff --git a/detections/endpoint/suspicious_process_dns_query_known_abuse_web_services.yml b/detections/endpoint/suspicious_process_dns_query_known_abuse_web_services.yml
index 0157a3a692..7688f3f101 100644
--- a/detections/endpoint/suspicious_process_dns_query_known_abuse_web_services.yml
+++ b/detections/endpoint/suspicious_process_dns_query_known_abuse_web_services.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious process making DNS queries to known, abused text-paste web services, VoIP, instant messaging, and digital distribution platforms. It leverages Sysmon EventID 22 logs to identify queries from processes like cmd.exe, powershell.exe, and others. This activity is significant as it may indicate an attempt to download malicious files, a common initial access technique. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the target host.
+description: The following analytic detects a suspicious process making DNS queries
+  to known, abused text-paste web services, VoIP, instant messaging, and digital distribution
+  platforms. It leverages Sysmon EventID 22 logs to identify queries from processes
+  like cmd.exe, powershell.exe, and others. This activity is significant as it may
+  indicate an attempt to download malicious files, a common initial access technique.
+  If confirmed malicious, this could lead to unauthorized code execution, data exfiltration,
+  or further compromise of the target host.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*", "*discord*", "*api.telegram*","*t.me*") process_name IN ("cmd.exe", "*powershell*", "pwsh.exe", "wscript.exe","cscript.exe") OR Image IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*") | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_dns_query_known_abuse_web_services_filter`'
-how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days.
-known_false_positives: Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
+search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*", "*discord*", "*api.telegram*","*t.me*")
+  process_name IN ("cmd.exe", "*powershell*", "pwsh.exe", "wscript.exe","cscript.exe")
+  OR Image IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*",
+  "*\\appdata\\*", "*\\perflogs\\*") | stats count min(_time) as firstTime max(_time)
+  as lastTime by Image QueryName QueryStatus process_name QueryResults Computer |
+  rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_process_dns_query_known_abuse_web_services_filter`'
+how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query.
+  We suggest you run this detection at least once a day over the last 14 days.
+known_false_positives: Noise and false positive can be seen if the following instant
+  messaging is allowed to use within corporate network. In this case, a filter is
+  needed.
 references:
 - https://urlhaus.abuse.ch/url/1798923/
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - QueryName
-  - QueryStatus
-  - process_name
-  - QueryResults
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_pastebin_download/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_pastebin_download/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_process_executed_from_container_file.yml b/detections/endpoint/suspicious_process_executed_from_container_file.yml
index 67ff44c942..b910c96f44 100644
--- a/detections/endpoint/suspicious_process_executed_from_container_file.yml
+++ b/detections/endpoint/suspicious_process_executed_from_container_file.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies a suspicious process executed from within common container/archive file types such as ZIP, ISO, IMG, and others. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it is a common technique used by adversaries to execute scripts or evade defenses. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk.
+description: The following analytic identifies a suspicious process executed from
+  within common container/archive file types such as ZIP, ISO, IMG, and others. It
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names and command-line executions. This activity is significant as it is a common
+  technique used by adversaries to execute scripts or evade defenses. If confirmed
+  malicious, this behavior could allow attackers to execute arbitrary code, escalate
+  privileges, or persist within the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*.ZIP\\*","*.ISO\\*","*.IMG\\*","*.CAB\\*","*.TAR\\*","*.GZ\\*","*.RAR\\*","*.7Z\\*") AND Processes.action="allowed" by Processes.dest Processes.parent_process Processes.process Processes.user| `drop_dm_object_name(Processes)`| regex process="(?i).*(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z)\\\\.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH)\"?$" | rex field=process "(?i).+\\\\(?<file_name>[^\\\]+\.(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z))\\\\((.+\\\\)+)?(?<process_name>.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH))\"?$"| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_executed_from_container_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process IN ("*.ZIP\\*","*.ISO\\*","*.IMG\\*","*.CAB\\*","*.TAR\\*","*.GZ\\*","*.RAR\\*","*.7Z\\*")
+  AND Processes.action="allowed" by Processes.dest Processes.parent_process Processes.process
+  Processes.user| `drop_dm_object_name(Processes)`| regex process="(?i).*(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z)\\\\.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH)\"?$"
+  | rex field=process "(?i).+\\\\(?<file_name>[^\\\]+\.(ZIP|ISO|IMG|CAB|TAR|GZ|RAR|7Z))\\\\((.+\\\\)+)?(?<process_name>.+\.(BAT|BIN|CAB|CMD|COM|CPL|EX_|EXE|GADGET|INF1|INS|INX||HTM|HTML|ISU|JAR|JOB|JS|JSE|LNK|MSC|MSI|MSP|MST|PAF|PIF|PS1|REG|RGS|SCR|SCT|SHB|SHS|U3P|VB|VBE|VBS|VBSCRIPT|WS|WSF|WSH))\"?$"|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_executed_from_container_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Various business process or userland applications and behavior.
 references:
 - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,16 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.parent_process
-  - Processes.process
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_process_file_path.yml b/detections/endpoint/suspicious_process_file_path.yml
index 7cafbe27e5..4d416bc837 100644
--- a/detections/endpoint/suspicious_process_file_path.yml
+++ b/detections/endpoint/suspicious_process_file_path.yml
@@ -5,14 +5,41 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies processes running from file paths not typically associated with legitimate software. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process paths within the Endpoint data model. This activity is significant because adversaries often use unconventional file paths to execute malicious code without requiring administrative privileges. If confirmed malicious, this behavior could indicate an attempt to bypass security controls, leading to unauthorized software execution, potential system compromise, and further malicious activities within the environment.
+description: The following analytic identifies processes running from file paths not
+  typically associated with legitimate software. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on specific process paths within the Endpoint
+  data model. This activity is significant because adversaries often use unconventional
+  file paths to execute malicious code without requiring administrative privileges.
+  If confirmed malicious, this behavior could indicate an attempt to bypass security
+  controls, leading to unauthorized software execution, potential system compromise,
+  and further malicious activities within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path = "*\\Windows\\servicing\\*" OR Processes.process_path = "*\\Users\\Default\\*" OR Processes.process_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" OR Processes.process_path = "*\\PerfLogs\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_file_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may allow execution of specific binaries in non-standard paths. Filter as needed.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*"
+  OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*"
+  OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path = "*\\Users\\Administrator\\Music\\*"
+  OR Processes.process_path = "*\\Windows\\servicing\\*" OR Processes.process_path
+  = "*\\Users\\Default\\*" OR Processes.process_path = "*Recycle.bin*" OR Processes.process_path
+  = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path
+  = "*\\temp\\*" OR Processes.process_path = "*\\PerfLogs\\*" by Processes.parent_process_name
+  Processes.parent_process Processes.process_path Processes.dest Processes.user |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_process_file_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may allow execution of specific binaries in
+  non-standard paths. Filter as needed.
 references:
 - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -25,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -66,7 +98,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: Suspicious process $process_name$ running from a suspicious process path- $process_path$ on host- $dest$
+  message: Suspicious process $process_name$ running from a suspicious process path-
+    $process_path$ on host- $dest$
   mitre_attack_id:
   - T1543
   observable:
@@ -82,19 +115,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_path
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_process_with_discord_dns_query.yml b/detections/endpoint/suspicious_process_with_discord_dns_query.yml
index dc07797821..5737199159 100644
--- a/detections/endpoint/suspicious_process_with_discord_dns_query.yml
+++ b/detections/endpoint/suspicious_process_with_discord_dns_query.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a process making a DNS query to Discord, excluding legitimate Discord application paths. It leverages Sysmon logs with Event ID 22 to detect DNS queries containing "discord" in the QueryName field. This activity is significant because Discord can be abused by adversaries to host and download malicious files, as seen in the WhisperGate campaign. If confirmed malicious, this could indicate malware attempting to download additional payloads from Discord, potentially leading to further code execution and compromise of the affected system.
+description: The following analytic identifies a process making a DNS query to Discord,
+  excluding legitimate Discord application paths. It leverages Sysmon logs with Event
+  ID 22 to detect DNS queries containing "discord" in the QueryName field. This activity
+  is significant because Discord can be abused by adversaries to host and download
+  malicious files, as seen in the WhisperGate campaign. If confirmed malicious, this
+  could indicate malware attempting to download additional payloads from Discord,
+  potentially leading to further code execution and compromise of the affected system.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode=22 QueryName IN ("*discord*") Image != "*\\AppData\\Local\\Discord\\*" AND Image != "*\\Program Files*" AND Image != "discord.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_process_with_discord_dns_query_filter`'
+search: '`sysmon` EventCode=22 QueryName IN ("*discord*") Image != "*\\AppData\\Local\\Discord\\*"
+  AND Image != "*\\Program Files*" AND Image != "discord.exe" | stats count min(_time)
+  as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name
+  QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_process_with_discord_dns_query_filter`'
 how_to_implement: his detection relies on sysmon logs with the Event ID 22, DNS Query.
-known_false_positives: Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
+known_false_positives: Noise and false positive can be seen if the following instant
+  messaging is allowed to use within corporate network. In this case, a filter is
+  needed.
 references:
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
 - https://medium.com/s2wblog/analysis-of-destructive-malware-whispergate-targeting-ukraine-9d5d158f19f3
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - QueryName
-  - QueryStatus
-  - process_name
-  - QueryResults
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/discord_dnsquery/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/discord_dnsquery/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_reg_exe_process.yml b/detections/endpoint/suspicious_reg_exe_process.yml
index ca2085dd62..69735729c6 100644
--- a/detections/endpoint/suspicious_reg_exe_process.yml
+++ b/detections/endpoint/suspicious_reg_exe_process.yml
@@ -5,14 +5,41 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies instances of reg.exe being launched from a command prompt (cmd.exe) that was not initiated by the user, as indicated by a parent process other than explorer.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names. This activity is significant because reg.exe is often used in registry manipulation, which can be indicative of malicious behavior such as persistence mechanisms or system configuration changes. If confirmed malicious, this could allow an attacker to modify critical system settings, potentially leading to privilege escalation or persistent access.
+description: The following analytic identifies instances of reg.exe being launched
+  from a command prompt (cmd.exe) that was not initiated by the user, as indicated
+  by a parent process other than explorer.exe. This detection leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process and parent process
+  names. This activity is significant because reg.exe is often used in registry manipulation,
+  which can be indicative of malicious behavior such as persistence mechanisms or
+  system configuration changes. If confirmed malicious, this could allow an attacker
+  to modify critical system settings, potentially leading to privilege escalation
+  or persistent access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup process_id| table process_id dest] | `suspicious_reg_exe_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name
+  != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name
+  Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | search [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id
+  Processes.dest Processes.process_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | rename parent_process_id as process_id |dedup
+  process_id| table process_id dest] | `suspicious_reg_exe_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It's possible for system administrators to write scripts that
+  exhibit this behavior. If this is the case, the search will need to be modified
+  to filter them out.
 references:
 - https://car.mitre.org/wiki/CAR-2013-03-001/
 drilldown_searches:
@@ -21,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a registry entry.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to add a registry entry.
   mitre_attack_id:
   - T1112
   observable:
@@ -56,19 +89,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml b/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml
index 51cce08869..1c41049997 100644
--- a/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml
+++ b/detections/endpoint/suspicious_regsvr32_register_suspicious_path.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Regsvr32.exe to register DLLs from suspicious paths such as AppData, ProgramData, or Windows Temp directories. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because Regsvr32.exe can be abused to proxy execution of malicious code, bypassing traditional security controls. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects the use of Regsvr32.exe to register DLLs
+  from suspicious paths such as AppData, ProgramData, or Windows Temp directories.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process execution logs and command-line arguments. This activity is significant
+  because Regsvr32.exe can be abused to proxy execution of malicious code, bypassing
+  traditional security controls. If confirmed malicious, this could allow an attacker
+  to execute arbitrary code, potentially leading to system compromise, data exfiltration,
+  or further lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process IN ("*\\appdata\\*", "*\\programdata\\*","*\\windows\\temp\\*") NOT (Processes.process IN ("*.dll*", "*.ax*", "*.ocx*")) by Processes.dest Processes.user  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_regsvr32` Processes.process
+  IN ("*\\appdata\\*", "*\\programdata\\*","*\\windows\\temp\\*") NOT (Processes.process
+  IN ("*.dll*", "*.ax*", "*.ocx*")) by Processes.dest Processes.user  Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.original_file_name
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_regsvr32_register_suspicious_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives with the query restricted to specified
+  paths. Add more world writeable paths as tuning continues.
 references:
 - https://attack.mitre.org/techniques/T1218/010/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
@@ -25,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +64,9 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to evade detection by using a non-standard file extension.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to evade detection by using a non-standard
+    file extension.
   mitre_attack_id:
   - T1218
   - T1218.010
@@ -62,23 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_rundll32_dllregisterserver.yml b/detections/endpoint/suspicious_rundll32_dllregisterserver.yml
index a093ed2c68..a7977f0a5c 100644
--- a/detections/endpoint/suspicious_rundll32_dllregisterserver.yml
+++ b/detections/endpoint/suspicious_rundll32_dllregisterserver.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe with the DllRegisterServer command to load a DLL. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to register a malicious DLL, which can be a method for code execution or persistence. If confirmed malicious, an attacker could gain unauthorized code execution, escalate privileges, or maintain persistence within the environment, posing a severe security risk.
+description: The following analytic detects the execution of rundll32.exe with the
+  DllRegisterServer command to load a DLL. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions and process details.
+  This activity is significant as it may indicate an attempt to register a malicious
+  DLL, which can be a method for code execution or persistence. If confirmed malicious,
+  an attacker could gain unauthorized code execution, escalate privileges, or maintain
+  persistence within the environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*dllregisterserver*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.original_file_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_rundll32_dllregisterserver_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This is likely to produce false positives and will require
+  some filtering. Tune the query by adding command line paths to known good DLLs,
+  or filtering based on parent process names.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -27,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +64,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to register a DLL. code
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to register a DLL. code
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -63,23 +90,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml b/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml
index 5315cc037d..41159adb39 100644
--- a/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml
+++ b/detections/endpoint/suspicious_rundll32_no_command_line_arguments.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of rundll32.exe without any command line arguments. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process execution logs. It is significant because rundll32.exe typically requires command line arguments to function properly, and its absence is often associated with malicious activities, such as those performed by Cobalt Strike. If confirmed malicious, this activity could indicate an attempt to execute arbitrary code, potentially leading to credential dumping, unauthorized file writes, or other malicious actions.
+description: The following analytic detects the execution of rundll32.exe without
+  any command line arguments. This behavior is identified using Endpoint Detection
+  and Response (EDR) telemetry, focusing on process execution logs. It is significant
+  because rundll32.exe typically requires command line arguments to function properly,
+  and its absence is often associated with malicious activities, such as those performed
+  by Cobalt Strike. If confirmed malicious, this activity could indicate an attempt
+  to execute arbitrary code, potentially leading to credential dumping, unauthorized
+  file writes, or other malicious actions.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_rundll32` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(rundll32\.exe.{0,4}$)" | `suspicious_rundll32_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_rundll32` by _time span=1h  Processes.process_id Processes.process_name
+  Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | regex process="(?i)(rundll32\.exe.{0,4}$)" | `suspicious_rundll32_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use a moved
+  copy of rundll32, triggering a false positive.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +64,8 @@ tags:
   cve:
   - CVE-2021-34527
   impact: 70
-  message: Suspicious rundll32.exe process with no command line arguments executed on $dest$ by $user$
+  message: Suspicious rundll32.exe process with no command line arguments executed
+    on $dest$ by $user$
   mitre_attack_id:
   - T1218
   - T1218.011
@@ -56,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_rundll32_plugininit.yml b/detections/endpoint/suspicious_rundll32_plugininit.yml
index 9253ac8d57..5122130f61 100644
--- a/detections/endpoint/suspicious_rundll32_plugininit.yml
+++ b/detections/endpoint/suspicious_rundll32_plugininit.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of the rundll32.exe process with the "plugininit" parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because the "plugininit" parameter is commonly associated with IcedID malware, which uses it to execute an initial DLL stager to download additional payloads. If confirmed malicious, this behavior could lead to further malware infections, data exfiltration, or complete system compromise.
+description: The following analytic identifies the execution of the rundll32.exe process
+  with the "plugininit" parameter. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events and command-line
+  arguments. This activity is significant because the "plugininit" parameter is commonly
+  associated with IcedID malware, which uses it to execute an initial DLL stager to
+  download additional payloads. If confirmed malicious, this behavior could lead to
+  further malware infections, data exfiltration, or complete system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*PluginInit* by  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_plugininit_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: third party application may used this dll export name to execute function.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*PluginInit*
+  by  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name
+  Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `suspicious_rundll32_plugininit_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: third party application may used this dll export name to execute
+  function.
 references:
 - https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/
 drilldown_searches:
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_rundll32_startw.yml b/detections/endpoint/suspicious_rundll32_startw.yml
index cb2a3a1ac6..a66e8201c4 100644
--- a/detections/endpoint/suspicious_rundll32_startw.yml
+++ b/detections/endpoint/suspicious_rundll32_startw.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of rundll32.exe with the DLL function names "Start" and "StartW," commonly associated with Cobalt Strike payloads. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. This activity is significant as it often indicates the presence of malicious payloads, such as Cobalt Strike, which can lead to unauthorized code execution. If confirmed malicious, this activity could allow attackers to inject shellcode, escalate privileges, and maintain persistence within the environment.
+description: The following analytic identifies the execution of rundll32.exe with
+  the DLL function names "Start" and "StartW," commonly associated with Cobalt Strike
+  payloads. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on command-line executions and process metadata. This activity
+  is significant as it often indicates the presence of malicious payloads, such as
+  Cobalt Strike, which can lead to unauthorized code execution. If confirmed malicious,
+  this activity could allow attackers to inject shellcode, escalate privileges, and
+  maintain persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_rundll32_startw_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*start*
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `suspicious_rundll32_startw_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Although unlikely, some legitimate applications may use Start
+  as a function and call it via the command line. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1218/011/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -25,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,23 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml b/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml
index 7c163e3b76..9a7dbfb15c 100644
--- a/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml
+++ b/detections/endpoint/suspicious_scheduled_task_from_public_directory.yml
@@ -5,14 +5,38 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the creation of scheduled tasks that execute binaries or scripts from public directories, such as users\public, \programdata\, or \windows\temp, using schtasks.exe with the /create command. It leverages Sysmon Event ID 1 data to detect this behavior. This activity is significant because it often indicates an attempt to maintain persistence or execute malicious scripts, which are common tactics in malware deployment. If confirmed as malicious, this could lead to data compromise, unauthorized access, and potential lateral movement within the network.
+description: The following analytic identifies the creation of scheduled tasks that
+  execute binaries or scripts from public directories, such as users\public, \programdata\,
+  or \windows\temp, using schtasks.exe with the /create command. It leverages Sysmon
+  Event ID 1 data to detect this behavior. This activity is significant because it
+  often indicates an attempt to maintain persistence or execute malicious scripts,
+  which are common tactics in malware deployment. If confirmed as malicious, this
+  could lead to data compromise, unauthorized access, and potential lateral movement
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR Processes.process=*windows\\temp*)  Processes.process=*/create* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_scheduled_task_from_public_directory_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: The main source of false positives could be the legitimate use of scheduled tasks from these directories. Careful tuning of this search may be necessary to suit the specifics of your environment, reducing the rate of false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe
+  (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR
+  Processes.process=*windows\\temp*)  Processes.process=*/create* by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `suspicious_scheduled_task_from_public_directory_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: The main source of false positives could be the legitimate
+  use of scheduled tasks from these directories. Careful tuning of this search may
+  be necessary to suit the specifics of your environment, reducing the rate of false
+  positives.
 references:
 - https://attack.mitre.org/techniques/T1053/005/
 drilldown_searches:
@@ -21,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,20 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml b/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml
index 0e962bdd16..ab91169203 100644
--- a/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml
+++ b/detections/endpoint/suspicious_searchprotocolhost_no_command_line_arguments.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances of searchprotocolhost.exe running without command line arguments. This behavior is unusual and often associated with malicious activities, such as those performed by Cobalt Strike. The detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process execution data. This activity is significant because searchprotocolhost.exe typically runs with specific arguments, and its absence may indicate an attempt to evade detection. If confirmed malicious, this could lead to unauthorized code execution, potential credential dumping, or other malicious actions within the environment.
+description: The following analytic detects instances of searchprotocolhost.exe running
+  without command line arguments. This behavior is unusual and often associated with
+  malicious activities, such as those performed by Cobalt Strike. The detection leverages
+  Endpoint Detection and Response (EDR) telemetry, focusing on process execution data.
+  This activity is significant because searchprotocolhost.exe typically runs with
+  specific arguments, and its absence may indicate an attempt to evade detection.
+  If confirmed malicious, this could lead to unauthorized code execution, potential
+  credential dumping, or other malicious actions within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)" | `suspicious_searchprotocolhost_no_command_line_arguments_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives may be present in small environments. Tuning may be required based on parent process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id
+  Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | regex process="(?i)(searchprotocolhost\.exe.{0,4}$)"
+  | `suspicious_searchprotocolhost_no_command_line_arguments_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives may be present in small environments.
+  Tuning may be required based on parent process.
 references:
 - https://github.com/mandiant/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Suspicious searchprotocolhost.exe process with no command line arguments executed on $dest$ by $user$
+  message: Suspicious searchprotocolhost.exe process with no command line arguments
+    executed on $dest$ by $user$
   mitre_attack_id:
   - T1055
   observable:
@@ -48,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml b/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml
index 343caae002..73cadb81f8 100644
--- a/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml
+++ b/detections/endpoint/suspicious_sqlite3_lsquarantine_behavior.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the use of SQLite3 querying the MacOS preferences to determine the original URL from which a package was downloaded. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions involving LSQuarantine. This activity is significant as it is commonly associated with MacOS adware and other malicious software. If confirmed malicious, this behavior could indicate an attempt to track or manipulate downloaded packages, potentially leading to further system compromise or persistent adware infections.
+description: The following analytic identifies the use of SQLite3 querying the MacOS
+  preferences to determine the original URL from which a package was downloaded. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line executions involving LSQuarantine. This activity
+  is significant as it is commonly associated with MacOS adware and other malicious
+  software. If confirmed malicious, this behavior could indicate an attempt to track
+  or manipulate downloaded packages, potentially leading to further system compromise
+  or persistent adware infections.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3 Processes.process=*LSQuarantine* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `suspicious_sqlite3_lsquarantine_behavior_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3
+  Processes.process=*LSQuarantine* by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `suspicious_sqlite3_lsquarantine_behavior_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown.
 references:
 - https://redcanary.com/blog/clipping-silver-sparrows-wings/
@@ -38,13 +58,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/endpoint/suspicious_ticket_granting_ticket_request.yml b/detections/endpoint/suspicious_ticket_granting_ticket_request.yml
index bca1bf752a..8a8793a65c 100644
--- a/detections/endpoint/suspicious_ticket_granting_ticket_request.yml
+++ b/detections/endpoint/suspicious_ticket_granting_ticket_request.yml
@@ -5,13 +5,29 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects suspicious Kerberos Ticket Granting Ticket (TGT) requests that may indicate exploitation of CVE-2021-42278 and CVE-2021-42287. It leverages Event ID 4781 (account name change) and Event ID 4768 (TGT request) to identify sequences where a newly renamed computer account requests a TGT. This behavior is significant as it could represent an attempt to escalate privileges by impersonating a Domain Controller. If confirmed malicious, this activity could allow attackers to gain elevated access and potentially control over the domain environment.
+description: The following analytic detects suspicious Kerberos Ticket Granting Ticket
+  (TGT) requests that may indicate exploitation of CVE-2021-42278 and CVE-2021-42287.
+  It leverages Event ID 4781 (account name change) and Event ID 4768 (TGT request)
+  to identify sequences where a newly renamed computer account requests a TGT. This
+  behavior is significant as it could represent an attempt to escalate privileges
+  by impersonating a Domain Controller. If confirmed malicious, this activity could
+  allow attackers to gain elevated access and potentially control over the domain
+  environment.
 data_source:
 - Windows Event Log Security 4768
 - Windows Event Log Security 4781
-search: '`wineventlog_security` (EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$") OR (EventCode=4768 TargetUserName!="*$") | eval RenamedComputerAccount = coalesce(NewTargetUserName, TargetUserName) | transaction RenamedComputerAccount startswith=(EventCode=4781) endswith=(EventCode=4768) | eval short_lived=case((duration<2),"TRUE") | search short_lived = TRUE | table _time, Computer, EventCode, TargetUserName, RenamedComputerAccount, short_lived | rename Computer as dest | `suspicious_ticket_granting_ticket_request_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: A computer account name change event inmediately followed by a kerberos TGT request with matching fields is unsual. However, legitimate behavior may trigger it. Filter as needed.
+search: '`wineventlog_security` (EventCode=4781 OldTargetUserName="*$" NewTargetUserName!="*$")
+  OR (EventCode=4768 TargetUserName!="*$") | eval RenamedComputerAccount = coalesce(NewTargetUserName,
+  TargetUserName) | transaction RenamedComputerAccount startswith=(EventCode=4781)
+  endswith=(EventCode=4768) | eval short_lived=case((duration<2),"TRUE") | search
+  short_lived = TRUE | table _time, Computer, EventCode, TargetUserName, RenamedComputerAccount,
+  short_lived | rename Computer as dest | `suspicious_ticket_granting_ticket_request_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: A computer account name change event inmediately followed by
+  a kerberos TGT request with matching fields is unsual. However, legitimate behavior
+  may trigger it. Filter as needed.
 references:
 - https://exploit.ph/cve-2021-42287-cve-2021-42278-weaponisation.html
 - https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-42278
@@ -37,17 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Old_Account_Name
-  - New_Account_Name
-  - Account_Name
-  - ComputerName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_ticket_granting_ticket_request/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/suspicious_ticket_granting_ticket_request/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml b/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml
index e4bf5b924e..07157352de 100644
--- a/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml
+++ b/detections/endpoint/suspicious_wav_file_in_appdata_folder.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of .wav files in the AppData folder, a behavior associated with Remcos RAT malware, which stores audio recordings in this location for data exfiltration. The detection leverages endpoint process and filesystem data to identify .wav file creation within the AppData\Roaming directory. This activity is significant as it indicates potential unauthorized data collection and exfiltration by malware. If confirmed malicious, this could lead to sensitive information being sent to an attacker's command and control server, compromising the affected system's confidentiality.
+description: The following analytic detects the creation of .wav files in the AppData
+  folder, a behavior associated with Remcos RAT malware, which stores audio recordings
+  in this location for data exfiltration. The detection leverages endpoint process
+  and filesystem data to identify .wav file creation within the AppData\Roaming directory.
+  This activity is significant as it indicates potential unauthorized data collection
+  and exfiltration by malware. If confirmed malicious, this could lead to sensitive
+  information being sent to an attacker's command and control server, compromising
+  the affected system's confidentiality.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*" by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.wav") Filesystem.file_path = "*\\appdata\\Roaming\\*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields file_name file_path process_name process_path process dest file_create_time _time proc_guid] | `suspicious_wav_file_in_appdata_folder_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, file_name, file_path and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=*.exe Processes.process_path="*\\appdata\\Roaming\\*"
+  by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest
+  Processes.process_guid | `drop_dm_object_name(Processes)` |rename process_guid as
+  proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count
+  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_name IN ("*.wav") Filesystem.file_path = "*\\appdata\\Roaming\\*"
+  by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)`
+  |rename process_guid as proc_guid | fields file_name file_path process_name process_path
+  process dest file_create_time _time proc_guid] | `suspicious_wav_file_in_appdata_folder_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, file_name, file_path and command-line
+  executions from your endpoints. If you are using Sysmon, you must have at least
+  version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://success.trendmicro.com/dcx/s/solution/1123281-remcos-malware-information?language=en_US
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - file_create_time
-  - file_name
-  - file_path
-  - process_name
-  - process_path
-  - process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon_wav.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos_agent/sysmon_wav.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_wevtutil_usage.yml b/detections/endpoint/suspicious_wevtutil_usage.yml
index 35b6ef0c60..df1522cb3e 100644
--- a/detections/endpoint/suspicious_wevtutil_usage.yml
+++ b/detections/endpoint/suspicious_wevtutil_usage.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: David Dorsey, Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the usage of wevtutil.exe with parameters for clearing event logs such as Application, Security, Setup, Trace, or System. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because clearing event logs can be an attempt to cover tracks after malicious actions, hindering forensic investigations. If confirmed malicious, this behavior could allow an attacker to erase evidence of their activities, making it difficult to trace their actions and understand the full scope of the compromise.
+description: The following analytic detects the usage of wevtutil.exe with parameters
+  for clearing event logs such as Application, Security, Setup, Trace, or System.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names and command-line arguments. This activity is significant because clearing
+  event logs can be an attempt to cover tracks after malicious actions, hindering
+  forensic investigations. If confirmed malicious, this behavior could allow an attacker
+  to erase evidence of their activities, making it difficult to trace their actions
+  and understand the full scope of the compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*", "* -cl *") Processes.process IN ("*System*", "*Security*", "*Setup*", "*Application*", "*trace*", "*powershell*") by Processes.parent_process_name Processes.parent_process Processes.process Processes.process_guid Processes.process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `suspicious_wevtutil_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process_name=wevtutil.exe Processes.process IN ("* cl *", "*clear-log*",
+  "* -cl *") Processes.process IN ("*System*", "*Security*", "*Setup*", "*Application*",
+  "*trace*", "*powershell*") by Processes.parent_process_name Processes.parent_process
+  Processes.process Processes.process_guid Processes.process_id Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
+  | `suspicious_wevtutil_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: The wevtutil.exe application is a legitimate Windows event
+  log utility. Administrators may use it to manage Windows event logs.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,17 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_pwh_log_cleared/wevtutil_clear_log.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_pwh_log_cleared/wevtutil_clear_log.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml b/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml
index ccfc5a3193..b13fecca59 100644
--- a/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml
+++ b/detections/endpoint/suspicious_writes_to_windows_recycle_bin.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects when a process other than explorer.exe writes to the Windows Recycle Bin. It leverages the Endpoint.Filesystem and Endpoint.Processes data models in Splunk to identify any process writing to the "*$Recycle.Bin*" file path, excluding explorer.exe. This activity is significant because it may indicate an attacker attempting to hide their actions, potentially leading to data theft, ransomware, or other malicious outcomes. If confirmed malicious, this behavior could allow an attacker to persist in the environment and evade detection by security tools.
+description: The following analytic detects when a process other than explorer.exe
+  writes to the Windows Recycle Bin. It leverages the Endpoint.Filesystem and Endpoint.Processes
+  data models in Splunk to identify any process writing to the "*$Recycle.Bin*" file
+  path, excluding explorer.exe. This activity is significant because it may indicate
+  an attacker attempting to hide their actions, potentially leading to data theft,
+  ransomware, or other malicious outcomes. If confirmed malicious, this behavior could
+  allow an attacker to persist in the environment and evade detection by security
+  tools.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_name Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")` | join  process_id [| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")` | table user process_name process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
-known_false_positives: Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name)
+  as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*"
+  by Filesystem.process_name Filesystem.process_id Filesystem.dest | `drop_dm_object_name("Filesystem")`
+  | join  process_id [| tstats `security_content_summariesonly` values(Processes.user)
+  as user values(Processes.process_name) as process_name values(Processes.parent_process_name)
+  as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name
+  != "explorer.exe" by Processes.process_id Processes.dest | `drop_dm_object_name("Processes")`
+  | table user process_name process_id dest] | `suspicious_writes_to_windows_recycle_bin_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on filesystem and process logs responsible for the changes from your endpoints into
+  the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
+known_false_positives: Because the Recycle Bin is a hidden folder in modern versions
+  of Windows, it would be unusual for a process other than explorer.exe to write to
+  it. Incidents should be investigated as appropriate.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$"
@@ -18,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,21 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.file_name
-  - Filesystem.process_id
-  - Filesystem.dest
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process_name
-  - Processes.process_id
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/svchost_lolbas_execution_process_spawn.yml b/detections/endpoint/svchost_lolbas_execution_process_spawn.yml
index 573e004e62..e6fe8cad3d 100644
--- a/detections/endpoint/svchost_lolbas_execution_process_spawn.yml
+++ b/detections/endpoint/svchost_lolbas_execution_process_spawn.yml
@@ -5,14 +5,44 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances of 'svchost.exe' spawning Living Off The Land Binaries and Scripts (LOLBAS) processes. It leverages Endpoint Detection and Response (EDR) data to monitor child processes of 'svchost.exe' that match known LOLBAS executables. This activity is significant as adversaries often use LOLBAS techniques to execute malicious code stealthily, potentially indicating lateral movement or code execution attempts. If confirmed malicious, this behavior could allow attackers to execute arbitrary commands, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
+description: The following analytic detects instances of 'svchost.exe' spawning Living
+  Off The Land Binaries and Scripts (LOLBAS) processes. It leverages Endpoint Detection
+  and Response (EDR) data to monitor child processes of 'svchost.exe' that match known
+  LOLBAS executables. This activity is significant as adversaries often use LOLBAS
+  techniques to execute malicious code stealthily, potentially indicating lateral
+  movement or code execution attempts. If confirmed malicious, this behavior could
+  allow attackers to execute arbitrary commands, escalate privileges, or maintain
+  persistence within the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `svchost_lolbas_execution_process_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate applications may trigger this behavior, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe)
+  (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe",
+  "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe",
+  "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe",
+  "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe",
+  "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe",
+  "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe",
+  "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe",
+  "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe",
+  "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe",
+  "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe",
+  "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `svchost_lolbas_execution_process_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate applications may trigger this behavior, filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1053/005/
 - https://www.ired.team/offensive-security/persistence/t1053-schtask
@@ -23,7 +53,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/svchost_lolbas_execution_process_spawn/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/svchost_lolbas_execution_process_spawn/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/system_info_gathering_using_dxdiag_application.yml b/detections/endpoint/system_info_gathering_using_dxdiag_application.yml
index 016be35821..1653127295 100644
--- a/detections/endpoint/system_info_gathering_using_dxdiag_application.yml
+++ b/detections/endpoint/system_info_gathering_using_dxdiag_application.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of the dxdiag.exe process with specific command-line arguments, which is used to gather system information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line details. This activity is significant because dxdiag.exe is rarely used in corporate environments and its execution may indicate reconnaissance efforts by malicious actors. If confirmed malicious, this activity could allow attackers to collect detailed system information, aiding in further exploitation or lateral movement within the network.
+description: The following analytic identifies the execution of the dxdiag.exe process
+  with specific command-line arguments, which is used to gather system information.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events and command-line details. This activity is significant
+  because dxdiag.exe is rarely used in corporate environments and its execution may
+  indicate reconnaissance efforts by malicious actors. If confirmed malicious, this
+  activity could allow attackers to collect detailed system information, aiding in
+  further exploitation or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_dxdiag` AND Processes.process = "* /t *" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_info_gathering_using_dxdiag_application_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This commandline can be used by a network administrator to audit host machine specifications. Thus, a filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_dxdiag` AND Processes.process
+  = "* /t *" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `system_info_gathering_using_dxdiag_application_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This commandline can be used by a network administrator to
+  audit host machine specifications. Thus, a filter is needed.
 references:
 - https://app.any.run/tasks/df0baf9f-8baf-4c32-a452-16562ecb19be/
 tags:
@@ -37,23 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/host_info_dxdiag/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/t1592/host_info_dxdiag/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/system_information_discovery_detection.yml b/detections/endpoint/system_information_discovery_detection.yml
index 28e61a488a..048d74b2b6 100644
--- a/detections/endpoint/system_information_discovery_detection.yml
+++ b/detections/endpoint/system_information_discovery_detection.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Patrick Bareiss, Splunk
 status: production
 type: TTP
-description: The following analytic identifies system information discovery techniques, such as the execution of commands like `wmic qfe`, `systeminfo`, and `hostname`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because attackers often use these commands to gather system configuration details, which can aid in further exploitation. If confirmed malicious, this behavior could allow attackers to tailor their attacks based on the discovered system information, potentially leading to privilege escalation, persistence, or data exfiltration.
+description: The following analytic identifies system information discovery techniques,
+  such as the execution of commands like `wmic qfe`, `systeminfo`, and `hostname`.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process execution logs. This activity is significant because attackers often use
+  these commands to gather system configuration details, which can aid in further
+  exploitation. If confirmed malicious, this behavior could allow attackers to tailor
+  their attacks based on the discovered system information, potentially leading to
+  privilege escalation, persistence, or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest by dest | where dc_processes_by_dest > 2 | stats values(process) as process min(firstTime) as firstTime max(lastTime) as lastTime by user, dest parent_process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*"
+  OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user
+  Processes.process_name Processes.process Processes.dest Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | eventstats dc(process) as dc_processes_by_dest
+  by dest | where dc_processes_by_dest > 2 | stats values(process) as process min(firstTime)
+  as firstTime max(lastTime) as lastTime by user, dest parent_process_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `system_information_discovery_detection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators debugging servers
 references:
 - https://web.archive.org/web/20210119205146/https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,16 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.user
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/system_processes_run_from_unexpected_locations.yml b/detections/endpoint/system_processes_run_from_unexpected_locations.yml
index 6c3dc37647..4589c04e51 100644
--- a/detections/endpoint/system_processes_run_from_unexpected_locations.yml
+++ b/detections/endpoint/system_processes_run_from_unexpected_locations.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: David Dorsey, Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies system processes running from unexpected locations outside `C:\Windows\System32\` or `C:\Windows\SysWOW64`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths, names, and hashes. This activity is significant as it may indicate a malicious process attempting to masquerade as a legitimate system process. If confirmed malicious, this behavior could allow an attacker to execute code, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
+description: The following analytic identifies system processes running from unexpected
+  locations outside `C:\Windows\System32\` or `C:\Windows\SysWOW64`. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process paths,
+  names, and hashes. This activity is significant as it may indicate a malicious process
+  attempting to masquerade as a legitimate system process. If confirmed malicious,
+  this behavior could allow an attacker to execute code, escalate privileges, or maintain
+  persistence within the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user Processes.parent_process Processes.process_path Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file_macro` | `system_processes_run_from_unexpected_locations_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This detection may require tuning based on third party applications utilizing native Windows binaries in non-standard paths.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*"
+  Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user
+  Processes.parent_process Processes.process_path Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| `is_windows_system_file_macro`
+  | `system_processes_run_from_unexpected_locations_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This detection may require tuning based on third party applications
+  utilizing native Windows binaries in non-standard paths.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml
 - https://attack.mitre.org/techniques/T1036/003/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: A System process $process_name$ is running from $process_path$ on $dest$, potentially non-standard.
+  message: A System process $process_name$ is running from $process_path$ on $dest$,
+    potentially non-standard.
   mitre_attack_id:
   - T1036
   - T1036.003
@@ -54,19 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_path
-  - Processes.user
-  - Processes.dest
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.parent_process_name
-  - Processes.process_hash
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/system_user_discovery_with_query.yml b/detections/endpoint/system_user_discovery_with_query.yml
index 96055bf615..dd04f1ed2e 100644
--- a/detections/endpoint/system_user_discovery_with_query.yml
+++ b/detections/endpoint/system_user_discovery_with_query.yml
@@ -5,13 +5,32 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `query.exe` with command-line arguments aimed at discovering logged-in users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use `query.exe` to gain situational awareness and perform Active Directory discovery on compromised endpoints. If confirmed malicious, this behavior could allow attackers to identify active users, aiding in further lateral movement and privilege escalation within the network.
+description: The following analytic detects the execution of `query.exe` with command-line
+  arguments aimed at discovering logged-in users. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as adversaries may use `query.exe` to gain
+  situational awareness and perform Active Directory discovery on compromised endpoints.
+  If confirmed malicious, this behavior could allow attackers to identify active users,
+  aiding in further lateral movement and privilege escalation within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="query.exe") (Processes.process=*user*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_user_discovery_with_query_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="query.exe")
+  (Processes.process=*user*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `system_user_discovery_with_query_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1033/
@@ -33,23 +52,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/system_user_discovery_with_whoami.yml b/detections/endpoint/system_user_discovery_with_whoami.yml
index aca557de30..1b0a21a559 100644
--- a/detections/endpoint/system_user_discovery_with_whoami.yml
+++ b/detections/endpoint/system_user_discovery_with_whoami.yml
@@ -5,13 +5,32 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `whoami.exe` without any arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because both Red Teams and adversaries use `whoami.exe` to identify the current logged-in user, aiding in situational awareness and Active Directory discovery. If confirmed malicious, this behavior could indicate an attacker is gathering information to further compromise the system, potentially leading to privilege escalation or lateral movement within the network.
+description: The following analytic detects the execution of `whoami.exe` without
+  any arguments. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process execution logs. This activity is significant because both Red
+  Teams and adversaries use `whoami.exe` to identify the current logged-in user, aiding
+  in situational awareness and Active Directory discovery. If confirmed malicious,
+  this behavior could indicate an attacker is gathering information to further compromise
+  the system, potentially leading to privilege escalation or lateral movement within
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="whoami.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_user_discovery_with_whoami_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="whoami.exe")
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `system_user_discovery_with_whoami_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1033/
@@ -37,23 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/time_provider_persistence_registry.yml b/detections/endpoint/time_provider_persistence_registry.yml
index 5b6a9ad1fa..e2c70b264d 100644
--- a/detections/endpoint/time_provider_persistence_registry.yml
+++ b/detections/endpoint/time_provider_persistence_registry.yml
@@ -5,12 +5,19 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to the time provider registry for persistence and autostart. It leverages data from the Endpoint.Registry data model, focusing on changes to the "CurrentControlSet\\Services\\W32Time\\TimeProviders" registry path. This activity is significant because such modifications are uncommon and can indicate an attempt to establish persistence on a compromised host. If confirmed malicious, this technique allows an attacker to maintain access and execute code automatically upon system boot, potentially leading to further exploitation and control over the affected system.
+description: The following analytic detects suspicious modifications to the time provider
+  registry for persistence and autostart. It leverages data from the Endpoint.Registry
+  data model, focusing on changes to the "CurrentControlSet\\Services\\W32Time\\TimeProviders"
+  registry path. This activity is significant because such modifications are uncommon
+  and can indicate an attempt to establish persistence on a compromised host. If confirmed
+  malicious, this technique allows an attacker to maintain access and execute code
+  automatically upon system boot, potentially leading to further exploitation and
+  control over the affected system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*")
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Services\\W32Time\\TimeProviders*")
   BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
   Registry.registry_value_name Registry.registry_value_data Registry.process_guid
   | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -59,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.003/timeprovider_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.003/timeprovider_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/trickbot_named_pipe.yml b/detections/endpoint/trickbot_named_pipe.yml
index 102b9ccd80..79e9e2ccf9 100644
--- a/detections/endpoint/trickbot_named_pipe.yml
+++ b/detections/endpoint/trickbot_named_pipe.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation or connection to a named pipe associated with Trickbot malware. It leverages Sysmon EventCodes 17 and 18 to identify named pipes with the pattern "\\pipe\\*lacesomepipe". This activity is significant as Trickbot uses named pipes for communication with its command and control (C2) servers, facilitating data exfiltration and command execution. If confirmed malicious, this behavior could allow attackers to maintain persistence, execute arbitrary commands, and exfiltrate sensitive information from the compromised system.
+description: The following analytic detects the creation or connection to a named
+  pipe associated with Trickbot malware. It leverages Sysmon EventCodes 17 and 18
+  to identify named pipes with the pattern "\\pipe\\*lacesomepipe". This activity
+  is significant as Trickbot uses named pipes for communication with its command and
+  control (C2) servers, facilitating data exfiltration and command execution. If confirmed
+  malicious, this behavior could allow attackers to maintain persistence, execute
+  arbitrary commands, and exfiltrate sensitive information from the compromised system.
 data_source:
 - Sysmon EventID 17
 - Sysmon EventID 18
-search: '`sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats  min(_time) as firstTime max(_time) as lastTime count by dest user_id EventCode PipeName signature Image process_id | rename Image as process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `trickbot_named_pipe_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and pipename from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. .
+search: '`sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" | stats  min(_time)
+  as firstTime max(_time) as lastTime count by dest user_id EventCode PipeName signature
+  Image process_id | rename Image as process_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `trickbot_named_pipe_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and pipename from your endpoints. If you are using Sysmon,
+  you must have at least version 6.0.4 of the Sysmon TA. .
 known_false_positives: unknown
 references:
 - https://labs.vipre.com/trickbot-and-its-modules/
@@ -21,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - user_id
-  - EventCode
-  - PipeName
-  - signature
-  - Image
-  - process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/namedpipe/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/namedpipe/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml b/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml
index 8c5327c59c..10dea0c752 100644
--- a/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml
+++ b/detections/endpoint/uac_bypass_mmc_load_unsigned_dll.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the loading of an unsigned DLL by the MMC.exe application, which is indicative of a potential UAC bypass or privilege escalation attempt. It leverages Sysmon EventCode 7 to identify instances where MMC.exe loads a non-Microsoft, unsigned DLL. This activity is significant because attackers often use this technique to modify CLSID registry entries, causing MMC.exe to load malicious DLLs, thereby bypassing User Account Control (UAC) and gaining elevated privileges. If confirmed malicious, this could allow an attacker to execute arbitrary code with higher privileges, leading to further system compromise and persistence.
+description: The following analytic detects the loading of an unsigned DLL by the
+  MMC.exe application, which is indicative of a potential UAC bypass or privilege
+  escalation attempt. It leverages Sysmon EventCode 7 to identify instances where
+  MMC.exe loads a non-Microsoft, unsigned DLL. This activity is significant because
+  attackers often use this technique to modify CLSID registry entries, causing MMC.exe
+  to load malicious DLLs, thereby bypassing User Account Control (UAC) and gaining
+  elevated privileges. If confirmed malicious, this could allow an attacker to execute
+  arbitrary code with higher privileges, leading to further system compromise and
+  persistence.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7  ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false Company != "Microsoft Corporation" | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed ProcessId OriginalFileName dest EventCode Company | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_mmc_load_unsigned_dll_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: unknown. all of the dll loaded by mmc.exe is microsoft signed dll.
+search: '`sysmon` EventCode=7  ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false
+  Company != "Microsoft Corporation" | stats count min(_time) as firstTime max(_time)
+  as lastTime by Image ImageLoaded Signed ProcessId OriginalFileName dest EventCode
+  Company | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `uac_bypass_mmc_load_unsigned_dll_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: unknown. all of the dll loaded by mmc.exe is microsoft signed
+  dll.
 references:
 - https://offsec.almond.consulting/UAC-bypass-dotnet.html
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: Suspicious unsigned $ImageLoaded$ loaded by $Image$ on endpoint $dest$ with EventCode $EventCode$
+  message: Suspicious unsigned $ImageLoaded$ loaded by $Image$ on endpoint $dest$
+    with EventCode $EventCode$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -42,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - Signed
-  - ProcessId
-  - OriginalFileName
-  - dest
-  - EventCode
-  - Company
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/uac_bypass_with_colorui_com_object.yml b/detections/endpoint/uac_bypass_with_colorui_com_object.yml
index 07865ead2e..9787905f94 100644
--- a/detections/endpoint/uac_bypass_with_colorui_com_object.yml
+++ b/detections/endpoint/uac_bypass_with_colorui_com_object.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a potential UAC bypass using the colorui.dll COM Object. It leverages Sysmon EventCode 7 to identify instances where colorui.dll is loaded by a process other than colorcpl.exe, excluding common system directories. This activity is significant because UAC bypass techniques are often used by malware, such as LockBit ransomware, to gain elevated privileges without user consent. If confirmed malicious, this could allow an attacker to execute code with higher privileges, leading to further system compromise and persistence within the environment.
+description: The following analytic detects a potential UAC bypass using the colorui.dll
+  COM Object. It leverages Sysmon EventCode 7 to identify instances where colorui.dll
+  is loaded by a process other than colorcpl.exe, excluding common system directories.
+  This activity is significant because UAC bypass techniques are often used by malware,
+  such as LockBit ransomware, to gain elevated privileges without user consent. If
+  confirmed malicious, this could allow an attacker to execute code with higher privileges,
+  leading to further system compromise and persistence within the environment.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 ImageLoaded="*\\colorui.dll" process_name != "colorcpl.exe" NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest user_id EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uac_bypass_with_colorui_com_object_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=7 ImageLoaded="*\\colorui.dll" process_name != "colorcpl.exe"
+  NOT(Image IN("*\\windows\\*", "*\\program files*")) | stats count min(_time) as
+  firstTime max(_time) as lastTime by Image ImageLoaded process_name dest user_id
+  EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `uac_bypass_with_colorui_com_object_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
 known_false_positives: not so common. but 3rd part app may load this dll.
 references:
 - https://news.sophos.com/en-us/2020/04/24/lockbit-ransomware-borrows-tricks-to-keep-up-with-revil-and-maze/
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 60
-  message: The following module $ImageLoaded$ was loaded by a non-standard application on endpoint $dest$.
+  message: The following module $ImageLoaded$ was loaded by a non-standard application
+    on endpoint $dest$.
   mitre_attack_id:
   - T1218
   - T1218.003
@@ -46,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/uac_colorui/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/uac_colorui/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/uninstall_app_using_msiexec.yml b/detections/endpoint/uninstall_app_using_msiexec.yml
index 35414e2794..ab673c75f1 100644
--- a/detections/endpoint/uninstall_app_using_msiexec.yml
+++ b/detections/endpoint/uninstall_app_using_msiexec.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the uninstallation of applications using msiexec with specific command-line arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it is an uncommon practice in enterprise environments and has been associated with malicious behavior, such as disabling antivirus software. If confirmed malicious, this could allow an attacker to remove security software, potentially leading to further compromise and persistence within the network.
+description: The following analytic detects the uninstallation of applications using
+  msiexec with specific command-line arguments. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs that include command-line
+  details. This activity is significant because it is an uncommon practice in enterprise
+  environments and has been associated with malicious behavior, such as disabling
+  antivirus software. If confirmed malicious, this could allow an attacker to remove
+  security software, potentially leading to further compromise and persistence within
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msiexec.exe Processes.process= "* /qn *" Processes.process= "*/X*" Processes.process= "*REBOOT=*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uninstall_app_using_msiexec_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msiexec.exe
+  Processes.process= "* /qn *" Processes.process= "*/X*" Processes.process= "*REBOOT=*"
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `uninstall_app_using_msiexec_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown.
 references:
 - https://threadreaderapp.com/thread/1423361119926816776.html
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/conti_leak/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml b/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml
index 4d09d5afb8..36dad669a7 100644
--- a/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml
+++ b/detections/endpoint/unknown_process_using_the_kerberos_protocol.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a non-lsass.exe process making an outbound connection on port 88, which is typically used by the Kerberos authentication protocol. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. This activity is significant because, under normal circumstances, only the lsass.exe process should interact with the Kerberos Distribution Center. If confirmed malicious, this behavior could indicate an adversary attempting to abuse the Kerberos protocol, potentially leading to unauthorized access or lateral movement within the network.
+description: The following analytic identifies a non-lsass.exe process making an outbound
+  connection on port 88, which is typically used by the Kerberos authentication protocol.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process and network traffic logs. This activity is significant because,
+  under normal circumstances, only the lsass.exe process should interact with the
+  Kerberos Distribution Center. If confirmed malicious, this behavior could indicate
+  an adversary attempting to abuse the Kerberos protocol, potentially leading to unauthorized
+  access or lateral movement within the network.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name!=lsass.exe by _time Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id dest [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port = 88 by All_Traffic.src All_Traffic.process_id  All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename src as dest ] |  table _time dest parent_process_name process_name process_path process process_id dest_port | `unknown_process_using_the_kerberos_protocol_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Custom applications may leverage the Kerberos protocol. Filter as needed.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name!=lsass.exe by _time Processes.process_id Processes.process_name
+  Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | join  process_id dest [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port = 88 by All_Traffic.src All_Traffic.process_id  All_Traffic.dest_port
+  | `drop_dm_object_name(All_Traffic)` | rename src as dest ] |  table _time dest
+  parent_process_name process_name process_path process process_id dest_port | `unknown_process_using_the_kerberos_protocol_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Custom applications may leverage the Kerberos protocol. Filter
+  as needed.
 references:
 - https://stealthbits.com/blog/how-to-detect-overpass-the-hash-attacks/
 - https://www.thehacker.recipes/ad/movement/kerberos/ptk
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: Unknown process $process_name$ using the kerberos protocol detected on host $dest$
+  message: Unknown process $process_name$ using the kerberos protocol detected on
+    host $dest$
   mitre_attack_id:
   - T1550
   observable:
@@ -42,24 +71,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
-  - All_Traffic.src_ip
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
-  - Processes.process_path
-  - Processes.process
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550/rubeus/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/unload_sysmon_filter_driver.yml b/detections/endpoint/unload_sysmon_filter_driver.yml
index 53329f735f..2b86a26512 100644
--- a/detections/endpoint/unload_sysmon_filter_driver.yml
+++ b/detections/endpoint/unload_sysmon_filter_driver.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `fltMC.exe` to unload the Sysmon driver, which stops Sysmon from collecting data. It leverages Endpoint Detection and Response (EDR) logs, focusing on process names and command-line executions. This activity is significant because disabling Sysmon can blind security monitoring, allowing malicious actions to go undetected. If confirmed malicious, this could enable attackers to execute further attacks without being logged, leading to potential data breaches, privilege escalation, or persistent access within the environment.
+description: The following analytic detects the use of `fltMC.exe` to unload the Sysmon
+  driver, which stops Sysmon from collecting data. It leverages Endpoint Detection
+  and Response (EDR) logs, focusing on process names and command-line executions.
+  This activity is significant because disabling Sysmon can blind security monitoring,
+  allowing malicious actions to go undetected. If confirmed malicious, this could
+  enable attackers to execute further attacks without being logged, leading to potential
+  data breaches, privilege escalation, or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv*  by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | table firstTime lastTime dest user count process_name process_id parent_process_name process | `unload_sysmon_filter_driver_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process)
+  as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe
+  AND Processes.process=*unload* AND Processes.process=*SysmonDrv*  by Processes.process_name
+  Processes.process_id Processes.parent_process_name Processes.process Processes.dest
+  Processes.user | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  | table firstTime lastTime dest user count process_name process_id parent_process_name
+  process | `unload_sysmon_filter_driver_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown at the moment
 references:
 - https://www.ired.team/offensive-security/defense-evasion/unloading-sysmon-driver
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/unload_sysmon/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/unload_sysmon/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/unloading_amsi_via_reflection.yml b/detections/endpoint/unloading_amsi_via_reflection.yml
index 9f024415ed..3b98211bcc 100644
--- a/detections/endpoint/unloading_amsi_via_reflection.yml
+++ b/detections/endpoint/unloading_amsi_via_reflection.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the tampering of AMSI (Antimalware Scan Interface) via PowerShell reflection. It leverages PowerShell Script Block Logging (EventCode=4104) to capture and analyze suspicious PowerShell commands, specifically those involving `system.management.automation.amsi`. This activity is significant as it indicates an attempt to bypass AMSI, a critical security feature that helps detect and block malicious scripts. If confirmed malicious, this could allow an attacker to execute harmful code undetected, leading to potential system compromise and data exfiltration.
+description: The following analytic detects the tampering of AMSI (Antimalware Scan
+  Interface) via PowerShell reflection. It leverages PowerShell Script Block Logging
+  (EventCode=4104) to capture and analyze suspicious PowerShell commands, specifically
+  those involving `system.management.automation.amsi`. This activity is significant
+  as it indicates an attempt to bypass AMSI, a critical security feature that helps
+  detect and block malicious scripts. If confirmed malicious, this could allow an
+  attacker to execute harmful code undetected, leading to potential system compromise
+  and data exfiltration.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = *system.management.automation.amsi* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `unloading_amsi_via_reflection_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Potential for some third party applications to disable AMSI upon invocation. Filter as needed.
+search: '`powershell` EventCode=4104 ScriptBlockText = *system.management.automation.amsi*
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `unloading_amsi_via_reflection_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Potential for some third party applications to disable AMSI
+  upon invocation. Filter as needed.
 references:
 - https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 - https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml b/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml
index 8e7e9a22a6..8c57ad12a7 100644
--- a/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml
+++ b/detections/endpoint/unusual_number_of_computer_service_tickets_requested.yml
@@ -5,12 +5,29 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies an unusual number of computer service ticket requests from a single source, leveraging Event ID 4769, "A Kerberos service ticket was requested." It uses statistical analysis, including standard deviation and the 3-sigma rule, to detect anomalies in service ticket requests. This activity is significant as it may indicate malicious behavior such as lateral movement, malware staging, or reconnaissance. If confirmed malicious, an attacker could gain unauthorized access to multiple endpoints, facilitating further compromise and potential data exfiltration.
+description: The following analytic identifies an unusual number of computer service
+  ticket requests from a single source, leveraging Event ID 4769, "A Kerberos service
+  ticket was requested." It uses statistical analysis, including standard deviation
+  and the 3-sigma rule, to detect anomalies in service ticket requests. This activity
+  is significant as it may indicate malicious behavior such as lateral movement, malware
+  staging, or reconnaissance. If confirmed malicious, an attacker could gain unauthorized
+  access to multiple endpoints, facilitating further compromise and potential data
+  exfiltration.
 data_source:
 - Windows Event Log Security 4769
-search: '`wineventlog_security` EventCode=4769 Service_Name="*$" Account_Name!="*$*" | bucket span=2m _time | stats dc(Service_Name) AS unique_targets values(Service_Name) as host_targets by _time, Client_Address, Account_Name | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std by Client_Address, Account_Name | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) | `unusual_number_of_computer_service_tickets_requested_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: An single endpoint requesting a large number of computer service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systeams and missconfigured systems.
+search: '`wineventlog_security` EventCode=4769 Service_Name="*$" Account_Name!="*$*"
+  | bucket span=2m _time | stats dc(Service_Name) AS unique_targets values(Service_Name)
+  as host_targets by _time, Client_Address, Account_Name | eventstats avg(unique_targets)
+  as comp_avg , stdev(unique_targets) as comp_std by Client_Address, Account_Name
+  | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_targets >10 and
+  unique_targets >= upperBound, 1, 0) | `unusual_number_of_computer_service_tickets_requested_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: An single endpoint requesting a large number of computer service
+  tickets is not common behavior. Possible false positive scenarios include but are
+  not limited to vulnerability scanners, administration systeams and missconfigured
+  systems.
 references:
 - https://attack.mitre.org/techniques/T1078/
 tags:
@@ -33,12 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Ticket_Options
-  - Ticket_Encryption_Type
-  - dest
-  - service
-  - service_id
   security_domain: endpoint
diff --git a/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml b/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml
index 04d468ae38..823e6f6d92 100644
--- a/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml
+++ b/detections/endpoint/unusual_number_of_kerberos_service_tickets_requested.yml
@@ -5,21 +5,28 @@ date: '2024-10-17'
 author: Mauricio Velazco, Dean Luxton, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies an unusual number of Kerberos service ticket requests, potentially indicating a kerberoasting attack. It leverages Kerberos Event 4769 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This activity is significant as kerberoasting allows adversaries to request service tickets and crack them offline, potentially gaining privileged access to the domain. If confirmed malicious, this could lead to unauthorized access to sensitive accounts and escalation of privileges within the Active Directory environment.
+description: The following analytic identifies an unusual number of Kerberos service
+  ticket requests, potentially indicating a kerberoasting attack. It leverages Kerberos
+  Event 4769 and calculates the standard deviation for each host, using the 3-sigma
+  rule to detect anomalies. This activity is significant as kerberoasting allows adversaries
+  to request service tickets and crack them offline, potentially gaining privileged
+  access to the domain. If confirmed malicious, this could lead to unauthorized access
+  to sensitive accounts and escalation of privileges within the Active Directory environment.
 data_source:
 - Windows Event Log Security 4769
 search: >-
   `wineventlog_security` EventCode=4769 ServiceName!="*$" TicketEncryptionType=0x17
-  | bucket span=2m _time 
-  | stats dc(ServiceName) AS unique_services values(ServiceName) as requested_services values(user_category) as user_category values(src_category) as src_category by _time, user, src
-  | eventstats avg(unique_services) as comp_avg , stdev(unique_services) as comp_std by user, src
-  | eval upperBound=(comp_avg+comp_std*3) 
-  | eval isOutlier=if(unique_services > 2 and unique_services >= upperBound, 1, 0) 
-  | search isOutlier=1
+  | bucket span=2m _time  | stats dc(ServiceName) AS unique_services values(ServiceName)
+  as requested_services values(user_category) as user_category values(src_category)
+  as src_category by _time, user, src
+  | eventstats avg(unique_services) as comp_avg , stdev(unique_services) as comp_std
+  by user, src
+  | eval upperBound=(comp_avg+comp_std*3)  | eval isOutlier=if(unique_services > 2
+  and unique_services >= upperBound, 1, 0)  | search isOutlier=1
   | `unusual_number_of_kerberos_service_tickets_requested_filter`
 how_to_implement: To successfully implement this search, you need to be ingesting
   Domain Controller and Kerberos events. The Advanced Security Audit policy setting
-  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled. 
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 known_false_positives: An single endpoint requesting a large number of kerberos service
   tickets is not common behavior. Possible false positive scenarios include but are
   not limited to vulnerability scanners, administration systems and missconfigured
@@ -33,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: User $user$ requested a service ticket for $unique_services$ services indicating a potential kerberoasting attack
+  message: User $user$ requested a service ticket for $unique_services$ services indicating
+    a potential kerberoasting attack
   mitre_attack_id:
   - T1558
   - T1558.003
@@ -59,20 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Ticket_Options
-  - Ticket_Encryption_Type
-  - user
-  - src
-  - Service_Name
-  - service_id
-  - Client_Address
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/unusual_number_of_kerberos_service_tickets_requested/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/unusual_number_of_kerberos_service_tickets_requested/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml b/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml
index a28540543b..d5b540a551 100644
--- a/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml
+++ b/detections/endpoint/unusual_number_of_remote_endpoint_authentication_events.yml
@@ -5,12 +5,29 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies an unusual number of remote authentication attempts from a single source by leveraging Windows Event ID 4624, which logs successful account logons. It uses statistical analysis, specifically the 3-sigma rule, to detect deviations from normal behavior. This activity is significant for a SOC as it may indicate lateral movement, malware staging, or reconnaissance. If confirmed malicious, this behavior could allow an attacker to move laterally within the network, escalate privileges, or gather information for further attacks.
+description: The following analytic identifies an unusual number of remote authentication
+  attempts from a single source by leveraging Windows Event ID 4624, which logs successful
+  account logons. It uses statistical analysis, specifically the 3-sigma rule, to
+  detect deviations from normal behavior. This activity is significant for a SOC as
+  it may indicate lateral movement, malware staging, or reconnaissance. If confirmed
+  malicious, this behavior could allow an attacker to move laterally within the network,
+  escalate privileges, or gather information for further attacks.
 data_source:
 - Windows Event Log Security 4624
-search: '`wineventlog_security` EventCode=4624 Logon_Type=3 Account_Name!="*$" | eval Source_Account = mvindex(Account_Name, 1) | bucket span=2m _time | stats dc(ComputerName) AS unique_targets values(ComputerName) as target_hosts by _time, Source_Network_Address, Source_Account | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std by Source_Network_Address, Source_Account | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) | `unusual_number_of_remote_endpoint_authentication_events_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
-known_false_positives: An single endpoint authenticating to a large number of hosts is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, jump servers and missconfigured systems.
+search: '`wineventlog_security` EventCode=4624 Logon_Type=3 Account_Name!="*$" | eval
+  Source_Account = mvindex(Account_Name, 1) | bucket span=2m _time | stats dc(ComputerName)
+  AS unique_targets values(ComputerName) as target_hosts by _time, Source_Network_Address,
+  Source_Account | eventstats avg(unique_targets) as comp_avg , stdev(unique_targets)
+  as comp_std by Source_Network_Address, Source_Account | eval upperBound=(comp_avg+comp_std*3)
+  | eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) |
+  `unusual_number_of_remote_endpoint_authentication_events_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
+known_false_positives: An single endpoint authenticating to a large number of hosts
+  is not common behavior. Possible false positive scenarios include but are not limited
+  to vulnerability scanners, jump servers and missconfigured systems.
 references:
 - https://attack.mitre.org/techniques/T1078/
 tags:
@@ -32,12 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - Caller_Process_Name
-  - Security_ID
-  - Account_Name
-  - ComputerName
   security_domain: endpoint
diff --git a/detections/endpoint/unusually_long_command_line.yml b/detections/endpoint/unusually_long_command_line.yml
index b574da1b90..0f93130716 100644
--- a/detections/endpoint/unusually_long_command_line.yml
+++ b/detections/endpoint/unusually_long_command_line.yml
@@ -5,13 +5,35 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects unusually long command lines, which may indicate malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on the length of command lines executed on hosts. This behavior is significant because attackers often use obfuscated or complex command lines to evade detection and execute malicious payloads. If confirmed malicious, this activity could lead to data theft, ransomware deployment, or further system compromise. Analysts should investigate the source and content of the command line, inspect relevant artifacts, and review concurrent processes to identify potential threats.
+description: The following analytic detects unusually long command lines, which may
+  indicate malicious activity. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on the length of command lines executed on hosts. This behavior
+  is significant because attackers often use obfuscated or complex command lines to
+  evade detection and execute malicious payloads. If confirmed malicious, this activity
+  could lead to data theft, ransomware deployment, or further system compromise. Analysts
+  should investigate the source and content of the command line, inspect relevant
+  artifacts, and review concurrent processes to identify potential threats.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`|  eval processlen=len(process) | eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost) + avgperhost)'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name
+  Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`|  eval processlen=len(process) | eventstats stdev(processlen)
+  as stdev, avg(processlen) as avg by dest | stats max(processlen) as maxlen, values(stdev)
+  as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process
+  | `unusually_long_command_line_filter` |eval threshold = 3 | where maxlen > ((threshold*stdevperhost)
+  + avgperhost)'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Some legitimate applications start with long command lines.
 references: []
 tags:
@@ -37,10 +59,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_name
-  - Processes.process
   security_domain: endpoint
diff --git a/detections/endpoint/unusually_long_command_line___mltk.yml b/detections/endpoint/unusually_long_command_line___mltk.yml
index 084a5c0d8f..6ee0eb665d 100644
--- a/detections/endpoint/unusually_long_command_line___mltk.yml
+++ b/detections/endpoint/unusually_long_command_line___mltk.yml
@@ -5,14 +5,41 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies unusually long command lines executed on hosts, which may indicate malicious activity. It leverages the Machine Learning Toolkit (MLTK) to detect command lines with lengths that deviate from the norm for a given user. This is significant for a SOC as unusually long command lines can be a sign of obfuscation or complex malicious scripts. If confirmed malicious, this activity could allow attackers to execute sophisticated commands, potentially leading to unauthorized access, data exfiltration, or further compromise of the system.
+description: The following analytic identifies unusually long command lines executed
+  on hosts, which may indicate malicious activity. It leverages the Machine Learning
+  Toolkit (MLTK) to detect command lines with lengths that deviate from the norm for
+  a given user. This is significant for a SOC as unusually long command lines can
+  be a sign of obfuscation or complex malicious scripts. If confirmed malicious, this
+  activity could allow attackers to execute sophisticated commands, potentially leading
+  to unauthorized access, data exfiltration, or further compromise of the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier | search isOutlier > 0 | table firstTime lastTime user dest process_name process processlen count | `unusually_long_command_line___mltk_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name
+  Processes.process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)`| eval processlen=len(process) | search user!=unknown
+  | apply cmdline_pdfmodel threshold=0.01 | rename "IsOutlier(processlen)" as isOutlier
+  | search isOutlier > 0 | table firstTime lastTime user dest process_name process
+  processlen count | `unusually_long_command_line___mltk_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legitimate applications use long command lines for installs
+  or updates. You should review identified command lines for legitimacy. You may modify
+  the first part of the search to omit legitimate command lines from consideration.
+  If you are seeing more results than desired, you may consider changing the value
+  of threshold in the search to a smaller value. You should also periodically re-run
+  the support search to re-build the ML model on the latest data. You may get unexpected
+  results if the user identified in the results is not present in the data used to
+  build the associated model.
 references: []
 tags:
   analytic_story:
@@ -37,10 +64,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_name
-  - Processes.process
   security_domain: endpoint
diff --git a/detections/endpoint/user_discovery_with_env_vars_powershell.yml b/detections/endpoint/user_discovery_with_env_vars_powershell.yml
index a8d554402f..f8b8187a77 100644
--- a/detections/endpoint/user_discovery_with_env_vars_powershell.yml
+++ b/detections/endpoint/user_discovery_with_env_vars_powershell.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of `powershell.exe` with command-line arguments that use PowerShell environment variables to identify the current logged user. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use it for situational awareness and Active Directory discovery on compromised endpoints. If confirmed malicious, this behavior could allow attackers to gather critical user information, aiding in further exploitation and lateral movement within the network.
+description: The following analytic detects the execution of `powershell.exe` with
+  command-line arguments that use PowerShell environment variables to identify the
+  current logged user. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names and command-line executions. This activity is
+  significant as adversaries may use it for situational awareness and Active Directory
+  discovery on compromised endpoints. If confirmed malicious, this behavior could
+  allow attackers to gather critical user information, aiding in further exploitation
+  and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe") (Processes.process="*$env:UserName*" OR Processes.process="*[System.Environment]::UserName*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `user_discovery_with_env_vars_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="powershell.exe")
+  (Processes.process="*$env:UserName*" OR Processes.process="*[System.Environment]::UserName*")
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `user_discovery_with_env_vars_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1033/
@@ -33,23 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml b/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml
index 3c274d5ac8..0dda4a397e 100644
--- a/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml
+++ b/detections/endpoint/user_discovery_with_env_vars_powershell_script_block.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Mauricio Velazco, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of PowerShell environment variables to identify the current logged user by leveraging PowerShell Script Block Logging (EventCode=4104). This method monitors script blocks containing `$env:UserName` or `[System.Environment]::UserName`. Identifying this activity is significant as adversaries and Red Teams may use it for situational awareness and Active Directory discovery on compromised endpoints. If confirmed malicious, this activity could allow attackers to gain insights into user context, aiding in further exploitation and lateral movement within the network.
+description: The following analytic detects the use of PowerShell environment variables
+  to identify the current logged user by leveraging PowerShell Script Block Logging
+  (EventCode=4104). This method monitors script blocks containing `$env:UserName`
+  or `[System.Environment]::UserName`. Identifying this activity is significant as
+  adversaries and Red Teams may use it for situational awareness and Active Directory
+  discovery on compromised endpoints. If confirmed malicious, this activity could
+  allow attackers to gain insights into user context, aiding in further exploitation
+  and lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*$env:UserName*" OR ScriptBlockText = "*[System.Environment]::UserName*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `user_discovery_with_env_vars_powershell_script_block_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators or power users may use this PowerShell commandlet for troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*$env:UserName*" OR ScriptBlockText
+  = "*[System.Environment]::UserName*") | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest,
+  user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `user_discovery_with_env_vars_powershell_script_block_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators or power users may use this PowerShell commandlet
+  for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1033/
 tags:
@@ -35,18 +49,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Path
-  - Message
-  - OpCode
-  - ComputerName
-  - User
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/AD_discovery/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/usn_journal_deletion.yml b/detections/endpoint/usn_journal_deletion.yml
index b76c7561d4..a76bfbcd6d 100644
--- a/detections/endpoint/usn_journal_deletion.yml
+++ b/detections/endpoint/usn_journal_deletion.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: David Dorsey, Splunk
 status: production
 type: TTP
-description: The following analytic detects the deletion of the USN Journal using the fsutil.exe utility. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because the USN Journal maintains a log of all changes made to files on the disk, and its deletion can be an indicator of an attempt to cover tracks or hinder forensic investigations. If confirmed malicious, this action could allow an attacker to obscure their activities, making it difficult to trace file modifications and potentially compromising incident response efforts.
+description: The following analytic detects the deletion of the USN Journal using
+  the fsutil.exe utility. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process execution logs that include command-line details. This
+  activity is significant because the USN Journal maintains a log of all changes made
+  to files on the disk, and its deletion can be an indicator of an attempt to cover
+  tracks or hinder forensic investigations. If confirmed malicious, this action could
+  allow an attacker to obscure their activities, making it difficult to trace file
+  modifications and potentially compromising incident response efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count values(Processes.process)
+  as process values(Processes.parent_process) as parent_process min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe
+  by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | search process="*deletejournal*" AND process="*usn*" | `usn_journal_deletion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: None identified
 references: []
 drilldown_searches:
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/vbscript_execution_using_wscript_app.yml b/detections/endpoint/vbscript_execution_using_wscript_app.yml
index 433f054b79..66bb4ab941 100644
--- a/detections/endpoint/vbscript_execution_using_wscript_app.yml
+++ b/detections/endpoint/vbscript_execution_using_wscript_app.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of VBScript using the wscript.exe application. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because wscript.exe is typically not used to execute VBScript, which is usually associated with cscript.exe. This deviation can indicate an attempt to evade traditional process monitoring and antivirus defenses. If confirmed malicious, this technique could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects the execution of VBScript using the wscript.exe
+  application. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process and command-line telemetry. This activity is significant because
+  wscript.exe is typically not used to execute VBScript, which is usually associated
+  with cscript.exe. This deviation can indicate an attempt to evade traditional process
+  monitoring and antivirus defenses. If confirmed malicious, this technique could
+  allow attackers to execute arbitrary code, potentially leading to system compromise,
+  data exfiltration, or further lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "wscript.exe" AND Processes.parent_process = "*//e:vbscript*") OR (Processes.process_name = "wscript.exe" AND Processes.process = "*//e:vbscript*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vbscript_execution_using_wscript_app_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  = "wscript.exe" AND Processes.parent_process = "*//e:vbscript*") OR (Processes.process_name
+  = "wscript.exe" AND Processes.process = "*//e:vbscript*") by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process_id Processes.process
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `vbscript_execution_using_wscript_app_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.joesandbox.com/analysis/369332/0/html
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,23 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/verclsid_clsid_execution.yml b/detections/endpoint/verclsid_clsid_execution.yml
index 77952e0626..1dcb352b71 100644
--- a/detections/endpoint/verclsid_clsid_execution.yml
+++ b/detections/endpoint/verclsid_clsid_execution.yml
@@ -5,14 +5,37 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the potential abuse of the verclsid.exe utility to execute malicious files via generated CLSIDs. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with verclsid.exe. This activity is significant because verclsid.exe is a legitimate Windows application used to verify CLSID COM objects, and its misuse can indicate an attempt to bypass security controls. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise or further malicious activities.
+description: The following analytic detects the potential abuse of the verclsid.exe
+  utility to execute malicious files via generated CLSIDs. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on specific command-line
+  patterns associated with verclsid.exe. This activity is significant because verclsid.exe
+  is a legitimate Windows application used to verify CLSID COM objects, and its misuse
+  can indicate an attempt to bypass security controls. If confirmed malicious, this
+  technique could allow an attacker to execute arbitrary code, potentially leading
+  to system compromise or further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_verclsid` AND Processes.process="*/S*" Processes.process="*/C*" AND  Processes.process="*{*" AND Processes.process="*}*" by  Processes.process_name Processes.original_file_name Processes.dest Processes.user Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `verclsid_clsid_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: windows can used this application for its normal COM object validation.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.parent_process) as parent_process values(Processes.process_id)
+  as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_verclsid` AND Processes.process="*/S*" Processes.process="*/C*" AND  Processes.process="*{*"
+  AND Processes.process="*}*" by  Processes.process_name Processes.original_file_name
+  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `verclsid_clsid_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: windows can used this application for its normal COM object
+  validation.
 references:
 - https://gist.github.com/NickTyrer/0598b60112eaafe6d07789f7964290d5
 - https://bohops.com/2018/08/18/abusing-the-com-registry-structure-part-2-loading-techniques-for-evasion-and-persistence/
@@ -22,7 +45,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: process $process_name$ to execute possible clsid commandline $process$ in $dest$
+  message: process $process_name$ to execute possible clsid commandline $process$
+    in $dest$
   mitre_attack_id:
   - T1218.012
   - T1218
@@ -39,23 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.012/verclsid_exec/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.012/verclsid_exec/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/w3wp_spawning_shell.yml b/detections/endpoint/w3wp_spawning_shell.yml
index 495589eb64..a63c4d3aa6 100644
--- a/detections/endpoint/w3wp_spawning_shell.yml
+++ b/detections/endpoint/w3wp_spawning_shell.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where a shell (PowerShell.exe or Cmd.exe) is spawned from W3WP.exe, the IIS worker process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the parent process is W3WP.exe. This activity is significant as it may indicate webshell activity, often associated with exploitation attempts like those by the HAFNIUM Group on Exchange servers. If confirmed malicious, this behavior could allow attackers to execute arbitrary commands, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies instances where a shell (PowerShell.exe
+  or Cmd.exe) is spawned from W3WP.exe, the IIS worker process. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process creation
+  events where the parent process is W3WP.exe. This activity is significant as it
+  may indicate webshell activity, often associated with exploitation attempts like
+  those by the HAFNIUM Group on Exchange servers. If confirmed malicious, this behavior
+  could allow attackers to execute arbitrary commands, potentially leading to system
+  compromise, data exfiltration, or further lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=w3wp.exe AND `process_cmd` OR `process_powershell` by Processes.dest Processes.parent_process Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `w3wp_spawning_shell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Baseline your environment before production. It is possible build systems using IIS will spawn cmd.exe to perform a software build. Filter as needed.
+search: '| tstats `security_content_summariesonly` count values(Processes.process_name)
+  as process_name values(Processes.process) as process min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=w3wp.exe
+  AND `process_cmd` OR `process_powershell` by Processes.dest Processes.parent_process
+  Processes.original_file_name Processes.user | `drop_dm_object_name(Processes)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `w3wp_spawning_shell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Baseline your environment before production. It is possible
+  build systems using IIS will spawn cmd.exe to perform a software build. Filter as
+  needed.
 references:
 - https://www.microsoft.com/security/blog/2020/02/04/ghost-in-the-shell-investigating-web-shell-attacks/
 - https://www.zerodayinitiative.com/blog/2021/8/17/from-pwn2own-2021-a-new-attack-surface-on-microsoft-exchange-proxyshell
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -59,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wbadmin_delete_system_backups.yml b/detections/endpoint/wbadmin_delete_system_backups.yml
index a2cbda5f3d..fc2a74b651 100644
--- a/detections/endpoint/wbadmin_delete_system_backups.yml
+++ b/detections/endpoint/wbadmin_delete_system_backups.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of wbadmin.exe with flags that delete backup files, specifically targeting catalog or system state backups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is commonly used by ransomware to prevent recovery by deleting system backups. If confirmed malicious, this action could severely hinder recovery efforts, leading to prolonged downtime and potential data loss.
+description: The following analytic detects the execution of wbadmin.exe with flags
+  that delete backup files, specifically targeting catalog or system state backups.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names and command-line arguments. This activity is significant because it
+  is commonly used by ransomware to prevent recovery by deleting system backups. If
+  confirmed malicious, this action could severely hinder recovery efforts, leading
+  to prolonged downtime and potential data loss.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe
+  Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*")
+  by Processes.process_name Processes.process Processes.parent_process_name Processes.dest
+  Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `wbadmin_delete_system_backups_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators may modify the boot configuration.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md
@@ -24,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wbemprox_com_object_execution.yml b/detections/endpoint/wbemprox_com_object_execution.yml
index ee7db518ce..fd8d443112 100644
--- a/detections/endpoint/wbemprox_com_object_execution.yml
+++ b/detections/endpoint/wbemprox_com_object_execution.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious process loading a COM object from wbemprox.dll, fastprox.dll, or wbemcomn.dll. It leverages Sysmon EventCode 7 to identify instances where these DLLs are loaded by processes not typically associated with them, excluding known legitimate processes and directories. This activity is significant as it may indicate an attempt by threat actors to abuse COM objects for privilege escalation or evasion of detection mechanisms. If confirmed malicious, this could allow attackers to gain elevated privileges or maintain persistence within the environment, posing a significant security risk.
+description: The following analytic detects a suspicious process loading a COM object
+  from wbemprox.dll, fastprox.dll, or wbemcomn.dll. It leverages Sysmon EventCode
+  7 to identify instances where these DLLs are loaded by processes not typically associated
+  with them, excluding known legitimate processes and directories. This activity is
+  significant as it may indicate an attempt by threat actors to abuse COM objects
+  for privilege escalation or evasion of detection mechanisms. If confirmed malicious,
+  this could allow attackers to gain elevated privileges or maintain persistence within
+  the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7  ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wbemprox_com_object_execution_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: legitimate process that are not in the exception list may trigger this event.
+search: '`sysmon` EventCode=7  ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll",
+  "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe"))
+  NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) | stats count min(_time)
+  as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode
+  Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `wbemprox_com_object_execution_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: legitimate process that are not in the exception list may trigger
+  this event.
 references:
 - https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/
 - https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,21 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
-  - Hashes
-  - IMPHASH
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf2/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf2/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wermgr_process_connecting_to_ip_check_web_services.yml b/detections/endpoint/wermgr_process_connecting_to_ip_check_web_services.yml
index 4f7da7eb01..cc320a081e 100644
--- a/detections/endpoint/wermgr_process_connecting_to_ip_check_web_services.yml
+++ b/detections/endpoint/wermgr_process_connecting_to_ip_check_web_services.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the wermgr.exe process attempting to connect to known IP check web services. It leverages Sysmon EventCode 22 to identify DNS queries made by wermgr.exe to specific IP check services. This activity is significant because wermgr.exe is typically used for Windows error reporting, and its connection to these services may indicate malicious code injection, often associated with malware like Trickbot. If confirmed malicious, this behavior could allow attackers to recon the infected machine's IP address, aiding in further exploitation and evasion tactics.
+description: The following analytic detects the wermgr.exe process attempting to connect
+  to known IP check web services. It leverages Sysmon EventCode 22 to identify DNS
+  queries made by wermgr.exe to specific IP check services. This activity is significant
+  because wermgr.exe is typically used for Windows error reporting, and its connection
+  to these services may indicate malicious code injection, often associated with malware
+  like Trickbot. If confirmed malicious, this behavior could allow attackers to recon
+  the infected machine's IP address, aiding in further exploitation and evasion tactics.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") |  stats  min(_time) as firstTime max(_time) as lastTime count by Image process_name ProcessId QueryName QueryStatus QueryResults EventCode Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_connecting_to_ip_check_web_services_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA.
+search: '`sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com",
+  "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com",
+  "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org",
+  "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net")
+  |  stats  min(_time) as firstTime max(_time) as lastTime count by Image process_name
+  ProcessId QueryName QueryStatus QueryResults EventCode Computer | rename Computer
+  as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `wermgr_process_connecting_to_ip_check_web_services_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, dns query name process path , and query ststus from
+  your endpoints like EventCode 22. If you are using Sysmon, you must have at least
+  version 12 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://labs.vipre.com/trickbot-and-its-modules/
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_path
-  - process_name
-  - process_id
-  - QueryName
-  - QueryStatus
-  - QueryResults
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wermgr_process_create_executable_file.yml b/detections/endpoint/wermgr_process_create_executable_file.yml
index 2104203a16..cac420136c 100644
--- a/detections/endpoint/wermgr_process_create_executable_file.yml
+++ b/detections/endpoint/wermgr_process_create_executable_file.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the wermgr.exe process creating an executable file. It leverages Sysmon EventCode 11 to identify instances where wermgr.exe generates a .exe file. This behavior is unusual because wermgr.exe is typically associated with error reporting, not file creation. Such activity is significant as it may indicate TrickBot malware, which injects code into wermgr.exe to execute malicious actions like downloading additional payloads. If confirmed malicious, this could lead to further malware infections, data exfiltration, or system compromise.
+description: The following analytic detects the wermgr.exe process creating an executable
+  file. It leverages Sysmon EventCode 11 to identify instances where wermgr.exe generates
+  a .exe file. This behavior is unusual because wermgr.exe is typically associated
+  with error reporting, not file creation. Such activity is significant as it may
+  indicate TrickBot malware, which injects code into wermgr.exe to execute malicious
+  actions like downloading additional payloads. If confirmed malicious, this could
+  lead to further malware infections, data exfiltration, or system compromise.
 data_source:
 - Sysmon EventID 11
-search: '`sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" | stats  min(_time) as firstTime max(_time) as lastTime count by  Image TargetFilename process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_create_executable_file_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of wermgr.exe may be used.
+search: '`sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe"
+  | stats  min(_time) as firstTime max(_time) as lastTime count by  Image TargetFilename
+  process_name dest EventCode ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `wermgr_process_create_executable_file_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Tune and filter known instances of wermgr.exe may be used.
 known_false_positives: unknown
 references:
 - https://labs.vipre.com/trickbot-and-its-modules/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - TargetFilename
-  - process_name
-  - dest
-  - EventCode
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml b/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml
index 652e24b34a..880d0de8d8 100644
--- a/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml
+++ b/detections/endpoint/wermgr_process_spawned_cmd_or_powershell_process.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the spawning of cmd or PowerShell processes by the wermgr.exe process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process telemetry, including parent-child process relationships and command-line executions. This behavior is significant as it is commonly associated with code injection techniques used by malware like TrickBot to execute shellcode or malicious DLL modules. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to system security.
+description: The following analytic detects the spawning of cmd or PowerShell processes
+  by the wermgr.exe process. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process telemetry, including parent-child process relationships
+  and command-line executions. This behavior is significant as it is commonly associated
+  with code injection techniques used by malware like TrickBot to execute shellcode
+  or malicious DLL modules. If confirmed malicious, this activity could allow attackers
+  to execute arbitrary code, escalate privileges, or maintain persistence within the
+  environment, posing a severe threat to system security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" `process_cmd` OR `process_powershell` by Processes.parent_process_name  Processes.original_file_name Processes.parent_process_id  Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wermgr_process_spawned_cmd_or_powershell_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.parent_process_name = "wermgr.exe" `process_cmd` OR `process_powershell`
+  by Processes.parent_process_name  Processes.original_file_name Processes.parent_process_id  Processes.process_name
+  Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `wermgr_process_spawned_cmd_or_powershell_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://labs.vipre.com/trickbot-and-its-modules/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wget_download_and_bash_execution.yml b/detections/endpoint/wget_download_and_bash_execution.yml
index 32ef082220..14cc6c3a94 100644
--- a/detections/endpoint/wget_download_and_bash_execution.yml
+++ b/detections/endpoint/wget_download_and_bash_execution.yml
@@ -5,7 +5,13 @@ date: '2024-09-30'
 author: Michael Haag, Splunk, DipsyTipsy
 status: production
 type: TTP
-description: The following analytic detects the use of wget on Linux or MacOS to download a file from a remote source and pipe it to bash. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it is commonly associated with malicious actions like coinminers and exploits such as CVE-2021-44228 in Log4j. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to system compromise and unauthorized access to sensitive data.
+description: The following analytic detects the use of wget on Linux or MacOS to download
+  a file from a remote source and pipe it to bash. This detection leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant as it is commonly associated with malicious
+  actions like coinminers and exploits such as CVE-2021-44228 in Log4j. If confirmed
+  malicious, this behavior could allow attackers to execute arbitrary code, potentially
+  leading to system compromise and unauthorized access to sensitive data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
@@ -39,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,7 +62,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 80
-  message: An instance of $process_name$ was identified on endpoint $dest$ attempting to download a remote file and run it with bash.
+  message: An instance of $process_name$ was identified on endpoint $dest$ attempting
+    to download a remote file and run it with bash.
   mitre_attack_id:
   - T1105
   observable:
@@ -71,23 +83,14 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
-  manual_test: Due to current limitations in command line extraction capabilities with Sysmon for Linux, full CommandLine data cannot be collected for complete validation. Setting to manual test to prevent integration test failures.
+  manual_test: Due to current limitations in command line extraction capabilities
+    with Sysmon for Linux, full CommandLine data cannot be collected for complete
+    validation. Setting to manual test to prevent integration test failures.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/linux-sysmon_curlwget.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/linux-sysmon_curlwget.log
     source: Syslog:Linux-Sysmon/Operational
     sourcetype: sysmon:linux
diff --git a/detections/endpoint/windows_abused_web_services.yml b/detections/endpoint/windows_abused_web_services.yml
index b888c94540..49345f1ab7 100644
--- a/detections/endpoint/windows_abused_web_services.yml
+++ b/detections/endpoint/windows_abused_web_services.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 22
-description: The following analytic detects a suspicious process making DNS queries to known, abused web services such as text-paste sites, VoIP, secure tunneling, instant messaging, and digital distribution platforms. This detection leverages Sysmon logs with Event ID 22, focusing on specific query names. This activity is significant as it may indicate an adversary attempting to download malicious files, a common initial access technique. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the target host.
-search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*",""*textbin*"", "*ngrok.io*", "*discord*", "*duckdns.org*", "*pasteio.com*") | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_abused_web_services_filter`'
-how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days.
-known_false_positives: Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
+description: The following analytic detects a suspicious process making DNS queries
+  to known, abused web services such as text-paste sites, VoIP, secure tunneling,
+  instant messaging, and digital distribution platforms. This detection leverages
+  Sysmon logs with Event ID 22, focusing on specific query names. This activity is
+  significant as it may indicate an adversary attempting to download malicious files,
+  a common initial access technique. If confirmed malicious, this could lead to unauthorized
+  code execution, data exfiltration, or further compromise of the target host.
+search: '`sysmon` EventCode=22 QueryName IN ("*pastebin*",""*textbin*"", "*ngrok.io*",
+  "*discord*", "*duckdns.org*", "*pasteio.com*") | stats count min(_time) as firstTime
+  max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults
+  Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_abused_web_services_filter`'
+how_to_implement: This detection relies on sysmon logs with the Event ID 22, DNS Query.
+  We suggest you run this detection at least once a day over the last 14 days.
+known_false_positives: Noise and false positive can be seen if the following instant
+  messaging is allowed to use within corporate network. In this case, a filter is
+  needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - QueryName
-  - QueryStatus
-  - process_name
-  - QueryResults
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102/njrat_ngrok_connection/ngrok.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1102/njrat_ngrok_connection/ngrok.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml b/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml
index 1120d4d82d..2258444e22 100644
--- a/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml
+++ b/detections/endpoint/windows_access_token_manipulation_sedebugprivilege.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a process enabling the "SeDebugPrivilege" privilege token. It leverages Windows Security Event Logs with EventCode 4703, filtering out common legitimate processes. This activity is significant because SeDebugPrivilege allows a process to inspect and modify the memory of other processes, potentially leading to credential dumping or code injection. If confirmed malicious, an attacker could gain extensive control over system processes, enabling them to escalate privileges, persist in the environment, or access sensitive information.
+description: The following analytic detects a process enabling the "SeDebugPrivilege"
+  privilege token. It leverages Windows Security Event Logs with EventCode 4703, filtering
+  out common legitimate processes. This activity is significant because SeDebugPrivilege
+  allows a process to inspect and modify the memory of other processes, potentially
+  leading to credential dumping or code injection. If confirmed malicious, an attacker
+  could gain extensive control over system processes, enabling them to escalate privileges,
+  persist in the environment, or access sensitive information.
 data_source:
 - Windows Event Log Security 4703
-search: '`wineventlog_security` EventCode=4703 EnabledPrivilegeList = "*SeDebugPrivilege*" AND NOT(ProcessName IN ("*\\Program File*", "*\\System32\\lsass.exe*", "*\\SysWOW64\\lsass.exe*", "*\\SysWOW64\\svchost.exe*", "*\\System32\\svchost.exe*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer ProcessName ProcessId SubjectDomainName SubjectUserName SubjectUserSid TargetUserName TargetLogonId TargetDomainName EnabledPrivilegeList action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_sedebugprivilege_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4703 EventCode enabled. The Windows TA is also required.
-known_false_positives: Some native binaries and browser applications may request SeDebugPrivilege. Filter as needed.
+search: '`wineventlog_security` EventCode=4703 EnabledPrivilegeList = "*SeDebugPrivilege*"
+  AND NOT(ProcessName IN ("*\\Program File*", "*\\System32\\lsass.exe*", "*\\SysWOW64\\lsass.exe*",
+  "*\\SysWOW64\\svchost.exe*", "*\\System32\\svchost.exe*")) | stats count min(_time)
+  as firstTime max(_time) as lastTime by Computer ProcessName ProcessId SubjectDomainName
+  SubjectUserName SubjectUserSid TargetUserName TargetLogonId TargetDomainName EnabledPrivilegeList
+  action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_access_token_manipulation_sedebugprivilege_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4703 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: Some native binaries and browser applications may request SeDebugPrivilege.
+  Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4703
 - https://devblogs.microsoft.com/oldnewthing/20080314-00/?p=23113
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A process $ProcessName$ adjust its privileges with SeDebugPrivilege on $Computer$.
+  message: A process $ProcessName$ adjust its privileges with SeDebugPrivilege on
+    $Computer$.
   mitre_attack_id:
   - T1134.002
   - T1134
@@ -50,21 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ProcessName
-  - Security_ID
-  - action
-  - result
-  - Process_ID
-  - Message
-  - member_dn
-  - ComputerName
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/sedebugprivilege_token/security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/sedebugprivilege_token/security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml b/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml
index c4a7ebff00..0e05239583 100644
--- a/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml
+++ b/detections/endpoint/windows_access_token_manipulation_winlogon_duplicate_token_handle.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects a process attempting to access winlogon.exe to duplicate its handle. This is identified using Sysmon EventCode 10, focusing on processes targeting winlogon.exe with specific access rights. This activity is significant because it is a common technique used by adversaries to escalate privileges by leveraging the high privileges and security tokens associated with winlogon.exe. If confirmed malicious, this could allow an attacker to gain elevated privileges, potentially leading to full system compromise and unauthorized access to sensitive information.
+description: The following analytic detects a process attempting to access winlogon.exe
+  to duplicate its handle. This is identified using Sysmon EventCode 10, focusing
+  on processes targeting winlogon.exe with specific access rights. This activity is
+  significant because it is a common technique used by adversaries to escalate privileges
+  by leveraging the high privileges and security tokens associated with winlogon.exe.
+  If confirmed malicious, this could allow an attacker to gain elevated privileges,
+  potentially leading to full system compromise and unauthorized access to sensitive
+  information.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") GrantedAccess = 0x1040 | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId GrantedAccess CallTrace dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_manipulation_winlogon_duplicate_token_handle_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: It is possible legitimate applications will request access to winlogon, filter as needed.
+search: '`sysmon` EventCode=10 TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*")
+  GrantedAccess = 0x1040 | stats count min(_time) as firstTime max(_time) as lastTime
+  by SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId
+  GrantedAccess CallTrace dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_access_token_manipulation_winlogon_duplicate_token_handle_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+known_false_positives: It is possible legitimate applications will request access
+  to winlogon, filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/api/handleapi/nf-handleapi-duplicatehandle
 - https://attack.mitre.org/techniques/T1134/001/
@@ -20,7 +35,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A process $SourceImage$ is duplicating the handle token of winlogon.exe in $dest$
+  message: A process $SourceImage$ is duplicating the handle token of winlogon.exe
+    in $dest$
   mitre_attack_id:
   - T1134.001
   - T1134
@@ -37,22 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - SourceProcessGUID
-  - TargetProcessGUID
-  - SourceProcessId
-  - TargetProcessId
-  - GrantedAccess
-  - CallTrace
-  - dest
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml b/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml
index d1f99b71e5..2babf99670 100644
--- a/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml
+++ b/detections/endpoint/windows_access_token_winlogon_duplicate_handle_in_uncommon_path.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a process attempting to duplicate the handle of winlogon.exe from an uncommon or public source path. This is identified using Sysmon EventCode 10, focusing on processes targeting winlogon.exe with specific access rights and excluding common system paths. This activity is significant because it may indicate an adversary trying to escalate privileges by leveraging the high-privilege tokens associated with winlogon.exe. If confirmed malicious, this could allow the attacker to gain elevated access, potentially leading to full system compromise and persistent control over the affected host.
+description: The following analytic detects a process attempting to duplicate the
+  handle of winlogon.exe from an uncommon or public source path. This is identified
+  using Sysmon EventCode 10, focusing on processes targeting winlogon.exe with specific
+  access rights and excluding common system paths. This activity is significant because
+  it may indicate an adversary trying to escalate privileges by leveraging the high-privilege
+  tokens associated with winlogon.exe. If confirmed malicious, this could allow the
+  attacker to gain elevated access, potentially leading to full system compromise
+  and persistent control over the affected host.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10  TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*") AND GrantedAccess = 0x1040 AND NOT (SourceImage IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId GrantedAccess CallTrace | rename Computer as dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_winlogon_duplicate_handle_in_uncommon_path_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: It is possible legitimate applications will request access to winlogon, filter as needed.
+search: '`sysmon` EventCode=10  TargetImage IN("*\\system32\\winlogon.exe*", "*\\SysWOW64\\winlogon.exe*")
+  AND GrantedAccess = 0x1040 AND NOT (SourceImage IN("C:\\Windows\\*", "C:\\Program
+  File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as
+  lastTime by Computer SourceImage TargetImage SourceProcessGUID TargetProcessGUID
+  SourceProcessId TargetProcessId GrantedAccess CallTrace | rename Computer as dest|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_access_token_winlogon_duplicate_handle_in_uncommon_path_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+known_false_positives: It is possible legitimate applications will request access
+  to winlogon, filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/api/handleapi/nf-handleapi-duplicatehandle
 - https://attack.mitre.org/techniques/T1134/001/
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A process $SourceImage$ is duplicating the handle token of winlogon.exe on $dest$
+  message: A process $SourceImage$ is duplicating the handle token of winlogon.exe
+    on $dest$
   mitre_attack_id:
   - T1134.001
   - T1134
@@ -46,22 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - SourceProcessGUID
-  - TargetProcessGUID
-  - SourceProcessId
-  - TargetProcessId
-  - GrantedAccess
-  - CallTrace
-  - dest
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/brute_duplicate_token/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml b/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml
index d1b73944f7..1abae42033 100644
--- a/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml
+++ b/detections/endpoint/windows_account_discovery_for_none_disable_user_account.yml
@@ -7,10 +7,24 @@ status: production
 type: Hunting
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetUser with the UACFilter parameter set to NOT_ACCOUNTDISABLE, indicating an attempt to enumerate Active Directory user accounts that are not disabled. This detection leverages PowerShell Script Block Logging (EventCode 4104) to identify the specific script block text. Monitoring this activity is significant as it may indicate reconnaissance efforts by an attacker to identify active user accounts for further exploitation. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or lateral movement within the network.
-search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetUser*" ScriptBlockText = "*NOT_ACCOUNTDISABLE*" ScriptBlockText = "*-UACFilter*" | rename Computer as dest, UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_account_discovery_for_none_disable_user_account_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
-known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed.
+description: The following analytic detects the execution of the PowerView PowerShell
+  cmdlet Get-NetUser with the UACFilter parameter set to NOT_ACCOUNTDISABLE, indicating
+  an attempt to enumerate Active Directory user accounts that are not disabled. This
+  detection leverages PowerShell Script Block Logging (EventCode 4104) to identify
+  the specific script block text. Monitoring this activity is significant as it may
+  indicate reconnaissance efforts by an attacker to identify active user accounts
+  for further exploitation. If confirmed malicious, this activity could lead to unauthorized
+  access, privilege escalation, or lateral movement within the network.
+search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetUser*" ScriptBlockText
+  = "*NOT_ACCOUNTDISABLE*" ScriptBlockText = "*-UACFilter*" | rename Computer as dest,
+  UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode
+  ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_account_discovery_for_none_disable_user_account_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
+known_false_positives: Administrators may leverage PowerView for legitimate purposes,
+  filter as needed.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
 - https://powersploit.readthedocs.io/en/stable/Recon/README/
@@ -22,7 +36,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: Windows Account Discovery for None Disable User Account using PowerView's Get-NetUser on $dest$.
+  message: Windows Account Discovery for None Disable User Account using PowerView's
+    Get-NetUser on $dest$.
   mitre_attack_id:
   - T1087
   - T1087.001
@@ -35,16 +50,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_account_discovery_for_sam_account_name.yml b/detections/endpoint/windows_account_discovery_for_sam_account_name.yml
index 16f898be86..df60f3526c 100644
--- a/detections/endpoint/windows_account_discovery_for_sam_account_name.yml
+++ b/detections/endpoint/windows_account_discovery_for_sam_account_name.yml
@@ -7,10 +7,24 @@ status: production
 type: Anomaly
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetUser, specifically querying for "samaccountname" and "pwdlastset" attributes. It leverages Event ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior is significant as it may indicate an attempt to gather user account information from Active Directory, which is a common reconnaissance step in lateral movement or privilege escalation attacks. If confirmed malicious, this activity could allow an attacker to map out user accounts, potentially leading to further exploitation and unauthorized access within the network.
-search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetUser*" ScriptBlockText IN ("*samaccountname*", "*pwdlastset*") | rename Computer as dest, UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_account_discovery_for_sam_account_name_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
-known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed.
+description: The following analytic detects the execution of the PowerView PowerShell
+  cmdlet Get-NetUser, specifically querying for "samaccountname" and "pwdlastset"
+  attributes. It leverages Event ID 4104 from PowerShell Script Block Logging to identify
+  this activity. This behavior is significant as it may indicate an attempt to gather
+  user account information from Active Directory, which is a common reconnaissance
+  step in lateral movement or privilege escalation attacks. If confirmed malicious,
+  this activity could allow an attacker to map out user accounts, potentially leading
+  to further exploitation and unauthorized access within the network.
+search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetUser*" ScriptBlockText
+  IN ("*samaccountname*", "*pwdlastset*") | rename Computer as dest, UserID as user
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_account_discovery_for_sam_account_name_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
+known_false_positives: Administrators may leverage PowerView for legitimate purposes,
+  filter as needed.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,16 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml b/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml
index 522ebf2efe..506dadfeef 100644
--- a/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml
+++ b/detections/endpoint/windows_account_discovery_with_netuser_preauthnotrequire.yml
@@ -7,10 +7,23 @@ status: production
 type: Hunting
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetUser with the -PreauthNotRequire parameter, leveraging Event ID 4104. This method identifies attempts to query Active Directory user accounts that do not require Kerberos preauthentication. Monitoring this activity is crucial as it can indicate reconnaissance efforts by an attacker to identify potentially vulnerable accounts. If confirmed malicious, this behavior could lead to further exploitation, such as unauthorized access or privilege escalation within the network.
-search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetUser*" ScriptBlockText = "*-PreauthNotRequire*" | rename Computer as dest, UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_account_discovery_with_netuser_preauthnotrequire_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
-known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed.
+description: The following analytic detects the execution of the PowerView PowerShell
+  cmdlet Get-NetUser with the -PreauthNotRequire parameter, leveraging Event ID 4104.
+  This method identifies attempts to query Active Directory user accounts that do
+  not require Kerberos preauthentication. Monitoring this activity is crucial as it
+  can indicate reconnaissance efforts by an attacker to identify potentially vulnerable
+  accounts. If confirmed malicious, this behavior could lead to further exploitation,
+  such as unauthorized access or privilege escalation within the network.
+search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetUser*" ScriptBlockText
+  = "*-PreauthNotRequire*" | rename Computer as dest, UserID as user | stats count
+  min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest
+  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_account_discovery_with_netuser_preauthnotrequire_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
+known_false_positives: Administrators may leverage PowerView for legitimate purposes,
+  filter as needed.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
 tags:
@@ -19,7 +32,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: A user dicovery using powerview commandlet Get-NetUser with PreauthNotRequire parameter on $dest$.
+  message: A user dicovery using powerview commandlet Get-NetUser with PreauthNotRequire
+    parameter on $dest$.
   mitre_attack_id:
   - T1087
   observable:
@@ -31,16 +45,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_abnormal_object_access_activity.yml b/detections/endpoint/windows_ad_abnormal_object_access_activity.yml
index d9e8c922ac..36544b50c5 100644
--- a/detections/endpoint/windows_ad_abnormal_object_access_activity.yml
+++ b/detections/endpoint/windows_ad_abnormal_object_access_activity.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic identifies a statistically significant increase in access to Active Directory objects, which may indicate attacker enumeration. It leverages Windows Security Event Code 4662 to monitor and analyze access patterns, comparing them against historical averages to detect anomalies. This activity is significant for a SOC because abnormal access to AD objects can be an early indicator of reconnaissance efforts by an attacker. If confirmed malicious, this behavior could lead to unauthorized access, privilege escalation, or further compromise of the Active Directory environment.
+description: The following analytic identifies a statistically significant increase
+  in access to Active Directory objects, which may indicate attacker enumeration.
+  It leverages Windows Security Event Code 4662 to monitor and analyze access patterns,
+  comparing them against historical averages to detect anomalies. This activity is
+  significant for a SOC because abnormal access to AD objects can be an early indicator
+  of reconnaissance efforts by an attacker. If confirmed malicious, this behavior
+  could lead to unauthorized access, privilege escalation, or further compromise of
+  the Active Directory environment.
 data_source:
 - Windows Event Log Security 4662
-search: '`wineventlog_security` EventCode=4662 | stats min(_time) AS firstTime, max(_time) AS lastTime, dc(ObjectName) AS ObjectName_count, values(ObjectType) AS ObjectType, latest(Computer) AS dest count BY SubjectUserName | eventstats avg(ObjectName_count) AS average stdev(ObjectName_count) AS standarddev | eval limit = round((average+(standarddev*3)),0), user = SubjectUserName | where ObjectName_count > limit | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_ad_abnormal_object_access_activity_filter`'
-how_to_implement: Enable Audit Directory Service Access via GPO and collect event code 4662. The required SACLs need to be created for the relevant objects. Be aware Splunk filters this event by default on the Windows TA. Recommend pre-filtering any known service accounts that frequently query AD to make detection more accurate. Setting wide search window of 48~72hr may smooth out misfires.
-known_false_positives: Service accounts or applications that routinely query Active Directory for information.
+search: '`wineventlog_security` EventCode=4662 | stats min(_time) AS firstTime, max(_time)
+  AS lastTime, dc(ObjectName) AS ObjectName_count, values(ObjectType) AS ObjectType,
+  latest(Computer) AS dest count BY SubjectUserName | eventstats avg(ObjectName_count)
+  AS average stdev(ObjectName_count) AS standarddev | eval limit = round((average+(standarddev*3)),0),
+  user = SubjectUserName | where ObjectName_count > limit | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `windows_ad_abnormal_object_access_activity_filter`'
+how_to_implement: Enable Audit Directory Service Access via GPO and collect event
+  code 4662. The required SACLs need to be created for the relevant objects. Be aware
+  Splunk filters this event by default on the Windows TA. Recommend pre-filtering
+  any known service accounts that frequently query AD to make detection more accurate.
+  Setting wide search window of 48~72hr may smooth out misfires.
+known_false_positives: Service accounts or applications that routinely query Active
+  Directory for information.
 references:
 - https://medium.com/securonix-tech-blog/detecting-ldap-enumeration-and-bloodhound-s-sharphound-collector-using-active-directory-decoys-dfc840f2f644
 - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4662
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: The account $user$ accessed an abnormal amount ($ObjectName_count$) of [$ObjectType$] AD object(s) between $firstTime$ and $lastTime$.
+  message: The account $user$ accessed an abnormal amount ($ObjectName_count$) of
+    [$ObjectType$] AD object(s) between $firstTime$ and $lastTime$.
   mitre_attack_id:
   - T1087
   - T1087.002
@@ -44,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectName
-  - EventCode
-  - SubjectUserName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml b/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml
index b922bf1bc8..eaeb1350f1 100644
--- a/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml
+++ b/detections/endpoint/windows_ad_adminsdholder_acl_modified.yml
@@ -7,10 +7,49 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 5136
-description: The following analytic detects modifications to the Access Control List (ACL) of the AdminSDHolder object in a Windows domain, specifically the addition of new rules. It leverages EventCode 5136 from the Security Event Log, focusing on changes to the nTSecurityDescriptor attribute. This activity is significant because the AdminSDHolder object secures privileged group members, and unauthorized changes can allow attackers to establish persistence and escalate privileges. If confirmed malicious, this could enable an attacker to control domain-level permissions, compromising the entire Active Directory environment.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=container ObjectDN="CN=AdminSDHolder,CN=System*"  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$" | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUTNEW builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user user | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights="","All rights",''aceControlAccessRights'') | search NOT aceType IN (*denied*,D,OD,XD) AND aceAccessRights IN ("Full control","All extended rights","All validated writes","Create all child objects","Delete all child objects","Delete subtree","Delete","Modify permissions","Modify owner","Write all properties",CC,CR,DC,DT,SD,SW,WD,WO,WP) | `windows_ad_adminsdholder_acl_modified_filter`'
-how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for the AdminSDHolder object in order to log modifications.
-known_false_positives: Adding new users or groups to the AdminSDHolder ACL is not usual. Filter as needed
+description: The following analytic detects modifications to the Access Control List
+  (ACL) of the AdminSDHolder object in a Windows domain, specifically the addition
+  of new rules. It leverages EventCode 5136 from the Security Event Log, focusing
+  on changes to the nTSecurityDescriptor attribute. This activity is significant because
+  the AdminSDHolder object secures privileged group members, and unauthorized changes
+  can allow attackers to establish persistence and escalate privileges. If confirmed
+  malicious, this could enable an attacker to control domain-level permissions, compromising
+  the entire Active Directory environment.
+search: "`wineventlog_security` EventCode=5136 ObjectClass=container ObjectDN=\"CN=AdminSDHolder,CN=System*\"\
+  \  | stats min(_time) as _time values(eval(if(OperationType==\"%%14675\",AttributeValue,null)))
+  as old_value values(eval(if(OperationType==\"%%14674\",AttributeValue,null))) as
+  new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID
+  src_user SubjectLogonId  | rex field=old_value max_match=10000 \"\\((?P<old_values>.*?)\\\
+  )\"  | rex field=new_value max_match=10000 \"\\((?P<new_ace>.*?)\\)\"  | mvexpand
+  new_ace | where NOT new_ace IN (old_values)  | rex field=new_ace \"(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);(?P<aceInheritedTypeGuid>.*?);(?P<aceSid>.*?)$\"\
+  \ | rex max_match=100 field=aceAccessRights \"(?P<AccessRights>[A-Z]{2})\"  | rex
+  max_match=100 field=aceFlags \"(?P<aceFlags>[A-Z]{2})\"  | lookup msad_guid_lookup
+  guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup
+  access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup
+  ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string
+  as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups
+  | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName
+  as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group``` | lookup
+  builtin_groups_lookup builtin_group_string  as aceSid OUTPUTNEW builtin_group_name
+  as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,\"\
+  This object only\"), aceAccessRights=if(aceAccessRights=\"CCDCLCSWRPWPDTLOCRSDRCWDWO\"\
+  ,\"Full control\",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType)
+  as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights)
+  as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace)
+  as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user
+  user | eval aceControlAccessRights=if(mvcount(aceControlAccessRights)=1 AND aceControlAccessRights=\"\
+  \",\"All rights\",'aceControlAccessRights') | search NOT aceType IN (*denied*,D,OD,XD)
+  AND aceAccessRights IN (\"Full control\",\"All extended rights\",\"All validated
+  writes\",\"Create all child objects\",\"Delete all child objects\",\"Delete subtree\"\
+  ,\"Delete\",\"Modify permissions\",\"Modify owner\",\"Write all properties\",CC,CR,DC,DT,SD,SW,WD,WO,WP)
+  | `windows_ad_adminsdholder_acl_modified_filter`"
+how_to_implement: To successfully implement this search, you ned to be ingesting eventcode
+  `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes`
+  within `DS Access` needs to be enabled. Additionally, a SACL needs to be created
+  for the AdminSDHolder object in order to log modifications.
+known_false_positives: Adding new users or groups to the AdminSDHolder ACL is not
+  usual. Filter as needed
 references:
 - https://learn.microsoft.com/en-us/windows-server/identity/ad-ds/plan/security-best-practices/appendix-c--protected-accounts-and-groups-in-active-directory
 - https://social.technet.microsoft.com/wiki/contents/articles/22331.adminsdholder-protected-groups-and-security-descriptor-propagator.aspx
@@ -26,7 +65,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,19 +95,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - AttributeLDAPDisplayName
-  - OperationType
-  - ObjectDN
-  - Computer
-  - SubjectUserName
-  - AttributeValue
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/adminsdholder_modified/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546/adminsdholder_modified/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml b/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml
index 08b9198623..f41177f585 100644
--- a/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml
+++ b/detections/endpoint/windows_ad_cross_domain_sid_history_addition.yml
@@ -8,10 +8,25 @@ status: production
 data_source:
 - Windows Event Log Security 4742
 - Windows Event Log Security 4738
-description: The following analytic detects changes to the sIDHistory attribute of user or computer objects across different domains. It leverages Windows Security Event Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. This activity is significant because the sIDHistory attribute allows users to inherit permissions from other AD accounts, which can be exploited by adversaries for inter-domain privilege escalation and persistence. If confirmed malicious, this could enable attackers to gain unauthorized access to resources, maintain persistence, and escalate privileges across domain boundaries.
-search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistoryMatch>.*)(\-|\\\)" | rex field=TargetSid "^(?P<TargetSidmatch>.*)(\-|\\\)" | where SidHistoryMatch!=TargetSidmatch AND SidHistoryMatch!=TargetDomainName | rename TargetSid as userSid | table _time action status host user userSid SidHistory Logon_ID src_user | `windows_ad_cross_domain_sid_history_addition_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit User Account Management` and  `Audit Computer Account Management` within `Account Management` all need to be enabled.
-known_false_positives: Domain mergers and migrations may generate large volumes of false positives for this analytic.
+description: The following analytic detects changes to the sIDHistory attribute of
+  user or computer objects across different domains. It leverages Windows Security
+  Event Codes 4738 and 4742 to identify when the sIDHistory attribute is modified.
+  This activity is significant because the sIDHistory attribute allows users to inherit
+  permissions from other AD accounts, which can be exploited by adversaries for inter-domain
+  privilege escalation and persistence. If confirmed malicious, this could enable
+  attackers to gain unauthorized access to resources, maintain persistence, and escalate
+  privileges across domain boundaries.
+search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory
+  IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistoryMatch>.*)(\-|\\\)"
+  | rex field=TargetSid "^(?P<TargetSidmatch>.*)(\-|\\\)" | where SidHistoryMatch!=TargetSidmatch
+  AND SidHistoryMatch!=TargetDomainName | rename TargetSid as userSid | table _time
+  action status host user userSid SidHistory Logon_ID src_user | `windows_ad_cross_domain_sid_history_addition_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit
+  User Account Management` and  `Audit Computer Account Management` within `Account
+  Management` all need to be enabled.
+known_false_positives: Domain mergers and migrations may generate large volumes of
+  false positives for this analytic.
 references:
 - https://adsecurity.org/?p=1772
 - https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - SidHistory
-  - TargetSid
-  - TargetDomainName
-  - user
-  - src_user
-  - Logon_ID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml b/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml
index c9cf51db8c..58cfa72bed 100644
--- a/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml
+++ b/detections/endpoint/windows_ad_domain_controller_audit_policy_disabled.yml
@@ -7,9 +7,26 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 4719
-description: The following analytic detects the disabling of audit policies on a domain controller. It leverages EventCode 4719 from Windows Security Event Logs to identify changes where success or failure auditing is removed. This activity is significant as it suggests an attacker may have gained access to the domain controller and is attempting to evade detection by tampering with audit policies. If confirmed malicious, this could lead to severe consequences, including data theft, privilege escalation, and full network compromise. Immediate investigation is required to determine the source and intent of the change.
-search: '`wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448, %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure removed")) dest_category="domain_controller"| replace "%%8448" with "Success removed", "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure removed" in AuditPolicyChanges | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes), SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) | stats min(_time) as _time values(host) as dest by AuditPolicyChanges SubcategoryGuid | lookup advanced_audit_policy_guids GUID as SubcategoryGuid OUTPUT Category SubCategory | `windows_ad_domain_controller_audit_policy_disabled_filter`'
-how_to_implement: Ensure you are ingesting EventCode `4719` from your domain controllers, the category domain_controller exists in assets and identities, and that assets and identities is enabled. If A&I is not configured, you will need to manually filter the results within the base search.
+description: The following analytic detects the disabling of audit policies on a domain
+  controller. It leverages EventCode 4719 from Windows Security Event Logs to identify
+  changes where success or failure auditing is removed. This activity is significant
+  as it suggests an attacker may have gained access to the domain controller and is
+  attempting to evade detection by tampering with audit policies. If confirmed malicious,
+  this could lead to severe consequences, including data theft, privilege escalation,
+  and full network compromise. Immediate investigation is required to determine the
+  source and intent of the change.
+search: '`wineventlog_security` EventCode=4719 (AuditPolicyChanges IN ("%%8448","%%8450","%%8448,
+  %%8450") OR Changes IN ("Failure removed","Success removed","Success removed, Failure
+  removed")) dest_category="domain_controller"| replace "%%8448" with "Success removed",
+  "%%8450" with "Failure removed", "%%8448, %%8450" with "Success removed, Failure
+  removed" in AuditPolicyChanges | eval AuditPolicyChanges=coalesce(AuditPolicyChanges,Changes),
+  SubcategoryGuid=coalesce(SubcategoryGuid,Subcategory_GUID) | stats min(_time) as
+  _time values(host) as dest by AuditPolicyChanges SubcategoryGuid | lookup advanced_audit_policy_guids
+  GUID as SubcategoryGuid OUTPUT Category SubCategory | `windows_ad_domain_controller_audit_policy_disabled_filter`'
+how_to_implement: Ensure you are ingesting EventCode `4719` from your domain controllers,
+  the category domain_controller exists in assets and identities, and that assets
+  and identities is enabled. If A&I is not configured, you will need to manually filter
+  the results within the base search.
 known_false_positives: Unknown
 references:
 - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4719
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,16 +62,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - AuditPolicyChanges
-  - SubcategoryGuid
   security_domain: endpoint
-  manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested
+  manual_test: This search uses a lookup provided by Enterprise Security and needs
+    to be manually tested
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_gpo/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_gpo/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_domain_controller_promotion.yml b/detections/endpoint/windows_ad_domain_controller_promotion.yml
index 2c13d4d040..c04a53f36e 100644
--- a/detections/endpoint/windows_ad_domain_controller_promotion.yml
+++ b/detections/endpoint/windows_ad_domain_controller_promotion.yml
@@ -7,9 +7,28 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 4742
-description: The following analytic identifies a genuine Domain Controller (DC) promotion event by detecting when a computer assigns itself the necessary Service Principal Names (SPNs) to function as a domain controller. It leverages Windows Security Event Code 4742 to monitor existing domain controllers for these changes. This activity is significant as it can help identify rogue DCs added to the network, which could indicate a DCShadow attack. If confirmed malicious, this could allow an attacker to manipulate Active Directory, leading to potential privilege escalation and persistent access within the environment.
-search: '`wineventlog_security` EventCode=4742 ServicePrincipalNames IN ("*E3514235-4B06-11D1-AB04-00C04FC2DCD2/*","*GC/*")| stats min(_time) as _time latest(ServicePrincipalNames) as ServicePrincipalNames,values(signature) as signature, values(src_user) as src_user, values(user) as user by Logon_ID, dvc| where src_user=user| rename Logon_ID as TargetLogonId, user as dest | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$" | fields - dest, dvc, signature]| stats min(_time) as _time, values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip values(ServicePrincipalNames) as ServicePrincipalNames values(signature) as signature values(dest) as dest values(dvc) as dvc by TargetLogonId | eval dest=trim(dest,"$") | `windows_ad_domain_controller_promotion_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4742`. The Advanced Security Audit policy setting `Audit Computer Account Management` within `Account Management` needs to be enabled.
+description: The following analytic identifies a genuine Domain Controller (DC) promotion
+  event by detecting when a computer assigns itself the necessary Service Principal
+  Names (SPNs) to function as a domain controller. It leverages Windows Security Event
+  Code 4742 to monitor existing domain controllers for these changes. This activity
+  is significant as it can help identify rogue DCs added to the network, which could
+  indicate a DCShadow attack. If confirmed malicious, this could allow an attacker
+  to manipulate Active Directory, leading to potential privilege escalation and persistent
+  access within the environment.
+search: '`wineventlog_security` EventCode=4742 ServicePrincipalNames IN ("*E3514235-4B06-11D1-AB04-00C04FC2DCD2/*","*GC/*")|
+  stats min(_time) as _time latest(ServicePrincipalNames) as ServicePrincipalNames,values(signature)
+  as signature, values(src_user) as src_user, values(user) as user by Logon_ID, dvc|
+  where src_user=user| rename Logon_ID as TargetLogonId, user as dest | appendpipe
+  [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"
+  | fields - dest, dvc, signature]| stats min(_time) as _time, values(TargetUserSid)
+  as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user,
+  values(status) as status, values(src_category) as src_category, values(src_ip) as
+  src_ip values(ServicePrincipalNames) as ServicePrincipalNames values(signature)
+  as signature values(dest) as dest values(dvc) as dvc by TargetLogonId | eval dest=trim(dest,"$")
+  | `windows_ad_domain_controller_promotion_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcode `4742`. The Advanced Security Audit policy setting `Audit Computer Account
+  Management` within `Account Management` needs to be enabled.
 known_false_positives: None.
 references:
 - https://attack.mitre.org/techniques/T1207/
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ServicePrincipalNames
-  - src_user
-  - user
-  - Logon_ID
-  - dvc
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/dc_promo/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/dc_promo/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_domain_replication_acl_addition.yml b/detections/endpoint/windows_ad_domain_replication_acl_addition.yml
index 3792ff5de9..d669b67dd2 100644
--- a/detections/endpoint/windows_ad_domain_replication_acl_addition.yml
+++ b/detections/endpoint/windows_ad_domain_replication_acl_addition.yml
@@ -7,10 +7,54 @@ type: TTP
 status: production
 data_source:
 - Windows Security 5136
-description: The following analytic detects the addition of permissions required for a DCSync attack, specifically DS-Replication-Get-Changes, DS-Replication-Get-Changes-All, and DS-Replication-Get-Changes-In-Filtered-Set. It leverages EventCode 5136 from the Windows Security Event Log to identify when these permissions are granted. This activity is significant because it indicates potential preparation for a DCSync attack, which can be used to replicate AD objects and exfiltrate sensitive data. If confirmed malicious, an attacker could gain extensive access to Active Directory, leading to severe data breaches and privilege escalation.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time) as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType) as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  | rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace | where NOT new_ace IN (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$" | search aceObjectGuid IN ("1131f6aa-9c07-11d1-f79f-00c04fc2dcd2","1131f6ad-9c07-11d1-f79f-00c04fc2dcd2","89e95b76-444d-4c62-991a-0facbeda640c") | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100 field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  | lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid), user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType) as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights) as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace) as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user user | search (aceControlAccessRights="DS-Replication-Get-Changes" AND aceControlAccessRights="DS-Replication-Get-Changes-All") OR (aceControlAccessRights="1131f6aa-9c07-11d1-f79f-00c04fc2dcd2" AND aceControlAccessRights="1131f6ad-9c07-11d1-f79f-00c04fc2dcd2") | `windows_ad_domain_replication_acl_addition_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting the eventcode 5136. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` to  `Write All Properties` applied to the domain root and all descendant objects. Once the necessary logging has been enabled, enumerate the domain policy to verify if existing accounts with access need to be whitelisted, or revoked. Assets and Identities is also leveraged to automatically translate the objectSid into username. Ensure your identities lookup is configured with the sAMAccountName and objectSid of all AD user and computer objects.
-known_false_positives: When there is a change to nTSecurityDescriptor, Windows logs the entire ACL with the newly added components. If existing accounts are present with this permission, they will raise an alert each time the nTSecurityDescriptor is updated unless whitelisted.
+description: The following analytic detects the addition of permissions required for
+  a DCSync attack, specifically DS-Replication-Get-Changes, DS-Replication-Get-Changes-All,
+  and DS-Replication-Get-Changes-In-Filtered-Set. It leverages EventCode 5136 from
+  the Windows Security Event Log to identify when these permissions are granted. This
+  activity is significant because it indicates potential preparation for a DCSync
+  attack, which can be used to replicate AD objects and exfiltrate sensitive data.
+  If confirmed malicious, an attacker could gain extensive access to Active Directory,
+  leading to severe data breaches and privilege escalation.
+search: '`wineventlog_security` EventCode=5136 ObjectClass=domainDNS  | stats min(_time)
+  as _time values(eval(if(OperationType=="%%14675",AttributeValue,null))) as old_value
+  values(eval(if(OperationType=="%%14674",AttributeValue,null))) as new_value values(OperationType)
+  as OperationType by ObjectClass ObjectDN OpCorrelationID src_user SubjectLogonId  |
+  rex field=old_value max_match=10000 "\((?P<old_values>.*?)\)"  | rex field=new_value
+  max_match=10000 "\((?P<new_ace>.*?)\)"  | mvexpand new_ace | where NOT new_ace IN
+  (old_values)  | rex field=new_ace "(?P<aceType>.*?);(?P<aceFlags>.*?);(?P<aceAccessRights>.*?);(?P<aceObjectGuid>.*?);;(?P<aceSid>.*?)$"
+  | search aceObjectGuid IN ("1131f6aa-9c07-11d1-f79f-00c04fc2dcd2","1131f6ad-9c07-11d1-f79f-00c04fc2dcd2","89e95b76-444d-4c62-991a-0facbeda640c")
+  | rex max_match=100 field=aceAccessRights "(?P<AccessRights>[A-Z]{2})"  | rex max_match=100
+  field=aceFlags "(?P<aceFlags>[A-Z]{2})"  | lookup msad_guid_lookup guid as aceObjectGuid
+  OUTPUT displayName as ControlAccessRights | lookup ace_access_rights_lookup access_rights_string
+  as AccessRights OUTPUT access_rights_value  | lookup ace_type_lookup ace_type_string
+  as aceType OUTPUT ace_type_value  | lookup ace_flag_lookup flag_string as aceFlags
+  OUTPUT flag_value as ace_flag_value ``` Optional SID resolution lookups | lookup
+  identity_lookup_expanded objectSid as aceSid OUTPUT downLevelDomainName as user  |
+  lookup admon_groups_def objectSid as aceSid OUTPUT cn as group ``` | lookup builtin_groups_lookup
+  builtin_group_string  as aceSid OUTPUT builtin_group_name as builtin_group | eval
+  aceType=coalesce(ace_type_value,aceType), aceFlags=coalesce(ace_flag_value,"This
+  object only"), aceAccessRights=if(aceAccessRights="CCDCLCSWRPWPDTLOCRSDRCWDWO","Full
+  control",coalesce(access_rights_value,AccessRights)), aceControlAccessRights=coalesce(ControlAccessRights,aceObjectGuid),
+  user=coalesce(user, group, builtin_group, aceSid) | stats min(_time) as _time values(aceType)
+  as aceType values(aceFlags) as aceFlags(inheritance) values(aceControlAccessRights)
+  as aceControlAccessRights values(aceAccessRights) as aceAccessRights values(new_ace)
+  as new_ace values(SubjectLogonId) as SubjectLogonId by ObjectClass ObjectDN src_user
+  user | search (aceControlAccessRights="DS-Replication-Get-Changes" AND aceControlAccessRights="DS-Replication-Get-Changes-All")
+  OR (aceControlAccessRights="1131f6aa-9c07-11d1-f79f-00c04fc2dcd2" AND aceControlAccessRights="1131f6ad-9c07-11d1-f79f-00c04fc2dcd2")
+  | `windows_ad_domain_replication_acl_addition_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  the eventcode 5136. The Advanced Security Audit policy setting `Audit Directory
+  Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody`
+  to  `Write All Properties` applied to the domain root and all descendant objects.
+  Once the necessary logging has been enabled, enumerate the domain policy to verify
+  if existing accounts with access need to be whitelisted, or revoked. Assets and
+  Identities is also leveraged to automatically translate the objectSid into username.
+  Ensure your identities lookup is configured with the sAMAccountName and objectSid
+  of all AD user and computer objects.
+known_false_positives: When there is a change to nTSecurityDescriptor, Windows logs
+  the entire ACL with the newly added components. If existing accounts are present
+  with this permission, they will raise an alert each time the nTSecurityDescriptor
+  is updated unless whitelisted.
 references:
 - https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-adts/1522b774-6464-41a3-87a5-1e5633c3fbbb
 - https://github.com/SigmaHQ/sigma/blob/29a5c62784faf986dc03952ae3e90e3df3294284/rules/windows/builtin/security/win_security_account_backdoor_dcsync_rights.yml
@@ -21,7 +65,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +95,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OperationType
-  - src_user
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - ObjectClass
   security_domain: endpoint
-  manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested.
+  manual_test: This search uses a lookup provided by Enterprise Security and needs
+    to be manually tested.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/aclmodification/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_dsrm_account_changes.yml b/detections/endpoint/windows_ad_dsrm_account_changes.yml
index 5604dd55c2..3bb05bcf79 100644
--- a/detections/endpoint/windows_ad_dsrm_account_changes.yml
+++ b/detections/endpoint/windows_ad_dsrm_account_changes.yml
@@ -8,9 +8,33 @@ status: production
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-description: The following analytic identifies changes to the Directory Services Restore Mode (DSRM) account behavior via registry modifications. It detects alterations in the registry path "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" with specific values indicating potential misuse. This activity is significant because the DSRM account, if misconfigured, can be exploited to persist within a domain, similar to a local administrator account. If confirmed malicious, an attacker could gain persistent administrative access to a Domain Controller, leading to potential domain-wide compromise and unauthorized access to sensitive information.
-search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" Registry.registry_value_data IN ("*1","*2") by Registry.action Registry.registry_path Registry.registry_value_data Registry.registry_value_type Registry.process_guid Registry.dest Registry.user | `drop_dm_object_name(Registry)` | join type=outer process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`] | table _time action dest user parent_process_name parent_process process_name process process_guid registry_path registry_value_data registry_value_type | `windows_ad_dsrm_account_changes_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies changes to the Directory Services Restore
+  Mode (DSRM) account behavior via registry modifications. It detects alterations
+  in the registry path "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior"
+  with specific values indicating potential misuse. This activity is significant because
+  the DSRM account, if misconfigured, can be exploited to persist within a domain,
+  similar to a local administrator account. If confirmed malicious, an attacker could
+  gain persistent administrative access to a Domain Controller, leading to potential
+  domain-wide compromise and unauthorized access to sensitive information.
+search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Endpoint.Registry
+  where Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior"
+  Registry.registry_value_data IN ("*1","*2") by Registry.action Registry.registry_path
+  Registry.registry_value_data Registry.registry_value_type Registry.process_guid
+  Registry.dest Registry.user | `drop_dm_object_name(Registry)` | join type=outer
+  process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  by Processes.user Processes.process_name Processes.process Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`]
+  | table _time action dest user parent_process_name parent_process process_name process
+  process_guid registry_path registry_value_data registry_value_type | `windows_ad_dsrm_account_changes_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Disaster recovery events.
 references:
 - https://adsecurity.org/?p=1714
@@ -20,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_value_data
-  - Registry.registry_path
-  - Registry.dest
-  - Registry.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_dsrm_password_reset.yml b/detections/endpoint/windows_ad_dsrm_password_reset.yml
index e164a90f81..8da6a70628 100644
--- a/detections/endpoint/windows_ad_dsrm_password_reset.yml
+++ b/detections/endpoint/windows_ad_dsrm_password_reset.yml
@@ -7,10 +7,23 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 4794
-description: The following analytic detects attempts to reset the Directory Services Restore Mode (DSRM) administrator password on a Domain Controller. It leverages event code 4794 from the Windows Security Event Log, specifically looking for events where the DSRM password reset is attempted. This activity is significant because the DSRM account can be used similarly to a local administrator account, providing potential persistence for an attacker. If confirmed malicious, this could allow an attacker to maintain administrative access to the Domain Controller, posing a severe risk to the domain's security.
-search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Change where All_Changes.result_id="4794" AND All_Changes.result="set the Directory Services Restore Mode administrator password" by All_Changes.action, All_Changes.dest, All_Changes.src, All_Changes.user | `drop_dm_object_name(All_Changes)` | `windows_ad_dsrm_password_reset_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4794` and  have the Advanced Security Audit policy `Audit User Account Management` within `Account Management` enabled.
-known_false_positives: Resetting the DSRM password for legitamate reasons, i.e. forgot the password. Disaster recovery. Deploying AD backdoor deliberately.
+description: The following analytic detects attempts to reset the Directory Services
+  Restore Mode (DSRM) administrator password on a Domain Controller. It leverages
+  event code 4794 from the Windows Security Event Log, specifically looking for events
+  where the DSRM password reset is attempted. This activity is significant because
+  the DSRM account can be used similarly to a local administrator account, providing
+  potential persistence for an attacker. If confirmed malicious, this could allow
+  an attacker to maintain administrative access to the Domain Controller, posing a
+  severe risk to the domain's security.
+search: '| tstats `security_content_summariesonly` min(_time) as _time from datamodel=Change
+  where All_Changes.result_id="4794" AND All_Changes.result="set the Directory Services
+  Restore Mode administrator password" by All_Changes.action, All_Changes.dest, All_Changes.src,
+  All_Changes.user | `drop_dm_object_name(All_Changes)` | `windows_ad_dsrm_password_reset_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcode `4794` and  have the Advanced Security Audit policy `Audit User Account
+  Management` within `Account Management` enabled.
+known_false_positives: Resetting the DSRM password for legitamate reasons, i.e. forgot
+  the password. Disaster recovery. Deploying AD backdoor deliberately.
 references:
 - https://adsecurity.org/?p=1714
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Changes.result_id
-  - All_Changes.result
-  - All_Changes.action
-  - All_Changes.dest
-  - All_Changes.src
-  - All_Changes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dsrm_account/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml b/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml
index 64df8fe865..2d18994499 100644
--- a/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml
+++ b/detections/endpoint/windows_ad_privileged_account_sid_history_addition.yml
@@ -8,9 +8,24 @@ status: production
 data_source:
 - Windows Event Log Security 4742
 - Windows Event Log Security 4738
-description: The following analytic identifies when the SID of a privileged user is added to the SID History attribute of another user. It leverages Windows Security Event Codes 4742 and 4738, combined with identity lookups, to detect this activity. This behavior is significant as it may indicate an attempt to abuse SID history for unauthorized access across multiple domains. If confirmed malicious, this activity could allow an attacker to escalate privileges or maintain persistent access within the environment, posing a significant security risk.
-search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistory>.*?)(}$|$)" | eval category="privileged" | lookup identity_lookup_expanded category, identity as SidHistory OUTPUT identity_tag as match | where isnotnull(match) | rename TargetSid as userSid | table _time action status host user userSid SidHistory Logon_ID src_user | `windows_ad_privileged_account_sid_history_addition_filter`'
-how_to_implement: Ensure you have objectSid and the Down Level Logon Name `DOMAIN\sAMACountName` added to the identity field of your Asset and Identities lookup, along with the category of privileged for the applicable users. Ensure you are ingesting eventcodes 4742 and 4738. Two advanced audit policies `Audit User Account Management` and `Audit Computer Account Management` under `Account Management` are required to generate these event codes.
+description: The following analytic identifies when the SID of a privileged user is
+  added to the SID History attribute of another user. It leverages Windows Security
+  Event Codes 4742 and 4738, combined with identity lookups, to detect this activity.
+  This behavior is significant as it may indicate an attempt to abuse SID history
+  for unauthorized access across multiple domains. If confirmed malicious, this activity
+  could allow an attacker to escalate privileges or maintain persistent access within
+  the environment, posing a significant security risk.
+search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory
+  IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistory>.*?)(}$|$)" | eval
+  category="privileged" | lookup identity_lookup_expanded category, identity as SidHistory
+  OUTPUT identity_tag as match | where isnotnull(match) | rename TargetSid as userSid
+  | table _time action status host user userSid SidHistory Logon_ID src_user | `windows_ad_privileged_account_sid_history_addition_filter`'
+how_to_implement: Ensure you have objectSid and the Down Level Logon Name `DOMAIN\sAMACountName`
+  added to the identity field of your Asset and Identities lookup, along with the
+  category of privileged for the applicable users. Ensure you are ingesting eventcodes
+  4742 and 4738. Two advanced audit policies `Audit User Account Management` and `Audit
+  Computer Account Management` under `Account Management` are required to generate
+  these event codes.
 known_false_positives: Migration of privileged accounts.
 references:
 - https://adsecurity.org/?p=1772
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 100
-  message: A Privileged User Account SID History Attribute was added to $userSid$ by $src_user$
+  message: A Privileged User Account SID History Attribute was added to $userSid$
+    by $src_user$
   mitre_attack_id:
   - T1134.005
   - T1134
@@ -42,20 +63,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - SidHistory
-  - TargetSid
-  - TargetDomainName
-  - user
-  - src_user
-  - Logon_ID
   security_domain: endpoint
-  manual_test: This search uses a lookup provided by Enterprise Security and needs to be manually tested.
+  manual_test: This search uses a lookup provided by Enterprise Security and needs
+    to be manually tested.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_privileged_object_access_activity.yml b/detections/endpoint/windows_ad_privileged_object_access_activity.yml
index 23781c185f..78fded8294 100644
--- a/detections/endpoint/windows_ad_privileged_object_access_activity.yml
+++ b/detections/endpoint/windows_ad_privileged_object_access_activity.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects access attempts to privileged Active Directory objects, such as Domain Admins or Enterprise Admins. It leverages Windows Security Event Code 4662 to identify when these sensitive objects are accessed. This activity is significant because such objects should rarely be accessed by normal users or processes, and unauthorized access attempts may indicate attacker enumeration or lateral movement within the domain. If confirmed malicious, this activity could allow attackers to escalate privileges, persist in the environment, or gain control over critical domain resources.
+description: The following analytic detects access attempts to privileged Active Directory
+  objects, such as Domain Admins or Enterprise Admins. It leverages Windows Security
+  Event Code 4662 to identify when these sensitive objects are accessed. This activity
+  is significant because such objects should rarely be accessed by normal users or
+  processes, and unauthorized access attempts may indicate attacker enumeration or
+  lateral movement within the domain. If confirmed malicious, this activity could
+  allow attackers to escalate privileges, persist in the environment, or gain control
+  over critical domain resources.
 data_source:
 - Windows Event Log Security 4662
-search: '`wineventlog_security` EventCode=4662 ObjectName IN ( "CN=Account Operators,*", "CN=Administrators,*", "CN=Backup Operators,*", "CN=Cert Publishers,*", "CN=Certificate Service DCOM Access,*", "CN=Domain Admins,*", "CN=Domain Controllers,*", "CN=Enterprise Admins,*", "CN=Enterprise Read-only Domain Controllers,*", "CN=Group Policy Creator Owners,*", "CN=Incoming Forest Trust Builders,*", "CN=Microsoft Exchange Servers,*", "CN=Network Configuration Operators,*", "CN=Power Users,*", "CN=Print Operators,*", "CN=Read-only Domain Controllers,*", "CN=Replicators,*", "CN=Schema Admins,*", "CN=Server Operators,*", "CN=Exchange Trusted Subsystem,*", "CN=Exchange Windows Permission,*", "CN=Organization Management,*") | rex field=ObjectName "CN\=(?<object_name>[^,]+)" | stats values(Computer) as dest, values(object_name) as object_name, dc(ObjectName) as object_count, min(_time) as firstTime, max(_time) as lastTime, count by SubjectUserName | rename SubjectUserName as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ad_privileged_object_access_activity_filter`'
-how_to_implement: Enable Audit Directory Service Access via GPO and collect event code 4662. The required SACLs need to be created for the relevant objects. Be aware Splunk filters this event by default on the Windows TA.
-known_false_positives: Service accounts or applications that routinely query Active Directory for information.
+search: '`wineventlog_security` EventCode=4662 ObjectName IN ( "CN=Account Operators,*",
+  "CN=Administrators,*", "CN=Backup Operators,*", "CN=Cert Publishers,*", "CN=Certificate
+  Service DCOM Access,*", "CN=Domain Admins,*", "CN=Domain Controllers,*", "CN=Enterprise
+  Admins,*", "CN=Enterprise Read-only Domain Controllers,*", "CN=Group Policy Creator
+  Owners,*", "CN=Incoming Forest Trust Builders,*", "CN=Microsoft Exchange Servers,*",
+  "CN=Network Configuration Operators,*", "CN=Power Users,*", "CN=Print Operators,*",
+  "CN=Read-only Domain Controllers,*", "CN=Replicators,*", "CN=Schema Admins,*", "CN=Server
+  Operators,*", "CN=Exchange Trusted Subsystem,*", "CN=Exchange Windows Permission,*",
+  "CN=Organization Management,*") | rex field=ObjectName "CN\=(?<object_name>[^,]+)"
+  | stats values(Computer) as dest, values(object_name) as object_name, dc(ObjectName)
+  as object_count, min(_time) as firstTime, max(_time) as lastTime, count by SubjectUserName
+  | rename SubjectUserName as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_ad_privileged_object_access_activity_filter`'
+how_to_implement: Enable Audit Directory Service Access via GPO and collect event
+  code 4662. The required SACLs need to be created for the relevant objects. Be aware
+  Splunk filters this event by default on the Windows TA.
+known_false_positives: Service accounts or applications that routinely query Active
+  Directory for information.
 references:
 - https://medium.com/securonix-tech-blog/detecting-ldap-enumeration-and-bloodhound-s-sharphound-collector-using-active-directory-decoys-dfc840f2f644
 - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4662
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectName
-  - EventCode
-  - Computer
-  - SubjectUserName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/4662_ad_enum/4662_priv_events.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml b/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml
index 478fc0a737..04e2525cfb 100644
--- a/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml
+++ b/detections/endpoint/windows_ad_replication_request_initiated_by_user_account.yml
@@ -7,9 +7,35 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 4662
-description: The following analytic detects a user account initiating an Active Directory replication request, indicative of a DCSync attack. It leverages EventCode 4662 from the Windows Security Event Log, focusing on specific object types and replication permissions. This activity is significant because it can allow an attacker with sufficient privileges to request password hashes for any or all users within the domain. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of the entire domain.
-search: '`wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}", "domainDNS") AND Properties IN ("*Replicating Directory Changes All*", "*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*", "*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") AND AccessMask="0x100" AND NOT (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") | stats min(_time) as _time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status | rename SubjectDomainName as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId, _time as attack_time | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | table attack_time, AuthenticationPackageName, LogonProcessName, LogonType, TargetUserSid, Target_Domain, user, Computer, TargetLogonId, status, src_ip, src_category, ObjectName, ObjectServer, ObjectType, OperationType | stats min(attack_time) as _time values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip by TargetLogonId | `windows_ad_replication_request_initiated_by_user_account_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services Access` within `DS Access` needs to be enabled, as well as the following SACLs applied to the domain root and all descendant objects. The principals `everybody`,  `Domain Computers`, and  `Domain Controllers` auditing the permissions `Replicating Directory Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes In Filtered Set`
+description: The following analytic detects a user account initiating an Active Directory
+  replication request, indicative of a DCSync attack. It leverages EventCode 4662
+  from the Windows Security Event Log, focusing on specific object types and replication
+  permissions. This activity is significant because it can allow an attacker with
+  sufficient privileges to request password hashes for any or all users within the
+  domain. If confirmed malicious, this could lead to unauthorized access, privilege
+  escalation, and potential compromise of the entire domain.
+search: '`wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}",
+  "domainDNS") AND Properties IN ("*Replicating Directory Changes All*", "*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*",
+  "*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*")
+  AND AccessMask="0x100" AND NOT (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18"
+  OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") | stats min(_time)
+  as _time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName,
+  ObjectServer, ObjectType, OperationType, status | rename SubjectDomainName as Target_Domain,
+  SubjectUserName as user, Logon_ID as TargetLogonId, _time as attack_time | appendpipe
+  [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"]
+  | table attack_time, AuthenticationPackageName, LogonProcessName, LogonType, TargetUserSid,
+  Target_Domain, user, Computer, TargetLogonId, status, src_ip, src_category, ObjectName,
+  ObjectServer, ObjectType, OperationType | stats min(attack_time) as _time values(TargetUserSid)
+  as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user,
+  values(Computer) as Computer, values(status) as status, values(src_category) as
+  src_category, values(src_ip) as src_ip by TargetLogonId | `windows_ad_replication_request_initiated_by_user_account_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services
+  Access` within `DS Access` needs to be enabled, as well as the following SACLs applied
+  to the domain root and all descendant objects. The principals `everybody`,  `Domain
+  Computers`, and  `Domain Controllers` auditing the permissions `Replicating Directory
+  Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes
+  In Filtered Set`
 known_false_positives: Azure AD Connect syncing operations.
 references:
 - https://adsecurity.org/?p=1729
@@ -21,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: Windows Active Directory Replication Request Initiated by User Account $user$ at $src_ip$
+  message: Windows Active Directory Replication Request Initiated by User Account
+    $user$ at $src_ip$
   mitre_attack_id:
   - T1003.006
   - T1003
@@ -48,26 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectType
-  - Properties
-  - AccessMask
-  - SubjectDomainName
-  - SubjectUserName
-  - SubjectUserSid
-  - Computer
-  - Logon_ID
-  - ObjectName
-  - ObjectServer
-  - ObjectType
-  - OperationType
-  - status
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/mimikatz/xml-windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/mimikatz/xml-windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml b/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml
index f457e83f5f..58c001c25a 100644
--- a/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml
+++ b/detections/endpoint/windows_ad_replication_request_initiated_from_unsanctioned_location.yml
@@ -8,9 +8,38 @@ status: production
 data_source:
 - Windows Event Log Security 4662
 - Windows Event Log Security 4624
-description: The following analytic identifies unauthorized Active Directory replication requests initiated from non-domain controller locations. It leverages EventCode 4662 to detect when a computer account with replication permissions creates a handle to domainDNS, filtering out known domain controller IP addresses. This activity is significant as it may indicate a DCSync attack, where an attacker with privileged access can request password hashes for any or all users within the domain. If confirmed malicious, this could lead to unauthorized access to sensitive information and potential full domain compromise.
-search: '`wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}", "domainDNS") AND Properties IN ("*Replicating Directory Changes All*", "*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*", "*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*") AND AccessMask="0x100" AND (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18" OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") | stats min(_time) as attack_time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID, ObjectName, ObjectServer, ObjectType, OperationType, status | rename SubjectDomainName as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | table attack_time, AuthenticationPackageName, LogonProcessName, LogonType, TargetUserSid, Target_Domain, user, Computer, TargetLogonId, status, src_ip, src_category, ObjectName, ObjectServer, ObjectType, OperationType | stats min(attack_time) as _time, values(TargetUserSid) as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user, values(Computer) as Computer, values(status) as status, values(src_category) as src_category, values(src_ip) as src_ip by TargetLogonId | search NOT src_category="domain_controller" | `windows_ad_replication_request_initiated_from_unsanctioned_location_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services Access` within `DS Access` needs to be enabled, as well as the following SACLs applied to the domain root and all descendant objects. The principals `everybody`,  `Domain Computers`, and  `Domain Controllers` auditing the permissions `Replicating Directory Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes In Filtered Set` Assets and Identities will also need to be configured, with the category of domain_controller added for domain controllers.
+description: The following analytic identifies unauthorized Active Directory replication
+  requests initiated from non-domain controller locations. It leverages EventCode
+  4662 to detect when a computer account with replication permissions creates a handle
+  to domainDNS, filtering out known domain controller IP addresses. This activity
+  is significant as it may indicate a DCSync attack, where an attacker with privileged
+  access can request password hashes for any or all users within the domain. If confirmed
+  malicious, this could lead to unauthorized access to sensitive information and potential
+  full domain compromise.
+search: '`wineventlog_security` EventCode=4662 ObjectType IN ("%{19195a5b-6da0-11d0-afd3-00c04fd930c9}",
+  "domainDNS") AND Properties IN ("*Replicating Directory Changes All*", "*{1131f6ad-9c07-11d1-f79f-00c04fc2dcd2}*",
+  "*{9923a32a-3607-11d2-b9be-0000f87a36b2}*","*{1131f6ac-9c07-11d1-f79f-00c04fc2dcd2}*")
+  AND AccessMask="0x100" AND (SubjectUserSid="NT AUT*" OR SubjectUserSid="S-1-5-18"
+  OR SubjectDomainName="Window Manager" OR SubjectUserName="*$") | stats min(_time)
+  as attack_time, count by SubjectDomainName, SubjectUserName, Computer, Logon_ID,
+  ObjectName, ObjectServer, ObjectType, OperationType, status | rename SubjectDomainName
+  as Target_Domain, SubjectUserName as user, Logon_ID as TargetLogonId | appendpipe
+  [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"]
+  | table attack_time, AuthenticationPackageName, LogonProcessName, LogonType, TargetUserSid,
+  Target_Domain, user, Computer, TargetLogonId, status, src_ip, src_category, ObjectName,
+  ObjectServer, ObjectType, OperationType | stats min(attack_time) as _time, values(TargetUserSid)
+  as TargetUserSid, values(Target_Domain) as Target_Domain, values(user) as user,
+  values(Computer) as Computer, values(status) as status, values(src_category) as
+  src_category, values(src_ip) as src_ip by TargetLogonId | search NOT src_category="domain_controller"
+  | `windows_ad_replication_request_initiated_from_unsanctioned_location_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcode `4662`. The Advanced Security Audit policy settings `Audit Directory Services
+  Access` within `DS Access` needs to be enabled, as well as the following SACLs applied
+  to the domain root and all descendant objects. The principals `everybody`,  `Domain
+  Computers`, and  `Domain Controllers` auditing the permissions `Replicating Directory
+  Changes`, `Replicating Directory Changes All`, and `Replicating Directory Changes
+  In Filtered Set` Assets and Identities will also need to be configured, with the
+  category of domain_controller added for domain controllers.
 known_false_positives: Genuine DC promotion may trigger this alert.
 references:
 - https://adsecurity.org/?p=1729
@@ -22,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: Windows Active Directory Replication Request Initiated from Unsanctioned Location $src_ip$ by $user$
+  message: Windows Active Directory Replication Request Initiated from Unsanctioned
+    Location $src_ip$ by $user$
   mitre_attack_id:
   - T1003.006
   - T1003
@@ -49,27 +84,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectType
-  - Properties
-  - AccessMask
-  - SubjectDomainName
-  - SubjectUserName
-  - SubjectUserSid
-  - Computer
-  - Logon_ID
-  - ObjectName
-  - ObjectServer
-  - ObjectType
-  - OperationType
-  - status
   security_domain: endpoint
-  manual_test: This detection runs correctly when run manually and given some time is given for data to settle in the splunk index.
+  manual_test: This detection runs correctly when run manually and given some time
+    is given for data to settle in the splunk index.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/impacket/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.006/impacket/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml b/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml
index f31376effa..196aef2431 100644
--- a/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml
+++ b/detections/endpoint/windows_ad_same_domain_sid_history_addition.yml
@@ -8,9 +8,23 @@ status: production
 data_source:
 - Windows Event Log Security 4742
 - Windows Event Log Security 4738
-description: The following analytic detects changes to the sIDHistory attribute of user or computer objects within the same domain. It leverages Windows Security Event Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. This activity is significant because the sIDHistory attribute can be abused by adversaries to grant unauthorized access by inheriting permissions from another account. If confirmed malicious, this could allow attackers to maintain persistent access or escalate privileges within the domain, posing a severe security risk.
-search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistoryMatch>.*)(\-|\\\)" | rex field=TargetSid "^(?P<TargetSidmatch>.*)(\-|\\\)" | where SidHistoryMatch=TargetSidmatch OR SidHistoryMatch=TargetDomainName | rename TargetSid as userSid, TargetDomainName as userDomainName | table _time action status host user userSid userDomainName SidHistory Logon_ID src_user | `windows_ad_same_domain_sid_history_addition_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit User Account Management` and  `Audit Computer Account Management` within `Account Management` all need to be enabled. SID resolution is not required..
+description: The following analytic detects changes to the sIDHistory attribute of
+  user or computer objects within the same domain. It leverages Windows Security Event
+  Codes 4738 and 4742 to identify when the sIDHistory attribute is modified. This
+  activity is significant because the sIDHistory attribute can be abused by adversaries
+  to grant unauthorized access by inheriting permissions from another account. If
+  confirmed malicious, this could allow attackers to maintain persistent access or
+  escalate privileges within the domain, posing a severe security risk.
+search: '`wineventlog_security` (EventCode=4742 OR EventCode=4738) NOT SidHistory
+  IN ("%%1793", -) | rex field=SidHistory "(^%{|^)(?P<SidHistoryMatch>.*)(\-|\\\)"
+  | rex field=TargetSid "^(?P<TargetSidmatch>.*)(\-|\\\)" | where SidHistoryMatch=TargetSidmatch
+  OR SidHistoryMatch=TargetDomainName | rename TargetSid as userSid, TargetDomainName
+  as userDomainName | table _time action status host user userSid userDomainName SidHistory
+  Logon_ID src_user | `windows_ad_same_domain_sid_history_addition_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcodes `4738` and `4742`. The Advanced Security Audit policy settings `Audit
+  User Account Management` and  `Audit Computer Account Management` within `Account
+  Management` all need to be enabled. SID resolution is not required..
 known_false_positives: Unknown
 references:
 - https://adsecurity.org/?p=1772
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - SidHistory
-  - TargetSid
-  - TargetDomainName
-  - user
-  - src_user
-  - Logon_ID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/mimikatz/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml b/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml
index 200c4727b4..5f03e98620 100644
--- a/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml
+++ b/detections/endpoint/windows_ad_serviceprincipalname_added_to_domain_account.yml
@@ -15,11 +15,11 @@ description: The following analytic detects the addition of a Service Principal
   allow an attacker to obtain cleartext passwords, leading to unauthorized access
   and potential lateral movement within the domain environment.
 search: >-
-  `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName OperationType="%%14674" ObjectClass=user
+  `wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName
+  OperationType="%%14674" ObjectClass=user
   | stats values(ObjectDN) as ObjectDN by _time, Computer, SubjectUserName, AttributeValue
   | rex field=ObjectDN "^CN=(?P<user>[a-zA-Z0-9!#$%&'@^_{}~.-]+),"
-  | rename Computer as dest, SubjectUserName as src_user 
-  | `windows_ad_serviceprincipalname_added_to_domain_account_filter`
+  | rename Computer as dest, SubjectUserName as src_user  | `windows_ad_serviceprincipalname_added_to_domain_account_filter`
 how_to_implement: To successfully implement this search, you ned to be ingesting eventcode
   `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes`
   within `DS Access` needs to be enabled. Additionally, a SACL needs to be created
@@ -38,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$ObjectDN$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ObjectDN$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$ObjectDN$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -63,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectDN
-  - signature
-  - SubjectUserName
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/service_principal_name_added/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/service_principal_name_added/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml b/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml
index d48621cab8..ef374b4089 100644
--- a/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml
+++ b/detections/endpoint/windows_ad_short_lived_domain_account_serviceprincipalname.yml
@@ -7,10 +7,25 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 5136
-description: The following analytic identifies the addition and quick deletion of a Service Principal Name (SPN) to a domain account within 5 minutes. This detection leverages EventCode 5136 from the Windows Security Event Log, focusing on changes to the servicePrincipalName attribute. This activity is significant as it may indicate an attempt to perform Kerberoasting, a technique used to crack the cleartext password of a domain account offline. If confirmed malicious, this could allow an attacker to gain unauthorized access to sensitive information or escalate privileges within the domain environment.
-search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName | transaction ObjectDN AttributeValue startswith=(EventCode=5136 OperationType="%%14674") endswith=(EventCode=5136 OperationType="%%14675") | eval short_lived=case((duration<300),"TRUE") | search short_lived = TRUE | rename ObjectDN as user | `windows_ad_short_lived_domain_account_serviceprincipalname_filter`'
-how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications.
-known_false_positives: A Service Principal Name should only be added to an account when an application requires it. Adding an SPN and quickly deleting it is less common but may be part of legitimate action. Filter as needed.
+description: The following analytic identifies the addition and quick deletion of
+  a Service Principal Name (SPN) to a domain account within 5 minutes. This detection
+  leverages EventCode 5136 from the Windows Security Event Log, focusing on changes
+  to the servicePrincipalName attribute. This activity is significant as it may indicate
+  an attempt to perform Kerberoasting, a technique used to crack the cleartext password
+  of a domain account offline. If confirmed malicious, this could allow an attacker
+  to gain unauthorized access to sensitive information or escalate privileges within
+  the domain environment.
+search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName
+  | transaction ObjectDN AttributeValue startswith=(EventCode=5136 OperationType="%%14674")
+  endswith=(EventCode=5136 OperationType="%%14675") | eval short_lived=case((duration<300),"TRUE")
+  | search short_lived = TRUE | rename ObjectDN as user | `windows_ad_short_lived_domain_account_serviceprincipalname_filter`'
+how_to_implement: To successfully implement this search, you ned to be ingesting eventcode
+  `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes`
+  within `DS Access` needs to be enabled. Additionally, a SACL needs to be created
+  for AD objects in order to ingest attribute modifications.
+known_false_positives: A Service Principal Name should only be added to an account
+  when an application requires it. Adding an SPN and quickly deleting it is less common
+  but may be part of legitimate action. Filter as needed.
 references:
 - https://adsecurity.org/?p=3466
 - https://www.thehacker.recipes/ad/movement/dacl/targeted-kerberoasting
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectDN
-  - signature
-  - SubjectUserName
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/short_lived_service_principal_name/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/short_lived_service_principal_name/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml b/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml
index 177d6dede2..f7f8ec7304 100644
--- a/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml
+++ b/detections/endpoint/windows_ad_short_lived_domain_controller_spn_attribute.yml
@@ -8,9 +8,29 @@ status: production
 data_source:
 - Windows Event Log Security 5136
 - Windows Event Log Security 4624
-description: The following analytic detects the temporary addition of a global catalog SPN or a DRS RPC SPN to an Active Directory computer object, indicative of a potential DCShadow attack. This detection leverages EventCode 5136 from the `wineventlog_security` data source, focusing on specific SPN attribute changes. This activity is significant as DCShadow attacks allow attackers with privileged access to register rogue Domain Controllers, enabling unauthorized changes to the AD infrastructure. If confirmed malicious, this could lead to unauthorized replication of changes, including credentials and keys, compromising the entire domain's security.
-search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName (AttributeValue="GC/*" OR AttributeValue="E3514235-4B06-11D1-AB04-00C04FC2DCD2/*") | stats min(_time) as _time range(_time) as duration values(OperationType) as OperationType values(user) as user values(src_ip) as src_ip values(src_nt_domain) as src_nt_domain values(src_user) as src_user values(Computer) as dest, values(ObjectDN) as ObjectDN by Logon_ID | eval short_lived=case((duration<30),"TRUE") | where short_lived="TRUE" AND mvcount(OperationType)>1 | replace "%%14674" with "Value Added", "%%14675" with "Value Deleted" in OperationType | rename Logon_ID as TargetLogonId | appendpipe [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"] | stats min(_time) as _time, values(ObjectDN) as ObjectDN values(OperationType) as OperationType by TargetLogonId src_user dest | `windows_ad_short_lived_domain_controller_spn_attribute_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody` to `Write All Properties` applied to the domain root and all descendant objects.
+description: The following analytic detects the temporary addition of a global catalog
+  SPN or a DRS RPC SPN to an Active Directory computer object, indicative of a potential
+  DCShadow attack. This detection leverages EventCode 5136 from the `wineventlog_security`
+  data source, focusing on specific SPN attribute changes. This activity is significant
+  as DCShadow attacks allow attackers with privileged access to register rogue Domain
+  Controllers, enabling unauthorized changes to the AD infrastructure. If confirmed
+  malicious, this could lead to unauthorized replication of changes, including credentials
+  and keys, compromising the entire domain's security.
+search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=servicePrincipalName
+  (AttributeValue="GC/*" OR AttributeValue="E3514235-4B06-11D1-AB04-00C04FC2DCD2/*")
+  | stats min(_time) as _time range(_time) as duration values(OperationType) as OperationType
+  values(user) as user values(src_ip) as src_ip values(src_nt_domain) as src_nt_domain
+  values(src_user) as src_user values(Computer) as dest, values(ObjectDN) as ObjectDN
+  by Logon_ID | eval short_lived=case((duration<30),"TRUE") | where short_lived="TRUE"
+  AND mvcount(OperationType)>1 | replace "%%14674" with "Value Added", "%%14675" with
+  "Value Deleted" in OperationType | rename Logon_ID as TargetLogonId | appendpipe
+  [| map search="search `wineventlog_security` EventCode=4624 TargetLogonId=$TargetLogonId$"]
+  | stats min(_time) as _time, values(ObjectDN) as ObjectDN values(OperationType)
+  as OperationType by TargetLogonId src_user dest | `windows_ad_short_lived_domain_controller_spn_attribute_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services
+  Changes` within `DS Access` needs to be enabled, alongside a SACL for `everybody`
+  to `Write All Properties` applied to the domain root and all descendant objects.
 known_false_positives: None.
 references:
 - https://www.dcshadow.com/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,21 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - AttributeLDAPDisplayName
-  - AttributeValue
-  - src_nt_domain
-  - src_user
-  - Computer
-  - ObjectDN
-  - Logon_ID
-  - signature
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/mimikatz/windows-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/mimikatz/windows-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_short_lived_server_object.yml b/detections/endpoint/windows_ad_short_lived_server_object.yml
index f1640e4f9c..388e1c15e5 100644
--- a/detections/endpoint/windows_ad_short_lived_server_object.yml
+++ b/detections/endpoint/windows_ad_short_lived_server_object.yml
@@ -8,10 +8,24 @@ status: production
 data_source:
 - Windows Event Log Security 5137
 - Windows Event Log Security 5141
-description: The following analytic identifies the creation and quick deletion of a Domain Controller (DC) object within 30 seconds in an Active Directory environment, indicative of a potential DCShadow attack. This detection leverages Windows Security Event Codes 5137 and 5141, analyzing the duration between these events. This activity is significant as DCShadow allows attackers with privileged access to register a rogue DC, enabling unauthorized changes to AD objects, including credentials. If confirmed malicious, this could lead to unauthorized AD modifications, compromising the integrity and security of the entire domain.
-search: '`wineventlog_security` EventCode=5137 OR EventCode=5141 ObjectDN="*CN=Servers,CN=Default-First-Site-Name,CN=Sites,CN=Configuration*" | transaction ObjectDN startswith=(EventCode=5137) endswith=(EventCode=5141) | eval short_lived=case((duration<30),"TRUE") | search short_lived = TRUE | stats values(ObjectDN) values(signature) values(EventCode) by _time, Computer, SubjectUserName | `windows_ad_short_lived_server_object_filter`'
-how_to_implement: To successfully implement this search, you ned to be ingesting Event codes `5137` and `5141`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. For these event codes to be generated, specific SACLs are required.
-known_false_positives: Creating and deleting a server object within 30 seconds or less is unusual but not impossible in a production environment. Filter as needed.
+description: The following analytic identifies the creation and quick deletion of
+  a Domain Controller (DC) object within 30 seconds in an Active Directory environment,
+  indicative of a potential DCShadow attack. This detection leverages Windows Security
+  Event Codes 5137 and 5141, analyzing the duration between these events. This activity
+  is significant as DCShadow allows attackers with privileged access to register a
+  rogue DC, enabling unauthorized changes to AD objects, including credentials. If
+  confirmed malicious, this could lead to unauthorized AD modifications, compromising
+  the integrity and security of the entire domain.
+search: '`wineventlog_security` EventCode=5137 OR EventCode=5141 ObjectDN="*CN=Servers,CN=Default-First-Site-Name,CN=Sites,CN=Configuration*"
+  | transaction ObjectDN startswith=(EventCode=5137) endswith=(EventCode=5141) | eval
+  short_lived=case((duration<30),"TRUE") | search short_lived = TRUE | stats values(ObjectDN)
+  values(signature) values(EventCode) by _time, Computer, SubjectUserName | `windows_ad_short_lived_server_object_filter`'
+how_to_implement: To successfully implement this search, you ned to be ingesting Event
+  codes `5137` and `5141`. The Advanced Security Audit policy setting `Audit Directory
+  Services Changes` within `DS Access` needs to be enabled. For these event codes
+  to be generated, specific SACLs are required.
+known_false_positives: Creating and deleting a server object within 30 seconds or
+  less is unusual but not impossible in a production environment. Filter as needed.
 references:
 - https://www.dcshadow.com/
 - https://attack.mitre.org/techniques/T1207/
@@ -25,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ObjectDN
-  - signature
-  - SubjectUserName
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/short_lived_server_object/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1207/short_lived_server_object/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ad_sid_history_attribute_modified.yml b/detections/endpoint/windows_ad_sid_history_attribute_modified.yml
index 650d71c661..d08cff2d3a 100644
--- a/detections/endpoint/windows_ad_sid_history_attribute_modified.yml
+++ b/detections/endpoint/windows_ad_sid_history_attribute_modified.yml
@@ -7,10 +7,23 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 5136
-description: The following analytic detects modifications to the SID History attribute in Active Directory by leveraging event code 5136. This detection uses logs from the `wineventlog_security` data source to identify changes to the sIDHistory attribute. Monitoring this activity is crucial as the SID History attribute can be exploited by adversaries to inherit permissions from other accounts, potentially granting unauthorized access. If confirmed malicious, this activity could allow attackers to maintain persistent access and escalate privileges within the domain, posing a significant security risk.
-search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=sIDHistory OperationType="%%14674" | stats values(ObjectDN) as ObjectDN by _time, Computer, SubjectUserName, AttributeValue | rename Computer as dest | `windows_ad_sid_history_attribute_modified_filter`'
-how_to_implement: To successfully implement this search, you ned to be ingesting eventcode `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes` within `DS Access` needs to be enabled. Additionally, a SACL needs to be created for AD objects in order to ingest attribute modifications.
-known_false_positives: Domain mergers and migrations may generate large volumes of false positives for this analytic.
+description: The following analytic detects modifications to the SID History attribute
+  in Active Directory by leveraging event code 5136. This detection uses logs from
+  the `wineventlog_security` data source to identify changes to the sIDHistory attribute.
+  Monitoring this activity is crucial as the SID History attribute can be exploited
+  by adversaries to inherit permissions from other accounts, potentially granting
+  unauthorized access. If confirmed malicious, this activity could allow attackers
+  to maintain persistent access and escalate privileges within the domain, posing
+  a significant security risk.
+search: '`wineventlog_security` EventCode=5136 AttributeLDAPDisplayName=sIDHistory
+  OperationType="%%14674" | stats values(ObjectDN) as ObjectDN by _time, Computer,
+  SubjectUserName, AttributeValue | rename Computer as dest | `windows_ad_sid_history_attribute_modified_filter`'
+how_to_implement: To successfully implement this search, you ned to be ingesting eventcode
+  `5136`. The Advanced Security Audit policy setting `Audit Directory Services Changes`
+  within `DS Access` needs to be enabled. Additionally, a SACL needs to be created
+  for AD objects in order to ingest attribute modifications.
+known_false_positives: Domain mergers and migrations may generate large volumes of
+  false positives for this analytic.
 references:
 - https://adsecurity.org/?p=1772
 - https://learn.microsoft.com/en-us/windows/win32/adschema/a-sidhistory?redirectedfrom=MSDN
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: SID History AD attribute modified by $SubjectUserName$ for $ObjectDN$ on $dest$
+  message: SID History AD attribute modified by $SubjectUserName$ for $ObjectDN$ on
+    $dest$
   mitre_attack_id:
   - T1134
   - T1134.005
@@ -44,19 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - AttributeLDAPDisplayName
-  - OperationType=
-  - ObjectDN
-  - Computer
-  - SubjectUserName
-  - AttributeValue
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/sid_history2/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134.005/sid_history2/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_adfind_exe.yml b/detections/endpoint/windows_adfind_exe.yml
index c7e52adcee..59b2791aa4 100644
--- a/detections/endpoint/windows_adfind_exe.yml
+++ b/detections/endpoint/windows_adfind_exe.yml
@@ -5,14 +5,38 @@ date: '2024-10-17'
 author: Jose Hernandez, Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of `adfind.exe` with specific command-line arguments related to Active Directory queries. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and parent processes. This activity is significant because `adfind.exe` is a powerful tool often used by threat actors like Wizard Spider and FIN6 to gather sensitive AD information. If confirmed malicious, this activity could allow attackers to map the AD environment, facilitating further attacks such as privilege escalation or lateral movement.
+description: The following analytic identifies the execution of `adfind.exe` with
+  specific command-line arguments related to Active Directory queries. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names,
+  command-line arguments, and parent processes. This activity is significant because
+  `adfind.exe` is a powerful tool often used by threat actors like Wizard Spider and
+  FIN6 to gather sensitive AD information. If confirmed malicious, this activity could
+  allow attackers to map the AD environment, facilitating further attacks such as
+  privilege escalation or lateral movement.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where ((Processes.process="* -f *" OR Processes.process="* -b *") AND (Processes.process=*objectcategory* OR Processes.process="*-gcb *" OR Processes.process="* -sc *" )) OR ((Processes.process="*trustdmp*" OR Processes.process="*dclist*")) by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.parent_process_id  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter`| `windows_adfind_exe_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: ADfind is a command-line tool for AD administration and management that is seen to be leveraged by various adversaries. Filter out legitimate administrator usage using the filter macro.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where ((Processes.process="* -f *"
+  OR Processes.process="* -b *") AND (Processes.process=*objectcategory* OR Processes.process="*-gcb
+  *" OR Processes.process="* -sc *" )) OR ((Processes.process="*trustdmp*" OR Processes.process="*dclist*"))
+  by Processes.dest Processes.user Processes.process_name Processes.process Processes.parent_process
+  Processes.process_id Processes.parent_process_id  | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_adfind_exe_filter`|
+  `windows_adfind_exe_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: ADfind is a command-line tool for AD administration and management
+  that is seen to be leveraged by various adversaries. Filter out legitimate administrator
+  usage using the filter macro.
 references:
 - https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/
 - https://www.mandiant.com/resources/a-nasty-trick-from-credential-theft-malware-to-business-disruption
@@ -24,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Windows AdFind Exe detected with command-line arguments associated with Active Directory queries on machine - [dest]
+  message: Windows AdFind Exe detected with command-line arguments associated with
+    Active Directory queries on machine - [dest]
   atomic_guid:
   - 736b4f53-f400-4c22-855d-1a6b5a551600
   - b95fd967-4e62-4109-b48d-265edfd28c3a
@@ -56,19 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_name
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_admin_permission_discovery.yml b/detections/endpoint/windows_admin_permission_discovery.yml
index 521f3d7bc8..ca594894b0 100644
--- a/detections/endpoint/windows_admin_permission_discovery.yml
+++ b/detections/endpoint/windows_admin_permission_discovery.yml
@@ -7,10 +7,29 @@ status: production
 type: Anomaly
 data_source:
 - Sysmon EventID 11
-description: The following analytic identifies the creation of a suspicious file named 'win.dat' in the root directory (C:\). It leverages data from the Endpoint.Filesystem datamodel to detect this activity. This behavior is significant as it is commonly used by malware like NjRAT to check for administrative privileges on a compromised host. If confirmed malicious, this activity could indicate that the malware has administrative access, allowing it to perform high-privilege actions, potentially leading to further system compromise and persistence.
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe", "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif", "*.lnk", "*.dat") by Filesystem.dest Filesystem.file_create_time Filesystem.process_id Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.user | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | where LIKE(root_drive, "C:") AND dropped_file_path_split_count = 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admin_permission_discovery_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: False positives may occur if there are legitimate accounts with the privilege to drop files in the root of the C drive. It's recommended to verify the legitimacy of such actions and the accounts involved.
+description: The following analytic identifies the creation of a suspicious file named
+  'win.dat' in the root directory (C:\). It leverages data from the Endpoint.Filesystem
+  datamodel to detect this activity. This behavior is significant as it is commonly
+  used by malware like NjRAT to check for administrative privileges on a compromised
+  host. If confirmed malicious, this activity could indicate that the malware has
+  administrative access, allowing it to perform high-privilege actions, potentially
+  leading to further system compromise and persistence.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.exe",
+  "*.dll", "*.sys", "*.com", "*.vbs", "*.vbe", "*.js", "*.bat", "*.cmd", "*.pif",
+  "*.lnk", "*.dat") by Filesystem.dest Filesystem.file_create_time Filesystem.process_id
+  Filesystem.process_guid Filesystem.file_name Filesystem.file_path Filesystem.user
+  | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path,
+  "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive
+  = mvindex(dropped_file_path,0) | where LIKE(root_drive, "C:") AND dropped_file_path_split_count
+  = 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |
+  `windows_admin_permission_discovery_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+known_false_positives: False positives may occur if there are legitimate accounts
+  with the privilege to drop files in the root of the C drive. It's recommended to
+  verify the legitimacy of such actions and the accounts involved.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.dest
-  - Filesystem.process_guid
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/njrat_admin_check/win_dat.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/njrat_admin_check/win_dat.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml b/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml
index 58208d8a34..9f377fc8cb 100644
--- a/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml
+++ b/detections/endpoint/windows_administrative_shares_accessed_on_multiple_hosts.yml
@@ -8,10 +8,25 @@ status: production
 data_source:
 - Windows Event Log Security 5140
 - Windows Event Log Security 5145
-description: The following analytic detects a source computer accessing Windows administrative shares (C$, Admin$, IPC$) on 30 or more remote endpoints within a 5-minute window. It leverages Event IDs 5140 and 5145 from file share events. This behavior is significant as it may indicate an adversary enumerating network shares to locate sensitive files, a common tactic used by threat actors. If confirmed malicious, this activity could lead to unauthorized access to critical data, lateral movement, and potential compromise of multiple systems within the network.
-search: '`wineventlog_security` EventCode=5140 OR EventCode=5145 (ShareName="\\\\*\\ADMIN$" OR ShareName="\\\\*\\IPC$" OR ShareName="\\\\*\\C$") | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets values(ShareName) as shares by _time, IpAddress, SubjectUserName, EventCode | where unique_targets > 30 | `windows_administrative_shares_accessed_on_multiple_hosts_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting file share events. The Advanced Security Audit policy setting `Audit Detailed File Share` or `Audit File Share` within `Object Access` need to be enabled.
-known_false_positives: An single endpoint accessing windows administrative shares across a large number of endpoints is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems.
+description: The following analytic detects a source computer accessing Windows administrative
+  shares (C$, Admin$, IPC$) on 30 or more remote endpoints within a 5-minute window.
+  It leverages Event IDs 5140 and 5145 from file share events. This behavior is significant
+  as it may indicate an adversary enumerating network shares to locate sensitive files,
+  a common tactic used by threat actors. If confirmed malicious, this activity could
+  lead to unauthorized access to critical data, lateral movement, and potential compromise
+  of multiple systems within the network.
+search: '`wineventlog_security` EventCode=5140 OR EventCode=5145 (ShareName="\\\\*\\ADMIN$"
+  OR ShareName="\\\\*\\IPC$" OR ShareName="\\\\*\\C$") | bucket span=5m _time | stats
+  dc(Computer) AS unique_targets values(Computer) as host_targets values(ShareName)
+  as shares by _time, IpAddress, SubjectUserName, EventCode | where unique_targets
+  > 30 | `windows_administrative_shares_accessed_on_multiple_hosts_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  file share events. The Advanced Security Audit policy setting `Audit Detailed File
+  Share` or `Audit File Share` within `Object Access` need to be enabled.
+known_false_positives: An single endpoint accessing windows administrative shares
+  across a large number of endpoints is not common behavior. Possible false positive
+  scenarios include but are not limited to vulnerability scanners, administration
+  systems and missconfigured systems.
 references:
 - https://attack.mitre.org/techniques/T1135/
 - https://en.wikipedia.org/wiki/Administrative_share
@@ -24,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host_targets$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: $IpAddress$ accessed the IPC share on more than 30 endpoints in a timespan of 5 minutes.
+  message: $IpAddress$ accessed the IPC share on more than 30 endpoints in a timespan
+    of 5 minutes.
   mitre_attack_id:
   - T1135
   observable:
@@ -50,17 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ShareName
-  - Computer
-  - IpAddress
-  - SubjectUserName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/ipc_share_accessed/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/ipc_share_accessed/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_admon_default_group_policy_object_modified.yml b/detections/endpoint/windows_admon_default_group_policy_object_modified.yml
index 1fcee629d8..c39fc452d3 100644
--- a/detections/endpoint/windows_admon_default_group_policy_object_modified.yml
+++ b/detections/endpoint/windows_admon_default_group_policy_object_modified.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Windows Active Directory Admon
-description: The following analytic detects modifications to the default Group Policy Objects (GPOs) in an Active Directory environment. It leverages Splunk's Admon to monitor updates to the "Default Domain Policy" and "Default Domain Controllers Policy." This activity is significant because changes to these default GPOs can indicate an adversary with privileged access attempting to gain further control, establish persistence, or deploy malware across multiple hosts. If confirmed malicious, such modifications could lead to widespread policy enforcement changes, unauthorized access, and potential compromise of the entire domain environment.
-search: '`admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" (displayName="Default Domain Policy" OR displayName="Default Domain Controllers Policy") | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) by dcName, displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admon_default_group_policy_object_modified_filter`'
-how_to_implement: To successfully implement this search, you need to be monitoring Active Directory logs using Admon. Details can be found here https://docs.splunk.com/Documentation/SplunkCloud/8.1.2101/Data/MonitorActiveDirectory
-known_false_positives: The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed.
+description: The following analytic detects modifications to the default Group Policy
+  Objects (GPOs) in an Active Directory environment. It leverages Splunk's Admon to
+  monitor updates to the "Default Domain Policy" and "Default Domain Controllers Policy."
+  This activity is significant because changes to these default GPOs can indicate
+  an adversary with privileged access attempting to gain further control, establish
+  persistence, or deploy malware across multiple hosts. If confirmed malicious, such
+  modifications could lead to widespread policy enforcement changes, unauthorized
+  access, and potential compromise of the entire domain environment.
+search: '`admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*"
+  (displayName="Default Domain Policy" OR displayName="Default Domain Controllers
+  Policy") | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath)
+  by dcName, displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_admon_default_group_policy_object_modified_filter`'
+how_to_implement: To successfully implement this search, you need to be monitoring
+  Active Directory logs using Admon. Details can be found here 
+  https://docs.splunk.com/Documentation/SplunkCloud/8.1.2101/Data/MonitorActiveDirectory
+known_false_positives: The default Group Policy Objects within an AD network may be
+  legitimately updated for administrative operations, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1484/
 - https://attack.mitre.org/techniques/T1484/001
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dcName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - admonEventType
-  - objectCategory
-  - displayName
-  - gPCFileSysPath
-  - dcName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log
     source: ActiveDirectory
     sourcetype: ActiveDirectory
diff --git a/detections/endpoint/windows_admon_group_policy_object_created.yml b/detections/endpoint/windows_admon_group_policy_object_created.yml
index 24c387e6ef..b44978eabe 100644
--- a/detections/endpoint/windows_admon_group_policy_object_created.yml
+++ b/detections/endpoint/windows_admon_group_policy_object_created.yml
@@ -7,10 +7,22 @@ status: production
 type: TTP
 data_source:
 - Windows Active Directory Admon
-description: The following analytic detects the creation of a new Group Policy Object (GPO) using Splunk's Admon data. It identifies events where a new GPO is created, excluding default "New Group Policy Object" entries. Monitoring GPO creation is crucial as adversaries can exploit GPOs to escalate privileges or deploy malware across an Active Directory network. If confirmed malicious, this activity could allow attackers to control system configurations, deploy ransomware, or propagate malware, significantly compromising the network's security.
-search: '`admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*" versionNumber=0 displayName!="New Group Policy Object" | stats min(_time) as firstTime max(_time) as lastTime values(gPCFileSysPath) by dcName, displayName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_admon_group_policy_object_created_filter`'
-how_to_implement: To successfully implement this search, you need to be monitoring Active Directory logs using Admon. Details can be found here https://docs.splunk.com/Documentation/SplunkCloud/8.1.2101/Data/MonitorActiveDirectory
-known_false_positives: Group Policy Objects are created as part of regular administrative operations, filter as needed.
+description: The following analytic detects the creation of a new Group Policy Object
+  (GPO) using Splunk's Admon data. It identifies events where a new GPO is created,
+  excluding default "New Group Policy Object" entries. Monitoring GPO creation is
+  crucial as adversaries can exploit GPOs to escalate privileges or deploy malware
+  across an Active Directory network. If confirmed malicious, this activity could
+  allow attackers to control system configurations, deploy ransomware, or propagate
+  malware, significantly compromising the network's security.
+search: '`admon` admonEventType=Update objectCategory="CN=Group-Policy-Container,CN=Schema,CN=Configuration,DC=*"
+  versionNumber=0 displayName!="New Group Policy Object" | stats min(_time) as firstTime
+  max(_time) as lastTime values(gPCFileSysPath) by dcName, displayName | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_admon_group_policy_object_created_filter`'
+how_to_implement: To successfully implement this search, you need to be monitoring
+  Active Directory logs using Admon. Details can be found here 
+  https://docs.splunk.com/Documentation/SplunkCloud/8.1.2101/Data/MonitorActiveDirectory
+known_false_positives: Group Policy Objects are created as part of regular administrative
+  operations, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1484/
 - https://attack.mitre.org/techniques/T1484/001
@@ -23,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dcName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dcName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - admonEventType
-  - objectCategory
-  - displayName
-  - gPCFileSysPath
-  - dcName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-admon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-admon.log
     source: ActiveDirectory
     sourcetype: ActiveDirectory
diff --git a/detections/endpoint/windows_alternate_datastream___base64_content.yml b/detections/endpoint/windows_alternate_datastream___base64_content.yml
index cf29606d34..c6bb06b085 100644
--- a/detections/endpoint/windows_alternate_datastream___base64_content.yml
+++ b/detections/endpoint/windows_alternate_datastream___base64_content.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of Alternate Data Streams (ADS) with Base64 content on Windows systems. It leverages Sysmon EventID 15, which captures file creation events, including the content of named streams. ADS can conceal malicious payloads, making them significant for SOC monitoring. This detection identifies hidden streams that may contain executables, scripts, or configuration data, often used by malware to evade detection. If confirmed malicious, this activity could allow attackers to hide and execute payloads, persist in the environment, or access sensitive information without being easily detected.
+description: The following analytic detects the creation of Alternate Data Streams
+  (ADS) with Base64 content on Windows systems. It leverages Sysmon EventID 15, which
+  captures file creation events, including the content of named streams. ADS can conceal
+  malicious payloads, making them significant for SOC monitoring. This detection identifies
+  hidden streams that may contain executables, scripts, or configuration data, often
+  used by malware to evade detection. If confirmed malicious, this activity could
+  allow attackers to hide and execute payloads, persist in the environment, or access
+  sensitive information without being easily detected.
 data_source:
 - Sysmon EventID 15
-search: '`sysmon` EventCode=15 NOT Contents IN ("-","[ZoneTransfer]*") | regex TargetFilename="(?<!\/)\b\w+(\.\w+)?:\w+(\.\w+)?$" | regex Contents="(?:[A-Za-z0-9+/]{128,})(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$" | eval file_name = replace(TargetFilename,"(.*\\\)",""), process = Image , file_path = TargetFilename , base64 = Contents, file_hash = coalesce(SHA256,SHA1,MD5,Hash) | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by dest, process, process_guid, file_name, file_path, file_hash, base64 | `base64decode(base64)` | fields - base64 | rename base64_decode as command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_alternate_datastream___base64_content_filter`'
-how_to_implement: Target environment must ingest sysmon data, specifically Event ID 15.
+search: '`sysmon` EventCode=15 NOT Contents IN ("-","[ZoneTransfer]*") | regex TargetFilename="(?<!\/)\b\w+(\.\w+)?:\w+(\.\w+)?$"
+  | regex Contents="(?:[A-Za-z0-9+/]{128,})(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$"
+  | eval file_name = replace(TargetFilename,"(.*\\\)",""), process = Image , file_path
+  = TargetFilename , base64 = Contents, file_hash = coalesce(SHA256,SHA1,MD5,Hash)
+  | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user
+  by dest, process, process_guid, file_name, file_path, file_hash, base64 | `base64decode(base64)`
+  | fields - base64 | rename base64_decode as command | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_alternate_datastream___base64_content_filter`'
+how_to_implement: Target environment must ingest sysmon data, specifically Event ID
+  15.
 known_false_positives: Unknown
 references:
 - https://gist.github.com/api0cradle/cdd2d0d0ec9abb686f0e89306e277b8f
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: Base64 content written to an NTFS alternate data stream by $user$, see command field for details.
+  message: Base64 content written to an NTFS alternate data stream by $user$, see
+    command field for details.
   mitre_attack_id:
   - T1564
   - T1564.004
@@ -53,17 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - TargetFilename
-  - Image
-  - Contents
-  - file_hash
-  - process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.004/ads_abuse/ads_abuse_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.004/ads_abuse/ads_abuse_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_alternate_datastream___executable_content.yml b/detections/endpoint/windows_alternate_datastream___executable_content.yml
index 8ce8a937bf..984e2db9a0 100644
--- a/detections/endpoint/windows_alternate_datastream___executable_content.yml
+++ b/detections/endpoint/windows_alternate_datastream___executable_content.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the writing of data with an IMPHASH value to an Alternate Data Stream (ADS) in the NTFS file system. It leverages Sysmon Event ID 15 and regex to identify files with a Portable Executable (PE) structure. This activity is significant as it may indicate a threat actor staging malicious code in hidden areas for persistence or future execution. If confirmed malicious, this could allow attackers to execute hidden code, maintain persistence, or escalate privileges within the environment.
+description: The following analytic detects the writing of data with an IMPHASH value
+  to an Alternate Data Stream (ADS) in the NTFS file system. It leverages Sysmon Event
+  ID 15 and regex to identify files with a Portable Executable (PE) structure. This
+  activity is significant as it may indicate a threat actor staging malicious code
+  in hidden areas for persistence or future execution. If confirmed malicious, this
+  could allow attackers to execute hidden code, maintain persistence, or escalate
+  privileges within the environment.
 data_source:
 - Sysmon EventID 15
-search: '`sysmon` EventCode=15 IMPHASH!=00000000000000000000000000000000 | regex TargetFilename="(?<!\/)\b\w+(\.\w+)?:\w+(\.\w+)?$" | eval file_name = replace(TargetFilename,"(.*\\\)",""), process = Image , file_path = TargetFilename, file_hash = coalesce(SHA256,SHA1,MD5,Hash) | stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by dest, process, process_guid, file_name, file_path, file_hash, IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_alternate_datastream___executable_content_filter`'
-how_to_implement: Target environment must ingest Sysmon data, specifically Event ID 15, and import hashing/imphash must be enabled within Sysmon.
+search: '`sysmon` EventCode=15 IMPHASH!=00000000000000000000000000000000 | regex TargetFilename="(?<!\/)\b\w+(\.\w+)?:\w+(\.\w+)?$"
+  | eval file_name = replace(TargetFilename,"(.*\\\)",""), process = Image , file_path
+  = TargetFilename, file_hash = coalesce(SHA256,SHA1,MD5,Hash) | stats count min(_time)
+  as firstTime max(_time) as lastTime values(user) as user by dest, process, process_guid,
+  file_name, file_path, file_hash, IMPHASH | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `windows_alternate_datastream___executable_content_filter`'
+how_to_implement: Target environment must ingest Sysmon data, specifically Event ID
+  15, and import hashing/imphash must be enabled within Sysmon.
 known_false_positives: Unknown
 references:
 - https://car.mitre.org/analytics/CAR-2020-08-001/
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: Base64 content written to an NTFS alternate data stream by $user$, see command field for details.
+  message: Base64 content written to an NTFS alternate data stream by $user$, see
+    command field for details.
   mitre_attack_id:
   - T1564
   - T1564.004
@@ -55,19 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - TargetFilename
-  - Image
-  - Contents
-  - file_hash
-  - process_guid
-  - IMPHASH
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.004/ads_abuse/ads_abuse_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.004/ads_abuse/ads_abuse_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_alternate_datastream___process_execution.yml b/detections/endpoint/windows_alternate_datastream___process_execution.yml
index d0dce48248..d189a01a29 100644
--- a/detections/endpoint/windows_alternate_datastream___process_execution.yml
+++ b/detections/endpoint/windows_alternate_datastream___process_execution.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a process attempts to execute a file from within an NTFS file system alternate data stream. This detection leverages process execution data from sources like Windows process monitoring or Sysmon Event ID 1, focusing on specific processes known for such behavior. This activity is significant because alternate data streams can be used by threat actors to hide malicious code, making it difficult to detect. If confirmed malicious, this could allow an attacker to execute hidden code, potentially leading to unauthorized actions and further compromise of the system.
+description: The following analytic detects when a process attempts to execute a file
+  from within an NTFS file system alternate data stream. This detection leverages
+  process execution data from sources like Windows process monitoring or Sysmon Event
+  ID 1, focusing on specific processes known for such behavior. This activity is significant
+  because alternate data streams can be used by threat actors to hide malicious code,
+  making it difficult to detect. If confirmed malicious, this could allow an attacker
+  to execute hidden code, potentially leading to unauthorized actions and further
+  compromise of the system.
 data_source:
 - Windows Event Log Security 4688
 - Sysmon EventID 1
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime values(Processes.process_current_directory) as directory from datamodel=Endpoint.Processes where Processes.parent_process_name != "unknown" Processes.process_name IN ("appvlp.exe","bitsadmin.exe","control.exe","cscript.exe","forfiles.exe","ftp.exe","mavinject.exe","mshta.exe","powershell.exe","powershell_ise.exe","pwsh.exe","regini.exe","regscr32.exe","rundll32.exe","sc.exe","wmic.exe","wscript.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | regex process="(\b)\w+(\.\w+)?:\w+(\.\w{2,4})(?!\.)(\b|\s|&)" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_alternate_datastream___process_execution_filter`'
-how_to_implement: Target environment must ingest process execution data sources such as Windows process monitoring and/or Sysmon EventID 1.
-known_false_positives: False positives may be generated by process executions within the commandline, regex has been provided to minimize the possibilty.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime values(Processes.process_current_directory)
+  as directory from datamodel=Endpoint.Processes where Processes.parent_process_name
+  != "unknown" Processes.process_name IN ("appvlp.exe","bitsadmin.exe","control.exe","cscript.exe","forfiles.exe","ftp.exe","mavinject.exe","mshta.exe","powershell.exe","powershell_ise.exe","pwsh.exe","regini.exe","regscr32.exe","rundll32.exe","sc.exe","wmic.exe","wscript.exe")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | regex process="(\b)\w+(\.\w+)?:\w+(\.\w{2,4})(?!\.)(\b|\s|&)" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_alternate_datastream___process_execution_filter`'
+how_to_implement: Target environment must ingest process execution data sources such
+  as Windows process monitoring and/or Sysmon EventID 1.
+known_false_positives: False positives may be generated by process executions within
+  the commandline, regex has been provided to minimize the possibilty.
 references:
 - https://gist.github.com/api0cradle/cdd2d0d0ec9abb686f0e89306e277b8f
 - https://car.mitre.org/analytics/CAR-2020-08-001/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: The $process_name$ process was executed by $user$ using data from an NTFS alternate data stream.
+  message: The $process_name$ process was executed by $user$ using data from an NTFS
+    alternate data stream.
   mitre_attack_id:
   - T1564
   - T1564.004
@@ -53,19 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.004/ads_abuse/ads_abuse_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.004/ads_abuse/ads_abuse_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_apache_benchmark_binary.yml b/detections/endpoint/windows_apache_benchmark_binary.yml
index 3b64b19f68..d397c319a0 100644
--- a/detections/endpoint/windows_apache_benchmark_binary.yml
+++ b/detections/endpoint/windows_apache_benchmark_binary.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the Apache Benchmark binary (ab.exe), commonly used by MetaSploit payloads. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the original file name is ab.exe. This activity is significant as it may indicate the presence of a MetaSploit attack, which uses Apache Benchmark to generate malicious payloads. If confirmed malicious, this could lead to unauthorized network connections, further system compromise, and potential data exfiltration. Immediate investigation is required to determine the intent and scope of the activity.
+description: The following analytic detects the execution of the Apache Benchmark
+  binary (ab.exe), commonly used by MetaSploit payloads. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process creation events where the
+  original file name is ab.exe. This activity is significant as it may indicate the
+  presence of a MetaSploit attack, which uses Apache Benchmark to generate malicious
+  payloads. If confirmed malicious, this could lead to unauthorized network connections,
+  further system compromise, and potential data exfiltration. Immediate investigation
+  is required to determine the intent and scope of the activity.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=ab.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_apache_benchmark_binary_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as there is a small subset of binaries that contain the original file name of ab.exe. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=ab.exe
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_apache_benchmark_binary_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as there is a small subset
+  of binaries that contain the original file name of ab.exe. Filter as needed.
 references:
 - https://seclists.org/metasploit/2013/q3/13
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: A known MetaSploit default payload has been identified on $dest$ ran by $user$, $parent_process_name$ spawning $process_name$.
+  message: A known MetaSploit default payload has been identified on $dest$ ran by
+    $user$, $parent_process_name$ spawning $process_name$.
   mitre_attack_id:
   - T1059
   observable:
@@ -54,23 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/metasploit/apachebench_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/metasploit/apachebench_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_app_layer_protocol_qakbot_namedpipe.yml b/detections/endpoint/windows_app_layer_protocol_qakbot_namedpipe.yml
index 6726f3ca23..a7b0c32795 100644
--- a/detections/endpoint/windows_app_layer_protocol_qakbot_namedpipe.yml
+++ b/detections/endpoint/windows_app_layer_protocol_qakbot_namedpipe.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a suspicious process creating or connecting to a potential Qakbot named pipe. It leverages Sysmon EventCodes 17 and 18, focusing on specific processes known to be abused by Qakbot and identifying randomly generated named pipes in GUID form. This activity is significant as Qakbot malware uses named pipes for inter-process communication after code injection, facilitating data theft. If confirmed malicious, this behavior could indicate a Qakbot infection, leading to unauthorized data access and potential exfiltration from the compromised host.
+description: The following analytic detects a suspicious process creating or connecting
+  to a potential Qakbot named pipe. It leverages Sysmon EventCodes 17 and 18, focusing
+  on specific processes known to be abused by Qakbot and identifying randomly generated
+  named pipes in GUID form. This activity is significant as Qakbot malware uses named
+  pipes for inter-process communication after code injection, facilitating data theft.
+  If confirmed malicious, this behavior could indicate a Qakbot infection, leading
+  to unauthorized data access and potential exfiltration from the compromised host.
 data_source:
 - Sysmon EventID 17
 - Sysmon EventID 18
-search: '`sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe") Image IN ("*\\calc.exe", "*\\notepad.exe", "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe") | regex PipeName="^\\\{[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{8}" | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessGuid ProcessId PipeName SecurityID EventCode dest UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_app_layer_protocol_qakbot_namedpipe_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, pipename, processguid and named pipe event type from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe")
+  Image IN ("*\\calc.exe", "*\\notepad.exe", "*\\rdpclip.exe", "*\\explorer.exe",
+  "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", "*\\dxdiag.exe", "*\\mobsync.exe",
+  "*\\msra.exe", "*\\xwizard.exe") | regex PipeName="^\\\{[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{8}"
+  | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType
+  ProcessGuid ProcessId PipeName SecurityID EventCode dest UserID | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_app_layer_protocol_qakbot_namedpipe_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, pipename, processguid and named pipe event type from
+  your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of
+  the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://strontic.github.io/xcyclopedia/library/wermgr.exe-0F652BF7ADA772981E8AAB0D108FCC92.html
@@ -22,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - EventType
-  - ProcessGuid
-  - ProcessId
-  - PipeName
-  - dest
-  - UserID
-  - SecurityID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_app_layer_protocol_wermgr_connect_to_namedpipe.yml b/detections/endpoint/windows_app_layer_protocol_wermgr_connect_to_namedpipe.yml
index 51ccd4d722..20e1c42251 100644
--- a/detections/endpoint/windows_app_layer_protocol_wermgr_connect_to_namedpipe.yml
+++ b/detections/endpoint/windows_app_layer_protocol_wermgr_connect_to_namedpipe.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the wermgr.exe process creating or connecting to a named pipe. It leverages Sysmon EventCodes 17 and 18 to identify these actions. This activity is significant because wermgr.exe, a legitimate Windows OS Problem Reporting application, is often abused by malware such as Trickbot and Qakbot to execute malicious code. If confirmed malicious, this behavior could indicate that an attacker has injected code into wermgr.exe, potentially allowing them to communicate covertly, escalate privileges, or persist within the environment.
+description: The following analytic detects the wermgr.exe process creating or connecting
+  to a named pipe. It leverages Sysmon EventCodes 17 and 18 to identify these actions.
+  This activity is significant because wermgr.exe, a legitimate Windows OS Problem
+  Reporting application, is often abused by malware such as Trickbot and Qakbot to
+  execute malicious code. If confirmed malicious, this behavior could indicate that
+  an attacker has injected code into wermgr.exe, potentially allowing them to communicate
+  covertly, escalate privileges, or persist within the environment.
 data_source:
 - Sysmon EventID 17
 - Sysmon EventID 18
-search: '`sysmon` EventCode IN (17, 18) Image= "*\\wermgr.exe" EventType IN ( "CreatePipe", "ConnectPipe") | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessGuid ProcessId PipeName SecurityID EventCode dest UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_app_layer_protocol_wermgr_connect_to_namedpipe_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, pipename, processguid and named pipe event type from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode IN (17, 18) Image= "*\\wermgr.exe" EventType IN ( "CreatePipe",
+  "ConnectPipe") | stats  min(_time) as firstTime max(_time) as lastTime count by
+  Image EventType ProcessGuid ProcessId PipeName SecurityID EventCode dest UserID
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_app_layer_protocol_wermgr_connect_to_namedpipe_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, pipename, processguid and named pipe event type from
+  your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of
+  the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://strontic.github.io/xcyclopedia/library/wermgr.exe-0F652BF7ADA772981E8AAB0D108FCC92.html
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: wermgr.exe process is creating or connecting to a named pipe $PipeName$ in $dest$
+  message: wermgr.exe process is creating or connecting to a named pipe $PipeName$
+    in $dest$
   mitre_attack_id:
   - T1071
   observable:
@@ -42,20 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - EventType
-  - ProcessGuid
-  - ProcessId
-  - PipeName
-  - dest
-  - UserID
-  - SecurityID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr2/sysmon_wermgr2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr2/sysmon_wermgr2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_application_layer_protocol_rms_radmin_tool_namedpipe.yml b/detections/endpoint/windows_application_layer_protocol_rms_radmin_tool_namedpipe.yml
index 9c644b0f2f..b0307dc4bd 100644
--- a/detections/endpoint/windows_application_layer_protocol_rms_radmin_tool_namedpipe.yml
+++ b/detections/endpoint/windows_application_layer_protocol_rms_radmin_tool_namedpipe.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of default or publicly known named pipes associated with the RMX remote admin tool. It leverages Sysmon EventCodes 17 and 18 to identify named pipe creation and connection events. This activity is significant as the RMX tool has been abused by adversaries and malware like Azorult to collect data from targeted hosts. If confirmed malicious, this could indicate unauthorized remote administration capabilities, leading to data exfiltration or further compromise of the affected system. Immediate investigation is required to determine the legitimacy of this tool's presence.
+description: The following analytic detects the use of default or publicly known named
+  pipes associated with the RMX remote admin tool. It leverages Sysmon EventCodes
+  17 and 18 to identify named pipe creation and connection events. This activity is
+  significant as the RMX tool has been abused by adversaries and malware like Azorult
+  to collect data from targeted hosts. If confirmed malicious, this could indicate
+  unauthorized remote administration capabilities, leading to data exfiltration or
+  further compromise of the affected system. Immediate investigation is required to
+  determine the legitimacy of this tool's presence.
 data_source:
 - Sysmon EventID 17
 - Sysmon EventID 18
-search: '`sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe") PipeName IN ("\\RManFUSServerNotify32", "\\RManFUSCallbackNotify32", "\\RMSPrint*") | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessId PipeName dest UserID | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_application_layer_protocol_rms_radmin_tool_namedpipe_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: False positives may be present. Filter based on pipe name or process.
+search: '`sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe")
+  PipeName IN ("\\RManFUSServerNotify32", "\\RManFUSCallbackNotify32", "\\RMSPrint*")
+  | stats  min(_time) as firstTime max(_time) as lastTime count by Image EventType
+  ProcessId PipeName dest UserID | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_application_layer_protocol_rms_radmin_tool_namedpipe_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: False positives may be present. Filter based on pipe name or
+  process.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 - https://attack.mitre.org/techniques/T1071/
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - EventType
-  - ProcessId
-  - PipeName
-  - dest
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_applocker_block_events.yml b/detections/endpoint/windows_applocker_block_events.yml
index de36b57c2a..17495ca154 100644
--- a/detections/endpoint/windows_applocker_block_events.yml
+++ b/detections/endpoint/windows_applocker_block_events.yml
@@ -6,9 +6,23 @@ author: Michael Haag, Splunk
 data_source: []
 type: Anomaly
 status: production
-description: The following analytic detects attempts to bypass application restrictions by identifying Windows AppLocker policy violations. It leverages Windows AppLocker event logs, specifically EventCodes 8007, 8004, 8022, 8025, 8029, and 8040, to pinpoint blocked actions. This activity is significant for a SOC as it highlights potential unauthorized application executions, which could indicate malicious intent or policy circumvention. If confirmed malicious, this activity could allow an attacker to execute unauthorized applications, potentially leading to further system compromise or data exfiltration.
-search: '`applocker`  EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) | spath input=UserData_Xml | rename RuleAndFileData.* as *, TargetUser as user, Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath, EventCode | lookup applockereventcodes EventCode OUTPUT Description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_applocker_block_events_filter`'
-how_to_implement: To implement this analytic, you must be ingesting Windows AppLocker logs into Splunk. Ensure proper logging is setup for AppLocker and data is being ingested into Splunk.
+description: The following analytic detects attempts to bypass application restrictions
+  by identifying Windows AppLocker policy violations. It leverages Windows AppLocker
+  event logs, specifically EventCodes 8007, 8004, 8022, 8025, 8029, and 8040, to pinpoint
+  blocked actions. This activity is significant for a SOC as it highlights potential
+  unauthorized application executions, which could indicate malicious intent or policy
+  circumvention. If confirmed malicious, this activity could allow an attacker to
+  execute unauthorized applications, potentially leading to further system compromise
+  or data exfiltration.
+search: '`applocker`  EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) | spath input=UserData_Xml
+  | rename RuleAndFileData.* as *, TargetUser as user, Computer as dest  | stats count
+  min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user,
+  TargetProcessId, FilePath, FullFilePath, EventCode | lookup applockereventcodes
+  EventCode OUTPUT Description | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_applocker_block_events_filter`'
+how_to_implement: To implement this analytic, you must be ingesting Windows AppLocker
+  logs into Splunk. Ensure proper logging is setup for AppLocker and data is being
+  ingested into Splunk.
 known_false_positives: Administrators may legitimately use AppLocker to allow applications.
 references:
 - https://attack.mitre.org/techniques/T1218
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +60,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - dest
-  - PolicyName
-  - RuleId
-  - user
-  - TargetProcessId
-  - FilePath
-  - FullFilePath
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script
diff --git a/detections/endpoint/windows_applocker_execution_from_uncommon_locations.yml b/detections/endpoint/windows_applocker_execution_from_uncommon_locations.yml
index 5da8951f37..e90f6d483c 100644
--- a/detections/endpoint/windows_applocker_execution_from_uncommon_locations.yml
+++ b/detections/endpoint/windows_applocker_execution_from_uncommon_locations.yml
@@ -6,10 +6,31 @@ author: Michael Haag, Splunk
 data_source: []
 type: Hunting
 status: production
-description: The following analytic identifies the execution of applications or scripts from uncommon or suspicious file paths, potentially indicating malware or unauthorized activity. It leverages Windows AppLocker event logs and uses statistical analysis to detect anomalies. By calculating the average and standard deviation of execution counts per file path, it flags paths with execution counts significantly higher than expected. This behavior is significant as it can uncover malicious activities or policy violations. If confirmed malicious, this activity could allow attackers to execute unauthorized code, leading to potential system compromise or data breaches.
-search: '`applocker` | spath input=UserData_Xml | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user | stats count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath | eventstats avg(count) as avg, stdev(count) as stdev | eval upperBound=(avg+stdev*2), anomaly=if(count > upperBound, "Yes", "No") | where anomaly="Yes" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_applocker_execution_from_uncommon_locations_filter`'
-how_to_implement: The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk. Note that, an additional method to reduce any false positives would be to add the specific EventCodes - 8003 or 8004 and filter from there. Upon tuning, modify to Anomaly or TTP.
-known_false_positives: False positives are possible if legitimate users are executing applications from file paths that are not permitted by AppLocker. It is recommended to investigate the context of the application execution to determine if it is malicious or not. Modify the threshold as needed to reduce false positives.
+description: The following analytic identifies the execution of applications or scripts
+  from uncommon or suspicious file paths, potentially indicating malware or unauthorized
+  activity. It leverages Windows AppLocker event logs and uses statistical analysis
+  to detect anomalies. By calculating the average and standard deviation of execution
+  counts per file path, it flags paths with execution counts significantly higher
+  than expected. This behavior is significant as it can uncover malicious activities
+  or policy violations. If confirmed malicious, this activity could allow attackers
+  to execute unauthorized code, leading to potential system compromise or data breaches.
+search: '`applocker` | spath input=UserData_Xml | rename RuleAndFileData.* as *, Computer
+  as dest, TargetUser AS user | stats count min(_time) as firstTime max(_time) as
+  lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath
+  | eventstats avg(count) as avg, stdev(count) as stdev | eval upperBound=(avg+stdev*2),
+  anomaly=if(count > upperBound, "Yes", "No") | where anomaly="Yes" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_applocker_execution_from_uncommon_locations_filter`'
+how_to_implement: The analytic is designed to be run against Windows AppLocker event
+  logs collected from endpoints with AppLocker enabled. If using Microsoft Defender
+  for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match
+  the block events for AppLocker. The analytic requires the AppLocker event logs to
+  be ingested into Splunk. Note that, an additional method to reduce any false positives
+  would be to add the specific EventCodes - 8003 or 8004 and filter from there. Upon
+  tuning, modify to Anomaly or TTP.
+known_false_positives: False positives are possible if legitimate users are executing
+  applications from file paths that are not permitted by AppLocker. It is recommended
+  to investigate the context of the application execution to determine if it is malicious
+  or not. Modify the threshold as needed to reduce false positives.
 references:
 - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting
 - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker
@@ -31,15 +52,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - FullFilePath
-  - dest
-  - user
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script
diff --git a/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml b/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml
index 576cbf6b58..3499680b06 100644
--- a/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml
+++ b/detections/endpoint/windows_applocker_privilege_escalation_via_unauthorized_bypass.yml
@@ -6,10 +6,31 @@ author: Michael Haag, Splunk
 data_source: []
 type: TTP
 status: production
-description: The following analytic utilizes Windows AppLocker event logs to identify attempts to bypass application restrictions. AppLocker is a feature that allows administrators to specify which applications are permitted to run on a system. This analytic is designed to identify attempts to bypass these restrictions, which could be indicative of an attacker attempting to escalate privileges. The analytic uses EventCodes 8007, 8004, 8022, 8025, 8029, and 8040 to identify these attempts. The analytic will identify the host, full file path, and target user associated with the bypass attempt. These EventCodes are related to block events and focus on 5 attempts or more.
-search: '`applocker` EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) | spath input=UserData_Xml | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user | stats count AS attempt_count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName, RuleId, user, TargetProcessId, FilePath, FullFilePath, EventCode | where attempt_count > 5 | sort - attempt_count | lookup applockereventcodes EventCode OUTPUT Description | `windows_applocker_privilege_escalation_via_unauthorized_bypass_filter`'
-how_to_implement: The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk.
-known_false_positives: False positives are possible if legitimate users are attempting to bypass application restrictions. This could occur if a user is attempting to run an application that is not permitted by AppLocker. It is recommended to investigate the context of the bypass attempt to determine if it is malicious or not. Modify the threshold as needed to reduce false positives.
+description: The following analytic utilizes Windows AppLocker event logs to identify
+  attempts to bypass application restrictions. AppLocker is a feature that allows
+  administrators to specify which applications are permitted to run on a system. This
+  analytic is designed to identify attempts to bypass these restrictions, which could
+  be indicative of an attacker attempting to escalate privileges. The analytic uses
+  EventCodes 8007, 8004, 8022, 8025, 8029, and 8040 to identify these attempts. The
+  analytic will identify the host, full file path, and target user associated with
+  the bypass attempt. These EventCodes are related to block events and focus on 5
+  attempts or more.
+search: '`applocker` EventCode IN (8007, 8004, 8022, 8025, 8029, 8040) | spath input=UserData_Xml
+  | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user | stats count
+  AS attempt_count min(_time) as firstTime max(_time) as lastTime by dest, PolicyName,
+  RuleId, user, TargetProcessId, FilePath, FullFilePath, EventCode | where attempt_count
+  > 5 | sort - attempt_count | lookup applockereventcodes EventCode OUTPUT Description
+  | `windows_applocker_privilege_escalation_via_unauthorized_bypass_filter`'
+how_to_implement: The analytic is designed to be run against Windows AppLocker event
+  logs collected from endpoints with AppLocker enabled. If using Microsoft Defender
+  for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match
+  the block events for AppLocker. The analytic requires the AppLocker event logs to
+  be ingested into Splunk.
+known_false_positives: False positives are possible if legitimate users are attempting
+  to bypass application restrictions. This could occur if a user is attempting to
+  run an application that is not permitted by AppLocker. It is recommended to investigate
+  the context of the bypass attempt to determine if it is malicious or not. Modify
+  the threshold as needed to reduce false positives.
 references:
 - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting
 - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker
@@ -19,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,15 +66,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Computer
-  - FullFilePath
-  - TargetUser
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script
diff --git a/detections/endpoint/windows_applocker_rare_application_launch_detection.yml b/detections/endpoint/windows_applocker_rare_application_launch_detection.yml
index 4b7293df30..f76048128f 100644
--- a/detections/endpoint/windows_applocker_rare_application_launch_detection.yml
+++ b/detections/endpoint/windows_applocker_rare_application_launch_detection.yml
@@ -6,10 +6,28 @@ author: Michael Haag, Splunk
 data_source: []
 type: Hunting
 status: production
-description: The following analytic detects the launch of rarely used applications within the environment, which may indicate the use of potentially malicious software or tools by attackers. It leverages Windows AppLocker event logs, aggregating application launch counts over time and flagging those that significantly deviate from the norm. This behavior is significant as it helps identify unusual application activity that could signal a security threat. If confirmed malicious, this activity could allow attackers to execute unauthorized code, potentially leading to further compromise of the system.
-search: '`applocker` | spath input=UserData_Xml | rename RuleAndFileData.* as *, Computer as dest, TargetUser AS user | stats dc(_time) as days, count by FullFilePath dest user | eventstats avg(count) as avg, stdev(count) as stdev | eval upperBound=(avg+stdev*3), lowerBound=(avg-stdev*3) | where count > upperBound OR count < lowerBound | `windows_applocker_rare_application_launch_detection_filter`'
-how_to_implement: The analytic is designed to be run against Windows AppLocker event logs collected from endpoints with AppLocker enabled. If using Microsoft Defender for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match the block events for AppLocker. The analytic requires the AppLocker event logs to be ingested into Splunk. Note that, an additional method to reduce any false positives would be to add the specific EventCodes - 8003 or 8004 and filter from there.
-known_false_positives: False positives are possible if legitimate users are launching applications that are not permitted by AppLocker. It is recommended to investigate the context of the application launch to determine if it is malicious or not. Modify the threshold as needed to reduce false positives.
+description: The following analytic detects the launch of rarely used applications
+  within the environment, which may indicate the use of potentially malicious software
+  or tools by attackers. It leverages Windows AppLocker event logs, aggregating application
+  launch counts over time and flagging those that significantly deviate from the norm.
+  This behavior is significant as it helps identify unusual application activity that
+  could signal a security threat. If confirmed malicious, this activity could allow
+  attackers to execute unauthorized code, potentially leading to further compromise
+  of the system.
+search: '`applocker` | spath input=UserData_Xml | rename RuleAndFileData.* as *, Computer
+  as dest, TargetUser AS user | stats dc(_time) as days, count by FullFilePath dest
+  user | eventstats avg(count) as avg, stdev(count) as stdev | eval upperBound=(avg+stdev*3),
+  lowerBound=(avg-stdev*3) | where count > upperBound OR count < lowerBound | `windows_applocker_rare_application_launch_detection_filter`'
+how_to_implement: The analytic is designed to be run against Windows AppLocker event
+  logs collected from endpoints with AppLocker enabled. If using Microsoft Defender
+  for Endpoint (MDE), modify the analytic to use EventTypes/ActionTypes that match
+  the block events for AppLocker. The analytic requires the AppLocker event logs to
+  be ingested into Splunk. Note that, an additional method to reduce any false positives
+  would be to add the specific EventCodes - 8003 or 8004 and filter from there.
+known_false_positives: False positives are possible if legitimate users are launching
+  applications that are not permitted by AppLocker. It is recommended to investigate
+  the context of the application launch to determine if it is malicious or not. Modify
+  the threshold as needed to reduce false positives.
 references:
 - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/applocker/using-event-viewer-with-applocker
 - https://learn.microsoft.com/en-us/windows/security/application-security/application-control/windows-defender-application-control/operations/querying-application-control-events-centrally-using-advanced-hunting
@@ -19,7 +37,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 50
-  message: An application launch that deviates from the norm was detected on a host $dest$.
+  message: An application launch that deviates from the norm was detected on a host
+    $dest$.
   mitre_attack_id:
   - T1218
   observable:
@@ -31,15 +50,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - FullFilePath
-  - dest
-  - user
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562/applocker/applocker.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-AppLocker/MSI and Script
diff --git a/detections/endpoint/windows_archive_collected_data_via_powershell.yml b/detections/endpoint/windows_archive_collected_data_via_powershell.yml
index 10508dc420..898c80e47f 100644
--- a/detections/endpoint/windows_archive_collected_data_via_powershell.yml
+++ b/detections/endpoint/windows_archive_collected_data_via_powershell.yml
@@ -7,9 +7,20 @@ status: production
 type: Anomaly
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the use of PowerShell scripts to archive files into a temporary folder. It leverages PowerShell Script Block Logging, specifically monitoring for the `Compress-Archive` command targeting the `Temp` directory. This activity is significant as it may indicate an adversary's attempt to collect and compress data for exfiltration. If confirmed malicious, this behavior could lead to unauthorized data access and exfiltration, posing a severe risk to sensitive information and overall network security.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Compress-Archive*"  ScriptBlockText = "*\\Temp\\*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_archive_collected_data_via_powershell_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+description: The following analytic detects the use of PowerShell scripts to archive
+  files into a temporary folder. It leverages PowerShell Script Block Logging, specifically
+  monitoring for the `Compress-Archive` command targeting the `Temp` directory. This
+  activity is significant as it may indicate an adversary's attempt to collect and
+  compress data for exfiltration. If confirmed malicious, this behavior could lead
+  to unauthorized data access and exfiltration, posing a severe risk to sensitive
+  information and overall network security.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Compress-Archive*"  ScriptBlockText
+  = "*\\Temp\\*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode
+  ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_archive_collected_data_via_powershell_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: powershell may used this function to archive data.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - dest
-  - user
-  - Score
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/powershell_archive/powershell_archive.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/powershell_archive/powershell_archive.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_archive_collected_data_via_rar.yml b/detections/endpoint/windows_archive_collected_data_via_rar.yml
index 2839a94042..0e1773bf41 100644
--- a/detections/endpoint/windows_archive_collected_data_via_rar.yml
+++ b/detections/endpoint/windows_archive_collected_data_via_rar.yml
@@ -9,9 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="Rar.exe" OR Processes.original_file_name = "Rar.exe" AND Processes.process = "*a*" Processes.process = "* -ep1*" Processes.process = "* -r*" Processes.process = "* -y*" Processes.process = "* -v5m*" Processes.process = "* -m1*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_archive_collected_data_via_rar_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies the execution of RAR utilities to archive
+  files on a system. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process names, GUIDs, and command-line arguments. This activity
+  is significant as threat actors, including red-teamers and malware like DarkGate,
+  use RAR archiving to compress and exfiltrate collected data from compromised hosts.
+  If confirmed malicious, this behavior could lead to the unauthorized transfer of
+  sensitive information to command and control servers, posing a severe risk to data
+  confidentiality and integrity.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="Rar.exe"
+  OR Processes.original_file_name = "Rar.exe" AND Processes.process = "*a*" Processes.process
+  = "* -ep1*" Processes.process = "* -r*" Processes.process = "* -y*" Processes.process
+  = "* -v5m*" Processes.process = "* -m1*" by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_archive_collected_data_via_rar_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: user and network administrator can execute this command.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,25 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility_darkgate/rar_sys.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility_darkgate/rar_sys.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml b/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml
index 687459d09a..6f7359aca3 100644
--- a/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml
+++ b/detections/endpoint/windows_archived_collected_data_in_temp_folder.yml
@@ -7,14 +7,19 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic detects the creation of archived files in a temporary folder, which may contain collected data. This behavior is often associated with malicious activity, where attackers compress sensitive information before exfiltration. The detection focuses on monitoring specific directories, such as temp folders, for the presence of newly created archive files (e.g., .zip, .rar, .tar). By identifying this pattern, security teams can quickly respond to potential data collection and exfiltration attempts, minimizing the risk of data breaches and improving overall threat detection.
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem 
-  where Filesystem.file_name IN ("*.zip", "*.rar", "*.tar", "*.7z") Filesystem.file_path = "*\\temp\\*" 
-  by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time 
-  | `drop_dm_object_name(Filesystem)`
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `windows_archived_collected_data_in_temp_folder_filter`'
+description: The following analytic detects the creation of archived files in a temporary
+  folder, which may contain collected data. This behavior is often associated with
+  malicious activity, where attackers compress sensitive information before exfiltration.
+  The detection focuses on monitoring specific directories, such as temp folders,
+  for the presence of newly created archive files (e.g., .zip, .rar, .tar). By identifying
+  this pattern, security teams can quickly respond to potential data collection and
+  exfiltration attempts, minimizing the risk of data breaches and improving overall
+  threat detection.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.zip",
+  "*.rar", "*.tar", "*.7z") Filesystem.file_path = "*\\temp\\*" by _time Filesystem.file_name
+  Filesystem.file_path Filesystem.dest Filesystem.file_create_time | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_archived_collected_data_in_temp_folder_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
@@ -30,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/archived_in_temp_dir/braodo_zip_temp.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560/archived_in_temp_dir/braodo_zip_temp.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
-    sourcetype: XmlWinEventLog
\ No newline at end of file
+    sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_autoit3_execution.yml b/detections/endpoint/windows_autoit3_execution.yml
index 2e7c0ba389..a248aece4d 100644
--- a/detections/endpoint/windows_autoit3_execution.yml
+++ b/detections/endpoint/windows_autoit3_execution.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of AutoIt3, a scripting language often used for automating Windows GUI tasks and general scripting. It identifies instances where AutoIt3 or its variants are executed by searching for process names or original file names matching 'autoit3.exe'. This activity is significant because attackers frequently use AutoIt3 to automate malicious actions, such as executing malware. If confirmed malicious, this activity could lead to unauthorized code execution, system compromise, or further propagation of malware within the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("autoit3.exe", "autoit*.exe") OR Processes.original_file_name IN ("autoit3.exe", "autoit*.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_autoit3_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if the application is legitimately used, filter by user or endpoint as needed.
+description: The following analytic detects the execution of AutoIt3, a scripting
+  language often used for automating Windows GUI tasks and general scripting. It identifies
+  instances where AutoIt3 or its variants are executed by searching for process names
+  or original file names matching 'autoit3.exe'. This activity is significant because
+  attackers frequently use AutoIt3 to automate malicious actions, such as executing
+  malware. If confirmed malicious, this activity could lead to unauthorized code execution,
+  system compromise, or further propagation of malware within the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("autoit3.exe",
+  "autoit*.exe") OR Processes.original_file_name IN ("autoit3.exe", "autoit*.exe")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_autoit3_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if the application is legitimately
+  used, filter by user or endpoint as needed.
 references:
 - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   atomic_guid: []
   confidence: 100
   impact: 50
-  message: Execution of AutoIt3 detected. The source process is $parent_process_name$ and the destination process is $process_name$ on $dest$ by
+  message: Execution of AutoIt3 detected. The source process is $parent_process_name$
+    and the destination process is $process_name$ on $dest$ by
   mitre_attack_id:
   - T1059
   observable:
@@ -56,19 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.original_file_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml b/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml
index f7d3062408..048428e86e 100644
--- a/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml
+++ b/detections/endpoint/windows_autostart_execution_lsass_driver_registry_modification.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications to undocumented registry keys that allow a DLL to load into lsass.exe, potentially capturing credentials. It leverages the Endpoint.Registry data model to identify changes to \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt or \CurrentControlSet\Services\NTDS\LsaDbExtPt. This activity is significant as it indicates a possible attempt to inject malicious code into the Local Security Authority Subsystem Service (LSASS), which can lead to credential theft. If confirmed malicious, this could allow attackers to gain unauthorized access to sensitive information and escalate privileges within the environment.
+description: The following analytic detects modifications to undocumented registry
+  keys that allow a DLL to load into lsass.exe, potentially capturing credentials.
+  It leverages the Endpoint.Registry data model to identify changes to \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt
+  or \CurrentControlSet\Services\NTDS\LsaDbExtPt. This activity is significant as
+  it indicates a possible attempt to inject malicious code into the Local Security
+  Authority Subsystem Service (LSASS), which can lead to credential theft. If confirmed
+  malicious, this could allow attackers to gain unauthorized access to sensitive information
+  and escalate privileges within the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\CurrentControlSet\\Services\\NTDS\\DirectoryServiceExtPt","*\\CurrentControlSet\\Services\\NTDS\\LsaDbExtPt") by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_autostart_execution_lsass_driver_registry_modification_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives may be present on recent Windows Operating Systems. Filtering may be required based on process_name. In addition, look for non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by adding Endpoint.processes process_name to query to identify the process making the modification.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\CurrentControlSet\\Services\\NTDS\\DirectoryServiceExtPt","*\\CurrentControlSet\\Services\\NTDS\\LsaDbExtPt")
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest Registry.process_guid | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_autostart_execution_lsass_driver_registry_modification_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives may be present on recent Windows Operating
+  Systems. Filtering may be required based on process_name. In addition, look for
+  non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by
+  adding Endpoint.processes process_name to query to identify the process making the
+  modification.
 references:
 - https://blog.xpnsec.com/exploring-mimikatz-part-1/
 - https://github.com/oxfemale/LogonCredentialsSteal/tree/master/lsass_lib
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The registry values for DirectoryServiceExtPt or LsaDbExtPt were modified on $dest$.
+  message: The registry values for DirectoryServiceExtPt or LsaDbExtPt were modified
+    on $dest$.
   mitre_attack_id:
   - T1547.008
   observable:
@@ -42,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.008/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.008/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml b/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml
index 5b517cb3d3..dd5f0fd489 100644
--- a/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml
+++ b/detections/endpoint/windows_binary_proxy_execution_mavinject_dll_injection.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of mavinject.exe for DLL injection into running processes, identified by specific command-line parameters such as /INJECTRUNNING and /HMODULE. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because it indicates potential arbitrary code execution, a common tactic for malware deployment and persistence. If confirmed malicious, this could allow attackers to execute unauthorized code, escalate privileges, and maintain persistence within the environment, posing a severe security risk.
+description: The following analytic detects the use of mavinject.exe for DLL injection
+  into running processes, identified by specific command-line parameters such as /INJECTRUNNING
+  and /HMODULE. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant because it indicates potential arbitrary code execution, a common
+  tactic for malware deployment and persistence. If confirmed malicious, this could
+  allow attackers to execute unauthorized code, escalate privileges, and maintain
+  persistence within the environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mavinject.exe Processes.process IN ("*injectrunning*", "*hmodule=0x*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_binary_proxy_execution_mavinject_dll_injection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present, filter on DLL name or parent process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mavinject.exe
+  Processes.process IN ("*injectrunning*", "*hmodule=0x*") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_binary_proxy_execution_mavinject_dll_injection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present, filter on DLL name or parent
+  process.
 references:
 - https://attack.mitre.org/techniques/T1218/013/
 - https://posts.specterops.io/mavinject-exe-functionality-deconstructed-c29ab2cf5c0e
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting load a DLL.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting load a DLL.
   mitre_attack_id:
   - T1218.013
   - T1218
@@ -57,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.013/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.013/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml b/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml
index 10e59f1316..ca5ab733b6 100644
--- a/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml
+++ b/detections/endpoint/windows_boot_or_logon_autostart_execution_in_startup_folder.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of files in the Windows %startup% folder, a common persistence technique. It leverages the Endpoint.Filesystem data model to identify file creation events in this specific directory. This activity is significant because adversaries often use the startup folder to ensure their malicious code executes automatically upon system boot or user logon. If confirmed malicious, this could allow attackers to maintain persistence on the host, potentially leading to further system compromise and unauthorized access to sensitive information.
+description: The following analytic detects the creation of files in the Windows %startup%
+  folder, a common persistence technique. It leverages the Endpoint.Filesystem data
+  model to identify file creation events in this specific directory. This activity
+  is significant because adversaries often use the startup folder to ensure their
+  malicious code executes automatically upon system boot or user logon. If confirmed
+  malicious, this could allow attackers to maintain persistence on the host, potentially
+  leading to further system compromise and unauthorized access to sensitive information.
 data_source:
 - Sysmon EventID 11
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\Microsoft\\Windows\\Start Menu\\Programs\\Startup\\*" by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.user Filesystem.file_path Filesystem.process_guid Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_boot_or_logon_autostart_execution_in_startup_folder_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: Administrators may allow creation of script or exe in this path.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\Microsoft\\Windows\\Start
+  Menu\\Programs\\Startup\\*" by Filesystem.file_create_time Filesystem.process_id
+  Filesystem.file_name Filesystem.user Filesystem.file_path Filesystem.process_guid
+  Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_boot_or_logon_autostart_execution_in_startup_folder_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+known_false_positives: Administrators may allow creation of script or exe in this
+  path.
 references:
 - https://attack.mitre.org/techniques/T1204/002/
 - https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
-  - Filesystem.process_guid
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_bootloader_inventory.yml b/detections/endpoint/windows_bootloader_inventory.yml
index 6f84f5ab91..3b2212219f 100644
--- a/detections/endpoint/windows_bootloader_inventory.yml
+++ b/detections/endpoint/windows_bootloader_inventory.yml
@@ -6,10 +6,22 @@ author: Michael Haag, Splunk
 status: experimental
 type: Hunting
 data_source: []
-description: The following analytic identifies the bootloader paths on Windows endpoints. It leverages a PowerShell Scripted input to capture this data, which is then processed and aggregated using Splunk. Monitoring bootloader paths is significant for a SOC as it helps detect unauthorized modifications that could indicate bootkits or other persistent threats. If confirmed malicious, such activity could allow attackers to maintain persistence, bypass security controls, and potentially control the boot process, leading to full system compromise.
-search: '`bootloader_inventory` | stats count min(_time) as firstTime max(_time) as lastTime values(_raw) by host | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_bootloader_inventory_filter`'
-how_to_implement: To implement this analytic, a new stanza will need to be added to a inputs.conf and deployed to all or some Windows endpoints. https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 provides the stanza. If modifying the sourcetype, be sure to update the Macro for this analytic. Recommend running it daily, or weekly, depending on threat model.
-known_false_positives: No false positives here, only bootloaders. Filter as needed or create a lookup as a baseline.
+description: The following analytic identifies the bootloader paths on Windows endpoints.
+  It leverages a PowerShell Scripted input to capture this data, which is then processed
+  and aggregated using Splunk. Monitoring bootloader paths is significant for a SOC
+  as it helps detect unauthorized modifications that could indicate bootkits or other
+  persistent threats. If confirmed malicious, such activity could allow attackers
+  to maintain persistence, bypass security controls, and potentially control the boot
+  process, leading to full system compromise.
+search: '`bootloader_inventory` | stats count min(_time) as firstTime max(_time) as
+  lastTime values(_raw) by host | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_bootloader_inventory_filter`'
+how_to_implement: To implement this analytic, a new stanza will need to be added to
+  a inputs.conf and deployed to all or some Windows endpoints. https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707
+  provides the stanza. If modifying the sourcetype, be sure to update the Macro for
+  this analytic. Recommend running it daily, or weekly, depending on threat model.
+known_false_positives: No false positives here, only bootloaders. Filter as needed
+  or create a lookup as a baseline.
 references:
 - https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707
 - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/
@@ -34,7 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - _raw
   security_domain: endpoint
diff --git a/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml b/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml
index e7faf6e938..bb6e813112 100644
--- a/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml
+++ b/detections/endpoint/windows_bypass_uac_via_pkgmgr_tool.yml
@@ -9,10 +9,36 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of the deprecated 'pkgmgr.exe' process with an XML input file, which is unusual and potentially suspicious. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process execution details and command-line arguments. The significance lies in the deprecated status of 'pkgmgr.exe' and the use of XML files, which could indicate an attempt to bypass User Account Control (UAC). If confirmed malicious, this activity could allow an attacker to execute commands with elevated privileges, leading to potential system compromise and unauthorized changes.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  Processes.process_name = pkgmgr.exe Processes.process = "*.xml*" NOT(Processes.parent_process_path IN("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "*:\\Program Files*")) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process_path Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_bypass_uac_via_pkgmgr_tool_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present on recent Windows Operating Systems. Filtering may be required based on process_name. In addition, look for non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by adding Endpoint.processes process_name to query to identify the process making the modification.
+description: The following analytic detects the execution of the deprecated 'pkgmgr.exe'
+  process with an XML input file, which is unusual and potentially suspicious. This
+  detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on
+  process execution details and command-line arguments. The significance lies in the
+  deprecated status of 'pkgmgr.exe' and the use of XML files, which could indicate
+  an attempt to bypass User Account Control (UAC). If confirmed malicious, this activity
+  could allow an attacker to execute commands with elevated privileges, leading to
+  potential system compromise and unauthorized changes.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  Processes.process_name = pkgmgr.exe
+  Processes.process = "*.xml*" NOT(Processes.parent_process_path IN("*:\\windows\\system32\\*",
+  "*:\\windows\\syswow64\\*", "*:\\Program Files*")) by Processes.dest Processes.user
+  Processes.parent_process_name Processes.parent_process_path Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_bypass_uac_via_pkgmgr_tool_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present on recent Windows Operating
+  Systems. Filtering may be required based on process_name. In addition, look for
+  non-standard, unsigned, module loads into LSASS. If query is too noisy, modify by
+  adding Endpoint.processes process_name to query to identify the process making the
+  modification.
 references:
 - https://asec.ahnlab.com/en/17692/
 - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer.
@@ -22,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/pkgmgr_uac_bypass/pkgmgr_create_file.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/pkgmgr_uac_bypass/pkgmgr_create_file.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_cab_file_on_disk.yml b/detections/endpoint/windows_cab_file_on_disk.yml
index dbe74199ea..9dd4167e33 100644
--- a/detections/endpoint/windows_cab_file_on_disk.yml
+++ b/detections/endpoint/windows_cab_file_on_disk.yml
@@ -7,10 +7,30 @@ status: production
 type: Anomaly
 data_source:
 - Sysmon EventID 11
-description: The following analytic detects .cab files being written to disk. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on events where the file name is '*.cab' and the action is 'write'. This activity is significant as .cab files can be used to deliver malicious payloads, including embedded .url files that execute harmful code. If confirmed malicious, this behavior could lead to unauthorized code execution and potential system compromise. Analysts should review the file path and associated artifacts for further investigation.
-search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.cab) by Filesystem.dest Filesystem.action Filesystem.process_id Filesystem.file_name | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cab_file_on_disk_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will only be present if a process legitimately writes a .cab file to disk. Modify the analytic as needed by file path. Filter as needed.
+description: The following analytic detects .cab files being written to disk. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on events where
+  the file name is '*.cab' and the action is 'write'. This activity is significant
+  as .cab files can be used to deliver malicious payloads, including embedded .url
+  files that execute harmful code. If confirmed malicious, this behavior could lead
+  to unauthorized code execution and potential system compromise. Analysts should
+  review the file path and associated artifacts for further investigation.
+search: '| tstats `security_content_summariesonly` count values(Filesystem.file_path)
+  as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem
+  where (Filesystem.file_name=*.cab) by Filesystem.dest Filesystem.action Filesystem.process_id
+  Filesystem.file_name | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_cab_file_on_disk_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will only be present if a process legitimately
+  writes a .cab file to disk. Modify the analytic as needed by file path. Filter as
+  needed.
 references:
 - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,15 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Filesystem.dest
-  - Filesystem.action
-  - Filesystem.process_id
-  - Filesystem.file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/cab_files.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/cab_files.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_cached_domain_credentials_reg_query.yml b/detections/endpoint/windows_cached_domain_credentials_reg_query.yml
index ff09efbc7b..c4c4508523 100644
--- a/detections/endpoint/windows_cached_domain_credentials_reg_query.yml
+++ b/detections/endpoint/windows_cached_domain_credentials_reg_query.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a process command line querying the CachedLogonsCount registry value in the Winlogon registry. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and registry queries. Monitoring this activity is significant as it can indicate the use of post-exploitation tools like Winpeas, which gather information about login caching settings. If confirmed malicious, this activity could help attackers understand login caching configurations, potentially aiding in credential theft or lateral movement within the network.
+description: The following analytic identifies a process command line querying the
+  CachedLogonsCount registry value in the Winlogon registry. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions and registry queries. Monitoring this activity is significant as it can
+  indicate the use of post-exploitation tools like Winpeas, which gather information
+  about login caching settings. If confirmed malicious, this activity could help attackers
+  understand login caching configurations, potentially aiding in credential theft
+  or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process = "* query *" AND Processes.process = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*" AND Processes.process = "*CACHEDLOGONSCOUNT*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_cached_domain_credentials_reg_query_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process
+  = "* query *" AND Processes.process = "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\Winlogon*"
+  AND Processes.process = "*CACHEDLOGONSCOUNT*" by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_cached_domain_credentials_reg_query_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: a process with commandline $process$ tries to retrieve cache domain credential logon count in $dest$
+  message: a process with commandline $process$ tries to retrieve cache domain credential
+    logon count in $dest$
   mitre_attack_id:
   - T1003.005
   - T1003
@@ -46,25 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_change_default_file_association_for_no_file_ext.yml b/detections/endpoint/windows_change_default_file_association_for_no_file_ext.yml
index 583191cd9a..928cb9dddc 100644
--- a/detections/endpoint/windows_change_default_file_association_for_no_file_ext.yml
+++ b/detections/endpoint/windows_change_default_file_association_for_no_file_ext.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to change the default file association for files without an extension to open with Notepad.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns and registry modifications. This activity is significant as it can indicate an attempt to manipulate file handling behavior, a technique observed in APT and ransomware attacks like Prestige. If confirmed malicious, this could allow attackers to execute arbitrary code by tricking users into opening files, potentially leading to system compromise or data exfiltration.
+description: The following analytic detects attempts to change the default file association
+  for files without an extension to open with Notepad.exe. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on specific command-line
+  patterns and registry modifications. This activity is significant as it can indicate
+  an attempt to manipulate file handling behavior, a technique observed in APT and
+  ransomware attacks like Prestige. If confirmed malicious, this could allow attackers
+  to execute arbitrary code by tricking users into opening files, potentially leading
+  to system compromise or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_reg` AND Processes.process="* add *" AND Processes.process="* HKCR\\*" AND Processes.process="*\\shell\\open\\command*" AND  Processes.process= *Notepad.exe* by  Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | rex field=process "Notepad\.exe (?<file_name_association>.*$)" | rex field=file_name_association "\.(?<extension>[^\.]*$)" | where isnull(extension) and isnotnull(file_name_association) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_change_default_file_association_for_no_file_ext_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where `process_reg` AND Processes.process="*
+  add *" AND Processes.process="* HKCR\\*" AND Processes.process="*\\shell\\open\\command*"
+  AND  Processes.process= *Notepad.exe* by  Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | rex field=process "Notepad\.exe (?<file_name_association>.*$)"
+  | rex field=file_name_association "\.(?<extension>[^\.]*$)" | where isnull(extension)
+  and isnotnull(file_name_association) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_change_default_file_association_for_no_file_ext_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/
@@ -21,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: process with commandline $process$ set or change the file association of a file with no file extension in $dest$
+  message: process with commandline $process$ set or change the file association of
+    a file with no file extension in $dest$
   mitre_attack_id:
   - T1546.001
   - T1546
@@ -43,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml b/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml
index aa07400fb6..7dff457a19 100644
--- a/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml
+++ b/detections/endpoint/windows_clipboard_data_via_get_clipboard.yml
@@ -5,11 +5,22 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the PowerShell command 'Get-Clipboard' to retrieve clipboard data. It leverages PowerShell Script Block Logging (EventCode 4104) to identify instances where this command is used. This activity is significant because it can indicate an attempt to steal sensitive information such as usernames, passwords, or other confidential data copied to the clipboard. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, potentially compromising user accounts and other critical assets.
+description: The following analytic detects the execution of the PowerShell command
+  'Get-Clipboard' to retrieve clipboard data. It leverages PowerShell Script Block
+  Logging (EventCode 4104) to identify instances where this command is used. This
+  activity is significant because it can indicate an attempt to steal sensitive information
+  such as usernames, passwords, or other confidential data copied to the clipboard.
+  If confirmed malicious, this behavior could lead to unauthorized access to sensitive
+  information, potentially compromising user accounts and other critical assets.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-Clipboard*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_clipboard_data_via_get_clipboard_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-Clipboard*" | stats count
+  min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer
+  UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_clipboard_data_via_get_clipboard_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: It is possible there will be false positives, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1115/
@@ -21,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Powershell script $ScriptBlockText$ execute Get-Clipboard commandlet on $dest$
+  message: Powershell script $ScriptBlockText$ execute Get-Clipboard commandlet on
+    $dest$
   mitre_attack_id:
   - T1115
   observable:
@@ -47,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/powershell/windows-powershell-xml2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/powershell/windows-powershell-xml2.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml b/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml
index 07d53cfd84..484f637c63 100644
--- a/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml
+++ b/detections/endpoint/windows_com_hijacking_inprocserver32_modification.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the modification of the InProcServer32 registry key by reg.exe, indicative of potential COM hijacking. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line execution logs. COM hijacking is significant as it allows adversaries to insert malicious code that executes in place of legitimate software, providing a means for persistence. If confirmed malicious, this activity could enable attackers to execute arbitrary code, disrupt legitimate system components, and maintain long-term access to the compromised environment.
+description: The following analytic detects the modification of the InProcServer32
+  registry key by reg.exe, indicative of potential COM hijacking. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process and
+  command-line execution logs. COM hijacking is significant as it allows adversaries
+  to insert malicious code that executes in place of legitimate software, providing
+  a means for persistence. If confirmed malicious, this activity could enable attackers
+  to execute arbitrary code, disrupt legitimate system components, and maintain long-term
+  access to the compromised environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` Processes.process=*inprocserver32* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_com_hijacking_inprocserver32_modification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_reg` Processes.process=*inprocserver32*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_com_hijacking_inprocserver32_modification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present and some filtering may be required.
 references:
 - https://attack.mitre.org/techniques/T1546/015/
@@ -23,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to modify InProcServer32 within the registry.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to modify InProcServer32 within the
+    registry.
   mitre_attack_id:
   - T1546.015
   - T1546
@@ -57,12 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - UPDATE
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.015/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml b/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml
index 0306b46a94..a585edccc3 100644
--- a/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml
+++ b/detections/endpoint/windows_command_and_scripting_interpreter_hunting_path_traversal.yml
@@ -5,14 +5,39 @@ date: '2024-10-17'
 author: Teoderick Contreras, Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies path traversal command-line executions, leveraging data from Endpoint Detection and Response (EDR) agents. It detects patterns in command-line arguments indicative of path traversal techniques, such as multiple instances of "/..", "\..", or "\\..". This activity is significant as it often indicates attempts to evade defenses by executing malicious code, such as through msdt.exe. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies path traversal command-line executions,
+  leveraging data from Endpoint Detection and Response (EDR) agents. It detects patterns
+  in command-line arguments indicative of path traversal techniques, such as multiple
+  instances of "/..", "\..", or "\\..". This activity is significant as it often indicates
+  attempts to evade defenses by executing malicious code, such as through msdt.exe.
+  If confirmed malicious, this behavior could allow attackers to execute arbitrary
+  code, potentially leading to system compromise, data exfiltration, or further lateral
+  movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes  by Processes.original_file_name Processes.process_id Processes.parent_process_id Processes.process_hash Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval count_of_pattern1 = (mvcount(split(process,"/.."))-1) | eval count_of_pattern2 = (mvcount(split(process,"\.."))-1) | eval count_of_pattern3 = (mvcount(split(process,"\\.."))-1) | eval count_of_pattern4 = (mvcount(split(process,"//.."))-1) | search count_of_pattern1 > 1 OR count_of_pattern2 > 1 OR count_of_pattern3 > 1 OR count_of_pattern4 > 1 | `windows_command_and_scripting_interpreter_hunting_path_traversal_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: false positive may vary depends on the score you want to check. The bigger number of path traversal string count the better.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes  by Processes.original_file_name Processes.process_id
+  Processes.parent_process_id Processes.process_hash Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval
+  count_of_pattern1 = (mvcount(split(process,"/.."))-1) | eval count_of_pattern2 =
+  (mvcount(split(process,"\.."))-1) | eval count_of_pattern3 = (mvcount(split(process,"\\.."))-1)
+  | eval count_of_pattern4 = (mvcount(split(process,"//.."))-1) | search count_of_pattern1
+  > 1 OR count_of_pattern2 > 1 OR count_of_pattern3 > 1 OR count_of_pattern4 > 1 |
+  `windows_command_and_scripting_interpreter_hunting_path_traversal_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: false positive may vary depends on the score you want to check.
+  The bigger number of path traversal string count the better.
 references:
 - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/
 tags:
@@ -22,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A parent process $parent_process_name$ has spawned a child $process_name$ with path traversal commandline $process$ in $dest$
+  message: A parent process $parent_process_name$ has spawned a child $process_name$
+    with path traversal commandline $process$ in $dest$
   mitre_attack_id:
   - T1059
   observable:
@@ -34,23 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml b/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml
index 713fb3a694..a79b516478 100644
--- a/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml
+++ b/detections/endpoint/windows_command_and_scripting_interpreter_path_traversal_exec.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects path traversal command-line execution, often used in malicious documents to execute code via msdt.exe for defense evasion. It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns in process paths. This activity is significant as it can indicate an attempt to bypass security controls and execute unauthorized code. If confirmed malicious, this behavior could lead to code execution, privilege escalation, or persistence within the environment, potentially allowing attackers to deploy malware or leverage other living-off-the-land binaries (LOLBins).
+description: The following analytic detects path traversal command-line execution,
+  often used in malicious documents to execute code via msdt.exe for defense evasion.
+  It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns
+  in process paths. This activity is significant as it can indicate an attempt to
+  bypass security controls and execute unauthorized code. If confirmed malicious,
+  this behavior could lead to code execution, privilege escalation, or persistence
+  within the environment, potentially allowing attackers to deploy malware or leverage
+  other living-off-the-land binaries (LOLBins).
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where  Processes.process="*\/..\/..\/..\/*" OR Processes.process="*\\..\\..\\..\\*" OR Processes.process="*\/\/..\/\/..\/\/..\/\/*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_and_scripting_interpreter_path_traversal_exec_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where  Processes.process="*\/..\/..\/..\/*"
+  OR Processes.process="*\\..\\..\\..\\*" OR Processes.process="*\/\/..\/\/..\/\/..\/\/*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id
+  Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_command_and_scripting_interpreter_path_traversal_exec_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Not known at this moment.
 references:
 - https://app.any.run/tasks/713f05d2-fe78-4b9d-a744-f7c133e3fafb/
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: A parent process $parent_process_name$ has spawned a child $process_name$ with path traversal commandline $process$ in $dest$
+  message: A parent process $parent_process_name$ has spawned a child $process_name$
+    with path traversal commandline $process$ in $dest$
   mitre_attack_id:
   - T1059
   observable:
@@ -43,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/path_traversal/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml b/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml
index 0bda1e9568..fce55a3294 100644
--- a/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml
+++ b/detections/endpoint/windows_command_shell_dcrat_forkbomb_payload.yml
@@ -5,13 +5,38 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of a DCRat "forkbomb" payload, which spawns multiple cmd.exe processes that launch notepad.exe instances in quick succession. This detection leverages Endpoint Detection and Response (EDR) data, focusing on the rapid creation of cmd.exe and notepad.exe processes within a 30-second window. This activity is significant as it indicates a potential DCRat infection, a known Remote Access Trojan (RAT) with destructive capabilities. If confirmed malicious, this behavior could lead to system instability, resource exhaustion, and potential disruption of services.
+description: The following analytic detects the execution of a DCRat "forkbomb" payload,
+  which spawns multiple cmd.exe processes that launch notepad.exe instances in quick
+  succession. This detection leverages Endpoint Detection and Response (EDR) data,
+  focusing on the rapid creation of cmd.exe and notepad.exe processes within a 30-second
+  window. This activity is significant as it indicates a potential DCRat infection,
+  a known Remote Access Trojan (RAT) with destructive capabilities. If confirmed malicious,
+  this behavior could lead to system instability, resource exhaustion, and potential
+  disruption of services.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.parent_process_id) as parent_process_id values(Processes.process_id) as process_id dc(Processes.parent_process_id) as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name= "cmd.exe" (Processes.process_name = "notepad.exe" OR Processes.original_file_name= "notepad.exe") Processes.parent_process = "*.bat*" by  Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.parent_process Processes.dest Processes.user  _time span=30s | where parent_process_id_count>= 10 AND process_id_count >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_shell_dcrat_forkbomb_payload_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.parent_process) as parent_process values(Processes.parent_process_id)
+  as parent_process_id values(Processes.process_id) as process_id dc(Processes.parent_process_id)
+  as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time)
+  as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=
+  "cmd.exe" (Processes.process_name = "notepad.exe" OR Processes.original_file_name=
+  "notepad.exe") Processes.parent_process = "*.bat*" by  Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.parent_process Processes.dest
+  Processes.user  _time span=30s | where parent_process_id_count>= 10 AND process_id_count
+  >=10 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `windows_command_shell_dcrat_forkbomb_payload_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://cert.gov.ua/article/405538
@@ -23,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: Multiple cmd.exe processes with child process of notepad.exe executed on $dest$
+  message: Multiple cmd.exe processes with child process of notepad.exe executed on
+    $dest$
   mitre_attack_id:
   - T1059.003
   - T1059
@@ -45,23 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_forkbomb/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_forkbomb/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_command_shell_fetch_env_variables.yml b/detections/endpoint/windows_command_shell_fetch_env_variables.yml
index 105610b53b..6adadd5e9b 100644
--- a/detections/endpoint/windows_command_shell_fetch_env_variables.yml
+++ b/detections/endpoint/windows_command_shell_fetch_env_variables.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a suspicious process command line fetching environment variables with a non-shell parent process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and parent process names. This activity is significant as it is commonly associated with malware like Qakbot, which uses this technique to gather system information. If confirmed malicious, this behavior could indicate that the parent process has been compromised, potentially allowing attackers to execute arbitrary commands, escalate privileges, or persist within the environment.
+description: The following analytic identifies a suspicious process command line fetching
+  environment variables with a non-shell parent process. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on command-line executions and parent
+  process names. This activity is significant as it is commonly associated with malware
+  like Qakbot, which uses this technique to gather system information. If confirmed
+  malicious, this behavior could indicate that the parent process has been compromised,
+  potentially allowing attackers to execute arbitrary commands, escalate privileges,
+  or persist within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*cmd /c set" OR Processes.process = "*cmd.exe /c set" AND NOT (Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell*" OR Processes.parent_process_name="pwsh.exe" OR Processes.parent_process_name = "explorer.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_shell_fetch_env_variables_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: shell process that are not included in this search may cause False positive. Filter is needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*cmd /c
+  set" OR Processes.process = "*cmd.exe /c set" AND NOT (Processes.parent_process_name
+  = "cmd.exe" OR Processes.parent_process_name = "powershell*" OR Processes.parent_process_name="pwsh.exe"
+  OR Processes.parent_process_name = "explorer.exe") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_command_shell_fetch_env_variables_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: shell process that are not included in this search may cause
+  False positive. Filter is needed.
 references:
 - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: non-shell parent process has a child process $process_name$ with a commandline $process$ to fetch env variables in $dest$
+  message: non-shell parent process has a child process $process_name$ with a commandline
+    $process$ to fetch env variables in $dest$
   mitre_attack_id:
   - T1055
   observable:
@@ -42,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml b/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml
index cd61d6ab4b..e94caa43a5 100644
--- a/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml
+++ b/detections/endpoint/windows_common_abused_cmd_shell_risk_behavior.yml
@@ -6,10 +6,39 @@ author: Teoderick Contreras, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies instances where four or more distinct detection analytics are associated with malicious command line behavior on a specific host. This detection leverages the Command Line Interface (CLI) data from various sources to identify suspicious activities. This behavior is significant as it often indicates attempts to execute malicious commands, access sensitive data, install backdoors, or perform other nefarious actions. If confirmed malicious, attackers could gain unauthorized control, exfiltrate information, escalate privileges, or launch further attacks within the network, leading to severe compromise.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*Cmdline Tool Not Executed In CMD Shell*", "*Windows System Network Config Discovery Display DNS*", "*Local Account Discovery With Wmic*", "*Net Localgroup Discovery*", "*Create local admin accounts using net exe*", "*Local Account Discovery with Net*", "*Icacls Deny Command*", "*ICACLS Grant Command*", "*Windows Proxy Via Netsh*", "*Processes launching netsh*", "*Disabling Firewall with Netsh*", "*Windows System Network Connections Discovery Netsh*", "*Network Connection Discovery With Arp*", "*Windows System Discovery Using ldap Nslookup*", "*Windows System Shutdown CommandLine*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `windows_common_abused_cmd_shell_risk_behavior_filter`'
-how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
-known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
+description: The following analytic identifies instances where four or more distinct
+  detection analytics are associated with malicious command line behavior on a specific
+  host. This detection leverages the Command Line Interface (CLI) data from various
+  sources to identify suspicious activities. This behavior is significant as it often
+  indicates attempts to execute malicious commands, access sensitive data, install
+  backdoors, or perform other nefarious actions. If confirmed malicious, attackers
+  could gain unauthorized control, exfiltrate information, escalate privileges, or
+  launch further attacks within the network, leading to severe compromise.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*Cmdline
+  Tool Not Executed In CMD Shell*", "*Windows System Network Config Discovery Display
+  DNS*", "*Local Account Discovery With Wmic*", "*Net Localgroup Discovery*", "*Create
+  local admin accounts using net exe*", "*Local Account Discovery with Net*", "*Icacls
+  Deny Command*", "*ICACLS Grant Command*", "*Windows Proxy Via Netsh*", "*Processes
+  launching netsh*", "*Disabling Firewall with Netsh*", "*Windows System Network Connections
+  Discovery Netsh*", "*Network Connection Discovery With Arp*", "*Windows System Discovery
+  Using ldap Nslookup*", "*Windows System Shutdown CommandLine*") by All_Risk.risk_object
+  All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 4 | `windows_common_abused_cmd_shell_risk_behavior_filter`'
+how_to_implement: Splunk Enterprise Security is required to utilize this correlation.
+  In addition, modify the source_count value to your environment. In our testing,
+  a count of 4 or 5 was decent in a lab, but the number may need to be increased base
+  on internal testing. In addition, based on false positives, modify any analytics
+  to be anomaly and lower or increase risk based on organization importance.
+known_false_positives: False positives will be present based on many factors. Tune
+  the correlation as needed to reduce too many triggers.
 references:
 - https://www.splunk.com/en_us/blog/security/from-macros-to-no-macros-continuous-malware-improvements-by-qakbot.html
 - https://www.splunk.com/en_us/blog/security/dark-crystal-rat-agent-deep-dive.html
@@ -19,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +72,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: series of process commandline being abused by threat actor have been identified on $risk_object$
+  message: series of process commandline being abused by threat actor have been identified
+    on $risk_object$
   mitre_attack_id:
   - T1222
   - T1049
@@ -55,17 +90,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/risk_behavior/abused_commandline/risk_recon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/risk_behavior/abused_commandline/risk_recon.log
     source: risk
     sourcetype: stash
diff --git a/detections/endpoint/windows_computer_account_created_by_computer_account.yml b/detections/endpoint/windows_computer_account_created_by_computer_account.yml
index 445a0fa187..2ca7c734db 100644
--- a/detections/endpoint/windows_computer_account_created_by_computer_account.yml
+++ b/detections/endpoint/windows_computer_account_created_by_computer_account.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a computer account creating a new computer account with a specific Service Principal Name (SPN) "RestrictedKrbHost". This detection leverages Windows Security Event Logs, specifically EventCode 4741, to identify such activities. This behavior is significant as it may indicate an attempt to establish unauthorized Kerberos authentication channels, potentially leading to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to impersonate services, access sensitive information, or maintain persistence within the network.
+description: The following analytic identifies a computer account creating a new computer
+  account with a specific Service Principal Name (SPN) "RestrictedKrbHost". This detection
+  leverages Windows Security Event Logs, specifically EventCode 4741, to identify
+  such activities. This behavior is significant as it may indicate an attempt to establish
+  unauthorized Kerberos authentication channels, potentially leading to lateral movement
+  or privilege escalation. If confirmed malicious, this activity could allow an attacker
+  to impersonate services, access sensitive information, or maintain persistence within
+  the network.
 data_source:
 - Windows Event Log Security 4741
-search: '`wineventlog_security` EventCode=4741 user_type=computer SubjectDomainName!="NT AUTHORITY" ServicePrincipalNames=*RestrictedKrbHost* | stats  count min(_time) as firstTime max(_time) as lastTime by dest, subject, action ,src_user, user, user_type, SubjectUserName,SubjectDomainName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_computer_account_created_by_computer_account_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also required.
-known_false_positives: It is possible third party applications may have a computer account that adds computer accounts, filtering may be required.
+search: '`wineventlog_security` EventCode=4741 user_type=computer SubjectDomainName!="NT
+  AUTHORITY" ServicePrincipalNames=*RestrictedKrbHost* | stats  count min(_time) as
+  firstTime max(_time) as lastTime by dest, subject, action ,src_user, user, user_type,
+  SubjectUserName,SubjectDomainName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_computer_account_created_by_computer_account_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: It is possible third party applications may have a computer
+  account that adds computer accounts, filtering may be required.
 references:
 - https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-kile/445e4499-7e49-4f2a-8d82-aaf2d1ee3c47
 - https://github.com/Dec0ne/KrbRelayUp
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 50
-  message: A Computer Account on $dest$ created by a computer account (possibly indicative of Kerberos relay attack).
+  message: A Computer Account on $dest$ created by a computer account (possibly indicative
+    of Kerberos relay attack).
   mitre_attack_id:
   - T1558
   observable:
@@ -42,20 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - subject
-  - action
-  - src_user
-  - user
-  - Account_Name
-  - Subject_Account_Name
-  - Subject_Account_Domain
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_created_by_computer_account/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_created_by_computer_account/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml b/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml
index 6242ba9f01..5a695a5ce3 100644
--- a/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml
+++ b/detections/endpoint/windows_computer_account_requesting_kerberos_ticket.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects a computer account requesting a Kerberos ticket, which is unusual as typically user accounts request these tickets. This detection leverages Windows Security Event Logs, specifically EventCode 4768, to identify instances where the TargetUserName ends with a dollar sign ($), indicating a computer account. This activity is significant because it may indicate the use of tools like KrbUpRelay or other Kerberos-based attacks. If confirmed malicious, this could allow attackers to impersonate computer accounts, potentially leading to unauthorized access and lateral movement within the network.
+description: The following analytic detects a computer account requesting a Kerberos
+  ticket, which is unusual as typically user accounts request these tickets. This
+  detection leverages Windows Security Event Logs, specifically EventCode 4768, to
+  identify instances where the TargetUserName ends with a dollar sign ($), indicating
+  a computer account. This activity is significant because it may indicate the use
+  of tools like KrbUpRelay or other Kerberos-based attacks. If confirmed malicious,
+  this could allow attackers to impersonate computer accounts, potentially leading
+  to unauthorized access and lateral movement within the network.
 data_source:
 - Windows Event Log Security 4768
-search: '`wineventlog_security`  EventCode=4768 TargetUserName="*$"  src_ip!="::1" | stats  count min(_time) as firstTime max(_time) as lastTime by dest, subject, action, user, TargetUserName, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_computer_account_requesting_kerberos_ticket_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4768 EventCode enabled. The Windows TA is also required.
-known_false_positives: It is possible false positives will be present based on third party applications. Filtering may be needed.
+search: '`wineventlog_security`  EventCode=4768 TargetUserName="*$"  src_ip!="::1"
+  | stats  count min(_time) as firstTime max(_time) as lastTime by dest, subject,
+  action, user, TargetUserName, src_ip | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_computer_account_requesting_kerberos_ticket_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4768 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: It is possible false positives will be present based on third
+  party applications. Filtering may be needed.
 references:
 - https://github.com/Dec0ne/KrbRelayUp
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 50
-  message: A Computer Account requested a Kerberos ticket on $dest$, possibly indicative of Kerberos relay attack.
+  message: A Computer Account requested a Kerberos ticket on $dest$, possibly indicative
+    of Kerberos relay attack.
   mitre_attack_id:
   - T1558
   observable:
@@ -41,19 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - subject
-  - action
-  - Supplied_Realm_Name
-  - user
-  - Account_Name
-  - src_ip
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_requesting_kerberos_ticket/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_requesting_kerberos_ticket/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_computer_account_with_spn.yml b/detections/endpoint/windows_computer_account_with_spn.yml
index 5dd93eb784..7e5be5bcd1 100644
--- a/detections/endpoint/windows_computer_account_with_spn.yml
+++ b/detections/endpoint/windows_computer_account_with_spn.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of Service Principal Names (SPNs) HOST and RestrictedKrbHost to a computer account, indicative of KrbRelayUp behavior. This detection leverages Windows Security Event Logs, specifically EventCode 4741, to identify changes in SPNs. This activity is significant as it is commonly associated with Kerberos-based attacks, which can be used to escalate privileges or perform lateral movement within a network. If confirmed malicious, this behavior could allow an attacker to impersonate services, potentially leading to unauthorized access to sensitive resources.
+description: The following analytic detects the addition of Service Principal Names
+  (SPNs) HOST and RestrictedKrbHost to a computer account, indicative of KrbRelayUp
+  behavior. This detection leverages Windows Security Event Logs, specifically EventCode
+  4741, to identify changes in SPNs. This activity is significant as it is commonly
+  associated with Kerberos-based attacks, which can be used to escalate privileges
+  or perform lateral movement within a network. If confirmed malicious, this behavior
+  could allow an attacker to impersonate services, potentially leading to unauthorized
+  access to sensitive resources.
 data_source:
 - Windows Event Log Security 4741
-search: '`wineventlog_security` EventCode=4741 NewUacValue="0x80" ServicePrincipalNames IN ("*HOST/*","*RestrictedKrbHost/*") | stats count min(_time) as firstTime max(_time) as lastTime values(EventCode),values(TargetDomainName),values(PrimaryGroupId), values(OldUacValue), values(NewUacValue),values(SamAccountName),values(DnsHostName),values(ServicePrincipalNames) by dest Logon_ID subject | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_computer_account_with_spn_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also required.
-known_false_positives: It is possible third party applications may add these SPNs to Computer Accounts, filtering may be needed.
+search: '`wineventlog_security` EventCode=4741 NewUacValue="0x80" ServicePrincipalNames
+  IN ("*HOST/*","*RestrictedKrbHost/*") | stats count min(_time) as firstTime max(_time)
+  as lastTime values(EventCode),values(TargetDomainName),values(PrimaryGroupId), values(OldUacValue),
+  values(NewUacValue),values(SamAccountName),values(DnsHostName),values(ServicePrincipalNames)
+  by dest Logon_ID subject | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_computer_account_with_spn_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4741 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: It is possible third party applications may add these SPNs
+  to Computer Accounts, filtering may be needed.
 references:
 - https://www.trustedsec.com/blog/an-attack-path-mapping-approach-to-cves-2021-42287-and-2021-42278
 - https://github.com/Dec0ne/KrbRelayUp
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A Computer Account was created with SPNs related to Kerberos on $dest$, possibly indicative of Kerberos relay attack.
+  message: A Computer Account was created with SPNs related to Kerberos on $dest$,
+    possibly indicative of Kerberos relay attack.
   mitre_attack_id:
   - T1558
   observable:
@@ -42,20 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - MSADChangedAttributes
-  - New_UAC_Value
-  - Security_ID
-  - Account_Domain
-  - SAM_Account_Name
-  - DNS_Host_Name
-  - Logon_Id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_with_spn/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_computer_account_with_spn/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_conhost_with_headless_argument.yml b/detections/endpoint/windows_conhost_with_headless_argument.yml
index 563fd557a8..582fc1b9f8 100644
--- a/detections/endpoint/windows_conhost_with_headless_argument.yml
+++ b/detections/endpoint/windows_conhost_with_headless_argument.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the unusual invocation of the Windows Console Host process (conhost.exe) with the undocumented --headless parameter. This detection leverages Endpoint Detection and Response (EDR) telemetry, specifically monitoring for command-line executions where conhost.exe is executed with the --headless argument. This activity is significant for a SOC as it is not commonly used in legitimate operations and may indicate an attacker's attempt to execute commands stealthily. If confirmed malicious, this behavior could lead to persistence, lateral movement, or other malicious activities, potentially resulting in data exfiltration or system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=conhost.exe Processes.process="*--headless *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_conhost_with_headless_argument_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if the application is legitimately used, filter by user or endpoint as needed.
+description: The following analytic detects the unusual invocation of the Windows
+  Console Host process (conhost.exe) with the undocumented --headless parameter. This
+  detection leverages Endpoint Detection and Response (EDR) telemetry, specifically
+  monitoring for command-line executions where conhost.exe is executed with the --headless
+  argument. This activity is significant for a SOC as it is not commonly used in legitimate
+  operations and may indicate an attacker's attempt to execute commands stealthily.
+  If confirmed malicious, this behavior could lead to persistence, lateral movement,
+  or other malicious activities, potentially resulting in data exfiltration or system
+  compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=conhost.exe
+  Processes.process="*--headless *" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_conhost_with_headless_argument_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if the application is legitimately
+  used, filter by user or endpoint as needed.
 references:
 - https://x.com/embee_research/status/1559410767564181504?s=20
 - https://x.com/GroupIB_TI/status/1719675754886131959?s=20
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,18 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.003/headless/4688_conhost_headless.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.003/headless/4688_conhost_headless.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_create_local_account.yml b/detections/endpoint/windows_create_local_account.yml
index f957e176df..f538e62c6d 100644
--- a/detections/endpoint/windows_create_local_account.yml
+++ b/detections/endpoint/windows_create_local_account.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of a new local user account on a Windows system. It leverages Windows Security Audit logs, specifically event ID 4720, to identify this activity. Monitoring the creation of local accounts is crucial for a SOC as it can indicate unauthorized access or lateral movement within the network. If confirmed malicious, this activity could allow an attacker to establish persistence, escalate privileges, or gain unauthorized access to sensitive systems and data.
+description: The following analytic detects the creation of a new local user account
+  on a Windows system. It leverages Windows Security Audit logs, specifically event
+  ID 4720, to identify this activity. Monitoring the creation of local accounts is
+  crucial for a SOC as it can indicate unauthorized access or lateral movement within
+  the network. If confirmed malicious, this activity could allow an attacker to establish
+  persistence, escalate privileges, or gain unauthorized access to sensitive systems
+  and data.
 data_source: []
-search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 by All_Changes.user All_Changes.dest All_Changes.result All_Changes.action | `drop_dm_object_name("All_Changes")` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_create_local_account_filter`'
-how_to_implement: 'This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/'
-known_false_positives: It is possible that an administrator created the account. Verifying activity with an administrator is advised. This analytic is set to anomaly to allow for risk to be added. Filter and tune as needed. Restrict to critical infrastructure to reduce any volume.
+search: '| tstats `security_content_summariesonly` values(All_Changes.result_id) as
+  result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change
+  where All_Changes.result_id=4720 by All_Changes.user All_Changes.dest All_Changes.result
+  All_Changes.action | `drop_dm_object_name("All_Changes")` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_create_local_account_filter`'
+how_to_implement: 'This search requires you to have enabled your Group Management
+  Audit Logs in your Local Windows Security Policy and be ingesting those logs.  More
+  information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/'
+known_false_positives: It is possible that an administrator created the account. Verifying
+  activity with an administrator is advised. This analytic is set to anomaly to allow
+  for risk to be added. Filter and tune as needed. Restrict to critical infrastructure
+  to reduce any volume.
 references:
 - https://thedfirreport.com/2022/03/21/apt35-automates-initial-access-using-proxyshell/
 drilldown_searches:
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,15 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Changes.user
-  - All_Changes.dest
-  - All_Changes.result
-  - All_Changes.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/4720.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/4720.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credential_access_from_browser_password_store.yml b/detections/endpoint/windows_credential_access_from_browser_password_store.yml
index 520b946189..46c430b5e7 100644
--- a/detections/endpoint/windows_credential_access_from_browser_password_store.yml
+++ b/detections/endpoint/windows_credential_access_from_browser_password_store.yml
@@ -7,10 +7,32 @@ data_source:
 - Windows Event Log Security 4663
 type: Anomaly
 status: production
-description: The following analytic identifies a possible non-common browser process accessing its browser user data profile. This tactic/technique has been observed in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive browser information and credentials as part of their exfiltration strategy. Detecting this anomaly can serve as a valuable pivot for identifying processes that access lists of browser user data profiles unexpectedly. This detection uses a lookup file `browser_app_list` that maintains a list of well known browser applications and the browser paths that are allowed to access the browser user data profiles.
-search: '`wineventlog_security` EventCode=4663 | stats count by _time object_file_path object_file_name dest process_name process_path process_id EventCode | lookup browser_app_list browser_object_path as object_file_path OUTPUT browser_process_name isAllowed | stats count min(_time) as firstTime max(_time) as lastTime values(object_file_name) values(object_file_path)  values(browser_process_name) as browser_process_name by dest process_name process_path process_id EventCode isAllowed | rex field=process_name "(?<extracted_process_name>[^\\\\]+)$" | eval isMalicious=if(match(browser_process_name, extracted_process_name), "0", "1") | where isMalicious=1 and isAllowed="false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_access_from_browser_password_store_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure." This search may trigger on a browser application that is not included in the browser_app_list lookup file.
-known_false_positives: The lookup file `browser_app_list` may not contain all the browser applications that are allowed to access the browser user data profiles. Consider updating the lookup files to add allowed object paths for the browser applications that are not included in the lookup file.
+description: The following analytic identifies a possible non-common browser process
+  accessing its browser user data profile. This tactic/technique has been observed
+  in various Trojan Stealers, such as SnakeKeylogger, which attempt to gather sensitive
+  browser information and credentials as part of their exfiltration strategy. Detecting
+  this anomaly can serve as a valuable pivot for identifying processes that access
+  lists of browser user data profiles unexpectedly. This detection uses a lookup file
+  `browser_app_list` that maintains a list of well known browser applications and
+  the browser paths that are allowed to access the browser user data profiles.
+search: '`wineventlog_security` EventCode=4663 | stats count by _time object_file_path
+  object_file_name dest process_name process_path process_id EventCode | lookup browser_app_list
+  browser_object_path as object_file_path OUTPUT browser_process_name isAllowed |
+  stats count min(_time) as firstTime max(_time) as lastTime values(object_file_name)
+  values(object_file_path)  values(browser_process_name) as browser_process_name by
+  dest process_name process_path process_id EventCode isAllowed | rex field=process_name
+  "(?<extracted_process_name>[^\\\\]+)$" | eval isMalicious=if(match(browser_process_name,
+  extracted_process_name), "0", "1") | where isMalicious=1 and isAllowed="false" |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_access_from_browser_password_store_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+  This search may trigger on a browser application that is not included in the browser_app_list
+  lookup file.
+known_false_positives: The lookup file `browser_app_list` may not contain all the
+  browser applications that are allowed to access the browser user data profiles.
+  Consider updating the lookup files to add allowed object paths for the browser applications
+  that are not included in the lookup file.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger
 - https://www.checkpoint.com/cyber-hub/threat-prevention/what-is-malware/snake-keylogger-malware/
@@ -20,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A non-common browser process $process_name$ accessing browser user data folder on $dest$
+  message: A non-common browser process $process_name$ accessing browser user data
+    folder on $dest$
   mitre_attack_id:
   - T1012
   observable:
@@ -43,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml b/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml
index 6733b4f819..2ba1efd988 100644
--- a/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml
+++ b/detections/endpoint/windows_credential_dumping_lsass_memory_createdump.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of CreateDump.exe to perform a process dump. This binary is not native to Windows and is often introduced by third-party applications, including PowerShell 7. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and complete command-line executions. This activity is significant as it may indicate an attempt to dump LSASS memory, which can be used to extract credentials. If confirmed malicious, this could lead to unauthorized access and lateral movement within the network.
+description: The following analytic detects the use of CreateDump.exe to perform a
+  process dump. This binary is not native to Windows and is often introduced by third-party
+  applications, including PowerShell 7. The detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, GUIDs, and complete
+  command-line executions. This activity is significant as it may indicate an attempt
+  to dump LSASS memory, which can be used to extract credentials. If confirmed malicious,
+  this could lead to unauthorized access and lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=createdump.exe OR Processes.original_file_name="FX_VER_INTERNALNAME_STR" Processes.process="*-u *" AND Processes.process="*-f *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_dumping_lsass_memory_createdump_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if an application is dumping processes, filter as needed. Recommend reviewing createdump.exe usage across the fleet to better understand all usage and by what.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=createdump.exe
+  OR Processes.original_file_name="FX_VER_INTERNALNAME_STR" Processes.process="*-u
+  *" AND Processes.process="*-f *" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_credential_dumping_lsass_memory_createdump_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if an application is dumping
+  processes, filter as needed. Recommend reviewing createdump.exe usage across the
+  fleet to better understand all usage and by what.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-11---dump-lsass-with-createdumpexe-from-net-v5
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to dump a process.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to dump a process.
   mitre_attack_id:
   - T1003.001
   observable:
@@ -54,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/createdump_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/createdump_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml
index a4d0897578..a68fcaa20d 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_copied_in_temp_dir.yml
@@ -7,13 +7,19 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic detects the copying of Chrome's Local State and Login Data files into temporary folders, a tactic often used by the Braodo stealer malware. These files contain encrypted user credentials, including saved passwords and login session details. The detection monitors for suspicious copying activity involving these specific Chrome files, particularly in temp directories where malware typically processes the stolen data. Identifying this behavior enables security teams to act quickly, preventing attackers from decrypting and exfiltrating sensitive browser credentials and mitigating the risk of unauthorized access.
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem 
-  where Filesystem.file_name IN ("Local State", "Login Data") Filesystem.file_path = "*\\temp\\*" 
-  by _time Filesystem.dest Filesystem.user Filesystem.file_name Filesystem.file_path  Filesystem.file_create_time 
-  | `drop_dm_object_name(Filesystem)`
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
+description: The following analytic detects the copying of Chrome's Local State and
+  Login Data files into temporary folders, a tactic often used by the Braodo stealer
+  malware. These files contain encrypted user credentials, including saved passwords
+  and login session details. The detection monitors for suspicious copying activity
+  involving these specific Chrome files, particularly in temp directories where malware
+  typically processes the stolen data. Identifying this behavior enables security
+  teams to act quickly, preventing attackers from decrypting and exfiltrating sensitive
+  browser credentials and mitigating the risk of unauthorized access.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("Local
+  State", "Login Data") Filesystem.file_path = "*\\temp\\*" by _time Filesystem.dest
+  Filesystem.user Filesystem.file_name Filesystem.file_path  Filesystem.file_create_time
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_credentials_from_password_stores_chrome_copied_in_temp_dir_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
@@ -30,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: Chrome Password Store File [$file_name$] was copied in %temp% folder on [$dest$].
+  message: Chrome Password Store File [$file_name$] was copied in %temp% folder on
+    [$dest$].
   mitre_attack_id:
   - T1555.003
   - T1555
@@ -52,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
-    sourcetype: XmlWinEventLog
\ No newline at end of file
+    sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml
index 956a7b4d6e..5fff6663e1 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_extension_access.yml
@@ -7,15 +7,25 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log Security 4663
-description: The following analytic detects non-Chrome processes attempting to access the Chrome extensions file. It leverages Windows Security Event logs, specifically event code 4663, to identify this behavior. This activity is significant because adversaries may exploit this file to extract sensitive information from the Chrome browser, posing a security risk. If confirmed malicious, this could lead to unauthorized access to stored credentials and other sensitive data, potentially compromising the security of the affected system and broader network.
-search: '`wineventlog_security` EventCode=4663 
-  object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Local Extension Settings\\*" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", "*\\chrome.exe")) 
-  | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest 
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
+description: The following analytic detects non-Chrome processes attempting to access
+  the Chrome extensions file. It leverages Windows Security Event logs, specifically
+  event code 4663, to identify this behavior. This activity is significant because
+  adversaries may exploit this file to extract sensitive information from the Chrome
+  browser, posing a security risk. If confirmed malicious, this could lead to unauthorized
+  access to stored credentials and other sensitive data, potentially compromising
+  the security of the affected system and broader network.
+search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User
+  Data\\Default\\Local Extension Settings\\*" AND NOT (process_path IN ("*:\\Windows\\explorer.exe",
+  "*\\chrome.exe")) | stats count min(_time) as firstTime max(_time) as lastTime by
+  object_file_name object_file_path process_name process_path  process_id EventCode
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_credentials_from_password_stores_chrome_extension_access_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: Uninstall chrome browser extension application may access this file and folder path to removed chrome installation in the target host. Filter is needed.
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: Uninstall chrome browser extension application may access this
+  file and folder path to removed chrome installation in the target host. Filter is
+  needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -24,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +54,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A non-chrome process $process_name$ accessing chrome browser extension folder files on $dest$
+  message: A non-chrome process $process_name$ accessing chrome browser extension
+    folder files on $dest$
   mitre_attack_id:
   - T1012
   observable:
@@ -51,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_ext_access/security-ext-raw.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_ext_access/security-ext-raw.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml
index a741b01e13..7e7336237f 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_localstate_access.yml
@@ -7,15 +7,24 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log Security 4663
-description: The following analytic detects non-Chrome processes accessing the Chrome "Local State" file, which contains critical settings and information. It leverages Windows Security Event logs, specifically event code 4663, to identify this behavior. This activity is significant because threat actors can exploit this file to extract the encrypted master key used for decrypting saved passwords in Chrome. If confirmed malicious, this could lead to unauthorized access to sensitive information, posing a severe security risk. Monitoring this anomaly helps identify potential threats and safeguard browser-stored data.
-search: '`wineventlog_security` EventCode=4663 
-  object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Local State" NOT (process_name IN ("*\\chrome.exe","*:\\Windows\\explorer.exe")) 
-  | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest 
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `windows_credentials_from_password_stores_chrome_localstate_access_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: Uninstall chrome application may access this file and folder path to removed chrome installation in target host. Filter is needed.
+description: The following analytic detects non-Chrome processes accessing the Chrome
+  "Local State" file, which contains critical settings and information. It leverages
+  Windows Security Event logs, specifically event code 4663, to identify this behavior.
+  This activity is significant because threat actors can exploit this file to extract
+  the encrypted master key used for decrypting saved passwords in Chrome. If confirmed
+  malicious, this could lead to unauthorized access to sensitive information, posing
+  a severe security risk. Monitoring this anomaly helps identify potential threats
+  and safeguard browser-stored data.
+search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User
+  Data\\Local State" NOT (process_name IN ("*\\chrome.exe","*:\\Windows\\explorer.exe"))
+  | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name
+  object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_localstate_access_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: Uninstall chrome application may access this file and folder
+  path to removed chrome installation in target host. Filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -24,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A non-chrome process $process_name$ accessing "Chrome\\User Data\\Local State" file on $dest$
+  message: A non-chrome process $process_name$ accessing "Chrome\\User Data\\Local
+    State" file on $dest$
   mitre_attack_id:
   - T1012
   observable:
@@ -53,19 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_local_state_simulate_access/redline-localstate-smalldata-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_local_state_simulate_access/redline-localstate-smalldata-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml b/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml
index af6696b330..83570746fc 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_chrome_login_data_access.yml
@@ -7,15 +7,25 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log Security 4663
-description: The following analytic identifies non-Chrome processes accessing the Chrome user data file "login data." This file is an SQLite database containing sensitive information, including saved passwords. The detection leverages Windows Security Event logs, specifically event code 4663, to monitor access attempts. This activity is significant as it may indicate attempts by threat actors to extract and decrypt stored passwords, posing a risk to user credentials. If confirmed malicious, attackers could gain unauthorized access to sensitive accounts and escalate their privileges within the environment.
-search: '`wineventlog_security` EventCode=4663 
-  object_file_path="*\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Login Data" AND NOT (process_path IN ("*:\\Windows\\explorer.exe", "*:\\Windows\\System32\\dllhost.exe", "*\\chrome.exe")) 
-  | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest 
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `windows_credentials_from_password_stores_chrome_login_data_access_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: Uninstall application may access this registry to remove the entry of the target application. filter is needed.
+description: The following analytic identifies non-Chrome processes accessing the
+  Chrome user data file "login data." This file is an SQLite database containing sensitive
+  information, including saved passwords. The detection leverages Windows Security
+  Event logs, specifically event code 4663, to monitor access attempts. This activity
+  is significant as it may indicate attempts by threat actors to extract and decrypt
+  stored passwords, posing a risk to user credentials. If confirmed malicious, attackers
+  could gain unauthorized access to sensitive accounts and escalate their privileges
+  within the environment.
+search: '`wineventlog_security` EventCode=4663 object_file_path="*\\AppData\\Local\\Google\\Chrome\\User
+  Data\\Default\\Login Data" AND NOT (process_path IN ("*:\\Windows\\explorer.exe",
+  "*:\\Windows\\System32\\dllhost.exe", "*\\chrome.exe")) | stats count min(_time)
+  as firstTime max(_time) as lastTime by object_file_name object_file_path process_name
+  process_path  process_id EventCode dest | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_chrome_login_data_access_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: Uninstall application may access this registry to remove the
+  entry of the target application. filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -24,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A non-chrome process $process_name$ accessing Chrome "Login Data" file on $dest$
+  message: A non-chrome process $process_name$ accessing Chrome "Login Data" file
+    on $dest$
   mitre_attack_id:
   - T1012
   observable:
@@ -53,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_login_data_simulate_access/redline-login-data-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/chrome_login_data_simulate_access/redline-login-data-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_creation.yml b/detections/endpoint/windows_credentials_from_password_stores_creation.yml
index 4df5a33762..bfa32e960f 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_creation.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_creation.yml
@@ -9,9 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of the Windows OS tool cmdkey.exe, which is used to create stored usernames, passwords, or credentials. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because cmdkey.exe is often abused by post-exploitation tools and malware, such as Darkgate, to gain unauthorized access. If confirmed malicious, this behavior could allow attackers to escalate privileges and maintain persistence on the targeted host, facilitating further attacks and potential data breaches.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/generic*" Processes.process IN ("*/user*", "*/password*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_creation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic detects the execution of the Windows OS tool cmdkey.exe,
+  which is used to create stored usernames, passwords, or credentials. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  execution logs and command-line arguments. This activity is significant because
+  cmdkey.exe is often abused by post-exploitation tools and malware, such as Darkgate,
+  to gain unauthorized access. If confirmed malicious, this behavior could allow attackers
+  to escalate privileges and maintain persistence on the targeted host, facilitating
+  further attacks and potential data breaches.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe"
+  OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/generic*"
+  Processes.process IN ("*/user*", "*/password*") by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_credentials_from_password_stores_creation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: network administrator can use this tool for auditing process.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,25 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_create_credential_store/cmdkey_gen_sys.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_create_credential_store/cmdkey_gen_sys.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_deletion.yml b/detections/endpoint/windows_credentials_from_password_stores_deletion.yml
index 9a9980a761..36dbeed365 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_deletion.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_deletion.yml
@@ -9,9 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of the Windows OS tool cmdkey.exe with the /delete parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. The activity is significant because cmdkey.exe can be used by attackers to delete stored credentials, potentially leading to privilege escalation and persistence. If confirmed malicious, this behavior could allow attackers to remove stored user credentials, hindering incident response efforts and enabling further unauthorized access to the compromised system.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/delete*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_deletion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic detects the execution of the Windows OS tool cmdkey.exe
+  with the /delete parameter. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs and command-line arguments.
+  The activity is significant because cmdkey.exe can be used by attackers to delete
+  stored credentials, potentially leading to privilege escalation and persistence.
+  If confirmed malicious, this behavior could allow attackers to remove stored user
+  credentials, hindering incident response efforts and enabling further unauthorized
+  access to the compromised system.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe"
+  OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/delete*"
+  by Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_deletion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: network administrator can use this tool for auditing process.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,25 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_delete_credentials_store/cmdkey_del_sys.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/cmdkey_delete_credentials_store/cmdkey_del_sys.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_password_stores_query.yml b/detections/endpoint/windows_credentials_from_password_stores_query.yml
index a312faaf06..83765c9688 100644
--- a/detections/endpoint/windows_credentials_from_password_stores_query.yml
+++ b/detections/endpoint/windows_credentials_from_password_stores_query.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the Windows OS tool cmdkey.exe, which is often abused by post-exploitation tools like winpeas, commonly used in ransomware attacks to list stored usernames, passwords, or credentials. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant as it indicates potential credential harvesting, which can lead to privilege escalation and persistence. If confirmed malicious, attackers could gain unauthorized access to sensitive information and maintain control over compromised systems for further exploitation.
+description: The following analytic detects the execution of the Windows OS tool cmdkey.exe,
+  which is often abused by post-exploitation tools like winpeas, commonly used in
+  ransomware attacks to list stored usernames, passwords, or credentials. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  execution logs. This activity is significant as it indicates potential credential
+  harvesting, which can lead to privilege escalation and persistence. If confirmed
+  malicious, attackers could gain unauthorized access to sensitive information and
+  maintain control over compromised systems for further exploitation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe" OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/list*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_query_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="cmdkey.exe"
+  OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/list*"
+  by Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_credentials_from_password_stores_query_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: network administrator can use this tool for auditing process.
 references:
 - https://ss64.com/nt/cmdkey.html
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: a process $process_name$ was executed in $dest$ to display stored username and credentials.
+  message: a process $process_name$ was executed in $dest$ to display stored username
+    and credentials.
   mitre_attack_id:
   - T1555
   observable:
@@ -46,25 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_cmdkeylist/cmdkey-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_cmdkeylist/cmdkey-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml b/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml
index 354a3ca4dc..94eb9720f3 100644
--- a/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml
+++ b/detections/endpoint/windows_credentials_from_web_browsers_saved_in_temp_folder.yml
@@ -7,14 +7,19 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic detects the creation of files containing passwords, cookies, and saved login account information by the Braodo stealer malware in temporary folders. Braodo often collects these credentials from browsers and applications, storing them in temp directories before exfiltration. This detection focuses on monitoring for the creation of files with patterns or formats commonly associated with stolen credentials. By identifying these activities, security teams can take needed action to prevent sensitive login data from being leaked, reducing the risk of unauthorized access to user accounts and systems.
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem 
-  where Filesystem.file_name IN ("login*", "pass*","cookie*","master_key*") Filesystem.file_path = "*\\temp\\*" 
-  by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time 
-  | `drop_dm_object_name(Filesystem)`
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `windows_credentials_from_web_browsers_saved_in_temp_folder_filter`'
+description: The following analytic detects the creation of files containing passwords,
+  cookies, and saved login account information by the Braodo stealer malware in temporary
+  folders. Braodo often collects these credentials from browsers and applications,
+  storing them in temp directories before exfiltration. This detection focuses on
+  monitoring for the creation of files with patterns or formats commonly associated
+  with stolen credentials. By identifying these activities, security teams can take
+  needed action to prevent sensitive login data from being leaked, reducing the risk
+  of unauthorized access to user accounts and systems.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("login*",
+  "pass*","cookie*","master_key*") Filesystem.file_path = "*\\temp\\*" by _time Filesystem.file_name
+  Filesystem.file_path Filesystem.dest Filesystem.file_create_time | `drop_dm_object_name(Filesystem)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_from_web_browsers_saved_in_temp_folder_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
@@ -30,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A known credential file name - [$file_name$] was saved in %temp% folder of [$dest$].
+  message: A known credential file name - [$file_name$] was saved in %temp% folder
+    of [$dest$].
   mitre_attack_id:
   - T1555.003
   - T1555
@@ -52,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.003/browser_credential_info_temp/braodo_browser_info.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_credentials_in_registry_reg_query.yml b/detections/endpoint/windows_credentials_in_registry_reg_query.yml
index 878c125caf..083dfe17b4 100644
--- a/detections/endpoint/windows_credentials_in_registry_reg_query.yml
+++ b/detections/endpoint/windows_credentials_in_registry_reg_query.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies processes querying the registry for potential passwords or credentials. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that access specific registry paths known to store sensitive information. This activity is significant as it may indicate credential theft attempts, often used by adversaries or post-exploitation tools like winPEAS. If confirmed malicious, this behavior could lead to privilege escalation, persistence, or lateral movement within the network, posing a severe security risk.
+description: The following analytic identifies processes querying the registry for
+  potential passwords or credentials. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on command-line executions that access specific
+  registry paths known to store sensitive information. This activity is significant
+  as it may indicate credential theft attempts, often used by adversaries or post-exploitation
+  tools like winPEAS. If confirmed malicious, this behavior could lead to privilege
+  escalation, persistence, or lateral movement within the network, posing a severe
+  security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process IN ("*\\Software\\ORL\\WinVNC3\\Password*", "*\\SOFTWARE\\RealVNC\\WinVNC4 /v password*", "*\\CurrentControlSet\\Services\\SNMP*", "*\\Software\\TightVNC\\Server*", "*\\Software\\SimonTatham\\PuTTY\\Sessions*", "*\\Software\\OpenSSH\\Agent\\Keys*", "*password*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credentials_in_registry_reg_query_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process
+  = "* query *" AND Processes.process IN ("*\\Software\\ORL\\WinVNC3\\Password*",
+  "*\\SOFTWARE\\RealVNC\\WinVNC4 /v password*", "*\\CurrentControlSet\\Services\\SNMP*",
+  "*\\Software\\TightVNC\\Server*", "*\\Software\\SimonTatham\\PuTTY\\Sessions*",
+  "*\\Software\\OpenSSH\\Agent\\Keys*", "*password*") by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_credentials_in_registry_reg_query_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1552/002/
@@ -23,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,25 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd/query-putty-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd/query-putty-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_curl_download_to_suspicious_path.yml b/detections/endpoint/windows_curl_download_to_suspicious_path.yml
index 69f832ed1c..f8f7a303b5 100644
--- a/detections/endpoint/windows_curl_download_to_suspicious_path.yml
+++ b/detections/endpoint/windows_curl_download_to_suspicious_path.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Windows Curl.exe to download a file to a suspicious location, such as AppData, ProgramData, or Public directories. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include the -O or --output options. This activity is significant because downloading files to these locations can indicate an attempt to bypass security controls or establish persistence. If confirmed malicious, this behavior could lead to unauthorized code execution, data exfiltration, or further compromise of the system.
+description: The following analytic detects the use of Windows Curl.exe to download
+  a file to a suspicious location, such as AppData, ProgramData, or Public directories.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  command-line executions that include the -O or --output options. This activity is
+  significant because downloading files to these locations can indicate an attempt
+  to bypass security controls or establish persistence. If confirmed malicious, this
+  behavior could lead to unauthorized code execution, data exfiltration, or further
+  compromise of the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_curl` Processes.process IN ("*-O *","*--output*") Processes.process IN ("*\\appdata\\*","*\\programdata\\*","*\\public\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_curl_download_to_suspicious_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible Administrators or super users will use Curl for legitimate purposes. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_curl` Processes.process
+  IN ("*-O *","*--output*") Processes.process IN ("*\\appdata\\*","*\\programdata\\*","*\\public\\*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_curl_download_to_suspicious_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible Administrators or super users will use Curl
+  for legitimate purposes. Filter as needed.
 references:
 - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/
 - https://attack.mitre.org/techniques/T1105/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ to download a file to a suspicious directory.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ to download a file to a suspicious directory.
   mitre_attack_id:
   - T1105
   observable:
@@ -58,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_curl_upload_to_remote_destination.yml b/detections/endpoint/windows_curl_upload_to_remote_destination.yml
index 0d3e281d18..38c1071e6c 100644
--- a/detections/endpoint/windows_curl_upload_to_remote_destination.yml
+++ b/detections/endpoint/windows_curl_upload_to_remote_destination.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Windows Curl.exe to upload a file to a remote destination. It identifies command-line arguments such as `-T`, `--upload-file`, `-d`, `--data`, and `-F` in process execution logs. This activity is significant because adversaries may use Curl to exfiltrate data or upload malicious payloads. If confirmed malicious, this could lead to data breaches or further compromise of the system. Analysts should review parallel processes and network logs to determine if the upload was successful and isolate the endpoint if necessary.
+description: The following analytic detects the use of Windows Curl.exe to upload
+  a file to a remote destination. It identifies command-line arguments such as `-T`,
+  `--upload-file`, `-d`, `--data`, and `-F` in process execution logs. This activity
+  is significant because adversaries may use Curl to exfiltrate data or upload malicious
+  payloads. If confirmed malicious, this could lead to data breaches or further compromise
+  of the system. Analysts should review parallel processes and network logs to determine
+  if the upload was successful and isolate the endpoint if necessary.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_curl` Processes.process IN ("*-T *","*--upload-file *", "*-d *", "*--data *", "*-F *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_curl_upload_to_remote_destination_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be limited to source control applications and may be required to be filtered out.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_curl` Processes.process
+  IN ("*-T *","*--upload-file *", "*-d *", "*--data *", "*-F *") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_curl_upload_to_remote_destination_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be limited to source control applications
+  and may be required to be filtered out.
 references:
 - https://everything.curl.dev/usingcurl/uploads
 - https://techcommunity.microsoft.com/t5/containers/tar-and-curl-come-to-windows/ba-p/382409
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ uploading a file to a remote destination.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ uploading a file to a remote destination.
   mitre_attack_id:
   - T1105
   observable:
@@ -56,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl_upload.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon_curl_upload.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml b/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml
index adb6936e6b..d754b17a6f 100644
--- a/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml
+++ b/detections/endpoint/windows_data_destruction_recursive_exec_files_deletion.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies a suspicious process that is recursively deleting executable files on a compromised host. It leverages Sysmon Event Codes 23 and 26 to detect this activity by monitoring for a high volume of deletions or overwrites of files with extensions like .exe, .sys, and .dll. This behavior is significant as it is commonly associated with destructive malware such as CaddyWiper, DoubleZero, and SwiftSlicer, which aim to make file recovery impossible. If confirmed malicious, this activity could lead to significant data loss and system instability, severely impacting business operations.
+description: The following analytic identifies a suspicious process that is recursively
+  deleting executable files on a compromised host. It leverages Sysmon Event Codes
+  23 and 26 to detect this activity by monitoring for a high volume of deletions or
+  overwrites of files with extensions like .exe, .sys, and .dll. This behavior is
+  significant as it is commonly associated with destructive malware such as CaddyWiper,
+  DoubleZero, and SwiftSlicer, which aim to make file recovery impossible. If confirmed
+  malicious, this activity could lead to significant data loss and system instability,
+  severely impacting business operations.
 data_source:
 - Sysmon EventID 23
 - Sysmon EventID 26
-search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.exe", "*.sys", "*.dll") | bin _time span=2m | stats count, values(TargetFilename) as deleted_files, min(_time) as firstTime, max(_time) as lastTime by user, dest, signature, signature_id, Image, process_name, process_guid | rename Image as process | where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_data_destruction_recursive_exec_files_deletion_filter`'
-how_to_implement: To successfully implement this search, you need to ingest logs that include the process name, TargetFilename, and ProcessID executions from your endpoints. If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed.
-known_false_positives: The uninstallation of a large software application or the use of cleanmgr.exe may trigger this detection. A filter is necessary to reduce false positives.
+search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.exe", "*.sys", "*.dll")
+  | bin _time span=2m | stats count, values(TargetFilename) as deleted_files, min(_time)
+  as firstTime, max(_time) as lastTime by user, dest, signature, signature_id, Image,
+  process_name, process_guid | rename Image as process | where count >=100 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_data_destruction_recursive_exec_files_deletion_filter`'
+how_to_implement: To successfully implement this search, you need to ingest logs that
+  include the process name, TargetFilename, and ProcessID executions from your endpoints.
+  If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed.
+known_false_positives: The uninstallation of a large software application or the use
+  of cleanmgr.exe may trigger this detection. A filter is necessary to reduce false
+  positives.
 references:
 - https://www.welivesecurity.com/2023/01/27/swiftslicer-new-destructive-wiper-malware-ukraine/
 drilldown_searches:
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: The process $process_name$ has removed a significant quantity of executable files, totaling [$count$], from the destination $dest$.
+  message: The process $process_name$ has removed a significant quantity of executable
+    files, totaling [$count$], from the destination $dest$.
   mitre_attack_id:
   - T1485
   observable:
@@ -51,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - TargetFilename
-  - dest
-  - user
-  - signature_id
-  - process_name
-  - process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/swift_slicer/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/swift_slicer/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_debugger_tool_execution.yml b/detections/endpoint/windows_debugger_tool_execution.yml
index 50cadd25ef..f599fcb798 100644
--- a/detections/endpoint/windows_debugger_tool_execution.yml
+++ b/detections/endpoint/windows_debugger_tool_execution.yml
@@ -6,10 +6,29 @@ author: Teoderick Contreras, Splunk
 data_source: []
 type: Hunting
 status: production
-description: This analysis detects the use of debugger tools within a production environment. While these tools are legitimate for file analysis and debugging, they are abused by malware like PlugX and DarkGate for malicious DLL side-loading. The hunting query aids Security Operations Centers (SOCs) in identifying potentially suspicious tool executions, particularly for non-technical users in the production network.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "x32dbg.exe" OR Processes.process_name = "x64dbg.exe" OR Processes.process_name = "windbg.exe" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_debugger_tool_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: administrator or IT professional may execute this application for verifying files or debugging application.
+description: This analysis detects the use of debugger tools within a production environment.
+  While these tools are legitimate for file analysis and debugging, they are abused
+  by malware like PlugX and DarkGate for malicious DLL side-loading. The hunting query
+  aids Security Operations Centers (SOCs) in identifying potentially suspicious tool
+  executions, particularly for non-technical users in the production network.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "x32dbg.exe"
+  OR Processes.process_name = "x64dbg.exe" OR Processes.process_name = "windbg.exe"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_debugger_tool_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: administrator or IT professional may execute this application
+  for verifying files or debugging application.
 references:
 - https://www.splunk.com/en_us/blog/security/enter-the-gates-an-analysis-of-the-darkgate-autoit-loader.html
 - https://www.trendmicro.com/en_us/research/23/b/investigating-the-plugx-trojan-disguised-as-a-legitimate-windows.html
@@ -36,21 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/debugger_execution/debugger.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/debugger_execution/debugger.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml b/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml
index e454a0f327..8763fbbfeb 100644
--- a/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml
+++ b/detections/endpoint/windows_defacement_modify_transcodedwallpaper_file.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies modifications to the TranscodedWallpaper file in the wallpaper theme directory, excluding changes made by explorer.exe. This detection leverages the Endpoint.Processes and Endpoint.Filesystem data models to correlate process activity with file modifications. This activity is significant as it may indicate an adversary attempting to deface or change the desktop wallpaper of a targeted host, a tactic often used to signal compromise or deliver a message. If confirmed malicious, this could be a sign of unauthorized access and tampering, potentially leading to further system compromise or data exfiltration.
+description: The following analytic identifies modifications to the TranscodedWallpaper
+  file in the wallpaper theme directory, excluding changes made by explorer.exe. This
+  detection leverages the Endpoint.Processes and Endpoint.Filesystem data models to
+  correlate process activity with file modifications. This activity is significant
+  as it may indicate an adversary attempting to deface or change the desktop wallpaper
+  of a targeted host, a tactic often used to signal compromise or deliver a message.
+  If confirmed malicious, this could be a sign of unauthorized access and tampering,
+  potentially leading to further system compromise or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_path !="*\\Windows\\Explorer.EXE" by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid  Processes.original_file_name | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid, _time [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*\\AppData\\Roaming\\Microsoft\\Windows\\Themes\\TranscodedWallpaper" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` |rename process_guid as proc_guid | fields file_name file_path process_name process_path process dest file_create_time _time proc_guid] | `windows_defacement_modify_transcodedwallpaper_file_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: 3rd part software application can change the wallpaper. Filter is needed.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_path !="*\\Windows\\Explorer.EXE" by _time span=1h Processes.process_id
+  Processes.process_name Processes.process Processes.dest Processes.process_guid  Processes.original_file_name
+  | `drop_dm_object_name(Processes)` |rename process_guid as proc_guid | join proc_guid,
+  _time [ | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_path = "*\\AppData\\Roaming\\Microsoft\\Windows\\Themes\\TranscodedWallpaper"
+  by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)`
+  |rename process_guid as proc_guid | fields file_name file_path process_name process_path
+  process dest file_create_time _time proc_guid] | `windows_defacement_modify_transcodedwallpaper_file_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: 3rd part software application can change the wallpaper. Filter
+  is needed.
 references:
 - https://forums.ivanti.com/s/article/Wallpaper-Windows-Settings-Desktop-Settings-and-the-transcodedwallpaper-jpg?language=en_US
 - https://www.trendmicro.com/vinfo/us/threat-encyclopedia/malware/ransom_sifreli.a
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: modification or creation of transcodedwallpaper file by $process_name$ in $dest$
+  message: modification or creation of transcodedwallpaper file by $process_name$
+    in $dest$
   mitre_attack_id:
   - T1491
   observable:
@@ -47,19 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - file_create_time
-  - file_name
-  - file_path
-  - process_name
-  - process_path
-  - process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/wallpaper_via_transcodedwallpaper/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/wallpaper_via_transcodedwallpaper/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_default_group_policy_object_modified.yml b/detections/endpoint/windows_default_group_policy_object_modified.yml
index ef15ade210..6a35586c52 100644
--- a/detections/endpoint/windows_default_group_policy_object_modified.yml
+++ b/detections/endpoint/windows_default_group_policy_object_modified.yml
@@ -7,10 +7,28 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 5136
-description: The following analytic detects modifications to default Group Policy Objects (GPOs) using Event ID 5136. It monitors changes to the `Default Domain Controllers Policy` and `Default Domain Policy`, which are critical for enforcing security settings across domain controllers and all users/computers, respectively. This activity is significant because unauthorized changes to these GPOs can indicate an adversary with privileged access attempting to deploy persistence mechanisms or execute malware across the network. If confirmed malicious, such modifications could lead to widespread compromise, allowing attackers to maintain control and execute arbitrary code on numerous hosts.
-search: '`wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=versionNumber (ObjectDN="CN={31B2F340-016D-11D2-945F-00C04FB984F9},CN=POLICIES,CN=SYSTEM,DC=*" OR ObjectDN="CN={6AC1786C-016F-11D2-945F-00C04fB984F9},CN=POLICIES,CN=SYSTEM,DC=*") | stats min(_time) as firstTime max(_time) as lastTime by ObjectDN SubjectUserSid AttributeValue Computer DSName | rename AttributeValue as versionNumber | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_filter`'
-how_to_implement: To successfully implement this search, the Advanced Security Audit policy setting `Audit Directory Service Changes` within `DS Access` needs to be enabled. Furthermore, the appropriate system access control lists (SACL) need to be created as the used events are not logged by default. A good guide to accomplish this can be found here https://jgspiers.com/audit-group-policy-changes/.
-known_false_positives: The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed.
+description: The following analytic detects modifications to default Group Policy
+  Objects (GPOs) using Event ID 5136. It monitors changes to the `Default Domain Controllers
+  Policy` and `Default Domain Policy`, which are critical for enforcing security settings
+  across domain controllers and all users/computers, respectively. This activity is
+  significant because unauthorized changes to these GPOs can indicate an adversary
+  with privileged access attempting to deploy persistence mechanisms or execute malware
+  across the network. If confirmed malicious, such modifications could lead to widespread
+  compromise, allowing attackers to maintain control and execute arbitrary code on
+  numerous hosts.
+search: '`wineventlog_security` EventCode=5136 ObjectClass=groupPolicyContainer AttributeLDAPDisplayName=versionNumber
+  (ObjectDN="CN={31B2F340-016D-11D2-945F-00C04FB984F9},CN=POLICIES,CN=SYSTEM,DC=*"
+  OR ObjectDN="CN={6AC1786C-016F-11D2-945F-00C04fB984F9},CN=POLICIES,CN=SYSTEM,DC=*")
+  | stats min(_time) as firstTime max(_time) as lastTime by ObjectDN SubjectUserSid
+  AttributeValue Computer DSName | rename AttributeValue as versionNumber | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_filter`'
+how_to_implement: To successfully implement this search, the Advanced Security Audit
+  policy setting `Audit Directory Service Changes` within `DS Access` needs to be
+  enabled. Furthermore, the appropriate system access control lists (SACL) need to
+  be created as the used events are not logged by default. A good guide to accomplish
+  this can be found here https://jgspiers.com/audit-group-policy-changes/.
+known_false_positives: The default Group Policy Objects within an AD network may be
+  legitimately updated for administrative operations, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1484/
 - https://attack.mitre.org/techniques/T1484/001
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,19 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - ObjectClass
-  - AttributeLDAPDisplayName
-  - ObjectDN
-  - Computer
-  - DSName
-  - AttributeValue
-  - SubjectUserSid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml b/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml
index 9eb7d51a69..c3c2cf1ac9 100644
--- a/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml
+++ b/detections/endpoint/windows_default_group_policy_object_modified_with_gpme.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects modifications to default Group Policy Objects (GPOs) using the Group Policy Management Editor (GPME). It leverages the Endpoint data model to identify processes where `mmc.exe` executes `gpme.msc` with specific GUIDs related to default GPOs. This activity is significant because default GPOs, such as the `Default Domain Controllers Policy` and `Default Domain Policy`, are critical for enforcing security policies across the domain. If malicious, such modifications could allow an attacker to gain further access, establish persistence, or deploy malware across numerous hosts, severely compromising the network's security.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mmc.exe (Processes.process =*gpme.msc*) AND (Processes.process = "*31B2F340-016D-11D2-945F-00C04FB984F9*" OR Processes.process = "*6AC1786C-016F-11D2-945F-00C04fB984F9*"  ) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_with_gpme_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: The default Group Policy Objects within an AD network may be legitimately updated for administrative operations, filter as needed.
+description: The following analytic detects modifications to default Group Policy
+  Objects (GPOs) using the Group Policy Management Editor (GPME). It leverages the
+  Endpoint data model to identify processes where `mmc.exe` executes `gpme.msc` with
+  specific GUIDs related to default GPOs. This activity is significant because default
+  GPOs, such as the `Default Domain Controllers Policy` and `Default Domain Policy`,
+  are critical for enforcing security policies across the domain. If malicious, such
+  modifications could allow an attacker to gain further access, establish persistence,
+  or deploy malware across numerous hosts, severely compromising the network's security.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mmc.exe
+  (Processes.process =*gpme.msc*) AND (Processes.process = "*31B2F340-016D-11D2-945F-00C04FB984F9*"
+  OR Processes.process = "*6AC1786C-016F-11D2-945F-00C04fB984F9*"  ) by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_default_group_policy_object_modified_with_gpme_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: The default Group Policy Objects within an AD network may be
+  legitimately updated for administrative operations, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1484/
 - https://attack.mitre.org/techniques/T1484/001
@@ -25,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 100
-  message: A default group policy object was opened with Group Policy Manage Editor on $dest$
+  message: A default group policy object was opened with Group Policy Manage Editor
+    on $dest$
   mitre_attack_id:
   - T1484
   - T1484.001
@@ -56,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/default_domain_policy_modified/windows-security.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defender_asr_audit_events.yml b/detections/endpoint/windows_defender_asr_audit_events.yml
index ef983f3f42..3ccb561c67 100644
--- a/detections/endpoint/windows_defender_asr_audit_events.yml
+++ b/detections/endpoint/windows_defender_asr_audit_events.yml
@@ -11,10 +11,28 @@ data_source:
 - Windows Event Log Defender 1126
 - Windows Event Log Defender 1132
 - Windows Event Log Defender 1134
-description: This detection searches for Windows Defender ASR audit events. ASR is a feature of Windows Defender Exploit Guard that prevents actions and apps that are typically used by exploit-seeking malware to infect machines. ASR rules are applied to processes and applications. When a process or application attempts to perform an action that is blocked by an ASR rule, an event is generated. This detection searches for ASR audit events that are generated when a process or application attempts to perform an action that would be blocked by an ASR rule, but is allowed to proceed for auditing purposes.
-search: '`ms_defender` EventCode IN (1122, 1125, 1126, 1132, 1134) | stats count min(_time) as firstTime max(_time) as lastTime by host, Process_Name, Target_Commandline, Path, ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_audit_events_filter`'
-how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore the analytic will need to be modified for each type of event.
-known_false_positives: False positives are expected from legitimate applications generating events that are similar to those generated by malicious activity. For example, Event ID 1122 is generated when a process attempts to load a DLL that is blocked by an ASR rule. This can be triggered by legitimate applications that attempt to load DLLs that are not blocked by ASR rules. This is audit only.
+description: This detection searches for Windows Defender ASR audit events. ASR is
+  a feature of Windows Defender Exploit Guard that prevents actions and apps that
+  are typically used by exploit-seeking malware to infect machines. ASR rules are
+  applied to processes and applications. When a process or application attempts to
+  perform an action that is blocked by an ASR rule, an event is generated. This detection
+  searches for ASR audit events that are generated when a process or application attempts
+  to perform an action that would be blocked by an ASR rule, but is allowed to proceed
+  for auditing purposes.
+search: '`ms_defender` EventCode IN (1122, 1125, 1126, 1132, 1134) | stats count min(_time)
+  as firstTime max(_time) as lastTime by host, Process_Name, Target_Commandline, Path,
+  ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_audit_events_filter`'
+how_to_implement: The following analytic requires collection of Windows Defender Operational
+  logs in either XML or multi-line. To collect, setup a new input for the Windows
+  Defender Operational logs. In addition, it does require a lookup that maps the ID
+  to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore
+  the analytic will need to be modified for each type of event.
+known_false_positives: False positives are expected from legitimate applications generating
+  events that are similar to those generated by malicious activity. For example, Event
+  ID 1122 is generated when a process attempts to load a DLL that is blocked by an
+  ASR rule. This can be triggered by legitimate applications that attempt to load
+  DLLs that are not blocked by ASR rules. This is audit only.
 references:
 - https://asrgen.streamlit.app/
 drilldown_searches:
@@ -23,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,17 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - host
-  - Process_Name
-  - Target_Commandline
-  - ID
-  - EventCode
-  - ASR_Rule
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_audit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_audit.log
     source: WinEventLog:Microsoft-Windows-Windows Defender/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defender_asr_block_events.yml b/detections/endpoint/windows_defender_asr_block_events.yml
index fc29136d59..29be39197e 100644
--- a/detections/endpoint/windows_defender_asr_block_events.yml
+++ b/detections/endpoint/windows_defender_asr_block_events.yml
@@ -11,10 +11,29 @@ data_source:
 - Windows Event Log Defender 1129
 - Windows Event Log Defender 1131
 - Windows Event Log Defender 1133
-description: This detection searches for Windows Defender ASR block events. ASR is a feature of Windows Defender Exploit Guard that prevents actions and apps that are typically used by exploit-seeking malware to infect machines. ASR rules are applied to processes and applications. When a process or application attempts to perform an action that is blocked by an ASR rule, an event is generated. This detection searches for ASR block events that are generated when a process or application attempts to perform an action that is blocked by an ASR rule. Typically, these will be enabled in block most after auditing and tuning the ASR rules themselves. Set to TTP once tuned.
-search: '`ms_defender` EventCode IN (1121, 1126, 1129, 1131, 1133) | stats count min(_time) as firstTime max(_time) as lastTime by host, Path, Parent_Commandline, Process_Name, ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_block_events_filter`'
-how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore the analytic will need to be modified for each type of event.
-known_false_positives: False positives are expected from legitimate applications generating events that are similar to those generated by malicious activity. For example, Event ID 1122 is generated when a process attempts to load a DLL that is blocked by an ASR rule. This can be triggered by legitimate applications that attempt to load DLLs that are not blocked by ASR rules. This is block only.
+description: This detection searches for Windows Defender ASR block events. ASR is
+  a feature of Windows Defender Exploit Guard that prevents actions and apps that
+  are typically used by exploit-seeking malware to infect machines. ASR rules are
+  applied to processes and applications. When a process or application attempts to
+  perform an action that is blocked by an ASR rule, an event is generated. This detection
+  searches for ASR block events that are generated when a process or application attempts
+  to perform an action that is blocked by an ASR rule. Typically, these will be enabled
+  in block most after auditing and tuning the ASR rules themselves. Set to TTP once
+  tuned.
+search: '`ms_defender` EventCode IN (1121, 1126, 1129, 1131, 1133) | stats count min(_time)
+  as firstTime max(_time) as lastTime by host, Path, Parent_Commandline, Process_Name,
+  ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_block_events_filter`'
+how_to_implement: The following analytic requires collection of Windows Defender Operational
+  logs in either XML or multi-line. To collect, setup a new input for the Windows
+  Defender Operational logs. In addition, it does require a lookup that maps the ID
+  to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore
+  the analytic will need to be modified for each type of event.
+known_false_positives: False positives are expected from legitimate applications generating
+  events that are similar to those generated by malicious activity. For example, Event
+  ID 1122 is generated when a process attempts to load a DLL that is blocked by an
+  ASR rule. This can be triggered by legitimate applications that attempt to load
+  DLLs that are not blocked by ASR rules. This is block only.
 references:
 - https://asrgen.streamlit.app/
 drilldown_searches:
@@ -23,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - host
-  - Process_Name
-  - Path
-  - ID
-  - EventCode
-  - ASR_Rule
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_block.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_block.log
     source: WinEventLog:Microsoft-Windows-Windows Defender/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defender_asr_registry_modification.yml b/detections/endpoint/windows_defender_asr_registry_modification.yml
index 7b0661a7ed..cd5c409730 100644
--- a/detections/endpoint/windows_defender_asr_registry_modification.yml
+++ b/detections/endpoint/windows_defender_asr_registry_modification.yml
@@ -7,10 +7,33 @@ status: production
 type: Hunting
 data_source:
 - Windows Event Log Defender 5007
-description: The following analytic detects modifications to Windows Defender Attack Surface Reduction (ASR) registry settings. It leverages Windows Defender Operational logs, specifically EventCode 5007, to identify changes in ASR rules. This activity is significant because ASR rules are designed to block actions commonly used by malware to exploit systems. Unauthorized modifications to these settings could indicate an attempt to weaken system defenses. If confirmed malicious, this could allow an attacker to bypass security measures, leading to potential system compromise and data breaches.
-search: '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?<New_Registry_Value>\\d+)$" | rex field=Old_Value "0x(?<Old_Registry_Value>\\d+)$" | rex field=New_Value "Rules\\\\(?<ASR_ID>[A-Fa-f0-9\\-]+)\\s*=" | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1", "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1", "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | stats count min(_time) as firstTime max(_time) as lastTime by host, New_Value, Old_Value, Old_Registry_Value, New_Registry_Value, ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT ASR_Rule | `security_content_ctime(firstTime)`| rename host as dest | `security_content_ctime(lastTime)` | `windows_defender_asr_registry_modification_filter`'
-how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name.
-known_false_positives: False positives are expected from legitimate applications generating events that are similar to those generated by malicious activity. For example, Event ID 5007 is generated when a process attempts to modify a registry key that is related to ASR rules. This can be triggered by legitimate applications that attempt to modify registry keys that are not blocked by ASR rules.
+description: The following analytic detects modifications to Windows Defender Attack
+  Surface Reduction (ASR) registry settings. It leverages Windows Defender Operational
+  logs, specifically EventCode 5007, to identify changes in ASR rules. This activity
+  is significant because ASR rules are designed to block actions commonly used by
+  malware to exploit systems. Unauthorized modifications to these settings could indicate
+  an attempt to weaken system defenses. If confirmed malicious, this could allow an
+  attacker to bypass security measures, leading to potential system compromise and
+  data breaches.
+search: '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?<New_Registry_Value>\\d+)$"
+  | rex field=Old_Value "0x(?<Old_Registry_Value>\\d+)$" | rex field=New_Value "Rules\\\\(?<ASR_ID>[A-Fa-f0-9\\-]+)\\s*="
+  | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1",
+  "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval
+  Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1",
+  "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | stats
+  count min(_time) as firstTime max(_time) as lastTime by host, New_Value, Old_Value,
+  Old_Registry_Value, New_Registry_Value, ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT
+  ASR_Rule | `security_content_ctime(firstTime)`| rename host as dest | `security_content_ctime(lastTime)`
+  | `windows_defender_asr_registry_modification_filter`'
+how_to_implement: The following analytic requires collection of Windows Defender Operational
+  logs in either XML or multi-line. To collect, setup a new input for the Windows
+  Defender Operational logs. In addition, it does require a lookup that maps the ID
+  to ASR Rule name.
+known_false_positives: False positives are expected from legitimate applications generating
+  events that are similar to those generated by malicious activity. For example, Event
+  ID 5007 is generated when a process attempts to modify a registry key that is related
+  to ASR rules. This can be triggered by legitimate applications that attempt to modify
+  registry keys that are not blocked by ASR rules.
 references:
 - https://asrgen.streamlit.app/
 tags:
@@ -32,17 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - host
-  - New_Value
-  - Old_Value
-  - Old_Registry_Value
-  - New_Registry_Value
-  - ASR_Rule
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_registry.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_registry.log
     source: WinEventLog:Microsoft-Windows-Windows Defender/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defender_asr_rule_disabled.yml b/detections/endpoint/windows_defender_asr_rule_disabled.yml
index d5f79a47d9..a0b59e0170 100644
--- a/detections/endpoint/windows_defender_asr_rule_disabled.yml
+++ b/detections/endpoint/windows_defender_asr_rule_disabled.yml
@@ -7,10 +7,30 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Defender 5007
-description: The following analytic identifies when a Windows Defender ASR rule disabled events. ASR is a feature of Windows Defender Exploit Guard that prevents actions and apps that are typically used by exploit-seeking malware to infect machines. ASR rules are applied to processes and applications. When a process or application attempts to perform an action that is blocked by an ASR rule, an event is generated. This detection searches for ASR rule disabled events that are generated when an ASR rule is disabled.
-search: '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?<New_Registry_Value>\\d+)$" | rex field=Old_Value "0x(?<Old_Registry_Value>\\d+)$" | rex field=New_Value "Rules\\\\(?<ASR_ID>[A-Fa-f0-9\\-]+)\\s*=" | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1", "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1", "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | search New_Registry_Value="Disabled" | stats count min(_time) as firstTime max(_time) as lastTime by host, New_Value, Old_Value, Old_Registry_Value, New_Registry_Value, ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT ASR_Rule | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_rule_disabled_filter`'
-how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name.
-known_false_positives: False positives may occur if applications are typically disabling ASR rules in the environment. Monitor for changes to ASR rules to determine if this is a false positive.
+description: The following analytic identifies when a Windows Defender ASR rule disabled
+  events. ASR is a feature of Windows Defender Exploit Guard that prevents actions
+  and apps that are typically used by exploit-seeking malware to infect machines.
+  ASR rules are applied to processes and applications. When a process or application
+  attempts to perform an action that is blocked by an ASR rule, an event is generated.
+  This detection searches for ASR rule disabled events that are generated when an
+  ASR rule is disabled.
+search: '`ms_defender` EventCode IN (5007) | rex field=New_Value "0x(?<New_Registry_Value>\\d+)$"
+  | rex field=Old_Value "0x(?<Old_Registry_Value>\\d+)$" | rex field=New_Value "Rules\\\\(?<ASR_ID>[A-Fa-f0-9\\-]+)\\s*="
+  | eval New_Registry_Value=case(New_Registry_Value=="0", "Disabled", New_Registry_Value=="1",
+  "Block", New_Registry_Value=="2", "Audit", New_Registry_Value=="6", "Warn") | eval
+  Old_Registry_Value=case(Old_Registry_Value=="0", "Disabled", Old_Registry_Value=="1",
+  "Block", Old_Registry_Value=="2", "Audit", Old_Registry_Value=="6", "Warn") | search
+  New_Registry_Value="Disabled" | stats count min(_time) as firstTime max(_time) as
+  lastTime by host, New_Value, Old_Value, Old_Registry_Value, New_Registry_Value,
+  ASR_ID | lookup asr_rules ID AS ASR_ID OUTPUT ASR_Rule | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_rule_disabled_filter`'
+how_to_implement: The following analytic requires collection of Windows Defender Operational
+  logs in either XML or multi-line. To collect, setup a new input for the Windows
+  Defender Operational logs. In addition, it does require a lookup that maps the ID
+  to ASR Rule name.
+known_false_positives: False positives may occur if applications are typically disabling
+  ASR rules in the environment. Monitor for changes to ASR rules to determine if this
+  is a false positive.
 references:
 - https://asrgen.streamlit.app/
 drilldown_searches:
@@ -19,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - host
-  - New_Value
-  - Old_Value
-  - Old_Registry_Value
-  - New_Registry_Value
-  - ASR_Rule
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_disabled_registry.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_disabled_registry.log
     source: WinEventLog:Microsoft-Windows-Windows Defender/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defender_asr_rules_stacking.yml b/detections/endpoint/windows_defender_asr_rules_stacking.yml
index fadaf5633a..3545c27588 100644
--- a/detections/endpoint/windows_defender_asr_rules_stacking.yml
+++ b/detections/endpoint/windows_defender_asr_rules_stacking.yml
@@ -15,10 +15,31 @@ data_source:
 - Windows Event Log Defender 1133
 - Windows Event Log Defender 1134
 - Windows Event Log Defender 5007
-description: The following analytic identifies security events from Microsoft Defender, focusing on Exploit Guard and Attack Surface Reduction (ASR) features. It detects Event IDs 1121, 1126, 1131, and 1133 for blocked operations, and Event IDs 1122, 1125, 1132, and 1134 for audit logs. Event ID 1129 indicates user overrides, while Event ID 5007 signals configuration changes. This detection uses a lookup to correlate ASR rule GUIDs with descriptive names. Monitoring these events is crucial for identifying unauthorized operations, potential security breaches, and policy enforcement issues. If confirmed malicious, attackers could bypass security measures, execute unauthorized actions, or alter system configurations.
-search: '`ms_defender` EventCode IN (1121, 1122, 1125, 1126, 1129, 1131, 1132, 1133, 1134, 5007) | stats count min(_time) as firstTime max(_time) as lastTime by host Parent_Commandline, Process_Name, Path, ID, EventCode | lookup asr_rules ID OUTPUT ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| rename host as dest | `windows_defender_asr_rules_stacking_filter`'
-how_to_implement: The following analytic requires collection of Windows Defender Operational logs in either XML or multi-line. To collect, setup a new input for the Windows Defender Operational logs. In addition, it does require a lookup that maps the ID to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore the analytic will need to be modified for each type of event. The analytic can be modified to look for specific ASR rules, or to look for specific Event IDs. EventID 5007 is a change in the registry, and may be a false positive. This can be removed from the search if desired.
-known_false_positives: False positives are not expected with this analytic, since it is a hunting analytic. It is meant to show the use of ASR rules and how they can be used to detect malicious activity.
+description: The following analytic identifies security events from Microsoft Defender,
+  focusing on Exploit Guard and Attack Surface Reduction (ASR) features. It detects
+  Event IDs 1121, 1126, 1131, and 1133 for blocked operations, and Event IDs 1122,
+  1125, 1132, and 1134 for audit logs. Event ID 1129 indicates user overrides, while
+  Event ID 5007 signals configuration changes. This detection uses a lookup to correlate
+  ASR rule GUIDs with descriptive names. Monitoring these events is crucial for identifying
+  unauthorized operations, potential security breaches, and policy enforcement issues.
+  If confirmed malicious, attackers could bypass security measures, execute unauthorized
+  actions, or alter system configurations.
+search: '`ms_defender` EventCode IN (1121, 1122, 1125, 1126, 1129, 1131, 1132, 1133,
+  1134, 5007) | stats count min(_time) as firstTime max(_time) as lastTime by host
+  Parent_Commandline, Process_Name, Path, ID, EventCode | lookup asr_rules ID OUTPUT
+  ASR_Rule | fillnull value=NULL | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  rename host as dest | `windows_defender_asr_rules_stacking_filter`'
+how_to_implement: The following analytic requires collection of Windows Defender Operational
+  logs in either XML or multi-line. To collect, setup a new input for the Windows
+  Defender Operational logs. In addition, it does require a lookup that maps the ID
+  to ASR Rule name. Note that Audit and block Event IDs have different fields, therefore
+  the analytic will need to be modified for each type of event. The analytic can be
+  modified to look for specific ASR rules, or to look for specific Event IDs. EventID
+  5007 is a change in the registry, and may be a false positive. This can be removed
+  from the search if desired.
+known_false_positives: False positives are not expected with this analytic, since
+  it is a hunting analytic. It is meant to show the use of ASR rules and how they
+  can be used to detect malicious activity.
 references:
 - https://asrgen.streamlit.app/
 - https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/attack-surface-reduction?view=o365-worldwide
@@ -47,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - host
-  - Parent_Commandline
-  - Target_Commandline
-  - ID
-  - EventCode
-  - ASR_Rule
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_defender_operational.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/defender/asr_defender_operational.log
     source: WinEventLog:Microsoft-Windows-Windows Defender/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_defender_exclusion_registry_entry.yml b/detections/endpoint/windows_defender_exclusion_registry_entry.yml
index 069ba2e428..8504999e22 100644
--- a/detections/endpoint/windows_defender_exclusion_registry_entry.yml
+++ b/detections/endpoint/windows_defender_exclusion_registry_entry.yml
@@ -5,16 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects modifications to the Windows Defender exclusion registry entries. It leverages endpoint registry data to identify changes in the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*". This activity is significant because adversaries often modify these entries to bypass Windows Defender, allowing malicious code to execute without detection. If confirmed malicious, this behavior could enable attackers to evade antivirus defenses, maintain persistence, and execute further malicious activities undetected.
+description: The following analytic detects modifications to the Windows Defender
+  exclusion registry entries. It leverages endpoint registry data to identify changes
+  in the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*".
+  This activity is significant because adversaries often modify these entries to bypass
+  Windows Defender, allowing malicious code to execute without detection. If confirmed
+  malicious, this behavior could enable attackers to evade antivirus defenses, maintain
+  persistence, and execute further malicious activities undetected.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Exclusions\\*")
-  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
-  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
-  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `windows_defender_exclusion_registry_entry_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\SOFTWARE\\Policies\\Microsoft\\Windows
+  Defender\\Exclusions\\*") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_defender_exclusion_registry_entry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -30,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: Exclusion registry $registry_path$ modified or added on $dest$ for Windows Defender
+  message: Exclusion registry $registry_path$ modified or added on $dest$ for Windows
+    Defender
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -61,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/defender_exclusion_sysmon/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_delete_or_modify_system_firewall.yml b/detections/endpoint/windows_delete_or_modify_system_firewall.yml
index bc5f130454..0b102dbfb2 100644
--- a/detections/endpoint/windows_delete_or_modify_system_firewall.yml
+++ b/detections/endpoint/windows_delete_or_modify_system_firewall.yml
@@ -9,9 +9,28 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies 'netsh' processes that delete or modify firewall configurations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing specific keywords. This activity is significant because it can indicate malware, such as NJRAT, attempting to alter firewall settings to evade detection or remove traces. If confirmed malicious, this behavior could allow an attacker to disable security measures, facilitating further compromise and persistence within the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process = "* firewall *" Processes.process = "* del*" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_delete_or_modify_system_firewall_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies 'netsh' processes that delete or modify
+  firewall configurations. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on command-line executions containing specific keywords.
+  This activity is significant because it can indicate malware, such as NJRAT, attempting
+  to alter firewall settings to evade detection or remove traces. If confirmed malicious,
+  this behavior could allow an attacker to disable security measures, facilitating
+  further compromise and persistence within the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process
+  = "* firewall *" Processes.process = "* del*" by Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process_id Processes.process_guid Processes.process
+  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_delete_or_modify_system_firewall_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrator may modify or delete firewall configuration.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_delete_firewall/njrat_delete_firewall.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_delete_firewall/njrat_delete_firewall.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml b/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml
index a7a440b7d8..eb0fd4386c 100644
--- a/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml
+++ b/detections/endpoint/windows_deleted_registry_by_a_non_critical_process_file_path.yml
@@ -5,13 +5,38 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of registry keys by non-critical processes. It leverages Endpoint Detection and Response (EDR) data, focusing on registry deletion events and correlating them with processes not typically associated with system or program files. This activity is significant as it may indicate malware, such as the Double Zero wiper, attempting to evade defenses or cause destructive payload impacts. If confirmed malicious, this behavior could lead to significant system damage, loss of critical configurations, and potential disruption of services.
+description: The following analytic detects the deletion of registry keys by non-critical
+  processes. It leverages Endpoint Detection and Response (EDR) data, focusing on
+  registry deletion events and correlating them with processes not typically associated
+  with system or program files. This activity is significant as it may indicate malware,
+  such as the Double Zero wiper, attempting to evade defenses or cause destructive
+  payload impacts. If confirmed malicious, this behavior could lead to significant
+  system damage, loss of critical configurations, and potential disruption of services.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry WHERE Registry.action=deleted BY _time span=1h Registry.dest Registry.registry_path Registry.registry_value_name Registry.registry_key_name Registry.process_guid Registry.registry_value_data Registry.action | `drop_dm_object_name(Registry)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT (Processes.process_path IN ("*\\windows\\*", "*\\program files*")) by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.user Processes.parent_process_name Processes.parent_process Processes.process_path Processes.process_guid | `drop_dm_object_name(Processes)`] | fields _time parent_process_name parent_process process_name process_path process process_guid registry_path registry_value_name registry_value_data registry_key_name action dest user | `windows_deleted_registry_by_a_non_critical_process_file_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This detection can catch for third party application updates or installation. In this scenario false positive filter is needed.
+search: '| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry
+  WHERE Registry.action=deleted BY _time span=1h Registry.dest Registry.registry_path
+  Registry.registry_value_name Registry.registry_key_name Registry.process_guid Registry.registry_value_data
+  Registry.action | `drop_dm_object_name(Registry)` | join process_guid [| tstats
+  `security_content_summariesonly` count FROM datamodel=Endpoint.Processes WHERE NOT
+  (Processes.process_path IN ("*\\windows\\*", "*\\program files*")) by _time span=1h
+  Processes.process_id Processes.process_name Processes.process Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_path Processes.process_guid | `drop_dm_object_name(Processes)`]
+  | fields _time parent_process_name parent_process process_name process_path process
+  process_guid registry_path registry_value_name registry_value_data registry_key_name
+  action dest user | `windows_deleted_registry_by_a_non_critical_process_file_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This detection can catch for third party application updates
+  or installation. In this scenario false positive filter is needed.
 references:
 - https://blog.talosintelligence.com/2022/03/threat-advisory-doublezero.html
 drilldown_searches:
@@ -20,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: The registry was deleted by a suspicious process named $process_name$ with the process path $process_path$ on dest $dest$.
+  message: The registry was deleted by a suspicious process named $process_name$ with
+    the process path $process_path$ on dest $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -42,26 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_change_password_through_registry.yml b/detections/endpoint/windows_disable_change_password_through_registry.yml
index 0e4f8385da..ce3ccc3499 100644
--- a/detections/endpoint/windows_disable_change_password_through_registry.yml
+++ b/detections/endpoint/windows_disable_change_password_through_registry.yml
@@ -5,12 +5,20 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects a suspicious registry modification that disables the Change Password feature on a Windows host. It identifies changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword" with a value of "0x00000001". This activity is significant as it can prevent users from changing their passwords, a tactic often used by ransomware to maintain control over compromised systems. If confirmed malicious, this could hinder user response to an attack, allowing the attacker to persist and potentially escalate their access within the network.
+description: The following analytic detects a suspicious registry modification that
+  disables the Change Password feature on a Windows host. It identifies changes to
+  the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword"
+  with a value of "0x00000001". This activity is significant as it can prevent users
+  from changing their passwords, a tactic often used by ransomware to maintain control
+  over compromised systems. If confirmed malicious, this could hinder user response
+  to an attack, allowing the attacker to persist and potentially escalate their access
+  within the network.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableChangePassword"
   Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
@@ -30,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml b/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml
index 56e34cd1b3..c060f60846 100644
--- a/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml
+++ b/detections/endpoint/windows_disable_lock_workstation_feature_through_registry.yml
@@ -5,14 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects a suspicious registry modification that disables the Lock Computer feature in Windows. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation" with a value of "0x00000001". This activity is significant because it prevents users from locking their screens, a tactic often used by malware, including ransomware, to maintain control over compromised systems. If confirmed malicious, this could allow attackers to sustain their presence and execute further malicious actions without user interruption.
+description: The following analytic detects a suspicious registry modification that
+  disables the Lock Computer feature in Windows. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation"
+  with a value of "0x00000001". This activity is significant because it prevents users
+  from locking their screens, a tactic often used by malware, including ransomware,
+  to maintain control over compromised systems. If confirmed malicious, this could
+  allow attackers to sustain their presence and execute further malicious actions
+  without user interruption.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation"
-  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableLockWorkstation"
+  Registry.registry_value_data = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_lock_workstation_feature_through_registry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -29,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_logoff_button_through_registry.yml b/detections/endpoint/windows_disable_logoff_button_through_registry.yml
index 1b4642de40..b7822e99fa 100644
--- a/detections/endpoint/windows_disable_logoff_button_through_registry.yml
+++ b/detections/endpoint/windows_disable_logoff_button_through_registry.yml
@@ -5,17 +5,24 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects a suspicious registry modification that disables the logoff feature on a Windows host. It leverages data from the Endpoint.Registry data model to identify changes to specific registry values associated with logoff functionality. This activity is significant because it can indicate ransomware attempting to make the compromised host unusable and hinder remediation efforts. If confirmed malicious, this action could prevent users from logging off, complicate incident response, and allow attackers to maintain persistence and control over the affected system.
+description: The following analytic detects a suspicious registry modification that
+  disables the logoff feature on a Windows host. It leverages data from the Endpoint.Registry
+  data model to identify changes to specific registry values associated with logoff
+  functionality. This activity is significant because it can indicate ransomware attempting
+  to make the compromised host unusable and hinder remediation efforts. If confirmed
+  malicious, this action could prevent users from logging off, complicate incident
+  response, and allow attackers to maintain persistence and control over the affected
+  system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*"
   Registry.registry_value_name IN ("NoLogOff", "StartMenuLogOff") Registry.registry_value_data
-  = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_logoff_button_through_registry_filter`'
+  = "0x00000001") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_disable_logoff_button_through_registry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -33,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_memory_crash_dump.yml b/detections/endpoint/windows_disable_memory_crash_dump.yml
index cb4a6d22c7..399e0cbe84 100644
--- a/detections/endpoint/windows_disable_memory_crash_dump.yml
+++ b/detections/endpoint/windows_disable_memory_crash_dump.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to disable the memory crash dump feature on Windows systems by setting the registry value to 0. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the CrashDumpEnabled registry key. This activity is significant because disabling crash dumps can hinder forensic analysis and incident response efforts. If confirmed malicious, this action could be part of a broader attack strategy, such as data destruction or system destabilization, as seen with HermeticWiper, potentially leading to significant operational disruptions and data loss.
+description: The following analytic detects attempts to disable the memory crash dump
+  feature on Windows systems by setting the registry value to 0. It leverages data
+  from the Endpoint.Registry datamodel, specifically monitoring changes to the CrashDumpEnabled
+  registry key. This activity is significant because disabling crash dumps can hinder
+  forensic analysis and incident response efforts. If confirmed malicious, this action
+  could be part of a broader attack strategy, such as data destruction or system destabilization,
+  as seen with HermeticWiper, potentially leading to significant operational disruptions
+  and data loss.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\CurrentControlSet\\Control\\CrashControl\\CrashDumpEnabled") AND Registry.registry_value_data="0x00000000" by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.registry_key_name  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_memory_crash_dump_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` and `Registry` node.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  where (Registry.registry_path="*\\CurrentControlSet\\Control\\CrashControl\\CrashDumpEnabled")
+  AND Registry.registry_value_data="0x00000000" by _time span=1h Registry.dest Registry.user
+  Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid Registry.registry_key_name  | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_memory_crash_dump_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` and `Registry`
+  node.
 known_false_positives: unknown
 references:
 - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_notification_center.yml b/detections/endpoint/windows_disable_notification_center.yml
index 83a7307929..db3dc8d99c 100644
--- a/detections/endpoint/windows_disable_notification_center.yml
+++ b/detections/endpoint/windows_disable_notification_center.yml
@@ -5,16 +5,23 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects the modification of the Windows registry to disable the Notification Center on a host machine. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "DisableNotificationCenter" registry value set to "0x00000001." This activity is significant because disabling the Notification Center can be a tactic used by RAT malware to hide its presence and subsequent actions. If confirmed malicious, this could allow an attacker to operate stealthily, potentially leading to further system compromise and data exfiltration.
+description: The following analytic detects the modification of the Windows registry
+  to disable the Notification Center on a host machine. It leverages data from the
+  Endpoint.Registry data model, specifically looking for changes to the "DisableNotificationCenter"
+  registry value set to "0x00000001." This activity is significant because disabling
+  the Notification Center can be a tactic used by RAT malware to hide its presence
+  and subsequent actions. If confirmed malicious, this could allow an attacker to
+  operate stealthily, potentially leading to further system compromise and data exfiltration.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_value_name= "DisableNotificationCenter" Registry.registry_value_data
-  = "0x00000001") BY Registry.dest Registry.user Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
-  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_notification_center_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_value_name=
+  "DisableNotificationCenter" Registry.registry_value_data = "0x00000001") BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
+  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_disable_notification_center_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -28,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_notif_center/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_notif_center/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml b/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml
index d30a554cd6..fe0b599f8c 100644
--- a/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml
+++ b/detections/endpoint/windows_disable_or_modify_tools_via_taskkill.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the use of taskkill.exe to forcibly terminate processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific taskkill parameters. This activity is significant because it can indicate attempts to disable security tools or disrupt legitimate applications, a common tactic in malware operations. If confirmed malicious, this behavior could allow attackers to evade detection, disrupt system stability, and potentially gain further control over the compromised system.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe" Processes.process IN ("* /f*", "* /t*") Processes.process IN ("* /im*", "* /pid*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_or_modify_tools_via_taskkill_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Network administrator can use this application to kill process during audit or investigation.
+description: The following analytic identifies the use of taskkill.exe to forcibly
+  terminate processes. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on command-line executions that include specific taskkill parameters.
+  This activity is significant because it can indicate attempts to disable security
+  tools or disrupt legitimate applications, a common tactic in malware operations.
+  If confirmed malicious, this behavior could allow attackers to evade detection,
+  disrupt system stability, and potentially gain further control over the compromised
+  system.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe"
+  Processes.process IN ("* /f*", "* /t*") Processes.process IN ("* /im*", "* /pid*")
+  by Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.process_guid  Processes.user Processes.dest
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_disable_or_modify_tools_via_taskkill_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Network administrator can use this application to kill process
+  during audit or investigation.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill/taskkill_im.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill/taskkill_im.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_or_stop_browser_process.yml b/detections/endpoint/windows_disable_or_stop_browser_process.yml
index d9def0ff3c..a70a0e3613 100644
--- a/detections/endpoint/windows_disable_or_stop_browser_process.yml
+++ b/detections/endpoint/windows_disable_or_stop_browser_process.yml
@@ -7,13 +7,20 @@ data_source:
 - Sysmon EventID 1
 type: TTP
 status: production
-description: The following analytic detects the use of the taskkill command in a process command line to terminate several known browser processes, a technique commonly employed by the Braodo stealer malware to steal credentials. By forcefully closing browsers like Chrome, Edge, and Firefox, the malware can unlock files that store sensitive information, such as passwords and login data. This detection focuses on identifying taskkill commands targeting these browsers, signaling malicious intent. Early detection allows security teams to investigate and prevent further credential theft and system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes 
-  where Processes.process = "*taskkill*" Processes.process IN("*chrome.exe","*firefox.exe","*brave.exe","*opera.exe","*msedge.exe","*chromium.exe")
-  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id
-  Processes.parent_process_id 
-  | `drop_dm_object_name(Processes)` 
-  | `security_content_ctime(firstTime)`
+description: The following analytic detects the use of the taskkill command in a process
+  command line to terminate several known browser processes, a technique commonly
+  employed by the Braodo stealer malware to steal credentials. By forcefully closing
+  browsers like Chrome, Edge, and Firefox, the malware can unlock files that store
+  sensitive information, such as passwords and login data. This detection focuses
+  on identifying taskkill commands targeting these browsers, signaling malicious intent.
+  Early detection allows security teams to investigate and prevent further credential
+  theft and system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*taskkill*"
+  Processes.process IN("*chrome.exe","*firefox.exe","*brave.exe","*opera.exe","*msedge.exe","*chromium.exe")
+  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `windows_disable_or_stop_browser_process_filter`'
 how_to_implement: The detection is based on data that originates from Endpoint Detection
   and Response (EDR) agents. These agents are designed to provide security-related
@@ -24,8 +31,8 @@ how_to_implement: The detection is based on data that originates from Endpoint D
   the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
   data model. Use the Splunk Common Information Model (CIM) to normalize the field
   names and speed up the data modeling process.
-known_false_positives: Admin or user may choose to terminate browser via taskkill.exe. Filter
-  as needed.
+known_false_positives: Admin or user may choose to terminate browser via taskkill.exe.
+  Filter as needed.
 references:
 - https://x.com/suyog41/status/1825869470323056748
 - https://g0njxa.medium.com/from-vietnam-to-united-states-malware-fraud-and-dropshipping-98b7a7b2c36d
@@ -35,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -61,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill_browser/braodo_taskkill.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/taskkill_browser/braodo_taskkill.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_shutdown_button_through_registry.yml b/detections/endpoint/windows_disable_shutdown_button_through_registry.yml
index c847b7595e..03bc158436 100644
--- a/detections/endpoint/windows_disable_shutdown_button_through_registry.yml
+++ b/detections/endpoint/windows_disable_shutdown_button_through_registry.yml
@@ -5,15 +5,22 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects suspicious registry modifications that disable the shutdown button on a user's logon screen. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to registry paths associated with shutdown policies. This activity is significant because it is a tactic used by malware, particularly ransomware like KillDisk, to hinder system usability and prevent the removal of malicious changes. If confirmed malicious, this could impede system recovery efforts, making it difficult to restart the machine and remove other harmful modifications.
+description: The following analytic detects suspicious registry modifications that
+  disable the shutdown button on a user's logon screen. It leverages data from the
+  Endpoint.Registry data model, specifically monitoring changes to registry paths
+  associated with shutdown policies. This activity is significant because it is a
+  tactic used by malware, particularly ransomware like KillDisk, to hinder system
+  usability and prevent the removal of malicious changes. If confirmed malicious,
+  this could impede system recovery efforts, making it difficult to restart the machine
+  and remove other harmful modifications.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE ((Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\shutdownwithoutlogon"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE ((Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\shutdownwithoutlogon"
   Registry.registry_value_data = "0x00000000") OR (Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoClose"
-  Registry.registry_value_data = "0x00000001")) BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.registry_value_data = "0x00000001")) BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_shutdown_button_through_registry_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
@@ -31,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml b/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml
index 9c0a85504d..042bf02464 100644
--- a/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml
+++ b/detections/endpoint/windows_disable_windows_event_logging_disable_http_logging.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of AppCmd.exe to disable HTTP logging on IIS servers. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution events where AppCmd.exe is used with specific parameters to alter logging settings. This activity is significant because disabling HTTP logging can help adversaries hide their tracks and avoid detection by removing evidence of their actions. If confirmed malicious, this could allow attackers to operate undetected, making it difficult to trace their activities and respond to the intrusion effectively.
+description: The following analytic detects the use of AppCmd.exe to disable HTTP
+  logging on IIS servers. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process execution events where AppCmd.exe is used with specific
+  parameters to alter logging settings. This activity is significant because disabling
+  HTTP logging can help adversaries hide their tracks and avoid detection by removing
+  evidence of their actions. If confirmed malicious, this could allow attackers to
+  operate undetected, making it difficult to trace their activities and respond to
+  the intrusion effectively.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name  IN ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process IN ("*set config*", "*httplogging*","*dontlog:true*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_event_logging_disable_http_logging_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present only if scripts or Administrators are disabling logging. Filter as needed by parent process or other.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name  IN
+  ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process
+  IN ("*set config*", "*httplogging*","*dontlog:true*") by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_event_logging_disable_http_logging_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present only if scripts or Administrators
+  are disabling logging. Filter as needed by parent process or other.
 references:
 - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf
 - https://unit42.paloaltonetworks.com/unit42-oilrig-uses-rgdoor-iis-backdoor-targets-middle-east/
@@ -24,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable IIS HTTP Logging.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to disable IIS HTTP Logging.
   mitre_attack_id:
   - T1562.002
   - T1562
@@ -62,23 +90,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/disable_http_logging_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/disable_http_logging_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml b/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml
index eeab4f1cac..7cb55ab6ac 100644
--- a/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml
+++ b/detections/endpoint/windows_disable_windows_group_policy_features_through_registry.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious registry modifications aimed at disabling Windows Group Policy features. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values associated with disabling key Windows functionalities. This activity is significant because it is commonly used by ransomware to hinder mitigation and forensic response efforts. If confirmed malicious, this behavior could severely impair the ability of security teams to analyze and respond to the attack, allowing the attacker to maintain control and persist within the compromised environment.
+description: The following analytic detects suspicious registry modifications aimed
+  at disabling Windows Group Policy features. It leverages data from the Endpoint.Registry
+  data model, focusing on specific registry paths and values associated with disabling
+  key Windows functionalities. This activity is significant because it is commonly
+  used by ransomware to hinder mitigation and forensic response efforts. If confirmed
+  malicious, this behavior could severely impair the ability of security teams to
+  analyze and respond to the attack, allowing the attacker to maintain control and
+  persist within the compromised environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\*" Registry.registry_value_name IN ("NoDesktop", "NoFind", "NoControlPanel", "NoFileMenu", "NoSetTaskbar", "NoTrayContextMenu", "TaskbarLockAll", "NoThemesTab","NoPropertiesMyDocuments","NoVisualStyleChoice","NoColorChoice","NoPropertiesMyDocuments") Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_group_policy_features_through_registry_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
-known_false_positives: Disabling these features for legitimate purposes is not a common use case but can still be implemented by the administrators. Filter as needed.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*"
+  OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\*"
+  Registry.registry_value_name IN ("NoDesktop", "NoFind", "NoControlPanel", "NoFileMenu",
+  "NoSetTaskbar", "NoTrayContextMenu", "TaskbarLockAll", "NoThemesTab","NoPropertiesMyDocuments","NoVisualStyleChoice","NoColorChoice","NoPropertiesMyDocuments")
+  Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.dest Registry.user
+  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_disable_windows_group_policy_features_through_registry_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
+known_false_positives: Disabling these features for legitimate purposes is not a common
+  use case but can still be implemented by the administrators. Filter as needed.
 references:
 - https://hybrid-analysis.com/sample/ef1c427394c205580576d18ba68d5911089c7da0386f19d1ca126929d3e671ab?environmentId=120&lang=en
 - https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/Troj~Krotten-N/detailed-analysis
@@ -22,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_disableantispyware_registry.yml b/detections/endpoint/windows_disableantispyware_registry.yml
index 482bbb400b..9f1ba27c68 100644
--- a/detections/endpoint/windows_disableantispyware_registry.yml
+++ b/detections/endpoint/windows_disableantispyware_registry.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: Rod Soto, Jose Hernandez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the modification of the Windows Registry key "DisableAntiSpyware" being set to disable. This detection leverages data from the Endpoint.Registry datamodel, specifically looking for the registry value name "DisableAntiSpyware" with a value of "0x00000001". This activity is significant as it is commonly associated with Ryuk ransomware infections, indicating potential malicious intent to disable Windows Defender. If confirmed malicious, this action could allow attackers to disable critical security defenses, facilitating further malicious activities such as data encryption, exfiltration, or additional system compromise.
+description: The following analytic detects the modification of the Windows Registry
+  key "DisableAntiSpyware" being set to disable. This detection leverages data from
+  the Endpoint.Registry datamodel, specifically looking for the registry value name
+  "DisableAntiSpyware" with a value of "0x00000001". This activity is significant
+  as it is commonly associated with Ryuk ransomware infections, indicating potential
+  malicious intent to disable Windows Defender. If confirmed malicious, this action
+  could allow attackers to disable critical security defenses, facilitating further
+  malicious activities such as data encryption, exfiltration, or additional system
+  compromise.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_value_name="DisableAntiSpyware" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_value_name="DisableAntiSpyware"
+  AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_disableantispyware_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
 drilldown_searches:
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_diskcryptor_usage.yml b/detections/endpoint/windows_diskcryptor_usage.yml
index 4b736d996b..2c39294e16 100644
--- a/detections/endpoint/windows_diskcryptor_usage.yml
+++ b/detections/endpoint/windows_diskcryptor_usage.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of DiskCryptor, identified by the process names "dcrypt.exe" or "dcinst.exe". This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. DiskCryptor is significant because adversaries use it to manually encrypt disks during an operation, potentially leading to data inaccessibility. If confirmed malicious, this activity could result in complete disk encryption, causing data loss and operational disruption. Immediate investigation is required to mitigate potential ransomware attacks.
+description: The following analytic detects the execution of DiskCryptor, identified
+  by the process names "dcrypt.exe" or "dcinst.exe". This detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  original file names. DiskCryptor is significant because adversaries use it to manually
+  encrypt disks during an operation, potentially leading to data inaccessibility.
+  If confirmed malicious, this activity could result in complete disk encryption,
+  causing data loss and operational disruption. Immediate investigation is required
+  to mitigate potential ransomware attacks.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dcrypt.exe" OR Processes.original_file_name=dcinst.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_diskcryptor_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible false positives may be present based on the internal name dcinst.exe, filter as needed. It may be worthy to alert on the service name.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="dcrypt.exe"
+  OR Processes.original_file_name=dcinst.exe) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_diskcryptor_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible false positives may be present based on the
+  internal name dcinst.exe, filter as needed. It may be worthy to alert on the service
+  name.
 references:
 - https://thedfirreport.com/2021/11/15/exchange-exploit-leads-to-domain-wide-ransomware/
 - https://github.com/DavidXanatos/DiskCryptor
@@ -22,7 +44,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to encrypt disks.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to encrypt disks.
   mitre_attack_id:
   - T1486
   observable:
@@ -46,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/dcrypt/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/dcrypt/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_diskshadow_proxy_execution.yml b/detections/endpoint/windows_diskshadow_proxy_execution.yml
index dddb49cb9d..7604d98f54 100644
--- a/detections/endpoint/windows_diskshadow_proxy_execution.yml
+++ b/detections/endpoint/windows_diskshadow_proxy_execution.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Lou Stella, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of DiskShadow.exe in scripting mode, which can execute arbitrary unsigned code. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions with scripting mode flags. This activity is significant because DiskShadow.exe is typically used for legitimate backup operations, but its misuse can indicate an attempt to execute unauthorized code. If confirmed malicious, this could lead to unauthorized code execution, potentially compromising the system and allowing further malicious activities.
+description: The following analytic detects the use of DiskShadow.exe in scripting
+  mode, which can execute arbitrary unsigned code. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on command-line executions with scripting
+  mode flags. This activity is significant because DiskShadow.exe is typically used
+  for legitimate backup operations, but its misuse can indicate an attempt to execute
+  unauthorized code. If confirmed malicious, this could lead to unauthorized code
+  execution, potentially compromising the system and allowing further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_diskshadow` (Processes.process=*-s* OR Processes.process=*/s*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_diskshadow_proxy_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators using the DiskShadow tool in their infrastructure as a main backup tool with scripts will cause false positives that can be filtered with `windows_diskshadow_proxy_execution_filter`
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_diskshadow` (Processes.process=*-s*
+  OR Processes.process=*/s*) by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_diskshadow_proxy_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators using the DiskShadow tool in their infrastructure
+  as a main backup tool with scripts will cause false positives that can be filtered
+  with `windows_diskshadow_proxy_execution_filter`
 references:
 - https://bohops.com/2018/03/26/diskshadow-the-return-of-vss-evasion-persistence-and-active-directory-database-extraction/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Porcesses.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.original_file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/diskshadow/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218/diskshadow/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dism_install_powershell_web_access.yml b/detections/endpoint/windows_dism_install_powershell_web_access.yml
index 5ea4e5193e..bc70f7e722 100644
--- a/detections/endpoint/windows_dism_install_powershell_web_access.yml
+++ b/detections/endpoint/windows_dism_install_powershell_web_access.yml
@@ -8,10 +8,32 @@ data_source:
 - Sysmon EventID 1
 type: TTP
 status: production
-description: The following analytic detects the installation of PowerShell Web Access using the Deployment Image Servicing and Management (DISM) tool. It leverages Sysmon EventID 1 to identify the execution of `dism.exe` with specific parameters related to enabling the WindowsPowerShellWebAccess feature. This activity is significant because enabling PowerShell Web Access can facilitate remote execution of PowerShell commands, potentially allowing an attacker to gain unauthorized access to systems and networks. If confirmed malicious, this action could lead to further exploitation and compromise of the affected system.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe (Processes.process="*WindowsPowerShellWebAccess*" AND Processes.process="*/online*" AND Processes.process="*/enable-feature*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dism_install_powershell_web_access_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators using the DISM tool to update and install Windows features may cause false positives that can be filtered with `windows_dism_install_powershell_web_access_filter`.
+description: The following analytic detects the installation of PowerShell Web Access
+  using the Deployment Image Servicing and Management (DISM) tool. It leverages Sysmon
+  EventID 1 to identify the execution of `dism.exe` with specific parameters related
+  to enabling the WindowsPowerShellWebAccess feature. This activity is significant
+  because enabling PowerShell Web Access can facilitate remote execution of PowerShell
+  commands, potentially allowing an attacker to gain unauthorized access to systems
+  and networks. If confirmed malicious, this action could lead to further exploitation
+  and compromise of the affected system.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe
+  (Processes.process="*WindowsPowerShellWebAccess*" AND Processes.process="*/online*"
+  AND Processes.process="*/enable-feature*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_dism_install_powershell_web_access_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators using the DISM tool to update and install Windows
+  features may cause false positives that can be filtered with `windows_dism_install_powershell_web_access_filter`.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a
 - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,21 +69,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - user
-  - parent_process_name
-  - process_name
-  - original_file_name
-  - process
-  - process_id
-  - parent_process_id
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/dism_pswa_4688_windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/dism_pswa_4688_windows-security.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Security
diff --git a/detections/endpoint/windows_dism_remove_defender.yml b/detections/endpoint/windows_dism_remove_defender.yml
index da15716ca6..91758ebdd9 100644
--- a/detections/endpoint/windows_dism_remove_defender.yml
+++ b/detections/endpoint/windows_dism_remove_defender.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `dism.exe` to remove Windows Defender. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that include specific parameters for disabling and removing Windows Defender. This activity is significant because adversaries may disable Defender to evade detection and carry out further malicious actions undetected. If confirmed malicious, this could lead to the attacker gaining persistent access, executing additional payloads, or exfiltrating sensitive data without being intercepted by Windows Defender.
+description: The following analytic detects the use of `dism.exe` to remove Windows
+  Defender. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on command-line executions that include specific parameters for disabling and removing
+  Windows Defender. This activity is significant because adversaries may disable Defender
+  to evade detection and carry out further malicious actions undetected. If confirmed
+  malicious, this could lead to the attacker gaining persistent access, executing
+  additional payloads, or exfiltrating sensitive data without being intercepted by
+  Windows Defender.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe (Processes.process="*/online*" AND Processes.process="*/disable-feature*" AND Processes.process="*Windows-Defender*" AND Processes.process="*/remove*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dism_remove_defender_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legitimate administrative tools leverage `dism.exe` to manipulate packages and features of the operating system. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dism.exe
+  (Processes.process="*/online*" AND Processes.process="*/disable-feature*" AND Processes.process="*Windows-Defender*"
+  AND Processes.process="*/remove*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_dism_remove_defender_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legitimate administrative tools leverage `dism.exe` to
+  manipulate packages and features of the operating system. Filter as needed.
 references:
 - https://thedfirreport.com/2020/11/23/pysa-mespinoza-ransomware/
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to disable Windows Defender.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to disable Windows Defender.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -56,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: access
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_dism.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_dism.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml b/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml
index da1099f295..a924b0b7bd 100644
--- a/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml
+++ b/detections/endpoint/windows_dll_search_order_hijacking_hunt_with_sysmon.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies potential DLL search order hijacking or DLL sideloading by detecting known Windows libraries loaded from non-standard directories. It leverages Sysmon EventCode 7 to monitor DLL loads and cross-references them with a lookup of known hijackable libraries. This activity is significant as it may indicate an attempt to execute malicious code by exploiting DLL search order vulnerabilities. If confirmed malicious, this could allow attackers to gain code execution, escalate privileges, or maintain persistence within the environment.
+description: The following analytic identifies potential DLL search order hijacking
+  or DLL sideloading by detecting known Windows libraries loaded from non-standard
+  directories. It leverages Sysmon EventCode 7 to monitor DLL loads and cross-references
+  them with a lookup of known hijackable libraries. This activity is significant as
+  it may indicate an attempt to execute malicious code by exploiting DLL search order
+  vulnerabilities. If confirmed malicious, this could allow attackers to gain code
+  execution, escalate privileges, or maintain persistence within the environment.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 NOT (process_path IN ("*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*")) | lookup hijacklibs library AS loaded_file OUTPUT islibrary | search islibrary = True | stats count min(_time) as firstTime max(_time) as lastTime values(process_name) as process_name by _time dest loaded_file | `windows_dll_search_order_hijacking_hunt_with_sysmon_filter`'
-how_to_implement: The search is written against the latest Sysmon TA 4.0 https://splunkbase.splunk.com/app/5709. For this specific event ID 7, the sysmon TA will extract the ImageLoaded name to the loaded_file field which is used in the search to compare against the hijacklibs lookup.
-known_false_positives: False positives will be present based on paths. Filter or add other paths to the exclusion as needed. Some applications may legitimately load libraries from non-standard paths.
+search: '`sysmon` EventCode=7 NOT (process_path IN ("*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*"))
+  | lookup hijacklibs library AS loaded_file OUTPUT islibrary | search islibrary =
+  True | stats count min(_time) as firstTime max(_time) as lastTime values(process_name)
+  as process_name by _time dest loaded_file | `windows_dll_search_order_hijacking_hunt_with_sysmon_filter`'
+how_to_implement: The search is written against the latest Sysmon TA 4.0 https://splunkbase.splunk.com/app/5709.
+  For this specific event ID 7, the sysmon TA will extract the ImageLoaded name to
+  the loaded_file field which is used in the search to compare against the hijacklibs
+  lookup.
+known_false_positives: False positives will be present based on paths. Filter or add
+  other paths to the exclusion as needed. Some applications may legitimately load
+  libraries from non-standard paths.
 references:
 - https://hijacklibs.net
 tags:
@@ -34,15 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - ImageLoaded
-  - Module_Path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml b/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml
index bb14b913dd..b1e782af25 100644
--- a/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml
+++ b/detections/endpoint/windows_dll_search_order_hijacking_with_iscsicpl.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects DLL search order hijacking involving iscsicpl.exe. It identifies when iscsicpl.exe loads a malicious DLL from a new path, triggering the payload execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on child processes spawned by iscsicpl.exe. This activity is significant as it indicates a potential attempt to execute unauthorized code via DLL hijacking. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic detects DLL search order hijacking involving iscsicpl.exe.
+  It identifies when iscsicpl.exe loads a malicious DLL from a new path, triggering
+  the payload execution. This detection leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on child processes spawned by iscsicpl.exe. This
+  activity is significant as it indicates a potential attempt to execute unauthorized
+  code via DLL hijacking. If confirmed malicious, this could allow an attacker to
+  execute arbitrary code, escalate privileges, or maintain persistence within the
+  environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=iscsicpl.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_dll_search_order_hijacking_with_iscsicpl_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present, filtering may be required. Remove the Windows Shells macro to determine if other utilities are using iscsicpl.exe.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=iscsicpl.exe
+  `windows_shells` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_dll_search_order_hijacking_with_iscsicpl_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present, filtering may be required.
+  Remove the Windows Shells macro to determine if other utilities are using iscsicpl.exe.
 references:
 - https://github.com/hackerhouse-opensource/iscsicpl_bypassUAC
 - https://github.com/422926799/csplugin/tree/master/bypassUAC
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to elevate access.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to elevate access.
   mitre_attack_id:
   - T1574.001
   observable:
@@ -56,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/iscsicpl/iscsicpl-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.001/iscsicpl/iscsicpl-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dll_side_loading_in_calc.yml b/detections/endpoint/windows_dll_side_loading_in_calc.yml
index 6be20c3205..daf52908c6 100644
--- a/detections/endpoint/windows_dll_side_loading_in_calc.yml
+++ b/detections/endpoint/windows_dll_side_loading_in_calc.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious DLL modules loaded by calc.exe that are not located in the %systemroot%\system32 or %systemroot%\sysWoW64 directories. This detection leverages Sysmon EventCode 7 to identify DLL side-loading, a technique often used by Qakbot malware to execute malicious DLLs. This activity is significant as it indicates potential malware execution through a trusted process, which can bypass security controls. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, and escalate privileges within the environment.
+description: The following analytic detects suspicious DLL modules loaded by calc.exe
+  that are not located in the %systemroot%\system32 or %systemroot%\sysWoW64 directories.
+  This detection leverages Sysmon EventCode 7 to identify DLL side-loading, a technique
+  often used by Qakbot malware to execute malicious DLLs. This activity is significant
+  as it indicates potential malware execution through a trusted process, which can
+  bypass security controls. If confirmed malicious, this could allow attackers to
+  execute arbitrary code, maintain persistence, and escalate privileges within the
+  environment.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 Image = "*\calc.exe" AND NOT (Image IN ("*:\\windows\\system32\\*", "*:\\windows\\sysWow64\\*")) AND NOT(ImageLoaded IN("*:\\windows\\system32\\*", "*:\\windows\\sysWow64\\*", "*:\\windows\\WinSXS\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName Product process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_side_loading_in_calc_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on processes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
+search: '`sysmon` EventCode=7 Image = "*\calc.exe" AND NOT (Image IN ("*:\\windows\\system32\\*",
+  "*:\\windows\\sysWow64\\*")) AND NOT(ImageLoaded IN("*:\\windows\\system32\\*",
+  "*:\\windows\\sysWow64\\*", "*:\\windows\\WinSXS\\*")) | stats count min(_time)
+  as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName Product
+  process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_dll_side_loading_in_calc_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on processes that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem`
+  node. In addition, confirm the latest CIM App 4.20 or higher is installed and the
+  latest TA for the endpoint product.
 known_false_positives: unknown
 references:
 - https://www.bitdefender.com/blog/hotforsecurity/new-qakbot-malware-strain-replaces-windows-calculator-dll-to-infected-pcs/
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 90
-  message: a dll modules is loaded by calc.exe in $ImageLoaded$ that are not in common windows OS installation folder in $dest$
+  message: a dll modules is loaded by calc.exe in $ImageLoaded$ that are not in common
+    windows OS installation folder in $dest$
   mitre_attack_id:
   - T1574.002
   - T1574
@@ -41,21 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - OriginalFileName
-  - Product
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml b/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml
index 5d7d1df53a..e6b542684b 100644
--- a/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml
+++ b/detections/endpoint/windows_dll_side_loading_process_child_of_calc.yml
@@ -9,9 +9,29 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies suspicious child processes spawned by calc.exe, indicative of DLL side-loading techniques. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, and parent processes. This activity is significant as it is commonly associated with Qakbot malware, which uses calc.exe to load malicious DLLs via regsvr32.exe. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, maintain persistence, and escalate privileges, posing a severe threat to the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "calc.exe")  AND Processes.process_name != "win32calc.exe" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dll_side_loading_process_child_of_calc_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies suspicious child processes spawned
+  by calc.exe, indicative of DLL side-loading techniques. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs,
+  names, and parent processes. This activity is significant as it is commonly associated
+  with Qakbot malware, which uses calc.exe to load malicious DLLs via regsvr32.exe.
+  If confirmed malicious, this behavior could allow attackers to execute arbitrary
+  code, maintain persistence, and escalate privileges, posing a severe threat to the
+  environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  = "calc.exe")  AND Processes.process_name != "win32calc.exe" by Processes.parent_process
+  Processes.process_name Processes.process_id Processes.process_guid Processes.process
+  Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_dll_side_loading_process_child_of_calc_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dns_gather_network_info.yml b/detections/endpoint/windows_dns_gather_network_info.yml
index d14c5992b6..c90342344a 100644
--- a/detections/endpoint/windows_dns_gather_network_info.yml
+++ b/detections/endpoint/windows_dns_gather_network_info.yml
@@ -9,10 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the use of the dnscmd.exe command to enumerate DNS records. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This activity is significant as it may indicate an adversary gathering network information, a common precursor to more targeted attacks. If confirmed malicious, this behavior could enable attackers to map the network, identify critical assets, and plan subsequent actions, potentially leading to data exfiltration or further compromise of the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dnscmd.exe" Processes.process = "* /enumrecords *" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dns_gather_network_info_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network administrator can execute this command to enumerate DNS record. Filter or add other paths to the exclusion as needed.
+description: The following analytic detects the use of the dnscmd.exe command to enumerate
+  DNS records. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process command-line executions. This activity is significant as it
+  may indicate an adversary gathering network information, a common precursor to more
+  targeted attacks. If confirmed malicious, this behavior could enable attackers to
+  map the network, identify critical assets, and plan subsequent actions, potentially
+  leading to data exfiltration or further compromise of the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "dnscmd.exe"
+  Processes.process = "* /enumrecords *" by Processes.parent_process Processes.process_name
+  Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_dns_gather_network_info_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network administrator can execute this command to enumerate
+  DNS record. Filter or add other paths to the exclusion as needed.
 references:
 - https://cert.gov.ua/article/3718487
 - https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1590.002/enum_dns_record/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1590.002/enum_dns_record/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dnsadmins_new_member_added.yml b/detections/endpoint/windows_dnsadmins_new_member_added.yml
index 27f95111ca..bf64cffe91 100644
--- a/detections/endpoint/windows_dnsadmins_new_member_added.yml
+++ b/detections/endpoint/windows_dnsadmins_new_member_added.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 4732
-description: The following analytic detects the addition of a new member to the DnsAdmins group in Active Directory by leveraging Event ID 4732. This detection uses security event logs to identify changes to this high-privilege group. Monitoring this activity is crucial because members of the DnsAdmins group can manage the DNS service, often running on Domain Controllers, and potentially execute malicious code with SYSTEM privileges. If confirmed malicious, this activity could allow an attacker to escalate privileges and gain control over critical domain services, posing a significant security risk.
-search: '`wineventlog_security` EventCode=4732 TargetUserName=DnsAdmins | stats min(_time) as firstTime max(_time) as lastTime values(TargetUserName) as target_users_added values(user) as user by  dest src_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_dnsadmins_new_member_added_filter`'
-how_to_implement: To successfully implement this search, Domain Controller events need to be ingested. The Advanced Security Audit policy setting `Audit Security Group Management` within `Account Management` needs to be enabled.
-known_false_positives: New members can be added to the DnsAdmins group as part of legitimate administrative tasks. Filter as needed.
+description: The following analytic detects the addition of a new member to the DnsAdmins
+  group in Active Directory by leveraging Event ID 4732. This detection uses security
+  event logs to identify changes to this high-privilege group. Monitoring this activity
+  is crucial because members of the DnsAdmins group can manage the DNS service, often
+  running on Domain Controllers, and potentially execute malicious code with SYSTEM
+  privileges. If confirmed malicious, this activity could allow an attacker to escalate
+  privileges and gain control over critical domain services, posing a significant
+  security risk.
+search: '`wineventlog_security` EventCode=4732 TargetUserName=DnsAdmins | stats min(_time)
+  as firstTime max(_time) as lastTime values(TargetUserName) as target_users_added
+  values(user) as user by  dest src_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_dnsadmins_new_member_added_filter`'
+how_to_implement: To successfully implement this search, Domain Controller events
+  need to be ingested. The Advanced Security Audit policy setting `Audit Security
+  Group Management` within `Account Management` needs to be enabled.
+known_false_positives: New members can be added to the DnsAdmins group as part of
+  legitimate administrative tasks. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 - https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/from-dnsadmins-to-system-to-domain-compromise
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetUserName
-  - SubjectUserName
-  - Computer
-  - MemberSid
-  - TargetUserName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dnsadmins_member_added/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/dnsadmins_member_added/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml b/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml
index 566208d19f..9b240d3f41 100644
--- a/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml
+++ b/detections/endpoint/windows_domain_account_discovery_via_get_netcomputer.yml
@@ -7,10 +7,24 @@ status: production
 type: Anomaly
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the PowerView PowerShell cmdlet Get-NetComputer, which is used to query Active Directory for user account details such as "samaccountname," "accountexpires," "lastlogon," and more. It leverages Event ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior is significant as it may indicate an attempt to gather user account information, which is often a precursor to further malicious actions. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or lateral movement within the network.
-search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetComputer*" ScriptBlockText IN ("*samaccountname*", "*accountexpires*", "*lastlogon*", "*lastlogoff*", "*pwdlastset*", "*logoncount*") | rename Computer as dest, UserID as user | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_domain_account_discovery_via_get_netcomputer_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
-known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed.
+description: The following analytic detects the execution of the PowerView PowerShell
+  cmdlet Get-NetComputer, which is used to query Active Directory for user account
+  details such as "samaccountname," "accountexpires," "lastlogon," and more. It leverages
+  Event ID 4104 from PowerShell Script Block Logging to identify this activity. This
+  behavior is significant as it may indicate an attempt to gather user account information,
+  which is often a precursor to further malicious actions. If confirmed malicious,
+  this activity could lead to unauthorized access, privilege escalation, or lateral
+  movement within the network.
+search: '`powershell` EventCode=4104  ScriptBlockText = "*Get-NetComputer*" ScriptBlockText
+  IN ("*samaccountname*", "*accountexpires*", "*lastlogon*", "*lastlogoff*", "*pwdlastset*",
+  "*logoncount*") | rename Computer as dest, UserID as user | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_domain_account_discovery_via_get_netcomputer_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
+known_false_positives: Administrators may leverage PowerView for legitimate purposes,
+  filter as needed.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,16 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087/powerview_get_netuser_preauthnotrequire/get-netuser-not-require-pwh.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_domain_admin_impersonation_indicator.yml b/detections/endpoint/windows_domain_admin_impersonation_indicator.yml
index 08a1935107..a0be222678 100644
--- a/detections/endpoint/windows_domain_admin_impersonation_indicator.yml
+++ b/detections/endpoint/windows_domain_admin_impersonation_indicator.yml
@@ -7,10 +7,38 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log Security 4627
-description: The following analytic identifies potential Kerberos ticket forging attacks, specifically the Diamond Ticket attack. This is detected when a user logs into a host and the GroupMembership field in event 4627 indicates a privileged group (e.g., Domain Admins), but the user does not actually belong to that group in the directory service. The detection leverages Windows Security Event Log 4627, which logs account logon events. The analytic cross-references the GroupMembership field from the event against a pre-populated lookup of actual group memberships. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table. Any discrepancies between the events GroupMembership and the lookup indicate potential ticket forging. Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers to impersonate any user and potentially gain unauthorized access to resources. By forging a ticket that indicates membership in a privileged group, an attacker can bypass security controls and gain elevated privileges. Detecting such discrepancies in group memberships during logon events can be a strong indicator of this attack in progress, making it crucial for security teams to monitor and investigate. If validated as a true positive, this indicates that an attacker has successfully forged a Kerberos ticket and may have gained unauthorized access to critical resources, potentially with elevated privileges.
-search: '`wineventlog_security`  EventCode=4627 LogonType=3 NOT TargetUserName IN ("*$", "SYSTEM", "DWM-*","LOCAL SERVICE","NETWORK SERVICE", "ANONYMOUS LOGON", "UMFD-*") | where match(GroupMembership, "Domain Admins") | stats  count by _time, TargetUserName, GroupMembership, host | lookup domain_admins username as TargetUserName OUTPUT username | fillnull value=NotDA username | search username = "NotDA" | `windows_domain_admin_impersonation_indicator_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Authentication events across all endpoints and ingest Event Id 4627. Specifically, the Audit Group Membership subcategory within the Logon Logooff category needs to be enabled. Its crucial to note that the accuracy and effectiveness of this detection heavily rely on the users diligence in populating and regularly updating this lookup table.
-known_false_positives: False positives may trigger the detections certain scenarios like directory service delays or out of date lookups. Filter as needed.
+description: The following analytic identifies potential Kerberos ticket forging attacks,
+  specifically the Diamond Ticket attack. This is detected when a user logs into a
+  host and the GroupMembership field in event 4627 indicates a privileged group (e.g.,
+  Domain Admins), but the user does not actually belong to that group in the directory
+  service. The detection leverages Windows Security Event Log 4627, which logs account
+  logon events. The analytic cross-references the GroupMembership field from the event
+  against a pre-populated lookup of actual group memberships. Its crucial to note
+  that the accuracy and effectiveness of this detection heavily rely on the users
+  diligence in populating and regularly updating this lookup table. Any discrepancies
+  between the events GroupMembership and the lookup indicate potential ticket forging.
+  Kerberos ticket forging, especially the Diamond Ticket attack, allows attackers
+  to impersonate any user and potentially gain unauthorized access to resources. By
+  forging a ticket that indicates membership in a privileged group, an attacker can
+  bypass security controls and gain elevated privileges. Detecting such discrepancies
+  in group memberships during logon events can be a strong indicator of this attack
+  in progress, making it crucial for security teams to monitor and investigate. If
+  validated as a true positive, this indicates that an attacker has successfully forged
+  a Kerberos ticket and may have gained unauthorized access to critical resources,
+  potentially with elevated privileges.
+search: '`wineventlog_security`  EventCode=4627 LogonType=3 NOT TargetUserName IN
+  ("*$", "SYSTEM", "DWM-*","LOCAL SERVICE","NETWORK SERVICE", "ANONYMOUS LOGON", "UMFD-*")
+  | where match(GroupMembership, "Domain Admins") | stats  count by _time, TargetUserName,
+  GroupMembership, host | lookup domain_admins username as TargetUserName OUTPUT username
+  | fillnull value=NotDA username | search username = "NotDA" | `windows_domain_admin_impersonation_indicator_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Authentication events across all endpoints and ingest Event Id 4627. Specifically,
+  the Audit Group Membership subcategory within the Logon Logooff category needs to
+  be enabled. Its crucial to note that the accuracy and effectiveness of this detection
+  heavily rely on the users diligence in populating and regularly updating this lookup
+  table.
+known_false_positives: False positives may trigger the detections certain scenarios
+  like directory service delays or out of date lookups. Filter as needed.
 references:
 - https://trustedsec.com/blog/a-diamond-in-the-ruff
 - https://unit42.paloaltonetworks.com/next-gen-kerberos-attacks
@@ -22,7 +50,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$TargetUserName$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$TargetUserName$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$TargetUserName$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: $TargetUserName$ may be impersonating a Domain Administrator through a forged Kerberos ticket.
+  message: $TargetUserName$ may be impersonating a Domain Administrator through a
+    forged Kerberos ticket.
   mitre_attack_id:
   - T1558
   observable:
@@ -45,16 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time,
-  - EventCode
-  - LogonType
-  - TargetUserName
-  - GroupMembership
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/diamond_ticket/security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/diamond_ticket/security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml b/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml
index 2c70fe5773..2709bcf6b8 100644
--- a/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml
+++ b/detections/endpoint/windows_dotnet_binary_in_non_standard_path.yml
@@ -5,14 +5,39 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of native .NET binaries from non-standard directories within the Windows operating system. It leverages Endpoint Detection and Response (EDR) telemetry, comparing process names and original file names against a predefined lookup using the `is_net_windows_file_macro` macro. This activity is significant because adversaries may move .NET binaries to unconventional paths to evade detection and execute malicious code. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
+description: The following analytic detects the execution of native .NET binaries
+  from non-standard directories within the Windows operating system. It leverages
+  Endpoint Detection and Response (EDR) telemetry, comparing process names and original
+  file names against a predefined lookup using the `is_net_windows_file_macro` macro.
+  This activity is significant because adversaries may move .NET binaries to unconventional
+  paths to evade detection and execute malicious code. If confirmed malicious, this
+  behavior could allow attackers to execute arbitrary code, escalate privileges, or
+  maintain persistence within the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where NOT (Processes.process_path IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*", "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_path Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `is_net_windows_file_macro` | `windows_dotnet_binary_in_non_standard_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering may be required. Certain utilities will run from non-standard paths based on the third-party application in use.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where NOT (Processes.process_path
+  IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*",
+  "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by
+  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.original_file_name Processes.process_path
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `is_net_windows_file_macro`
+  | `windows_dotnet_binary_in_non_standard_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering may be required.
+  Certain utilities will run from non-standard paths based on the third-party application
+  in use.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml
 - https://attack.mitre.org/techniques/T1036/003/
@@ -24,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +68,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard path was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard
+    path was identified on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1036
   - T1036.003
@@ -65,23 +96,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_driver_inventory.yml b/detections/endpoint/windows_driver_inventory.yml
index fc37b96880..646690304c 100644
--- a/detections/endpoint/windows_driver_inventory.yml
+++ b/detections/endpoint/windows_driver_inventory.yml
@@ -5,11 +5,20 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: Hunting
-description: The following analytic identifies drivers being loaded across the fleet. It leverages a PowerShell script input deployed to critical systems to capture driver data. This detection is significant as it helps monitor for unauthorized or malicious drivers that could compromise system integrity. If confirmed malicious, such drivers could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic identifies drivers being loaded across the fleet.
+  It leverages a PowerShell script input deployed to critical systems to capture driver
+  data. This detection is significant as it helps monitor for unauthorized or malicious
+  drivers that could compromise system integrity. If confirmed malicious, such drivers
+  could allow attackers to execute arbitrary code, escalate privileges, or maintain
+  persistence within the environment.
 data_source: []
-search: '`driverinventory` | stats values(Path) min(_time) as firstTime max(_time) as lastTime count by host DriverType | rename host as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_driver_inventory_filter`'
-how_to_implement: To capture the drivers by host, utilize the referenced Gist to create the inputs, props and transforms. Otherwise, this hunt query will not work.
-known_false_positives: Filter and modify the analytic as you'd like. Filter based on path. Remove the system32\drivers and look for non-standard paths.
+search: '`driverinventory` | stats values(Path) min(_time) as firstTime max(_time)
+  as lastTime count by host DriverType | rename host as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_driver_inventory_filter`'
+how_to_implement: To capture the drivers by host, utilize the referenced Gist to create
+  the inputs, props and transforms. Otherwise, this hunt query will not work.
+known_false_positives: Filter and modify the analytic as you'd like. Filter based
+  on path. Remove the system32\drivers and look for non-standard paths.
 references:
 - https://gist.github.com/MHaggis/3e4dc85c69b3f7a4595a06c8a692f244
 tags:
@@ -30,15 +39,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Path
-  - host
-  - DriverType
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/driver_inventory.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/driver_inventory.log
     source: PwSh:DriverInventory
     sourcetype: PwSh:DriverInventory
diff --git a/detections/endpoint/windows_driver_load_non_standard_path.yml b/detections/endpoint/windows_driver_load_non_standard_path.yml
index 7955826a70..9cf83357d6 100644
--- a/detections/endpoint/windows_driver_load_non_standard_path.yml
+++ b/detections/endpoint/windows_driver_load_non_standard_path.yml
@@ -1,21 +1,26 @@
 name: Windows Driver Load Non-Standard Path
 id: 9216ef3d-066a-4958-8f27-c84589465e62
 version: 4
-date: "2024-10-17"
+date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the loading of new Kernel Mode Drivers from non-standard paths using Windows EventCode 7045. It identifies drivers not located in typical directories like Windows, Program Files, or SystemRoot. This activity is significant because adversaries may use these non-standard paths to load malicious or vulnerable drivers, potentially bypassing security controls. If confirmed malicious, this could allow attackers to execute code at the kernel level, escalate privileges, or maintain persistence within the environment, posing a severe threat to system integrity and security.
+description: The following analytic detects the loading of new Kernel Mode Drivers
+  from non-standard paths using Windows EventCode 7045. It identifies drivers not
+  located in typical directories like Windows, Program Files, or SystemRoot. This
+  activity is significant because adversaries may use these non-standard paths to
+  load malicious or vulnerable drivers, potentially bypassing security controls. If
+  confirmed malicious, this could allow attackers to execute code at the kernel level,
+  escalate privileges, or maintain persistence within the environment, posing a severe
+  threat to system integrity and security.
 data_source:
 - Windows Event Log System 7045
 search: >-
   `wineventlog_system` EventCode=7045 ServiceType="kernel mode driver"
   | regex ImagePath!="(?i)^(\w:\\\\Windows\\\\|\w:\\\\Program\sFile|\\\\systemroot\\\\|%SystemRoot%|system32\\\\)"
-  | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType 
-  | rename Computer as dest 
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `windows_driver_load_non_standard_path_filter`
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode
+  ImagePath ServiceName ServiceType  | rename Computer as dest  | `security_content_ctime(firstTime)`  |
+  `security_content_ctime(lastTime)`  | `windows_driver_load_non_standard_path_filter`
 how_to_implement: To implement this analytic, the Windows EventCode 7045 will need
   to be logged. The Windows TA for Splunk is also recommended.
 known_false_positives: False positives may be present based on legitimate third party
@@ -31,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Computer
-  - EventCode
-  - ImagePath
-  - ServiceName
-  - ServiceType
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/xml7045_windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/xml7045_windows-system.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_drivers_loaded_by_signature.yml b/detections/endpoint/windows_drivers_loaded_by_signature.yml
index dc50b119ee..823dff261c 100644
--- a/detections/endpoint/windows_drivers_loaded_by_signature.yml
+++ b/detections/endpoint/windows_drivers_loaded_by_signature.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies all drivers being loaded on Windows systems using Sysmon EventCode 6 (Driver Load). It leverages fields such as driver path, signature status, and hash to detect potentially suspicious drivers. This activity is significant for a SOC as malicious drivers can be used to gain kernel-level access, bypass security controls, or persist in the environment. If confirmed malicious, this activity could allow an attacker to execute arbitrary code with high privileges, leading to severe system compromise and potential data exfiltration.
+description: The following analytic identifies all drivers being loaded on Windows
+  systems using Sysmon EventCode 6 (Driver Load). It leverages fields such as driver
+  path, signature status, and hash to detect potentially suspicious drivers. This
+  activity is significant for a SOC as malicious drivers can be used to gain kernel-level
+  access, bypass security controls, or persist in the environment. If confirmed malicious,
+  this activity could allow an attacker to execute arbitrary code with high privileges,
+  leading to severe system compromise and potential data exfiltration.
 data_source:
 - Sysmon EventID 6
-search: '`sysmon` EventCode=6 | stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) count by dest Signed Signature service_signature_verified service_signature_exists Hashes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_drivers_loaded_by_signature_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have the latest version of the Sysmon TA. Most EDR products provide the ability to review driver loads, or module loads, and using a query as such help with hunting for malicious drivers.
-known_false_positives: This analytic is meant to assist with identifying drivers loaded in the environment and not to be setup for notables off the bat.
+search: '`sysmon` EventCode=6 | stats min(_time) as firstTime max(_time) as lastTime
+  values(ImageLoaded) count by dest Signed Signature service_signature_verified service_signature_exists
+  Hashes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_drivers_loaded_by_signature_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have the latest version of the Sysmon
+  TA. Most EDR products provide the ability to review driver loads, or module loads,
+  and using a query as such help with hunting for malicious drivers.
+known_false_positives: This analytic is meant to assist with identifying drivers loaded
+  in the environment and not to be setup for notables off the bat.
 references:
 - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/
 - https://attack.mitre.org/techniques/T1014/
@@ -37,19 +51,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ImageLoaded
-  - dest
-  - Signed
-  - Signature
-  - service_signature_verified
-  - service_signature_exists
-  - Hashes
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_enable_powershell_web_access.yml b/detections/endpoint/windows_enable_powershell_web_access.yml
index 7a2ba60e59..e6d854c218 100644
--- a/detections/endpoint/windows_enable_powershell_web_access.yml
+++ b/detections/endpoint/windows_enable_powershell_web_access.yml
@@ -7,10 +7,21 @@ data_source:
 - Powershell Script Block Logging 4104
 type: TTP
 status: production
-description: The following analytic detects the enabling of PowerShell Web Access via PowerShell commands. It leverages PowerShell script block logging (EventCode 4104) to identify the execution of the `Install-WindowsFeature` cmdlet with the `WindowsPowerShellWebAccess` parameter. This activity is significant because enabling PowerShell Web Access can facilitate remote execution of PowerShell commands, potentially allowing an attacker to gain unauthorized access to systems and networks.
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*Install-WindowsFeature*WindowsPowerShellWebAccess*","*Install-PswaWebApplication*","*Add-PswaAuthorizationRule*UserName *ComputerName *") | rename Computer as dest | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_enable_powershell_web_access_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: It is possible that legitimate scripts or network administrators may enable PowerShell Web Access. Monitor and escalate as needed.
+description: The following analytic detects the enabling of PowerShell Web Access
+  via PowerShell commands. It leverages PowerShell script block logging (EventCode
+  4104) to identify the execution of the `Install-WindowsFeature` cmdlet with the
+  `WindowsPowerShellWebAccess` parameter. This activity is significant because enabling
+  PowerShell Web Access can facilitate remote execution of PowerShell commands, potentially
+  allowing an attacker to gain unauthorized access to systems and networks.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*Install-WindowsFeature*WindowsPowerShellWebAccess*","*Install-PswaWebApplication*","*Add-PswaAuthorizationRule*UserName
+  *ComputerName *") | rename Computer as dest | stats count min(_time) as firstTime
+  max(_time) as lastTime by EventCode ScriptBlockText dest UserID | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_enable_powershell_web_access_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: It is possible that legitimate scripts or network administrators
+  may enable PowerShell Web Access. Monitor and escalate as needed.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a
 - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +58,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - dest
-  - UserID
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/pswa_powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/pswa_powershell.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
diff --git a/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml b/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml
index 76fa24b0da..b4638d13ef 100644
--- a/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml
+++ b/detections/endpoint/windows_enable_win32_scheduledjob_via_registry.yml
@@ -8,10 +8,33 @@ status: production
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects the creation of a new DWORD value named "EnableAt" in the registry path "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration". This modification enables the use of the at.exe or wmi Win32_ScheduledJob commands to add scheduled tasks on a Windows endpoint. The detection leverages registry event data from the Endpoint datamodel. This activity is significant because it may indicate that an attacker is enabling the ability to schedule tasks, potentially to execute malicious code at specific times or intervals. If confirmed malicious, this could allow persistent code execution on the system.
-search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\CurrentVersion\\Schedule\\Configuration*" Registry.registry_value_name=EnableAt  by Registry.dest, Registry.user, Registry.registry_value_name, Registry.registry_value_type | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_enable_win32_scheduledjob_via_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: In some cases, an automated script or system may enable this setting continuously, leading to false positives. To avoid such situations, it is recommended to monitor the frequency and context of the registry modification and modify or filter the detection rules as needed. This can help to reduce the number of false positives and ensure that only genuine threats are identified. Additionally, it is important to investigate any detected instances of this modification and analyze them in the broader context of the system and network to determine if further action is necessary.
+description: The following analytic detects the creation of a new DWORD value named
+  "EnableAt" in the registry path "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration".
+  This modification enables the use of the at.exe or wmi Win32_ScheduledJob commands
+  to add scheduled tasks on a Windows endpoint. The detection leverages registry event
+  data from the Endpoint datamodel. This activity is significant because it may indicate
+  that an attacker is enabling the ability to schedule tasks, potentially to execute
+  malicious code at specific times or intervals. If confirmed malicious, this could
+  allow persistent code execution on the system.
+search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name)
+  as registry_key_name values(Registry.registry_path) as registry_path min(_time)
+  as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\CurrentVersion\\Schedule\\Configuration*"
+  Registry.registry_value_name=EnableAt  by Registry.dest, Registry.user, Registry.registry_value_name,
+  Registry.registry_value_type | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `drop_dm_object_name(Registry)` | `windows_enable_win32_scheduledjob_via_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: In some cases, an automated script or system may enable this
+  setting continuously, leading to false positives. To avoid such situations, it is
+  recommended to monitor the frequency and context of the registry modification and
+  modify or filter the detection rules as needed. This can help to reduce the number
+  of false positives and ensure that only genuine threats are identified. Additionally,
+  it is important to investigate any detected instances of this modification and analyze
+  them in the broader context of the system and network to determine if further action
+  is necessary.
 references:
 - https://securityonline.info/wmiexec-regout-get-outputdata-response-from-registry/
 - https://learn.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A process has modified the schedule task registry value - EnableAt - on endpoint $dest$ by user $user$.
+  message: A process has modified the schedule task registry value - EnableAt - on
+    endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1053.005
   observable:
@@ -47,17 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_value_name
-  - Registry.registry_value_type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/enableat_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/enableat_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_esx_admins_group_creation_security_event.yml b/detections/endpoint/windows_esx_admins_group_creation_security_event.yml
index 83a4e81a90..023ea35a20 100644
--- a/detections/endpoint/windows_esx_admins_group_creation_security_event.yml
+++ b/detections/endpoint/windows_esx_admins_group_creation_security_event.yml
@@ -9,10 +9,25 @@ data_source:
 - Windows Event Log Security 4737
 type: TTP
 status: production
-description: This analytic detects creation, deletion, or modification of the "ESX Admins" group in Active Directory. These events may indicate attempts to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085).
-search: '`wineventlog_security` EventCode IN (4727, 4730, 4737) (TargetUserName="ESX Admins" OR TargetUserName="*ESX Admins*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode TargetUserName TargetDomainName SubjectUserName SubjectDomainName Computer | rename Computer as dest | eval EventCodeDescription=case( EventCode=4727, "Security Enabled Global Group Created", EventCode=4730, "Security Enabled Global Group Deleted", EventCode=4737, "Security Enabled Global Group Modified" ) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_security_event_filter`'
-how_to_implement: To successfully implement this search, ensure that Windows Security Event logging is enabled and being ingested into Splunk, particularly for event codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific events.
-known_false_positives: Legitimate administrators might create, delete, or modify an "ESX Admins" group for valid reasons. Verify that the group changes are authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities.
+description: This analytic detects creation, deletion, or modification of the "ESX
+  Admins" group in Active Directory. These events may indicate attempts to exploit
+  the VMware ESXi Active Directory Integration Authentication Bypass vulnerability
+  (CVE-2024-37085).
+search: '`wineventlog_security` EventCode IN (4727, 4730, 4737) (TargetUserName="ESX
+  Admins" OR TargetUserName="*ESX Admins*") | stats count min(_time) as firstTime
+  max(_time) as lastTime by EventCode TargetUserName TargetDomainName SubjectUserName
+  SubjectDomainName Computer | rename Computer as dest | eval EventCodeDescription=case(
+  EventCode=4727, "Security Enabled Global Group Created", EventCode=4730, "Security
+  Enabled Global Group Deleted", EventCode=4737, "Security Enabled Global Group Modified"
+  ) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_security_event_filter`'
+how_to_implement: To successfully implement this search, ensure that Windows Security
+  Event logging is enabled and being ingested into Splunk, particularly for event
+  codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific
+  events.
+known_false_positives: Legitimate administrators might create, delete, or modify an
+  "ESX Admins" group for valid reasons. Verify that the group changes are authorized
+  and part of normal administrative tasks. Consider the context of the action, such
+  as the user performing it and any related activities.
 references:
 - https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505
 - https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,20 +69,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetUserName
-  - TargetDomainName
-  - SubjectUserName
-  - SubjectDomainName
-  - Computer
   security_domain: endpoint
   cve:
   - CVE-2024-37085
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log
     sourcetype: XmlWinEventLog
     source: Security
diff --git a/detections/endpoint/windows_esx_admins_group_creation_via_net.yml b/detections/endpoint/windows_esx_admins_group_creation_via_net.yml
index 9dee1b8382..6d763870c5 100644
--- a/detections/endpoint/windows_esx_admins_group_creation_via_net.yml
+++ b/detections/endpoint/windows_esx_admins_group_creation_via_net.yml
@@ -7,10 +7,26 @@ data_source:
 - Sysmon EventID 1
 type: TTP
 status: production
-description: This analytic detects attempts to create an "ESX Admins" group using the Windows net.exe or net1.exe commands. This activity may indicate an attempt to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085). Attackers can use this method to gain unauthorized access to ESXi hosts by recreating the "ESX Admins" group after its deletion from Active Directory.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe" OR Processes.process_name="net1.exe") AND (Processes.process="*group \"ESX Admins\"*" OR Processes.process="*group ESX Admins*") AND Processes.process="*/add*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_via_net_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Legitimate administrators might create an "ESX Admins" group for valid reasons. Verify that the group creation is authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities.
+description: This analytic detects attempts to create an "ESX Admins" group using
+  the Windows net.exe or net1.exe commands. This activity may indicate an attempt
+  to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability
+  (CVE-2024-37085). Attackers can use this method to gain unauthorized access to ESXi
+  hosts by recreating the "ESX Admins" group after its deletion from Active Directory.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe") AND (Processes.process="*group \"ESX Admins\"*"
+  OR Processes.process="*group ESX Admins*") AND Processes.process="*/add*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.original_file_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_via_net_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  data that records process activity from your hosts to populate the Endpoint data
+  model in the Processes node. If you are using Sysmon, you must have at least version
+  6.0.4 of the Sysmon TA.
+known_false_positives: Legitimate administrators might create an "ESX Admins" group
+  for valid reasons. Verify that the group creation is authorized and part of normal
+  administrative tasks. Consider the context of the action, such as the user performing
+  it and any related activities.
 references:
 - https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505
 - https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: An attempt to create an "ESX Admins" group was detected on $dest$ by user $user$.
+  message: An attempt to create an "ESX Admins" group was detected on $dest$ by user
+    $user$.
   mitre_attack_id:
   - T1136.002
   - T1136.001
@@ -47,22 +69,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.original_file_name
   security_domain: endpoint
   cve:
   - CVE-2024-37085
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon-esxadmins.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon-esxadmins.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml b/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml
index 06992495f5..2274c1250c 100644
--- a/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml
+++ b/detections/endpoint/windows_esx_admins_group_creation_via_powershell.yml
@@ -7,10 +7,23 @@ data_source:
 - Powershell Script Block Logging 4104
 type: TTP
 status: production
-description: This analytic detects attempts to create an "ESX Admins" group using PowerShell commands. This activity may indicate an attempt to exploit the VMware ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085). Attackers can use this method to gain unauthorized access to ESXi hosts by recreating the 'ESX Admins' group after its deletion from Active Directory.
-search: '`powershell` EventCode=4104 (ScriptBlockText="*New-ADGroup*" OR ScriptBlockText="*New-LocalGroup*") ScriptBlockText="*ESX Admins*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_esx_admins_group_creation_via_powershell_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. You can find additional setup instructions in the Splunk documentation for configuring PowerShell logging.
-known_false_positives: Legitimate administrators might create an "ESX Admins" group for valid reasons. Verify that the group creation is authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities.
+description: This analytic detects attempts to create an "ESX Admins" group using
+  PowerShell commands. This activity may indicate an attempt to exploit the VMware
+  ESXi Active Directory Integration Authentication Bypass vulnerability (CVE-2024-37085).
+  Attackers can use this method to gain unauthorized access to ESXi hosts by recreating
+  the 'ESX Admins' group after its deletion from Active Directory.
+search: '`powershell` EventCode=4104 (ScriptBlockText="*New-ADGroup*" OR ScriptBlockText="*New-LocalGroup*")
+  ScriptBlockText="*ESX Admins*" | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest
+  | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_esx_admins_group_creation_via_powershell_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. You can find additional
+  setup instructions in the Splunk documentation for configuring PowerShell logging.
+known_false_positives: Legitimate administrators might create an "ESX Admins" group
+  for valid reasons. Verify that the group creation is authorized and part of normal
+  administrative tasks. Consider the context of the action, such as the user performing
+  it and any related activities.
 references:
 - https://support.broadcom.com/web/ecx/support-content-notification/-/external/content/SecurityAdvisories/0/24505
 - https://www.microsoft.com/en-us/security/blog/2024/07/29/ransomware-operators-exploit-esxi-hypervisor-vulnerability-for-mass-encryption/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 80
-  message: PowerShell command to create "ESX Admins" group detected on host $dest$ by user $user$.
+  message: PowerShell command to create "ESX Admins" group detected on host $dest$
+    by user $user$.
   mitre_attack_id:
   - T1136.002
   - T1136.001
@@ -47,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-powershell-esxadmins.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-powershell-esxadmins.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_event_for_service_disabled.yml b/detections/endpoint/windows_event_for_service_disabled.yml
index cd03b4ee88..5abd985c34 100644
--- a/detections/endpoint/windows_event_for_service_disabled.yml
+++ b/detections/endpoint/windows_event_for_service_disabled.yml
@@ -5,12 +5,24 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects when a Windows service is modified from a start type to disabled. It leverages system event logs, specifically EventCode 7040, to identify this change. This activity is significant because adversaries often disable security or other critical services to evade detection and maintain control over a compromised host. If confirmed malicious, this action could allow attackers to bypass security defenses, leading to further exploitation and persistence within the environment.
+description: The following analytic detects when a Windows service is modified from
+  a start type to disabled. It leverages system event logs, specifically EventCode
+  7040, to identify this change. This activity is significant because adversaries
+  often disable security or other critical services to evade detection and maintain
+  control over a compromised host. If confirmed malicious, this action could allow
+  attackers to bypass security defenses, leading to further exploitation and persistence
+  within the environment.
 data_source:
 - Windows Event Log System 7040
-search: '`wineventlog_system` EventCode=7040  EventData_Xml="*disabled*" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode Name UserID service ServiceName | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_for_service_disabled_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-known_false_positives: Windows service update may cause this event. In that scenario, filtering is needed.
+search: '`wineventlog_system` EventCode=7040  EventData_Xml="*disabled*" | stats count
+  min(_time) as firstTime max(_time) as lastTime by Computer EventCode Name UserID
+  service ServiceName | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_event_for_service_disabled_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints.
+known_false_positives: Windows service update may cause this event. In that scenario,
+  filtering is needed.
 references:
 - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html
 tags:
@@ -33,17 +45,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ComputerName
-  - EventCode
-  - Message
-  - User
-  - Sid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_event_log_cleared.yml b/detections/endpoint/windows_event_log_cleared.yml
index 2cae4b5ddc..bb43249605 100644
--- a/detections/endpoint/windows_event_log_cleared.yml
+++ b/detections/endpoint/windows_event_log_cleared.yml
@@ -5,13 +5,23 @@ date: '2024-09-30'
 author: Rico Valdez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the clearing of Windows event logs by identifying Windows Security Event ID 1102 or System log event 104. This detection leverages Windows event logs to monitor for log clearing activities. Such behavior is significant as it may indicate an attempt to cover tracks after malicious activities. If confirmed malicious, this action could hinder forensic investigations and allow attackers to persist undetected, making it crucial to investigate further and correlate with other alerts and data sources.
+description: The following analytic detects the clearing of Windows event logs by
+  identifying Windows Security Event ID 1102 or System log event 104. This detection
+  leverages Windows event logs to monitor for log clearing activities. Such behavior
+  is significant as it may indicate an attempt to cover tracks after malicious activities.
+  If confirmed malicious, this action could hinder forensic investigations and allow
+  attackers to persist undetected, making it crucial to investigate further and correlate
+  with other alerts and data sources.
 data_source:
 - Windows Event Log Security 1102
 - Windows Event Log System 104
-search: (`wineventlog_security` EventCode=1102) OR (`wineventlog_system` EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by dest object EventCode | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter`
-how_to_implement: To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
-known_false_positives: It is possible that these logs may be legitimately cleared by Administrators. Filter as needed.
+search: (`wineventlog_security` EventCode=1102) OR (`wineventlog_system` EventCode=104)
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest object EventCode
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_log_cleared_filter`
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.
+known_false_positives: It is possible that these logs may be legitimately cleared
+  by Administrators. Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1102
 - https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads
@@ -23,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,14 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_event_log_cleared/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/windows_event_log_cleared/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml b/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml
index dcdc4a32f2..a5064569b4 100644
--- a/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml
+++ b/detections/endpoint/windows_event_triggered_image_file_execution_options_injection.yml
@@ -5,12 +5,24 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the creation or modification of Image File Execution Options (IFEO) registry keys, detected via EventCode 3000 in the Application channel. This detection leverages Windows Event Logs to monitor for process names added to IFEO under specific registry paths. This activity is significant as it can indicate attempts to set traps for process monitoring or debugging, often used by attackers for persistence or evasion. If confirmed malicious, this could allow an attacker to execute arbitrary code or manipulate process behavior, leading to potential system compromise.
+description: The following analytic identifies the creation or modification of Image
+  File Execution Options (IFEO) registry keys, detected via EventCode 3000 in the
+  Application channel. This detection leverages Windows Event Logs to monitor for
+  process names added to IFEO under specific registry paths. This activity is significant
+  as it can indicate attempts to set traps for process monitoring or debugging, often
+  used by attackers for persistence or evasion. If confirmed malicious, this could
+  allow an attacker to execute arbitrary code or manipulate process behavior, leading
+  to potential system compromise.
 data_source:
 - Windows Event Log Application 3000
-search: '`wineventlog_application` EventCode=3000 | rename param1 AS "Process" param2 AS "Exit_Code" | stats count min(_time) as firstTime max(_time) as lastTime by Process Exit_Code dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_event_triggered_image_file_execution_options_injection_filter`'
-how_to_implement: This analytic requires capturing the Windows Event Log Application channel in XML.
-known_false_positives: False positives may be present and tuning will be required before turning into a TTP or notable.
+search: '`wineventlog_application` EventCode=3000 | rename param1 AS "Process" param2
+  AS "Exit_Code" | stats count min(_time) as firstTime max(_time) as lastTime by Process
+  Exit_Code dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_event_triggered_image_file_execution_options_injection_filter`'
+how_to_implement: This analytic requires capturing the Windows Event Log Application
+  channel in XML.
+known_false_positives: False positives may be present and tuning will be required
+  before turning into a TTP or notable.
 references:
 - https://blog.thinkst.com/2022/09/sensitive-command-token-so-much-offense.html
 - https://docs.microsoft.com/en-us/windows-hardware/drivers/debugger/registry-entries-for-silent-process-exit
@@ -20,7 +32,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Windows eventcode 3000 triggered on $dest$ potentially indicating persistence or a monitoring of a process has occurred.
+  message: Windows eventcode 3000 triggered on $dest$ potentially indicating persistence
+    or a monitoring of a process has occurred.
   mitre_attack_id:
   - T1546.012
   observable:
@@ -32,16 +45,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Process
-  - Exit_Code
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-application.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-application.log
     source: XmlWinEventLog:Application
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_excessive_disabled_services_event.yml b/detections/endpoint/windows_excessive_disabled_services_event.yml
index 2cd18880ff..a18067b476 100644
--- a/detections/endpoint/windows_excessive_disabled_services_event.yml
+++ b/detections/endpoint/windows_excessive_disabled_services_event.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies an excessive number of system events where services are modified from start to disabled. It leverages Windows Event Logs (EventCode 7040) to detect multiple service state changes on a single host. This activity is significant as it may indicate an adversary attempting to disable security applications or other critical services, potentially leading to defense evasion or destructive actions. If confirmed malicious, this behavior could allow attackers to disable security defenses, disrupt system operations, and achieve their objectives on the compromised system.
+description: The following analytic identifies an excessive number of system events
+  where services are modified from start to disabled. It leverages Windows Event Logs
+  (EventCode 7040) to detect multiple service state changes on a single host. This
+  activity is significant as it may indicate an adversary attempting to disable security
+  applications or other critical services, potentially leading to defense evasion
+  or destructive actions. If confirmed malicious, this behavior could allow attackers
+  to disable security defenses, disrupt system operations, and achieve their objectives
+  on the compromised system.
 data_source:
 - Windows Event Log System 7040
-search: '`wineventlog_system` EventCode=7040 "disabled" | stats count values(EventData_Xml) as MessageList dc(EventData_Xml) as MessageCount min(_time) as firstTime max(_time) as lastTime by Computer EventCode UserID | rename Computer as dest | where count >=10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_excessive_disabled_services_event_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
+search: '`wineventlog_system` EventCode=7040 "disabled" | stats count values(EventData_Xml)
+  as MessageList dc(EventData_Xml) as MessageCount min(_time) as firstTime max(_time)
+  as lastTime by Computer EventCode UserID | rename Computer as dest | where count
+  >=10 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_excessive_disabled_services_event_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints.
 known_false_positives: Unknown
 references:
 - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: An excessive number (Count - $MessageCount$) of Windows services were disabled on dest - $dest$.
+  message: An excessive number (Count - $MessageCount$) of Windows services were disabled
+    on dest - $dest$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -42,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - src
-  - ComputerName
-  - EventCode
-  - Message
-  - User
-  - Sid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/windows_excessive_disabled_services_event/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_executable_in_loaded_modules.yml b/detections/endpoint/windows_executable_in_loaded_modules.yml
index 6ae2630da2..f5de3b29df 100644
--- a/detections/endpoint/windows_executable_in_loaded_modules.yml
+++ b/detections/endpoint/windows_executable_in_loaded_modules.yml
@@ -7,9 +7,21 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 7
-description: The following analytic identifies instances where executable files (.exe) are loaded as modules, detected through 'ImageLoaded' events in Sysmon logs. This method leverages Sysmon EventCode 7 to track unusual module loading behavior, which is significant as it deviates from the norm of loading .dll files. This activity is crucial for SOC monitoring because it can indicate the presence of malware like NjRAT, which uses this technique to load malicious modules. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, maintain persistence, and further compromise the host system.
-search: '`sysmon` EventCode=7 ImageLoaded= *.exe | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed SignatureStatus OriginalFileName process_name Computer EventCode ProcessId Hashes IMPHASH | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_executable_in_loaded_modules_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+description: The following analytic identifies instances where executable files (.exe)
+  are loaded as modules, detected through 'ImageLoaded' events in Sysmon logs. This
+  method leverages Sysmon EventCode 7 to track unusual module loading behavior, which
+  is significant as it deviates from the norm of loading .dll files. This activity
+  is crucial for SOC monitoring because it can indicate the presence of malware like
+  NjRAT, which uses this technique to load malicious modules. If confirmed malicious,
+  this behavior could allow attackers to execute arbitrary code, maintain persistence,
+  and further compromise the host system.
+search: '`sysmon` EventCode=7 ImageLoaded= *.exe | stats count min(_time) as firstTime
+  max(_time) as lastTime by Image ImageLoaded Signed SignatureStatus OriginalFileName
+  process_name Computer EventCode ProcessId Hashes IMPHASH | rename Computer as dest
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_executable_in_loaded_modules_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,23 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - Signed
-  - SignatureStatus
-  - OriginalFileName
-  - process_name
-  - Computer
-  - EventCode
-  - ProcessId
-  - Hashes
-  - IMPHASH
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1129/executable_shared_modules/image_loaded_exe.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1129/executable_shared_modules/image_loaded_exe.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml b/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml
index 2fd040f713..ddbd3f14bb 100644
--- a/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml
+++ b/detections/endpoint/windows_execute_arbitrary_commands_with_msdt.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects arbitrary command execution using Windows msdt.exe, a Diagnostics Troubleshooting Wizard. It leverages Endpoint Detection and Response (EDR) data to identify instances where msdt.exe is invoked via the ms-msdt:/ protocol handler to retrieve a remote payload. This activity is significant as it can indicate an exploitation attempt leveraging msdt.exe to execute arbitrary commands, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or persist within the environment, posing a severe security risk.
+description: The following analytic detects arbitrary command execution using Windows
+  msdt.exe, a Diagnostics Troubleshooting Wizard. It leverages Endpoint Detection
+  and Response (EDR) data to identify instances where msdt.exe is invoked via the
+  ms-msdt:/ protocol handler to retrieve a remote payload. This activity is significant
+  as it can indicate an exploitation attempt leveraging msdt.exe to execute arbitrary
+  commands, potentially leading to unauthorized code execution. If confirmed malicious,
+  this could allow an attacker to execute arbitrary code, escalate privileges, or
+  persist within the environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msdt.exe Processes.process IN ("*msdt*","*ms-msdt:*","*ms-msdt:/id*","*ms-msdt:-id*","*/id*") AND (Processes.process="*IT_BrowseForFile=*" OR Processes.process="*IT_RebrowseForFile=*" OR Processes.process="*.xml*") AND Processes.process="*PCWDiagnostic*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_execute_arbitrary_commands_with_msdt_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present, filter as needed. Added .xml to potentially capture any answer file usage. Remove as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msdt.exe
+  Processes.process IN ("*msdt*","*ms-msdt:*","*ms-msdt:/id*","*ms-msdt:-id*","*/id*")
+  AND (Processes.process="*IT_BrowseForFile=*" OR Processes.process="*IT_RebrowseForFile=*"
+  OR Processes.process="*.xml*") AND Processes.process="*PCWDiagnostic*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_execute_arbitrary_commands_with_msdt_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present, filter as needed. Added .xml
+  to potentially capture any answer file usage. Remove as needed.
 references:
 - https://isc.sans.edu/diary/rss/28694
 - https://doublepulsar.com/follina-a-microsoft-office-code-execution-vulnerability-1a47fce5629e
@@ -26,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +65,8 @@ tags:
   cve:
   - CVE-2022-30190
   impact: 100
-  message: A parent process $parent_process_name$ has spawned a child process $process_name$ on host $dest$ possibly indicative of indirect command execution.
+  message: A parent process $parent_process_name$ has spawned a child process $process_name$
+    on host $dest$ possibly indicative of indirect command execution.
   mitre_attack_id:
   - T1218
   observable:
@@ -61,23 +90,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml b/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml
index 896774f898..2a87790508 100644
--- a/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml
+++ b/detections/endpoint/windows_exfiltration_over_c2_via_invoke_restmethod.yml
@@ -7,9 +7,22 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects potential data exfiltration using PowerShell's Invoke-RestMethod. It leverages PowerShell Script Block Logging to identify scripts that attempt to upload files via HTTP POST requests. This activity is significant as it may indicate an attacker is exfiltrating sensitive data, such as desktop screenshots or files, to an external command and control (C2) server. If confirmed malicious, this could lead to data breaches, loss of sensitive information, and further compromise of the affected systems. Immediate investigation is recommended to determine the intent and scope of the activity.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Invoke-RestMethod *" AND ScriptBlockText = "* -Uri *" AND ScriptBlockText = "* -Method *" AND ScriptBlockText = "* Post *" AND ScriptBlockText = "* -InFile *" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_invoke_restmethod_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+description: The following analytic detects potential data exfiltration using PowerShell's
+  Invoke-RestMethod. It leverages PowerShell Script Block Logging to identify scripts
+  that attempt to upload files via HTTP POST requests. This activity is significant
+  as it may indicate an attacker is exfiltrating sensitive data, such as desktop screenshots
+  or files, to an external command and control (C2) server. If confirmed malicious,
+  this could lead to data breaches, loss of sensitive information, and further compromise
+  of the affected systems. Immediate investigation is recommended to determine the
+  intent and scope of the activity.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Invoke-RestMethod *" AND
+  ScriptBlockText = "* -Uri *" AND ScriptBlockText = "* -Method *" AND ScriptBlockText
+  = "* Post *" AND ScriptBlockText = "* -InFile *" | stats count min(_time) as firstTime
+  max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_invoke_restmethod_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives should be limited. Filter as needed.
 references:
 - https://twitter.com/_CERT_UA/status/1620781684257091584
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A PowerShell script on $Computer$ is attempting to transfer files to a remote URL.
+  message: A PowerShell script on $Computer$ is attempting to transfer files to a
+    remote URL.
   mitre_attack_id:
   - T1041
   observable:
@@ -41,16 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml b/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml
index 4f78b790f5..a5870f7ebc 100644
--- a/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml
+++ b/detections/endpoint/windows_exfiltration_over_c2_via_powershell_uploadstring.yml
@@ -7,9 +7,21 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic identifies potential data exfiltration using the PowerShell `net.webclient` command with the `UploadString` method. It leverages PowerShell Script Block Logging to detect instances where this command is executed. This activity is significant as it may indicate an attempt to upload sensitive data, such as desktop screenshots or files, to an external or internal URI, often associated with malware like Winter-Vivern. If confirmed malicious, this could lead to unauthorized data transfer, compromising sensitive information and potentially leading to further exploitation of the compromised host.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Net.webclient*" AND ScriptBlockText = "*.UploadString*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_powershell_uploadstring_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+description: The following analytic identifies potential data exfiltration using the
+  PowerShell `net.webclient` command with the `UploadString` method. It leverages
+  PowerShell Script Block Logging to detect instances where this command is executed.
+  This activity is significant as it may indicate an attempt to upload sensitive data,
+  such as desktop screenshots or files, to an external or internal URI, often associated
+  with malware like Winter-Vivern. If confirmed malicious, this could lead to unauthorized
+  data transfer, compromising sensitive information and potentially leading to further
+  exploitation of the compromised host.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Net.webclient*" AND ScriptBlockText
+  = "*.UploadString*" | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_exfiltration_over_c2_via_powershell_uploadstring_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives should be limited. Filter as needed.
 references:
 - https://twitter.com/_CERT_UA/status/1620781684257091584
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A PowerShell script on $Computer$ is attempting to transfer files to a remote URL.
+  message: A PowerShell script on $Computer$ is attempting to transfer files to a
+    remote URL.
   mitre_attack_id:
   - T1041
   observable:
@@ -41,16 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_uploadstring/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_uploadstring/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_export_certificate.yml b/detections/endpoint/windows_export_certificate.yml
index f555e83d7c..df6157905f 100644
--- a/detections/endpoint/windows_export_certificate.yml
+++ b/detections/endpoint/windows_export_certificate.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the export of a certificate from the Windows Certificate Store. It leverages the Certificates Lifecycle log channel, specifically event ID 1007, to identify this activity. Monitoring certificate exports is crucial as certificates can be used for authentication to VPNs or private resources. If malicious actors export certificates, they could potentially gain unauthorized access to sensitive systems or data, leading to significant security breaches.
+description: The following analytic detects the export of a certificate from the Windows
+  Certificate Store. It leverages the Certificates Lifecycle log channel, specifically
+  event ID 1007, to identify this activity. Monitoring certificate exports is crucial
+  as certificates can be used for authentication to VPNs or private resources. If
+  malicious actors export certificates, they could potentially gain unauthorized access
+  to sensitive systems or data, leading to significant security breaches.
 data_source:
 - Windows Event Log CertificateServicesClient 1007
-search: '`certificateservices_lifecycle` EventCode=1007 | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as lastTime by Computer, SubjectName, UserData_Xml | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_export_certificate_filter`'
-how_to_implement: To implement this analytic, you must collect Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational or Microsoft-Windows-CertificateServicesClient-Lifecycle-User/Operational.
-known_false_positives: False positives may be generated based on an automated process or service that exports certificates on the regular. Review is required before setting to alert. Monitor for abnormal processes performing an export.
+search: '`certificateservices_lifecycle` EventCode=1007 | xmlkv UserData_Xml | stats
+  count min(_time) as firstTime max(_time) as lastTime by Computer, SubjectName, UserData_Xml
+  | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `windows_export_certificate_filter`'
+how_to_implement: To implement this analytic, you must collect Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational
+  or Microsoft-Windows-CertificateServicesClient-Lifecycle-User/Operational.
+known_false_positives: False positives may be generated based on an automated process
+  or service that exports certificates on the regular. Review is required before setting
+  to alert. Monitor for abnormal processes performing an export.
 references:
 - https://atomicredteam.io/defense-evasion/T1553.004/#atomic-test-4---install-root-ca-on-windows
 drilldown_searches:
@@ -19,7 +30,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,15 +58,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - SubjectName
-  - UserData_Xml
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certificateservices-lifecycle.log
-    source: XmlWinEventLog:Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certificateservices-lifecycle.log
+    source: 
+      XmlWinEventLog:Microsoft-Windows-CertificateServicesClient-Lifecycle-System/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_file_share_discovery_with_powerview.yml b/detections/endpoint/windows_file_share_discovery_with_powerview.yml
index 8d8612f796..8ada9009b9 100644
--- a/detections/endpoint/windows_file_share_discovery_with_powerview.yml
+++ b/detections/endpoint/windows_file_share_discovery_with_powerview.yml
@@ -7,21 +7,40 @@ type: TTP
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the Invoke-ShareFinder PowerShell cmdlet from PowerView. This detection leverages PowerShell Script Block Logging to identify instances where this specific command is executed. Monitoring this activity is crucial as it indicates an attempt to enumerate network file shares, which may contain sensitive information such as backups, scripts, and credentials. If confirmed malicious, this activity could enable an attacker to escalate privileges or move laterally within the network, potentially compromising additional systems and sensitive data.
-search: '`powershell` EventCode=4104  (ScriptBlockText=Invoke-ShareFinder*) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_share_discovery_with_powerview_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
-known_false_positives: Security teams may leverage PowerView proactively to identify and remediate sensitive file shares. Filter as needed.
+description: The following analytic detects the execution of the Invoke-ShareFinder
+  PowerShell cmdlet from PowerView. This detection leverages PowerShell Script Block
+  Logging to identify instances where this specific command is executed. Monitoring
+  this activity is crucial as it indicates an attempt to enumerate network file shares,
+  which may contain sensitive information such as backups, scripts, and credentials.
+  If confirmed malicious, this activity could enable an attacker to escalate privileges
+  or move laterally within the network, potentially compromising additional systems
+  and sensitive data.
+search: '`powershell` EventCode=4104  (ScriptBlockText=Invoke-ShareFinder*) | stats
+  count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode
+  ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_file_share_discovery_with_powerview_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
+known_false_positives: Security teams may leverage PowerView proactively to identify
+  and remediate sensitive file shares. Filter as needed.
 references:
 - https://github.com/PowerShellEmpire/PowerTools/blob/master/PowerView/powerview.ps1
 - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/
 - https://attack.mitre.org/techniques/T1135/
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$UserID$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" UserID = "$UserID$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" UserID =
+    "$UserID$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$UserID$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/powerview_sharefinder/windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/powerview_sharefinder/windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml b/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml
index 3eec3d467e..3ff46a8460 100644
--- a/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml
+++ b/detections/endpoint/windows_file_transfer_protocol_in_non_common_process_path.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects FTP connections initiated by processes located in non-standard installation paths on Windows systems. It leverages Sysmon EventCode 3 to identify network connections where the process image path does not match common directories like "Program Files" or "Windows\System32". This activity is significant as FTP is often used by adversaries and malware, such as AgentTesla, for Command and Control (C2) communications to exfiltrate stolen data. If confirmed malicious, this could lead to unauthorized data transfer, exposing sensitive information and compromising the integrity of the affected host.
+description: The following analytic detects FTP connections initiated by processes
+  located in non-standard installation paths on Windows systems. It leverages Sysmon
+  EventCode 3 to identify network connections where the process image path does not
+  match common directories like "Program Files" or "Windows\System32". This activity
+  is significant as FTP is often used by adversaries and malware, such as AgentTesla,
+  for Command and Control (C2) communications to exfiltrate stolen data. If confirmed
+  malicious, this could lead to unauthorized data transfer, exposing sensitive information
+  and compromising the integrity of the affected host.
 data_source:
 - Sysmon EventID 3
-search: '`sysmon` EventCode=3 NOT(Image IN("*\\program files*", "*\\windows\\system32\\*","*\\windows\\SysWOW64\\*")) (DestinationPortName="ftp" OR DestinationPort=21) | stats count min(_time) as firstTime max(_time) as lastTime by Image DestinationPort DestinationPortName DestinationHostname DestinationIp SourcePort SourcePortName Protocol SourceHostname dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_transfer_protocol_in_non_common_process_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 3 connection events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: third party application may use this network protocol as part of its feature. Filter is needed.
+search: '`sysmon` EventCode=3 NOT(Image IN("*\\program files*", "*\\windows\\system32\\*","*\\windows\\SysWOW64\\*"))
+  (DestinationPortName="ftp" OR DestinationPort=21) | stats count min(_time) as firstTime
+  max(_time) as lastTime by Image DestinationPort DestinationPortName DestinationHostname
+  DestinationIp SourcePort SourcePortName Protocol SourceHostname dest user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_file_transfer_protocol_in_non_common_process_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and sysmon eventcode = 3 connection events from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: third party application may use this network protocol as part
+  of its feature. Filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: a process $Image$ is having a FTP connection to $DestinationHostname$ in $dest$
+  message: a process $Image$ is having a FTP connection to $DestinationHostname$ in
+    $dest$
   mitre_attack_id:
   - T1071.003
   - T1071
@@ -42,23 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - DestinationPort
-  - DestinationPortName
-  - DestinationHostname
-  - SourceHostname
-  - SourcePort
-  - SourcePortName
-  - Protocol
-  - DestinationIp
-  - dest
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_ftp/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_ftp/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_file_without_extension_in_critical_folder.yml b/detections/endpoint/windows_file_without_extension_in_critical_folder.yml
index 6cafb7c27c..5db5c768fd 100644
--- a/detections/endpoint/windows_file_without_extension_in_critical_folder.yml
+++ b/detections/endpoint/windows_file_without_extension_in_critical_folder.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of files without extensions in critical folders like "System32\Drivers." It leverages data from the Endpoint.Filesystem datamodel, focusing on file paths and creation times. This activity is significant as it may indicate the presence of destructive malware, such as HermeticWiper, which drops driver components in these directories. If confirmed malicious, this behavior could lead to severe system compromise, including boot sector wiping, resulting in potential data loss and system inoperability.
+description: The following analytic detects the creation of files without extensions
+  in critical folders like "System32\Drivers." It leverages data from the Endpoint.Filesystem
+  datamodel, focusing on file paths and creation times. This activity is significant
+  as it may indicate the presence of destructive malware, such as HermeticWiper, which
+  drops driver components in these directories. If confirmed malicious, this behavior
+  could lead to severe system compromise, including boot sector wiping, resulting
+  in potential data loss and system inoperability.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\System32\\drivers\\*", "*\\syswow64\\drivers\\*") by _time span=5m Filesystem.dest Filesystem.user Filesystem.file_name Filesystem.file_path Filesystem.process_guid Filesystem.file_create_time | `drop_dm_object_name(Filesystem)` | rex field="file_name" "\.(?<extension>[^\.]*$)" | where isnull(extension) | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes by _time span=5m Processes.process_name Processes.dest Processes.process_guid Processes.user | `drop_dm_object_name(Processes)`] | stats count min(_time) as firstTime max(_time) as lastTime by dest process_name process_guid file_name file_path file_create_time user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_file_without_extension_in_critical_folder_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_path IN ("*\\System32\\drivers\\*", "*\\syswow64\\drivers\\*")
+  by _time span=5m Filesystem.dest Filesystem.user Filesystem.file_name Filesystem.file_path
+  Filesystem.process_guid Filesystem.file_create_time | `drop_dm_object_name(Filesystem)`
+  | rex field="file_name" "\.(?<extension>[^\.]*$)" | where isnull(extension) | join
+  process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  by _time span=5m Processes.process_name Processes.dest Processes.process_guid Processes.user
+  | `drop_dm_object_name(Processes)`] | stats count min(_time) as firstTime max(_time)
+  as lastTime by dest process_name process_guid file_name file_path file_create_time
+  user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_file_without_extension_in_critical_folder_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
 known_false_positives: Unknown at this point
 references:
 - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,22 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
-  - Filesystem.dest
-  - Processes.process_name
-  - Processes.dest
-  - Processes.process_guid
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml b/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml
index 81f91b9ac1..b3cab41b18 100644
--- a/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml
+++ b/detections/endpoint/windows_files_and_dirs_access_rights_modification_via_icacls.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the modification of security permissions on files or directories using tools like icacls.exe, cacls.exe, or xcacls.exe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line executions. This activity is significant as it is commonly used by Advanced Persistent Threats (APTs) and coinminer scripts to evade detection and maintain control over compromised systems. If confirmed malicious, this behavior could allow attackers to hinder investigation, impede remediation efforts, and maintain persistent access to the compromised environment.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe", "cacls.exe","xcacls.exe") AND Processes.process IN ("*:R*", "*:W*", "*:F*", "*:C*",, "*:N*","*/P*", "*/E*") by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_files_and_dirs_access_rights_modification_via_icacls_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unknown. It is possible some administrative scripts use ICacls. Filter as needed.
+description: The following analytic identifies the modification of security permissions
+  on files or directories using tools like icacls.exe, cacls.exe, or xcacls.exe. It
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific
+  command-line executions. This activity is significant as it is commonly used by
+  Advanced Persistent Threats (APTs) and coinminer scripts to evade detection and
+  maintain control over compromised systems. If confirmed malicious, this behavior
+  could allow attackers to hinder investigation, impede remediation efforts, and maintain
+  persistent access to the compromised environment.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN( "icacls.exe",
+  "cacls.exe","xcacls.exe") AND Processes.process IN ("*:R*", "*:W*", "*:F*", "*:C*",,
+  "*:N*","*/P*", "*/E*") by Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_files_and_dirs_access_rights_modification_via_icacls_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unknown. It is possible some administrative scripts use ICacls.
+  Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.amadey
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,9 @@ tags:
   - 3309f53e-b22b-4eb6-8fd2-a6cf58b355a9
   confidence: 70
   impact: 70
-  message: Process name $process_name$ with access right modification argument executed by $user$ to change security permission of a specific file or directory on host $dest$
+  message: Process name $process_name$ with access right modification argument executed
+    by $user$ to change security permission of a specific file or directory on host
+    $dest$
   mitre_attack_id:
   - T1222.001
   - T1222
@@ -49,21 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_guid
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.process
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/amadey/access_permission/amadey_sysmon2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/amadey/access_permission/amadey_sysmon2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml b/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml
index cc5f7790e7..18406d81f6 100644
--- a/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml
+++ b/detections/endpoint/windows_find_domain_organizational_units_with_getdomainou.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the `Get-DomainOU` cmdlet, a part of the PowerView toolkit used for Windows domain enumeration. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. Detecting `Get-DomainOU` usage is significant as adversaries may use it to gather information about organizational units within Active Directory, which can facilitate lateral movement or privilege escalation. If confirmed malicious, this activity could allow attackers to map the domain structure, aiding in further exploitation and persistence within the network.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-DomainOU*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_find_domain_organizational_units_with_getdomainou_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
+description: The following analytic detects the execution of the `Get-DomainOU` cmdlet,
+  a part of the PowerView toolkit used for Windows domain enumeration. It leverages
+  PowerShell Script Block Logging (EventCode=4104) to identify this activity. Detecting
+  `Get-DomainOU` usage is significant as adversaries may use it to gather information
+  about organizational units within Active Directory, which can facilitate lateral
+  movement or privilege escalation. If confirmed malicious, this activity could allow
+  attackers to map the domain structure, aiding in further exploitation and persistence
+  within the network.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-DomainOU*" | stats count
+  min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer
+  UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_find_domain_organizational_units_with_getdomainou_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators may leverage PowerSploit tools for legitimate
+  reasons, filter as needed.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainOU/
 - https://attack.mitre.org/techniques/T1087/002/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious PowerShell Get-DomainOU was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Get-DomainOU was identified on endpoint $dest$ by
+    user $user$.
   mitre_attack_id:
   - T1087
   - T1087.002
@@ -47,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-DomainOU-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-DomainOU-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml b/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml
index 48125cf43a..fba268adfd 100644
--- a/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml
+++ b/detections/endpoint/windows_find_interesting_acl_with_findinterestingdomainacl.yml
@@ -7,10 +7,22 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the `Find-InterestingDomainAcl` cmdlet, part of the PowerView toolkit, using PowerShell Script Block Logging (EventCode=4104). This detection leverages logs to identify when this command is run, which is significant as adversaries may use it to find misconfigured or unusual Access Control Lists (ACLs) within a domain. If confirmed malicious, this activity could allow attackers to identify privilege escalation opportunities or weak security configurations in Active Directory, potentially leading to unauthorized access or further exploitation.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Find-InterestingDomainAcl*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_find_interesting_acl_with_findinterestingdomainacl_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
+description: The following analytic detects the execution of the `Find-InterestingDomainAcl`
+  cmdlet, part of the PowerView toolkit, using PowerShell Script Block Logging (EventCode=4104).
+  This detection leverages logs to identify when this command is run, which is significant
+  as adversaries may use it to find misconfigured or unusual Access Control Lists
+  (ACLs) within a domain. If confirmed malicious, this activity could allow attackers
+  to identify privilege escalation opportunities or weak security configurations in
+  Active Directory, potentially leading to unauthorized access or further exploitation.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Find-InterestingDomainAcl*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_find_interesting_acl_with_findinterestingdomainacl_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators may leverage PowerSploit tools for legitimate
+  reasons, filter as needed.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Find-InterestingDomainAcl/
 - https://attack.mitre.org/techniques/T1087/002/
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious PowerShell Find-InterestingDomainAcl was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Find-InterestingDomainAcl was identified on endpoint
+    $dest$ by user $user$.
   mitre_attack_id:
   - T1087
   - T1087.002
@@ -47,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-interestingACL-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-interestingACL-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_findstr_gpp_discovery.yml b/detections/endpoint/windows_findstr_gpp_discovery.yml
index d5c7e1af0e..3f2837034f 100644
--- a/detections/endpoint/windows_findstr_gpp_discovery.yml
+++ b/detections/endpoint/windows_findstr_gpp_discovery.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the use of the findstr command to search for unsecured credentials in Group Policy Preferences (GPP). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving findstr.exe with references to SYSVOL and cpassword. This activity is significant because it indicates an attempt to locate and potentially decrypt embedded credentials in GPP, which could lead to unauthorized access. If confirmed malicious, this could allow an attacker to escalate privileges or gain access to sensitive systems and data within the domain.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=findstr.exe AND Processes.process=*sysvol* AND Processes.process=*cpassword*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_findstr_gpp_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may leverage findstr to find passwords in GPO to validate exposure. Filter as needed.
+description: The following analytic detects the use of the findstr command to search
+  for unsecured credentials in Group Policy Preferences (GPP). It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  involving findstr.exe with references to SYSVOL and cpassword. This activity is
+  significant because it indicates an attempt to locate and potentially decrypt embedded
+  credentials in GPP, which could lead to unauthorized access. If confirmed malicious,
+  this could allow an attacker to escalate privileges or gain access to sensitive
+  systems and data within the domain.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=findstr.exe
+  AND Processes.process=*sysvol* AND Processes.process=*cpassword*) by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.original_file_name |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_findstr_gpp_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may leverage findstr to find passwords in GPO
+  to validate exposure. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1552/006/
 - https://pentestlab.blog/2017/03/20/group-policy-preferences/
@@ -25,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.original_file_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/findstr_gpp_discovery/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/findstr_gpp_discovery/windows-security.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml b/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml
index 350f33b270..c8ee99b2bd 100644
--- a/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml
+++ b/detections/endpoint/windows_forest_discovery_with_getforestdomain.yml
@@ -7,10 +7,22 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the `Get-ForestDomain` cmdlet, a component of the PowerView toolkit used for Windows domain enumeration. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. Detecting `Get-ForestDomain` is significant because adversaries and Red Teams use it to gather detailed information about Active Directory forest and domain configurations. If confirmed malicious, this activity could enable attackers to understand the domain structure, facilitating lateral movement or privilege escalation within the environment.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-ForestDomain*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_forest_discovery_with_getforestdomain_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
+description: The following analytic detects the execution of the `Get-ForestDomain`
+  cmdlet, a component of the PowerView toolkit used for Windows domain enumeration.
+  It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity.
+  Detecting `Get-ForestDomain` is significant because adversaries and Red Teams use
+  it to gather detailed information about Active Directory forest and domain configurations.
+  If confirmed malicious, this activity could enable attackers to understand the domain
+  structure, facilitating lateral movement or privilege escalation within the environment.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Get-ForestDomain*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_forest_discovery_with_getforestdomain_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators may leverage PowerSploit tools for legitimate
+  reasons, filter as needed.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-ForestDomain/
 - https://attack.mitre.org/techniques/T1087/002/
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious PowerShell Get-ForestDomain was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Get-ForestDomain was identified on endpoint $dest$
+    by user $user$.
   mitre_attack_id:
   - T1087
   - T1087.002
@@ -47,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-ForestDomain-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-ForestDomain-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_gather_victim_host_information_camera.yml b/detections/endpoint/windows_gather_victim_host_information_camera.yml
index 2ccf24f0ab..3e16436f98 100644
--- a/detections/endpoint/windows_gather_victim_host_information_camera.yml
+++ b/detections/endpoint/windows_gather_victim_host_information_camera.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a PowerShell script that enumerates camera devices on the targeted host. This detection leverages PowerShell Script Block Logging, specifically looking for commands querying Win32_PnPEntity for camera-related information. This activity is significant as it is commonly observed in DCRat malware, which collects camera data to send to its command-and-control server. If confirmed malicious, this behavior could indicate an attempt to gather sensitive visual information from the host, potentially leading to privacy breaches or further exploitation.
+description: The following analytic detects a PowerShell script that enumerates camera
+  devices on the targeted host. This detection leverages PowerShell Script Block Logging,
+  specifically looking for commands querying Win32_PnPEntity for camera-related information.
+  This activity is significant as it is commonly observed in DCRat malware, which
+  collects camera data to send to its command-and-control server. If confirmed malicious,
+  this behavior could indicate an attempt to gather sensitive visual information from
+  the host, potentially leading to privacy breaches or further exploitation.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText= "* Win32_PnPEntity *" ScriptBlockText= "*SELECT*" ScriptBlockText= "*WHERE*" ScriptBlockText = "*PNPClass*" ScriptBlockText IN ("*Image*", "*Camera*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_host_information_camera_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Administrators may execute this powershell command to get hardware information related to camera on $dest$.
+search: '`powershell` EventCode=4104 ScriptBlockText= "* Win32_PnPEntity *" ScriptBlockText=
+  "*SELECT*" ScriptBlockText= "*WHERE*" ScriptBlockText = "*PNPClass*" ScriptBlockText
+  IN ("*Image*", "*Camera*") | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename
+  UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_gather_victim_host_information_camera_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Administrators may execute this powershell command to get hardware
+  information related to camera on $dest$.
 references:
 - https://cert.gov.ua/article/405538
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,15 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_enum_camera/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_enum_camera/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_gather_victim_identity_sam_info.yml b/detections/endpoint/windows_gather_victim_identity_sam_info.yml
index 075937b8d5..d7bc07789d 100644
--- a/detections/endpoint/windows_gather_victim_identity_sam_info.yml
+++ b/detections/endpoint/windows_gather_victim_identity_sam_info.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects processes loading the samlib.dll or samcli.dll modules, which are often abused to access Security Account Manager (SAM) objects or credentials on domain controllers. This detection leverages Sysmon EventCode 7 to identify these DLLs being loaded outside typical system directories. Monitoring this activity is crucial as it may indicate attempts to gather sensitive identity information. If confirmed malicious, this behavior could allow attackers to obtain credentials, escalate privileges, or further infiltrate the network.
+description: The following analytic detects processes loading the samlib.dll or samcli.dll
+  modules, which are often abused to access Security Account Manager (SAM) objects
+  or credentials on domain controllers. This detection leverages Sysmon EventCode
+  7 to identify these DLLs being loaded outside typical system directories. Monitoring
+  this activity is crucial as it may indicate attempts to gather sensitive identity
+  information. If confirmed malicious, this behavior could allow attackers to obtain
+  credentials, escalate privileges, or further infiltrate the network.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7  (ImageLoaded = "*\\samlib.dll" AND OriginalFileName = "samlib.dll") OR (ImageLoaded = "*\\samcli.dll" AND OriginalFileName = "SAMCLI.DLL") AND NOT (Image IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_identity_sam_info_filter`'
-how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
-known_false_positives: this module can be loaded by a third party application. Filter is needed.
+search: '`sysmon` EventCode=7  (ImageLoaded = "*\\samlib.dll" AND OriginalFileName
+  = "samlib.dll") OR (ImageLoaded = "*\\samcli.dll" AND OriginalFileName = "SAMCLI.DLL")
+  AND NOT (Image IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats
+  count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name
+  dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_gather_victim_identity_sam_info_filter`'
+how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709
+  will add the ImageLoaded name to the process_name field, allowing this query to
+  work. Use as an example and implement for other products.
+known_false_positives: this module can be loaded by a third party application. Filter
+  is needed.
 references:
 - https://redcanary.com/blog/active-breach-evading-defenses/
 - https://strontic.github.io/xcyclopedia/library/samlib.dll-0BDF6351009F6EBA5BA7E886F23263B1.html
@@ -20,7 +34,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: An instance of $dest$ that loads $ImageLoaded$ that are related to accessing to SAM object information.
+  message: An instance of $dest$ that loads $ImageLoaded$ that are related to accessing
+    to SAM object information.
   mitre_attack_id:
   - T1589.001
   - T1589
@@ -33,18 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/loading_samlib/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/loading_samlib/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_gather_victim_network_info_through_ip_check_web_services.yml b/detections/endpoint/windows_gather_victim_network_info_through_ip_check_web_services.yml
index ee3d7a35ce..c9b6885c47 100644
--- a/detections/endpoint/windows_gather_victim_network_info_through_ip_check_web_services.yml
+++ b/detections/endpoint/windows_gather_victim_network_info_through_ip_check_web_services.yml
@@ -5,12 +5,29 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects processes attempting to connect to known IP check web services. This behavior is identified using Sysmon EventCode 22 logs, specifically monitoring DNS queries to services like "wtfismyip.com" and "ipinfo.io". This activity is significant as it is commonly used by malware, such as Trickbot, for reconnaissance to determine the infected machine's IP address. If confirmed malicious, this could allow attackers to gather network information, aiding in further attacks or lateral movement within the network.
+description: The following analytic detects processes attempting to connect to known
+  IP check web services. This behavior is identified using Sysmon EventCode 22 logs,
+  specifically monitoring DNS queries to services like "wtfismyip.com" and "ipinfo.io".
+  This activity is significant as it is commonly used by malware, such as Trickbot,
+  for reconnaissance to determine the infected machine's IP address. If confirmed
+  malicious, this could allow attackers to gather network information, aiding in further
+  attacks or lateral movement within the network.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode=22  QueryName IN ("*wtfismyip.com", "*checkip.*", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org", "*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net", "*iplogger.org*", "*ip-api.com*", "*geoip.*", "*icanhazip.*") |  stats  min(_time) as firstTime max(_time) as lastTime count by  Image ProcessId QueryName QueryStatus QueryResults EventCode Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_gather_victim_network_info_through_ip_check_web_services_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA.
-known_false_positives: Filter internet browser application to minimize the false positive of this detection.
+search: '`sysmon` EventCode=22  QueryName IN ("*wtfismyip.com", "*checkip.*", "*ipecho.net",
+  "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb",
+  "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org",
+  "*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net", "*iplogger.org*", "*ip-api.com*",
+  "*geoip.*", "*icanhazip.*") |  stats  min(_time) as firstTime max(_time) as lastTime
+  count by  Image ProcessId QueryName QueryStatus QueryResults EventCode Computer
+  | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_gather_victim_network_info_through_ip_check_web_services_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, dns query name process path , and query ststus from
+  your endpoints like EventCode 22. If you are using Sysmon, you must have at least
+  version 12 of the Sysmon TA.
+known_false_positives: Filter internet browser application to minimize the false positive
+  of this detection.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 tags:
@@ -36,19 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ProcessId
-  - QueryName
-  - QueryStatus
-  - QueryResults
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml b/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml
index 0618321580..b6079c5b30 100644
--- a/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml
+++ b/detections/endpoint/windows_get_adcomputer_unconstrained_delegation_discovery.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the Get-ADComputer cmdlet with parameters indicating a search for Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this specific activity. This behavior is significant as it may indicate an attempt by adversaries or Red Teams to gain situational awareness and perform Active Directory discovery. If confirmed malicious, this activity could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network.
+description: The following analytic detects the use of the Get-ADComputer cmdlet with
+  parameters indicating a search for Windows endpoints with Kerberos Unconstrained
+  Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify
+  this specific activity. This behavior is significant as it may indicate an attempt
+  by adversaries or Red Teams to gain situational awareness and perform Active Directory
+  discovery. If confirmed malicious, this activity could allow attackers to identify
+  high-value targets for further exploitation, potentially leading to privilege escalation
+  or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-ADComputer*" AND ScriptBlockText = "*TrustedForDelegation*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_get_adcomputer_unconstrained_delegation_discovery_filter`'
-how_to_implement: The following  analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators or power users may leverage PowerView for system management or troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-ADComputer*" AND ScriptBlockText
+  = "*TrustedForDelegation*") | stats count min(_time) as firstTime max(_time) as
+  lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest
+  | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_get_adcomputer_unconstrained_delegation_discovery_filter`'
+how_to_implement: The following  analytic requires PowerShell operational logs to
+  be imported. Modify the powershell macro as needed to match the sourcetype or add
+  index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators or power users may leverage PowerView for system
+  management or troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
 - https://adsecurity.org/?p=1667
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_get_adcomputer_unconstrained_delegation_discovery/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_get_adcomputer_unconstrained_delegation_discovery/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml b/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml
index a1f09ceb06..85f3b7ce1a 100644
--- a/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml
+++ b/detections/endpoint/windows_get_local_admin_with_findlocaladminaccess.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the `Find-LocalAdminAccess` cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part of PowerView, a toolkit for Windows domain enumeration. Identifying the use of `Find-LocalAdminAccess` is crucial as adversaries may use it to find machines where the current user has local administrator access, facilitating lateral movement or privilege escalation. If confirmed malicious, this activity could allow attackers to target and compromise additional systems within the network, significantly increasing their control and access to sensitive information.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*Find-LocalAdminAccess*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_get_local_admin_with_findlocaladminaccess_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators may leverage PowerSploit tools for legitimate reasons, filter as needed.
+description: The following analytic detects the execution of the `Find-LocalAdminAccess`
+  cmdlet using PowerShell Script Block Logging (EventCode=4104). This cmdlet is part
+  of PowerView, a toolkit for Windows domain enumeration. Identifying the use of `Find-LocalAdminAccess`
+  is crucial as adversaries may use it to find machines where the current user has
+  local administrator access, facilitating lateral movement or privilege escalation.
+  If confirmed malicious, this activity could allow attackers to target and compromise
+  additional systems within the network, significantly increasing their control and
+  access to sensitive information.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*Find-LocalAdminAccess*" |
+  stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest, UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_get_local_admin_with_findlocaladminaccess_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators may leverage PowerSploit tools for legitimate
+  reasons, filter as needed.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Find-LocalAdminAccess/
 - https://attack.mitre.org/techniques/T1087/002/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Suspicious PowerShell Find-LocalAdminAccess was identified on endpoint $dest$ by user $user$.
+  message: Suspicious PowerShell Find-LocalAdminAccess was identified on endpoint
+    $dest$ by user $user$.
   mitre_attack_id:
   - T1087
   - T1087.002
@@ -47,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-LocalAdminAccess-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/AD_discovery/windows-powershell-LocalAdminAccess-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_group_policy_object_created.yml b/detections/endpoint/windows_group_policy_object_created.yml
index fb69e13b70..18f5a58dae 100644
--- a/detections/endpoint/windows_group_policy_object_created.yml
+++ b/detections/endpoint/windows_group_policy_object_created.yml
@@ -8,10 +8,25 @@ type: TTP
 data_source:
 - Windows Event Log Security 5136
 - Windows Event Log Security 5137
-description: The following analytic detects the creation of a new Group Policy Object (GPO) by leveraging Event IDs 5136 and 5137. This detection uses directory service change events to identify when a new GPO is created. Monitoring GPO creation is crucial as adversaries can exploit GPOs to escalate privileges or deploy malware across an Active Directory network. If confirmed malicious, this activity could allow attackers to control system configurations, deploy ransomware, or propagate malware, leading to widespread compromise and significant operational disruption.
-search: '`wineventlog_security` EventCode=5137 OR (EventCode=5136 AttributeValue!="New Group Policy Object" AND (AttributeLDAPDisplayName=displayName OR AttributeLDAPDisplayName=gPCFileSysPath) ) ObjectClass=groupPolicyContainer | stats values(AttributeValue) as details values(SubjectUserSid) as User values(ObjectDN) as ObjectDN by ObjectGUID Computer | eval GPO_Name = mvindex(details, 0) | eval GPO_Path = mvindex(details, 1) | fields - details | `windows_group_policy_object_created_filter`'
-how_to_implement: To successfully implement this search, the Advanced Security Audit policy setting `Audit Directory Service Changes` within `DS Access` needs to be enabled. Furthermore, the appropriate system access control lists (SACL) need to be created as the used events are not logged by default. A good guide to accomplish this can be found here https://jgspiers.com/audit-group-policy-changes/.
-known_false_positives: Group Policy Objects are created as part of regular administrative operations, filter as needed.
+description: The following analytic detects the creation of a new Group Policy Object
+  (GPO) by leveraging Event IDs 5136 and 5137. This detection uses directory service
+  change events to identify when a new GPO is created. Monitoring GPO creation is
+  crucial as adversaries can exploit GPOs to escalate privileges or deploy malware
+  across an Active Directory network. If confirmed malicious, this activity could
+  allow attackers to control system configurations, deploy ransomware, or propagate
+  malware, leading to widespread compromise and significant operational disruption.
+search: '`wineventlog_security` EventCode=5137 OR (EventCode=5136 AttributeValue!="New
+  Group Policy Object" AND (AttributeLDAPDisplayName=displayName OR AttributeLDAPDisplayName=gPCFileSysPath)
+  ) ObjectClass=groupPolicyContainer | stats values(AttributeValue) as details values(SubjectUserSid)
+  as User values(ObjectDN) as ObjectDN by ObjectGUID Computer | eval GPO_Name = mvindex(details,
+  0) | eval GPO_Path = mvindex(details, 1) | fields - details | `windows_group_policy_object_created_filter`'
+how_to_implement: To successfully implement this search, the Advanced Security Audit
+  policy setting `Audit Directory Service Changes` within `DS Access` needs to be
+  enabled. Furthermore, the appropriate system access control lists (SACL) need to
+  be created as the used events are not logged by default. A good guide to accomplish
+  this can be found here https://jgspiers.com/audit-group-policy-changes/.
+known_false_positives: Group Policy Objects are created as part of regular administrative
+  operations, filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1484/
 - https://attack.mitre.org/techniques/T1484/001
@@ -25,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$User$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$User$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$User$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,20 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - AttributeValue
-  - AttributeLDAPDisplayName
-  - ObjectClass
-  - SubjectUserSid
-  - ObjectDN
-  - ObjectGUID
-  - Computer
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484.001/group_policy_created/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_hidden_schedule_task_settings.yml b/detections/endpoint/windows_hidden_schedule_task_settings.yml
index fb543adcd7..d2d2e20473 100644
--- a/detections/endpoint/windows_hidden_schedule_task_settings.yml
+++ b/detections/endpoint/windows_hidden_schedule_task_settings.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of hidden scheduled tasks on Windows systems, which are not visible in the UI. It leverages Windows Security EventCode 4698 to identify tasks where the 'Hidden' setting is enabled. This behavior is significant as it may indicate malware activity, such as Industroyer2, or the use of living-off-the-land binaries (LOLBINs) to download additional payloads. If confirmed malicious, this activity could allow attackers to execute code stealthily, maintain persistence, or further compromise the system by downloading additional malicious payloads.
+description: The following analytic detects the creation of hidden scheduled tasks
+  on Windows systems, which are not visible in the UI. It leverages Windows Security
+  EventCode 4698 to identify tasks where the 'Hidden' setting is enabled. This behavior
+  is significant as it may indicate malware activity, such as Industroyer2, or the
+  use of living-off-the-land binaries (LOLBINs) to download additional payloads. If
+  confirmed malicious, this activity could allow attackers to execute code stealthily,
+  maintain persistence, or further compromise the system by downloading additional
+  malicious payloads.
 data_source:
 - Windows Event Log Security 4698
-search: '`wineventlog_security` EventCode=4698 | xmlkv Message | search Hidden = true | stats count min(_time) as firstTime max(_time) as lastTime by  Task_Name, Command, Author, Hidden, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hidden_schedule_task_settings_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
+search: '`wineventlog_security` EventCode=4698 | xmlkv Message | search Hidden = true
+  | stats count min(_time) as firstTime max(_time) as lastTime by  Task_Name, Command,
+  Author, Hidden, dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_hidden_schedule_task_settings_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and
+  filter known instances of Task schedule used in your environment.
 known_false_positives: unknown
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,19 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Task_Name
-  - Command
-  - Author
-  - Enabled
-  - Hidden
-  - Arguments
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/hidden_schedule_task/security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/hidden_schedule_task/security.log
     source: WinEventLog:Security
     sourcetype: WinEventLog
diff --git a/detections/endpoint/windows_hide_notification_features_through_registry.yml b/detections/endpoint/windows_hide_notification_features_through_registry.yml
index 5188572e73..f33fdaca08 100644
--- a/detections/endpoint/windows_hide_notification_features_through_registry.yml
+++ b/detections/endpoint/windows_hide_notification_features_through_registry.yml
@@ -5,12 +5,18 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects suspicious registry modifications aimed at hiding common Windows notification features on a compromised host. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant as it is often used by ransomware to obscure visual indicators, increasing the impact of the attack. If confirmed malicious, this could prevent users from noticing critical system alerts, thereby aiding the attacker in maintaining persistence and furthering their malicious activities undetected.
+description: The following analytic detects suspicious registry modifications aimed
+  at hiding common Windows notification features on a compromised host. It leverages
+  data from the Endpoint.Registry data model, focusing on specific registry paths
+  and values. This activity is significant as it is often used by ransomware to obscure
+  visual indicators, increasing the impact of the attack. If confirmed malicious,
+  this could prevent users from noticing critical system alerts, thereby aiding the
+  attacker in maintaining persistence and furthering their malicious activities undetected.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\*"
   Registry.registry_value_name IN ("HideClock", "HideSCAHealth", "HideSCANetwork",
   "HideSCAPower", "HideSCAVolume") Registry.registry_value_data = "0x00000001") BY
   _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
@@ -30,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/ransomware_disable_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_high_file_deletion_frequency.yml b/detections/endpoint/windows_high_file_deletion_frequency.yml
index 564982f177..988bbe0717 100644
--- a/detections/endpoint/windows_high_file_deletion_frequency.yml
+++ b/detections/endpoint/windows_high_file_deletion_frequency.yml
@@ -5,13 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: Anomaly
-description: The following analytic identifies a high frequency of file deletions by monitoring Sysmon EventCodes 23 and 26 for specific file extensions. This detection leverages Sysmon logs to track deleted target filenames, process names, and process IDs. Such activity is significant as it often indicates ransomware behavior, where files are encrypted and the originals are deleted. If confirmed malicious, this activity could lead to extensive data loss and operational disruption, as ransomware can render critical files inaccessible, demanding a ransom for their recovery.
+description: The following analytic identifies a high frequency of file deletions
+  by monitoring Sysmon EventCodes 23 and 26 for specific file extensions. This detection
+  leverages Sysmon logs to track deleted target filenames, process names, and process
+  IDs. Such activity is significant as it often indicates ransomware behavior, where
+  files are encrypted and the originals are deleted. If confirmed malicious, this
+  activity could lead to extensive data loss and operational disruption, as ransomware
+  can render critical files inaccessible, demanding a ransom for their recovery.
 data_source:
 - Sysmon EventID 23
 - Sysmon EventID 26
-search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.cmd", "*.ini","*.gif", "*.jpg", "*.jpeg", "*.db", "*.ps1", "*.doc", "*.docx", "*.xls", "*.xlsx", "*.ppt", "*.pptx", "*.bmp","*.zip", "*.rar", "*.7z", "*.chm", "*.png", "*.log", "*.vbs", "*.js", "*.vhd", "*.bak", "*.wbcat", "*.bkf" , "*.backup*", "*.dsk", "*.win") NOT TargetFilename IN ("*\\INetCache\\Content.Outlook\\*") | stats count, values(TargetFilename) as deleted_files, min(_time) as firstTime, max(_time) as lastTime by user, dest, signature, signature_id, Image, process_name, process_guid | rename Image as process | where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_high_file_deletion_frequency_filter`'
-how_to_implement: To successfully implement this search, you need to ingest logs that include the deleted target file name, process name, and process ID from your endpoints. If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed.
-known_false_positives: Users may delete a large number of pictures or files in a folder, which could trigger this detection. Additionally, heavy usage of PowerBI and Outlook may also result in false positives.
+search: '`sysmon` EventCode IN ("23","26") TargetFilename IN ("*.cmd", "*.ini","*.gif",
+  "*.jpg", "*.jpeg", "*.db", "*.ps1", "*.doc", "*.docx", "*.xls", "*.xlsx", "*.ppt",
+  "*.pptx", "*.bmp","*.zip", "*.rar", "*.7z", "*.chm", "*.png", "*.log", "*.vbs",
+  "*.js", "*.vhd", "*.bak", "*.wbcat", "*.bkf" , "*.backup*", "*.dsk", "*.win") NOT
+  TargetFilename IN ("*\\INetCache\\Content.Outlook\\*") | stats count, values(TargetFilename)
+  as deleted_files, min(_time) as firstTime, max(_time) as lastTime by user, dest,
+  signature, signature_id, Image, process_name, process_guid | rename Image as process
+  | where count >=100 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_high_file_deletion_frequency_filter`'
+how_to_implement: To successfully implement this search, you need to ingest logs that
+  include the deleted target file name, process name, and process ID from your endpoints.
+  If you are using Sysmon, ensure you have at least version 2.0 of the Sysmon TA installed.
+known_false_positives: Users may delete a large number of pictures or files in a folder,
+  which could trigger this detection. Additionally, heavy usage of PowerBI and Outlook
+  may also result in false positives.
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
 - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: Elevated file deletion rate observed from process [$process_name$] on machine $dest$
+  message: Elevated file deletion rate observed from process [$process_name$] on machine
+    $dest$
   mitre_attack_id:
   - T1485
   observable:
@@ -61,18 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - TargetFilename
-  - dest
-  - user
-  - Image
-  - ProcessID
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml b/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml
index 48b788410d..b7ca791283 100644
--- a/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml
+++ b/detections/endpoint/windows_hijack_execution_flow_version_dll_side_load.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a process loading a version.dll file from a directory other than %windir%\system32 or %windir%\syswow64. This detection leverages Sysmon EventCode 7 to identify instances where an unsigned or improperly located version.dll is loaded. This activity is significant as it is a common technique used in ransomware and APT malware campaigns, including Brute Ratel C4, to execute malicious code via DLL side loading. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, and potentially compromise the target host.
+description: The following analytic detects a process loading a version.dll file from
+  a directory other than %windir%\system32 or %windir%\syswow64. This detection leverages
+  Sysmon EventCode 7 to identify instances where an unsigned or improperly located
+  version.dll is loaded. This activity is significant as it is a common technique
+  used in ransomware and APT malware campaigns, including Brute Ratel C4, to execute
+  malicious code via DLL side loading. If confirmed malicious, this could allow attackers
+  to execute arbitrary code, maintain persistence, and potentially compromise the
+  target host.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 ImageLoaded = "*\\version.dll" AND (Signed = "false" OR NOT(ImageLoaded IN("*\\windows\\system32*",  "*\\windows\\syswow64\\*"))) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hijack_execution_flow_version_dll_side_load_filter`'
-how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
+search: '`sysmon` EventCode=7 ImageLoaded = "*\\version.dll" AND (Signed = "false"
+  OR NOT(ImageLoaded IN("*\\windows\\system32*",  "*\\windows\\syswow64\\*"))) | stats
+  count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name
+  dest EventCode Signed ProcessId | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_hijack_execution_flow_version_dll_side_load_filter`'
+how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709
+  will add the ImageLoaded name to the process_name field, allowing this query to
+  work. Use as an example and implement for other products.
 known_false_positives: unknown
 references:
 - https://www.mdsec.co.uk/2022/08/part-3-how-i-met-your-beacon-brute-ratel/
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml b/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml
index f52757bbc0..99ab77ec43 100644
--- a/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml
+++ b/detections/endpoint/windows_hunting_system_account_targeting_lsass.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies processes attempting to access Lsass.exe, which may indicate credential dumping or applications needing credential access. It leverages Sysmon EventCode 10 to detect such activities by analyzing fields like TargetImage, GrantedAccess, and SourceImage. This behavior is significant as unauthorized access to Lsass.exe can lead to credential theft, posing a severe security risk. If confirmed malicious, attackers could gain access to sensitive credentials, potentially leading to privilege escalation and further compromise of the environment.
+description: The following analytic identifies processes attempting to access Lsass.exe,
+  which may indicate credential dumping or applications needing credential access.
+  It leverages Sysmon EventCode 10 to detect such activities by analyzing fields like
+  TargetImage, GrantedAccess, and SourceImage. This behavior is significant as unauthorized
+  access to Lsass.exe can lead to credential theft, posing a severe security risk.
+  If confirmed malicious, attackers could gain access to sensitive credentials, potentially
+  leading to privilege escalation and further compromise of the environment.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, TargetImage, GrantedAccess, SourceImage, SourceProcessId, SourceUser, TargetUser  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_hunting_system_account_targeting_lsass_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
-known_false_positives: False positives will occur based on GrantedAccess and SourceUser, filter based on source image as needed. Utilize this hunting analytic to tune out false positives in TTP or anomaly analytics.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe | stats count min(_time) as
+  firstTime max(_time) as lastTime by dest, TargetImage, GrantedAccess, SourceImage,
+  SourceProcessId, SourceUser, TargetUser  | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | `windows_hunting_system_account_targeting_lsass_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
+known_false_positives: False positives will occur based on GrantedAccess and SourceUser,
+  filter based on source image as needed. Utilize this hunting analytic to tune out
+  false positives in TTP or anomaly analytics.
 references:
 - https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service
 - https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump
@@ -24,7 +38,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A process, $SourceImage$, has requested access to LSASS on $dest$. Review for further details.
+  message: A process, $SourceImage$, has requested access to LSASS on $dest$. Review
+    for further details.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -41,19 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - TargetImage
-  - GrantedAccess
-  - SourceImage
-  - SourceProcessId
-  - SourceUser
-  - TargetUser
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml b/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml
index c0ebc30361..18021976f9 100644
--- a/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml
+++ b/detections/endpoint/windows_identify_powershell_web_access_iis_pool.yml
@@ -7,10 +7,40 @@ data_source:
 - Windows Event Log Security 4648
 type: Hunting
 status: production
-description: This analytic detects and analyzes PowerShell Web Access (PSWA) usage in Windows environments. It tracks both connection attempts (EventID 4648) and successful logons (EventID 4624) associated with PSWA, providing a comprehensive view of access patterns. The analytic identifies PSWA's operational status, host servers, processes, and connection metrics. It highlights unique target accounts, domains accessed, and verifies logon types. This information is crucial for detecting potential misuse, such as lateral movement, brute force attempts, or unusual access patterns. By offering insights into PSWA activity, it enables security teams to quickly assess and investigate potential security incidents involving this powerful administrative tool.
-search: '`wineventlog_security` (EventCode=4648 OR EventCode=4624 OR EventCode=4625) SubjectUserName="pswa_pool" | fields EventCode, SubjectUserName, TargetUserName, Computer, TargetDomainName, ProcessName, LogonType | rename Computer as dest | stats count(eval(EventCode=4648)) as "Connection Attempts", count(eval(EventCode=4624)) as "Successful Logons", count(eval(EventCode=4625)) as "Unsuccessful Logons", dc(TargetUserName) as "Unique Target Accounts", values(dest) as "PSWA Host", dc(TargetDomainName) as "Unique Target Domains", values(ProcessName) as "PSWA Process", values(TargetUserName) as "Target Users List", values(TargetServerName) as "Target Servers List", values(LogonType) as "Logon Types" | eval PSWA_Running = "Yes", "PSWA Process" = mvindex(split(mvindex("PSWA Process", 0), "\\"), -1) | fields PSWA_Running, "PSWA Host", "PSWA Process", "Connection Attempts", "Successful Logons","Unsuccessful Logons", "Unique Target Accounts", "Unique Target Domains", "Target Users List","Target Servers List", "Logon Types" | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_identify_powershell_web_access_iis_pool_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event logs, specifically Event ID 4648 (A logon was attempted using explicit credentials). Ensure that your Windows systems are configured to audit logon events and that these logs are being forwarded to your SIEM or log management solution. You may need to enable advanced audit policy settings in Windows to capture these events. Additionally, make sure that your environment is set up to capture the necessary fields such as SubjectUserName, TargetUserName, Computer, TargetServerName, and ProcessName from these events. If you're using Splunk, ensure that you have the appropriate Windows TA installed and configured to collect these security logs.
-known_false_positives: False positives may occur if legitimate PSWA processes are used for administrative tasks. Careful review of the logs is recommended to distinguish between legitimate and malicious activity.
+description: This analytic detects and analyzes PowerShell Web Access (PSWA) usage
+  in Windows environments. It tracks both connection attempts (EventID 4648) and successful
+  logons (EventID 4624) associated with PSWA, providing a comprehensive view of access
+  patterns. The analytic identifies PSWA's operational status, host servers, processes,
+  and connection metrics. It highlights unique target accounts, domains accessed,
+  and verifies logon types. This information is crucial for detecting potential misuse,
+  such as lateral movement, brute force attempts, or unusual access patterns. By offering
+  insights into PSWA activity, it enables security teams to quickly assess and investigate
+  potential security incidents involving this powerful administrative tool.
+search: '`wineventlog_security` (EventCode=4648 OR EventCode=4624 OR EventCode=4625)
+  SubjectUserName="pswa_pool" | fields EventCode, SubjectUserName, TargetUserName,
+  Computer, TargetDomainName, ProcessName, LogonType | rename Computer as dest | stats
+  count(eval(EventCode=4648)) as "Connection Attempts", count(eval(EventCode=4624))
+  as "Successful Logons", count(eval(EventCode=4625)) as "Unsuccessful Logons", dc(TargetUserName)
+  as "Unique Target Accounts", values(dest) as "PSWA Host", dc(TargetDomainName) as
+  "Unique Target Domains", values(ProcessName) as "PSWA Process", values(TargetUserName)
+  as "Target Users List", values(TargetServerName) as "Target Servers List", values(LogonType)
+  as "Logon Types" | eval PSWA_Running = "Yes", "PSWA Process" = mvindex(split(mvindex("PSWA
+  Process", 0), "\\"), -1) | fields PSWA_Running, "PSWA Host", "PSWA Process", "Connection
+  Attempts", "Successful Logons","Unsuccessful Logons", "Unique Target Accounts",
+  "Unique Target Domains", "Target Users List","Target Servers List", "Logon Types"
+  | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_identify_powershell_web_access_iis_pool_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event logs, specifically Event ID 4648 (A logon was attempted using
+  explicit credentials). Ensure that your Windows systems are configured to audit
+  logon events and that these logs are being forwarded to your SIEM or log management
+  solution. You may need to enable advanced audit policy settings in Windows to capture
+  these events. Additionally, make sure that your environment is set up to capture
+  the necessary fields such as SubjectUserName, TargetUserName, Computer, TargetServerName,
+  and ProcessName from these events. If you're using Splunk, ensure that you have
+  the appropriate Windows TA installed and configured to collect these security logs.
+known_false_positives: False positives may occur if legitimate PSWA processes are
+  used for administrative tasks. Careful review of the logs is recommended to distinguish
+  between legitimate and malicious activity.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a
 - https://gist.github.com/MHaggis/7e67b659af9148fa593cf2402edebb41
@@ -20,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: PowerShell Web Access (PSWA) IIS Application Pool activity detected on $PSWA Host$.
+  message: PowerShell Web Access (PSWA) IIS Application Pool activity detected on
+    $PSWA Host$.
   mitre_attack_id:
   - T1190
   observable:
@@ -32,19 +63,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - SubjectUserName
-  - TargetUserName
-  - dest
-  - TargetServerName
-  - ProcessName
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/4648_4624_pswa_pool.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/4648_4624_pswa_pool.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Security
diff --git a/detections/endpoint/windows_identify_protocol_handlers.yml b/detections/endpoint/windows_identify_protocol_handlers.yml
index 272c0b755b..8fbd71d6bc 100644
--- a/detections/endpoint/windows_identify_protocol_handlers.yml
+++ b/detections/endpoint/windows_identify_protocol_handlers.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the use of protocol handlers executed via the command line. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. This activity is significant because protocol handlers can be exploited to execute arbitrary commands or launch applications, potentially leading to unauthorized actions. If confirmed malicious, an attacker could use this technique to gain code execution, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
+description: The following analytic identifies the use of protocol handlers executed
+  via the command line. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process and command-line telemetry. This activity is significant
+  because protocol handlers can be exploited to execute arbitrary commands or launch
+  applications, potentially leading to unauthorized actions. If confirmed malicious,
+  an attacker could use this technique to gain code execution, escalate privileges,
+  or maintain persistence within the environment, posing a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes  by Processes.dest Processes.parent_process_name Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup windows_protocol_handlers handler AS process OUTPUT handler ishandler | where ishandler="TRUE" | `windows_identify_protocol_handlers_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be found. https and http is a URL Protocol handler that will trigger this analytic. Tune based on process or command-line.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Processes.process) as process values(Processes.parent_process)
+  as parent_process from datamodel=Endpoint.Processes  by Processes.dest Processes.parent_process_name
+  Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup
+  windows_protocol_handlers handler AS process OUTPUT handler ishandler | where ishandler="TRUE"
+  | `windows_identify_protocol_handlers_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be found. https and http is a URL Protocol
+  handler that will trigger this analytic. Tune based on process or command-line.
 references:
 - https://gist.github.com/MHaggis/a0d3edb57d36e0916c94c0a464b2722e
 - https://www.oreilly.com/library/view/learning-java/1565927184/apas02.html
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 20
   impact: 30
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing a protocol handler.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ utilizing a protocol handler.
   mitre_attack_id:
   - T1059
   observable:
@@ -53,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/protocol_handlers/protocolhandlers.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/protocol_handlers/protocolhandlers.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_iis_components_add_new_module.yml b/detections/endpoint/windows_iis_components_add_new_module.yml
index 93ab8ee563..eb9cfad1f1 100644
--- a/detections/endpoint/windows_iis_components_add_new_module.yml
+++ b/detections/endpoint/windows_iis_components_add_new_module.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of AppCmd.exe to install a new module in IIS. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use it to install webshells or backdoors, leading to credit card scraping, persistence, and further post-exploitation. If confirmed malicious, this could allow attackers to maintain persistent access, execute arbitrary code, and potentially exfiltrate sensitive information from the compromised web server.
+description: The following analytic detects the execution of AppCmd.exe to install
+  a new module in IIS. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant as adversaries may use it to install webshells or backdoors, leading
+  to credit card scraping, persistence, and further post-exploitation. If confirmed
+  malicious, this could allow attackers to maintain persistent access, execute arbitrary
+  code, and potentially exfiltrate sensitive information from the compromised web
+  server.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name  IN ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process IN ("*install *", "*module *") AND Processes.process="*image*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_add_new_module_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present until properly tuned. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where NOT (Processes.parent_process_name  IN
+  ("msiexec.exe", "iissetup.exe")) Processes.process_name=appcmd.exe Processes.process
+  IN ("*install *", "*module *") AND Processes.process="*image*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_add_new_module_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present until properly tuned. Filter
+  as needed.
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
 - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf
@@ -26,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to install a new IIS module.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to install a new IIS module.
   mitre_attack_id:
   - T1505
   - T1505.004
@@ -60,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/appcmd_install-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/appcmd_install-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml b/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml
index 3ff16da76e..312a890de4 100644
--- a/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml
+++ b/detections/endpoint/windows_iis_components_get_webglobalmodule_module_query.yml
@@ -5,12 +5,23 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of the PowerShell cmdlet Get-WebGlobalModule, which lists all IIS Modules installed on a system. It leverages PowerShell input data to detect this activity by capturing the module names and the image paths of the DLLs. This activity is significant for a SOC because it can indicate an attempt to enumerate installed IIS modules, which could be a precursor to exploiting vulnerabilities or misconfigurations. If confirmed malicious, this could allow an attacker to gain insights into the web server's configuration, potentially leading to further exploitation or privilege escalation.
+description: The following analytic identifies the execution of the PowerShell cmdlet
+  Get-WebGlobalModule, which lists all IIS Modules installed on a system. It leverages
+  PowerShell input data to detect this activity by capturing the module names and
+  the image paths of the DLLs. This activity is significant for a SOC because it can
+  indicate an attempt to enumerate installed IIS modules, which could be a precursor
+  to exploiting vulnerabilities or misconfigurations. If confirmed malicious, this
+  could allow an attacker to gain insights into the web server's configuration, potentially
+  leading to further exploitation or privilege escalation.
 data_source:
 - Powershell Installed IIS Modules
-search: '`iis_get_webglobalmodule` | stats count min(_time) as firstTime max(_time) as lastTime by host name image | rename host as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_get_webglobalmodule_module_query_filter`'
-how_to_implement: You must ingest the PwSh cmdlet Get-WebGlobalModule in order to utilize this analytic. Follow https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040
-known_false_positives: This analytic is meant to assist with hunting modules across a fleet of IIS servers. Filter and modify as needed.
+search: '`iis_get_webglobalmodule` | stats count min(_time) as firstTime max(_time)
+  as lastTime by host name image | rename host as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_iis_components_get_webglobalmodule_module_query_filter`'
+how_to_implement: You must ingest the PwSh cmdlet Get-WebGlobalModule in order to
+  utilize this analytic. Follow https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040
+known_false_positives: This analytic is meant to assist with hunting modules across
+  a fleet of IIS servers. Filter and modify as needed.
 references:
 - https://docs.splunk.com/Documentation/Splunk/9.0.2/Data/MonitorWindowsdatawithPowerShellscripts
 - https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040
@@ -35,15 +46,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - host
-  - name
-  - image
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_installediismodules.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_installediismodules.log
     source: powershell://AppCmdModules
     sourcetype: Pwsh:InstalledIISModules
diff --git a/detections/endpoint/windows_iis_components_module_failed_to_load.yml b/detections/endpoint/windows_iis_components_module_failed_to_load.yml
index a20726f025..8699412498 100644
--- a/detections/endpoint/windows_iis_components_module_failed_to_load.yml
+++ b/detections/endpoint/windows_iis_components_module_failed_to_load.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects when an IIS Module DLL fails to load due to a configuration problem, identified by EventCode 2282. This detection leverages Windows Application event logs to identify repeated failures in loading IIS modules. Such failures can indicate misconfigurations or potential tampering with IIS components. If confirmed malicious, this activity could lead to service disruptions or provide an attacker with opportunities to exploit vulnerabilities within the IIS environment. Immediate investigation is required to determine the legitimacy of the failing module and to mitigate any potential security risks.
+description: The following analytic detects when an IIS Module DLL fails to load due
+  to a configuration problem, identified by EventCode 2282. This detection leverages
+  Windows Application event logs to identify repeated failures in loading IIS modules.
+  Such failures can indicate misconfigurations or potential tampering with IIS components.
+  If confirmed malicious, this activity could lead to service disruptions or provide
+  an attacker with opportunities to exploit vulnerabilities within the IIS environment.
+  Immediate investigation is required to determine the legitimacy of the failing module
+  and to mitigate any potential security risks.
 data_source:
 - Windows Event Log Application 2282
-search: '`wineventlog_application` EventCode=2282 | stats  count min(_time) as firstTime max(_time) as lastTime by  EventCode dest Name ModuleDll | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_module_failed_to_load_filter`'
-how_to_implement: IIS must be installed and Application event logs must be collected in order to utilize this analytic.
-known_false_positives: False positives will be present until all module failures are resolved or reviewed.
+search: '`wineventlog_application` EventCode=2282 | stats  count min(_time) as firstTime
+  max(_time) as lastTime by  EventCode dest Name ModuleDll | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_iis_components_module_failed_to_load_filter`'
+how_to_implement: IIS must be installed and Application event logs must be collected
+  in order to utilize this analytic.
+known_false_positives: False positives will be present until all module failures are
+  resolved or reviewed.
 references:
 - https://social.technet.microsoft.com/wiki/contents/articles/21757.event-id-2282-iis-worker-process-availability.aspx
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
@@ -25,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OpCode
-  - EventCode
-  - ComputerName
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/2282_windows-application.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/2282_windows-application.log
     source: XmlWinEventLog:Application
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_iis_components_new_module_added.yml b/detections/endpoint/windows_iis_components_new_module_added.yml
index 8176e7b715..5cf4a933f4 100644
--- a/detections/endpoint/windows_iis_components_new_module_added.yml
+++ b/detections/endpoint/windows_iis_components_new_module_added.yml
@@ -5,12 +5,23 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of new IIS modules on a Windows IIS server. It leverages the Windows Event log - Microsoft-IIS-Configuration/Operational, specifically EventCode 29, to identify this activity. This behavior is significant because IIS modules are rarely added to production servers, and unauthorized modules could indicate malicious activity. If confirmed malicious, an attacker could use these modules to execute arbitrary code, escalate privileges, or maintain persistence within the environment, potentially compromising the server and sensitive data.
+description: The following analytic detects the addition of new IIS modules on a Windows
+  IIS server. It leverages the Windows Event log - Microsoft-IIS-Configuration/Operational,
+  specifically EventCode 29, to identify this activity. This behavior is significant
+  because IIS modules are rarely added to production servers, and unauthorized modules
+  could indicate malicious activity. If confirmed malicious, an attacker could use
+  these modules to execute arbitrary code, escalate privileges, or maintain persistence
+  within the environment, potentially compromising the server and sensitive data.
 data_source:
 - Windows IIS 29
-search: '`iis_operational_logs` EventCode=29 | stats  count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message | rename ComputerName AS dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_components_new_module_added_filter`'
-how_to_implement: You must enabled the IIS Configuration Operational log before ingesting in Splunk. Setup and inputs may be found here https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040.
-known_false_positives: False positives may be present when updates or an administrator adds a new module to IIS. Monitor and filter as needed.
+search: '`iis_operational_logs` EventCode=29 | stats  count min(_time) as firstTime
+  max(_time) as lastTime by OpCode EventCode ComputerName Message | rename ComputerName
+  AS dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_iis_components_new_module_added_filter`'
+how_to_implement: You must enabled the IIS Configuration Operational log before ingesting
+  in Splunk. Setup and inputs may be found here https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040.
+known_false_positives: False positives may be present when updates or an administrator
+  adds a new module to IIS. Monitor and filter as needed.
 references:
 - https://gist.github.com/MHaggis/64396dfd9fc3734e1d1901a8f2f07040
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
@@ -25,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OpCode
-  - EventCode
-  - ComputerName
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/IIS-Configuration-Operational.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/IIS-Configuration-Operational.log
     source: IIS:Configuration:Operational
     sourcetype: IIS:Configuration:Operational
diff --git a/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml b/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml
index 98a4a0add6..32893f80bb 100644
--- a/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml
+++ b/detections/endpoint/windows_impair_defense_add_xml_applocker_rules.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the use of a PowerShell commandlet to import an AppLocker XML policy. This behavior is identified by monitoring processes that execute the "Import-Module Applocker" and "Set-AppLockerPolicy" commands with the "-XMLPolicy" parameter. This activity is significant because it can indicate an attempt to disable or bypass security controls, as seen in the Azorult malware. If confirmed malicious, this could allow an attacker to disable antivirus products, leading to further compromise and persistence within the environment.
+description: The following analytic detects the use of a PowerShell commandlet to
+  import an AppLocker XML policy. This behavior is identified by monitoring processes
+  that execute the "Import-Module Applocker" and "Set-AppLockerPolicy" commands with
+  the "-XMLPolicy" parameter. This activity is significant because it can indicate
+  an attempt to disable or bypass security controls, as seen in the Azorult malware.
+  If confirmed malicious, this could allow an attacker to disable antivirus products,
+  leading to further compromise and persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` AND Processes.process="*Import-Module Applocker*" AND Processes.process="*Set-AppLockerPolicy *"  AND Processes.process="* -XMLPolicy *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_add_xml_applocker_rules_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may execute this command that may cause some false positive.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_powershell` AND Processes.process="*Import-Module Applocker*" AND
+  Processes.process="*Set-AppLockerPolicy *"  AND Processes.process="* -XMLPolicy
+  *" by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_add_xml_applocker_rules_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may execute this command that may cause some
+  false positive.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 tags:
@@ -34,18 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml b/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml
index 11919863a1..4f560ff96c 100644
--- a/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml
+++ b/detections/endpoint/windows_impair_defense_change_win_defender_health_check_intervals.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that change the health check interval of Windows Defender. It leverages data from the Endpoint datamodel, specifically monitoring changes to the "ServiceKeepAlive" registry path with a value of "0x00000001". This activity is significant because altering Windows Defender settings can impair its ability to perform timely health checks, potentially leaving the system vulnerable. If confirmed malicious, this could allow an attacker to disable or delay security scans, increasing the risk of undetected malware or other malicious activities.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\ServiceKeepAlive" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_health_check_intervals_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that change the health check interval of Windows Defender. It leverages data from
+  the Endpoint datamodel, specifically monitoring changes to the "ServiceKeepAlive"
+  registry path with a value of "0x00000001". This activity is significant because
+  altering Windows Defender settings can impair its ability to perform timely health
+  checks, potentially leaving the system vulnerable. If confirmed malicious, this
+  could allow an attacker to disable or delay security scans, increasing the risk
+  of undetected malware or other malicious activities.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\ServiceKeepAlive" Registry.registry_value_data="0x00000001" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_health_check_intervals_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml b/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml
index 3c810683d5..790f5c90e6 100644
--- a/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml
+++ b/detections/endpoint/windows_impair_defense_change_win_defender_quick_scan_interval.yml
@@ -8,10 +8,26 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that change the Windows Defender Quick Scan Interval. It leverages data from the Endpoint.Registry data model, focusing on changes to the "QuickScanInterval" registry path. This activity is significant because altering the scan interval can impair Windows Defender's ability to detect malware promptly, potentially allowing threats to persist undetected. If confirmed malicious, this modification could enable attackers to bypass security measures, maintain persistence, and execute further malicious activities without being detected by quick scans.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Scan\\QuickScanInterval" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_quick_scan_interval_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that change the Windows Defender Quick Scan Interval. It leverages data from the
+  Endpoint.Registry data model, focusing on changes to the "QuickScanInterval" registry
+  path. This activity is significant because altering the scan interval can impair
+  Windows Defender's ability to detect malware promptly, potentially allowing threats
+  to persist undetected. If confirmed malicious, this modification could enable attackers
+  to bypass security measures, maintain persistence, and execute further malicious
+  activities without being detected by quick scans.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Scan\\QuickScanInterval" by Registry.registry_key_name Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_quick_scan_interval_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml b/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml
index b9f07da267..9b22a43200 100644
--- a/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml
+++ b/detections/endpoint/windows_impair_defense_change_win_defender_throttle_rate.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the ThrottleDetectionEventsRate registry setting in Windows Defender. It leverages data from the Endpoint.Registry datamodel to identify changes in the registry path related to Windows Defender's event logging rate. This activity is significant because altering the ThrottleDetectionEventsRate can reduce the frequency of logged detection events, potentially masking malicious activities. If confirmed malicious, this could allow an attacker to evade detection by decreasing the visibility of security events, thereby hindering incident response and forensic investigations.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\NIS\\Consumers\\IPS\\ThrottleDetectionEventsRate" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_throttle_rate_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the ThrottleDetectionEventsRate
+  registry setting in Windows Defender. It leverages data from the Endpoint.Registry
+  datamodel to identify changes in the registry path related to Windows Defender's
+  event logging rate. This activity is significant because altering the ThrottleDetectionEventsRate
+  can reduce the frequency of logged detection events, potentially masking malicious
+  activities. If confirmed malicious, this could allow an attacker to evade detection
+  by decreasing the visibility of security events, thereby hindering incident response
+  and forensic investigations.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\NIS\\Consumers\\IPS\\ThrottleDetectionEventsRate" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_throttle_rate_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml b/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml
index f9f8238599..eea7e59f40 100644
--- a/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml
+++ b/detections/endpoint/windows_impair_defense_change_win_defender_tracing_level.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry specifically targeting the "WppTracingLevel" setting within Windows Defender. This detection leverages data from the Endpoint.Registry data model to identify changes in the registry path associated with Windows Defender tracing levels. Such modifications are significant as they can impair the diagnostic capabilities of Windows Defender, potentially hiding malicious activities. If confirmed malicious, this activity could allow an attacker to evade detection and maintain persistence within the environment, leading to further compromise and data exfiltration.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Reporting\\WppTracingLevel" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_tracing_level_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  specifically targeting the "WppTracingLevel" setting within Windows Defender. This
+  detection leverages data from the Endpoint.Registry data model to identify changes
+  in the registry path associated with Windows Defender tracing levels. Such modifications
+  are significant as they can impair the diagnostic capabilities of Windows Defender,
+  potentially hiding malicious activities. If confirmed malicious, this activity could
+  allow an attacker to evade detection and maintain persistence within the environment,
+  leading to further compromise and data exfiltration.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Reporting\\WppTracingLevel" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_change_win_defender_tracing_level_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_configure_app_install_control.yml b/detections/endpoint/windows_impair_defense_configure_app_install_control.yml
index 1a9d3fe1ec..ea93b660eb 100644
--- a/detections/endpoint/windows_impair_defense_configure_app_install_control.yml
+++ b/detections/endpoint/windows_impair_defense_configure_app_install_control.yml
@@ -16,11 +16,11 @@ description: The following analytic detects modifications to the Windows registr
   increasing the risk of security vulnerabilities. If confirmed malicious, this action
   could lead to the installation of harmful applications, potentially compromising
   the system and exposing sensitive information.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\Microsoft\\Windows Defender\\SmartScreen\\ConfigureAppInstallControl"
-  Registry.registry_value_data= "Anywhere") OR (Registry.registry_path= "*\\Microsoft\\Windows
-  Defender\\SmartScreen\\ConfigureAppInstallControlEnabled" Registry.registry_value_data=
-  "0x00000000") BY Registry.dest Registry.user Registry.registry_path
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows
+  Defender\\SmartScreen\\ConfigureAppInstallControl" Registry.registry_value_data=
+  "Anywhere") OR (Registry.registry_path= "*\\Microsoft\\Windows Defender\\SmartScreen\\ConfigureAppInstallControlEnabled"
+  Registry.registry_value_data= "0x00000000") BY Registry.dest Registry.user Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `windows_impair_defense_configure_app_install_control_filter`'
@@ -40,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Define Windows Defender App Install Control registry set to disable on $dest$.
+  message: Define Windows Defender App Install Control registry set to disable on
+    $dest$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -63,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml b/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml
index 737a37b0f5..4ce6de13d6 100644
--- a/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml
+++ b/detections/endpoint/windows_impair_defense_define_win_defender_threat_action.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows Defender ThreatSeverityDefaultAction registry setting. It leverages data from the Endpoint.Registry datamodel to identify changes in registry values that define how Windows Defender responds to threats. This activity is significant because altering these settings can impair the system's defense mechanisms, potentially allowing threats to go unaddressed. If confirmed malicious, this could enable attackers to bypass antivirus protections, leading to persistent threats and increased risk of data compromise or further system exploitation.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Threats\\ThreatSeverityDefaultAction*" Registry.registry_value_data IN ("0x00000001", "9") by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_define_win_defender_threat_action_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows Defender
+  ThreatSeverityDefaultAction registry setting. It leverages data from the Endpoint.Registry
+  datamodel to identify changes in registry values that define how Windows Defender
+  responds to threats. This activity is significant because altering these settings
+  can impair the system's defense mechanisms, potentially allowing threats to go unaddressed.
+  If confirmed malicious, this could enable attackers to bypass antivirus protections,
+  leading to persistent threats and increased risk of data compromise or further system
+  exploitation.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Threats\\ThreatSeverityDefaultAction*" Registry.registry_value_data IN
+  ("0x00000001", "9") by Registry.registry_key_name Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_define_win_defender_threat_action_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml b/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml
index 91d87fcfb9..d9741fd05f 100644
--- a/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml
+++ b/detections/endpoint/windows_impair_defense_delete_win_defender_context_menu.yml
@@ -5,13 +5,30 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the deletion of the Windows Defender context menu entry from the registry. It leverages data from the Endpoint datamodel, specifically monitoring registry actions where the path includes "*\\shellex\\ContextMenuHandlers\\EPP" and the action is 'deleted'. This activity is significant as it is commonly associated with Remote Access Trojan (RAT) malware attempting to disable security features. If confirmed malicious, this could allow an attacker to impair defenses, facilitating further malicious activities such as unauthorized access, persistence, and data exfiltration.
+description: The following analytic detects the deletion of the Windows Defender context
+  menu entry from the registry. It leverages data from the Endpoint datamodel, specifically
+  monitoring registry actions where the path includes "*\\shellex\\ContextMenuHandlers\\EPP"
+  and the action is 'deleted'. This activity is significant as it is commonly associated
+  with Remote Access Trojan (RAT) malware attempting to disable security features.
+  If confirmed malicious, this could allow an attacker to impair defenses, facilitating
+  further malicious activities such as unauthorized access, persistence, and data
+  exfiltration.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\shellex\\ContextMenuHandlers\\EPP" Registry.action = deleted by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_context_menu_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\shellex\\ContextMenuHandlers\\EPP"
+  Registry.action = deleted by  Registry.registry_path Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.action Registry.dest
+  Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_context_menu_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
 - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/
@@ -35,18 +52,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml b/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml
index 83ea02b03d..a34fea7561 100644
--- a/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml
+++ b/detections/endpoint/windows_impair_defense_delete_win_defender_profile_registry.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of the Windows Defender main profile registry key. It leverages data from the Endpoint.Registry datamodel, specifically monitoring for deleted actions within the Windows Defender registry path. This activity is significant as it indicates potential tampering with security defenses, often associated with Remote Access Trojans (RATs) and other malware. If confirmed malicious, this action could allow an attacker to disable Windows Defender, reducing the system's ability to detect and respond to further malicious activities, thereby compromising endpoint security.
+description: The following analytic detects the deletion of the Windows Defender main
+  profile registry key. It leverages data from the Endpoint.Registry datamodel, specifically
+  monitoring for deleted actions within the Windows Defender registry path. This activity
+  is significant as it indicates potential tampering with security defenses, often
+  associated with Remote Access Trojans (RATs) and other malware. If confirmed malicious,
+  this action could allow an attacker to disable Windows Defender, reducing the system's
+  ability to detect and respond to further malicious activities, thereby compromising
+  endpoint security.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows Defender" Registry.action = deleted by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action  Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_profile_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Policies\\Microsoft\\Windows
+  Defender" Registry.action = deleted by  Registry.registry_path Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.action  Registry.user
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_delete_win_defender_profile_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
 - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/delete_win_defender_context_menu/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml b/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml
index dcaf0cb4bb..71375a5d69 100644
--- a/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml
+++ b/detections/endpoint/windows_impair_defense_deny_security_software_with_applocker.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications in the Windows registry by the Applocker utility that deny the execution of various security products. This detection leverages data from the Endpoint.Registry datamodel, focusing on specific registry paths and values indicating a "Deny" action against known antivirus and security software. This activity is significant as it may indicate an attempt to disable security defenses, a tactic observed in malware like Azorult. If confirmed malicious, this could allow attackers to bypass security measures, facilitating further malicious activities and persistence within the environment.
+description: The following analytic detects modifications in the Windows registry
+  by the Applocker utility that deny the execution of various security products. This
+  detection leverages data from the Endpoint.Registry datamodel, focusing on specific
+  registry paths and values indicating a "Deny" action against known antivirus and
+  security software. This activity is significant as it may indicate an attempt to
+  disable security defenses, a tactic observed in malware like Azorult. If confirmed
+  malicious, this could allow attackers to bypass security measures, facilitating
+  further malicious activities and persistence within the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Group Policy Objects\\*" AND Registry.registry_path= "*}Machine\\Software\\Policies\\Microsoft\\Windows\\SrpV2*") OR Registry.registry_path="*\\Software\\Policies\\Microsoft\\Windows\\SrpV2*" AND Registry.registry_value_data = "*Action\=\"Deny\"*" AND Registry.registry_value_data IN("*O=SYMANTEC*","*O=MCAFEE*","*O=KASPERSKY*","*O=BLEEPING COMPUTER*", "*O=PANDA SECURITY*","*O=SYSTWEAK SOFTWARE*", "*O=TREND MICRO*", "*O=AVAST*", "*O=GRIDINSOFT*", "*O=MICROSOFT*", "*O=NANO SECURITY*", "*O=SUPERANTISPYWARE.COM*", "*O=DOCTOR WEB*", "*O=MALWAREBYTES*", "*O=ESET*", "*O=AVIRA*", "*O=WEBROOT*") by  Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.registry_key_name Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_deny_security_software_with_applocker_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: False positives may be present based on organization use of Applocker. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Group
+  Policy Objects\\*" AND Registry.registry_path= "*}Machine\\Software\\Policies\\Microsoft\\Windows\\SrpV2*")
+  OR Registry.registry_path="*\\Software\\Policies\\Microsoft\\Windows\\SrpV2*" AND
+  Registry.registry_value_data = "*Action\=\"Deny\"*" AND Registry.registry_value_data
+  IN("*O=SYMANTEC*","*O=MCAFEE*","*O=KASPERSKY*","*O=BLEEPING COMPUTER*", "*O=PANDA
+  SECURITY*","*O=SYSTWEAK SOFTWARE*", "*O=TREND MICRO*", "*O=AVAST*", "*O=GRIDINSOFT*",
+  "*O=MICROSOFT*", "*O=NANO SECURITY*", "*O=SUPERANTISPYWARE.COM*", "*O=DOCTOR WEB*",
+  "*O=MALWAREBYTES*", "*O=ESET*", "*O=AVIRA*", "*O=WEBROOT*") by  Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.action Registry.registry_key_name Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_deny_security_software_with_applocker_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: False positives may be present based on organization use of
+  Applocker. Filter as needed.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 - https://www.microsoftpressstore.com/articles/article.aspx?p=2228450&seqNum=11
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: Applocker registry modification to deny the action of several AV products on $dest$.
+  message: Applocker registry modification to deny the action of several AV products
+    on $dest$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -43,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml b/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml
index 875e151671..27e374d16d 100644
--- a/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml
+++ b/detections/endpoint/windows_impair_defense_disable_controlled_folder_access.yml
@@ -8,10 +8,28 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a modification in the Windows registry that disables the Windows Defender Controlled Folder Access feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the EnableControlledFolderAccess registry setting. This activity is significant because Controlled Folder Access is designed to protect critical folders from unauthorized access, including ransomware attacks. If this activity is confirmed malicious, it could allow attackers to bypass a key security feature, potentially leading to unauthorized access or modification of sensitive files.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Windows Defender Exploit Guard\\Controlled Folder Access\\EnableControlledFolderAccess" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_controlled_folder_access_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects a modification in the Windows registry
+  that disables the Windows Defender Controlled Folder Access feature. It leverages
+  data from the Endpoint.Registry data model, specifically monitoring changes to the
+  EnableControlledFolderAccess registry setting. This activity is significant because
+  Controlled Folder Access is designed to protect critical folders from unauthorized
+  access, including ransomware attacks. If this activity is confirmed malicious, it
+  could allow attackers to bypass a key security feature, potentially leading to unauthorized
+  access or modification of sensitive files.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Windows Defender Exploit Guard\\Controlled Folder Access\\EnableControlledFolderAccess"
+  Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_disable_controlled_folder_access_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml b/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml
index 2354e77431..d75c43d8db 100644
--- a/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml
+++ b/detections/endpoint/windows_impair_defense_disable_defender_firewall_and_network.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications in the Windows registry to disable firewall and network protection settings within Windows Defender Security Center. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the UILockdown registry value. This activity is significant as it may indicate an attempt to impair system defenses, potentially restricting users from modifying firewall or network protection settings. If confirmed malicious, this could allow an attacker to weaken the system's security posture, making it more vulnerable to further attacks and unauthorized access.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender Security Center\\Firewall and network protection\\UILockdown" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_defender_firewall_and_network_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications in the Windows registry
+  to disable firewall and network protection settings within Windows Defender Security
+  Center. It leverages data from the Endpoint.Registry data model, specifically monitoring
+  changes to the UILockdown registry value. This activity is significant as it may
+  indicate an attempt to impair system defenses, potentially restricting users from
+  modifying firewall or network protection settings. If confirmed malicious, this
+  could allow an attacker to weaken the system's security posture, making it more
+  vulnerable to further attacks and unauthorized access.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender Security Center\\Firewall and network protection\\UILockdown" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_defender_firewall_and_network_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Windows Defender firewall and network protection section feature set to disable on $dest$.
+  message: Windows Defender firewall and network protection section feature set to
+    disable on $dest$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml b/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml
index 60fcf1c2db..ba0deeaaa3 100644
--- a/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml
+++ b/detections/endpoint/windows_impair_defense_disable_defender_protocol_recognition.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable the Windows Defender protocol recognition feature. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the "DisableProtocolRecognition" setting. This activity is significant because disabling protocol recognition can hinder Windows Defender's ability to detect and respond to malware or suspicious software. If confirmed malicious, this action could allow an attacker to bypass antivirus defenses, facilitating further malicious activities such as data exfiltration or system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\NIS\\DisableProtocolRecognition" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_defender_protocol_recognition_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable the Windows Defender protocol recognition feature. It leverages data
+  from the Endpoint.Registry data model, specifically looking for changes to the "DisableProtocolRecognition"
+  setting. This activity is significant because disabling protocol recognition can
+  hinder Windows Defender's ability to detect and respond to malware or suspicious
+  software. If confirmed malicious, this action could allow an attacker to bypass
+  antivirus defenses, facilitating further malicious activities such as data exfiltration
+  or system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\NIS\\DisableProtocolRecognition" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_defender_protocol_recognition_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_pua_protection.yml b/detections/endpoint/windows_impair_defense_disable_pua_protection.yml
index 19e44c7bda..dd018ad4bb 100644
--- a/detections/endpoint/windows_impair_defense_disable_pua_protection.yml
+++ b/detections/endpoint/windows_impair_defense_disable_pua_protection.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a modification in the Windows registry to disable Windows Defender PUA protection by setting PUAProtection to 0. This detection leverages data from the Endpoint.Registry datamodel, focusing on registry path changes related to Windows Defender. Disabling PUA protection is significant as it reduces defenses against Potentially Unwanted Applications (PUAs), which, while not always malicious, can negatively impact user experience and security. If confirmed malicious, this activity could allow an attacker to introduce adware, browser toolbars, or other unwanted software, potentially compromising system integrity and user productivity.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\PUAProtection" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_pua_protection_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects a modification in the Windows registry
+  to disable Windows Defender PUA protection by setting PUAProtection to 0. This detection
+  leverages data from the Endpoint.Registry datamodel, focusing on registry path changes
+  related to Windows Defender. Disabling PUA protection is significant as it reduces
+  defenses against Potentially Unwanted Applications (PUAs), which, while not always
+  malicious, can negatively impact user experience and security. If confirmed malicious,
+  this activity could allow an attacker to introduce adware, browser toolbars, or
+  other unwanted software, potentially compromising system integrity and user productivity.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\PUAProtection" Registry.registry_value_data="0x00000000" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_pua_protection_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml b/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml
index 6adaf8235b..e5def1ca75 100644
--- a/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml
+++ b/detections/endpoint/windows_impair_defense_disable_realtime_signature_delivery.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable the Windows Defender real-time signature delivery feature. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path associated with Windows Defender signature updates. This activity is significant because disabling real-time signature delivery can prevent Windows Defender from receiving timely malware definitions, reducing its effectiveness. If confirmed malicious, this action could allow attackers to bypass malware detection, leading to potential system compromise and persistent threats.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Signature Updates\\RealtimeSignatureDelivery" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_realtime_signature_delivery_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable the Windows Defender real-time signature delivery feature. It leverages
+  data from the Endpoint.Registry data model, specifically monitoring changes to the
+  registry path associated with Windows Defender signature updates. This activity
+  is significant because disabling real-time signature delivery can prevent Windows
+  Defender from receiving timely malware definitions, reducing its effectiveness.
+  If confirmed malicious, this action could allow attackers to bypass malware detection,
+  leading to potential system compromise and persistent threats.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Signature Updates\\RealtimeSignatureDelivery" Registry.registry_value_data="0x00000000"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_realtime_signature_delivery_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml b/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml
index 0f772f2bd6..489ac6ade4 100644
--- a/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml
+++ b/detections/endpoint/windows_impair_defense_disable_web_evaluation.yml
@@ -16,12 +16,12 @@ description: The following analytic detects modifications to the Windows registr
   malicious web content to bypass security checks. If confirmed malicious, this could
   lead to users interacting with harmful scripts or unsafe web elements, increasing
   the risk of system exploitation and security breaches.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE Registry.registry_path= "*\\Windows\\CurrentVersion\\AppHost\\EnableWebContentEvaluation"  Registry.registry_value_data=
-  "0x00000000" BY Registry.dest Registry.user Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_web_evaluation_filter`'
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Windows\\CurrentVersion\\AppHost\\EnableWebContentEvaluation"  Registry.registry_value_data=
+  "0x00000000" BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_disable_web_evaluation_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
   your endpoints into the `Endpoint` datamodel in the `Registry` node.
@@ -38,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -61,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml
index 307d13e532..80c042dba3 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_app_guard.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable Windows Defender Application Guard auditing. It leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant because disabling auditing can hinder security monitoring and threat detection within the isolated environment, making it easier for malicious activities to go unnoticed. If confirmed malicious, this action could allow attackers to bypass Windows Defender protections, potentially leading to unauthorized access, data exfiltration, or further system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Policies\\Microsoft\\AppHVSI\\AuditApplicationGuard" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_app_guard_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable Windows Defender Application Guard auditing. It leverages data from
+  the Endpoint.Registry data model, focusing on specific registry paths and values.
+  This activity is significant because disabling auditing can hinder security monitoring
+  and threat detection within the isolated environment, making it easier for malicious
+  activities to go unnoticed. If confirmed malicious, this action could allow attackers
+  to bypass Windows Defender protections, potentially leading to unauthorized access,
+  data exfiltration, or further system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Policies\\Microsoft\\AppHVSI\\AuditApplicationGuard"
+  Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_disable_win_defender_app_guard_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml
index f80f278871..eaf5527c30 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_compute_file_hashes.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable Windows Defender's file hash computation by setting the EnableFileHashComputation value to 0. This detection leverages data from the Endpoint.Registry data model, focusing on changes to the specific registry path associated with Windows Defender. Disabling file hash computation can significantly impair Windows Defender's ability to detect and scan for malware, making it a critical behavior to monitor. If confirmed malicious, this activity could allow attackers to bypass Windows Defender, facilitating undetected malware execution and persistence in the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\MpEngine\\EnableFileHashComputation" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_compute_file_hashes_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable Windows Defender's file hash computation by setting the EnableFileHashComputation
+  value to 0. This detection leverages data from the Endpoint.Registry data model,
+  focusing on changes to the specific registry path associated with Windows Defender.
+  Disabling file hash computation can significantly impair Windows Defender's ability
+  to detect and scan for malware, making it a critical behavior to monitor. If confirmed
+  malicious, this activity could allow attackers to bypass Windows Defender, facilitating
+  undetected malware execution and persistence in the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\MpEngine\\EnableFileHashComputation" Registry.registry_value_data="0x00000000"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_compute_file_hashes_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml
index bd2118d25e..134218ebb3 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_gen_reports.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications in the Windows registry to disable Windows Defender generic reports. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "DisableGenericRePorts" registry value. This activity is significant as it can prevent the transmission of error reports to Microsoft's Windows Error Reporting service, potentially hiding malicious activities. If confirmed malicious, this action could allow attackers to bypass Windows Defender detections, reducing the visibility of their activities and increasing the risk of undetected system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Reporting\\DisableGenericRePorts" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_gen_reports_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications in the Windows registry
+  to disable Windows Defender generic reports. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the "DisableGenericRePorts" registry
+  value. This activity is significant as it can prevent the transmission of error
+  reports to Microsoft's Windows Error Reporting service, potentially hiding malicious
+  activities. If confirmed malicious, this action could allow attackers to bypass
+  Windows Defender detections, reducing the visibility of their activities and increasing
+  the risk of undetected system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Reporting\\DisableGenericRePorts" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_gen_reports_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml
index 61cf44a228..a8aea7ab43 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_network_protection.yml
@@ -8,10 +8,28 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable Windows Defender Network Protection. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the EnableNetworkProtection registry entry. This activity is significant because disabling Network Protection can leave the system vulnerable to network-based threats by preventing Windows Defender from analyzing and blocking malicious network activity. If confirmed malicious, this action could allow attackers to bypass security measures, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Windows Defender Exploit Guard\\Network Protection\\EnableNetworkProtection" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_network_protection_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable Windows Defender Network Protection. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the EnableNetworkProtection registry
+  entry. This activity is significant because disabling Network Protection can leave
+  the system vulnerable to network-based threats by preventing Windows Defender from
+  analyzing and blocking malicious network activity. If confirmed malicious, this
+  action could allow attackers to bypass security measures, potentially leading to
+  unauthorized access, data exfiltration, or further compromise of the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Windows Defender Exploit Guard\\Network Protection\\EnableNetworkProtection"
+  Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_disable_win_defender_network_protection_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml
index 7ec7078d44..e683ff85f4 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_report_infection.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable Windows Defender's infection reporting. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "DontReportInfectionInformation" registry key. This activity is significant because it can prevent Windows Defender from reporting detailed threat information to Microsoft, potentially allowing malware to evade detection. If confirmed malicious, this action could enable attackers to bypass security measures, maintain persistence, and avoid detection, leading to prolonged unauthorized access and potential data breaches.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\MRT\\DontReportInfectionInformation" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_report_infection_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable Windows Defender's infection reporting. It leverages data from the
+  Endpoint.Registry datamodel, specifically monitoring changes to the "DontReportInfectionInformation"
+  registry key. This activity is significant because it can prevent Windows Defender
+  from reporting detailed threat information to Microsoft, potentially allowing malware
+  to evade detection. If confirmed malicious, this action could enable attackers to
+  bypass security measures, maintain persistence, and avoid detection, leading to
+  prolonged unauthorized access and potential data breaches.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\MRT\\DontReportInfectionInformation"
+  Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_disable_win_defender_report_infection_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Windows Defender DontReportInfectionInformation registry is enabled on $dest$.
+  message: Windows Defender DontReportInfectionInformation registry is enabled on
+    $dest$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml
index d762a189ed..d56f2d8cec 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_scan_on_update.yml
@@ -8,10 +8,26 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable the Windows Defender Scan On Update feature. It leverages data from the Endpoint.Registry datamodel, specifically looking for changes to the "DisableScanOnUpdate" registry setting with a value of "0x00000001". This activity is significant because disabling automatic scans can leave systems vulnerable to malware and other threats. If confirmed malicious, this action could allow attackers to bypass Windows Defender, facilitating further compromise and persistence within the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Signature Updates\\DisableScanOnUpdate" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_scan_on_update_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable the Windows Defender Scan On Update feature. It leverages data from
+  the Endpoint.Registry datamodel, specifically looking for changes to the "DisableScanOnUpdate"
+  registry setting with a value of "0x00000001". This activity is significant because
+  disabling automatic scans can leave systems vulnerable to malware and other threats.
+  If confirmed malicious, this action could allow attackers to bypass Windows Defender,
+  facilitating further compromise and persistence within the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Signature Updates\\DisableScanOnUpdate" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_scan_on_update_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml b/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml
index 9e46e80641..47de5edda7 100644
--- a/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml
+++ b/detections/endpoint/windows_impair_defense_disable_win_defender_signature_retirement.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable Windows Defender Signature Retirement. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the DisableSignatureRetirement registry setting. This activity is significant because disabling signature retirement can prevent Windows Defender from removing outdated antivirus signatures, potentially reducing its effectiveness in detecting threats. If confirmed malicious, this action could allow an attacker to evade detection by using older, less relevant signatures, thereby compromising the system's security posture.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\NIS\\Consumers\\IPS\\DisableSignatureRetirement" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_signature_retirement_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable Windows Defender Signature Retirement. It leverages data from the Endpoint.Registry
+  data model, specifically monitoring changes to the DisableSignatureRetirement registry
+  setting. This activity is significant because disabling signature retirement can
+  prevent Windows Defender from removing outdated antivirus signatures, potentially
+  reducing its effectiveness in detecting threats. If confirmed malicious, this action
+  could allow an attacker to evade detection by using older, less relevant signatures,
+  thereby compromising the system's security posture.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\NIS\\Consumers\\IPS\\DisableSignatureRetirement" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_disable_win_defender_signature_retirement_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: Windows Defender DisableSignatureRetirement registry is set to enable on $dest$.
+  message: Windows Defender DisableSignatureRetirement registry is set to enable on
+    $dest$.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml b/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml
index fece49084d..0a5cd53b1d 100644
--- a/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml
+++ b/detections/endpoint/windows_impair_defense_overide_win_defender_phishing_filter.yml
@@ -8,10 +8,28 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable the Windows Defender phishing filter. It leverages data from the Endpoint.Registry data model, focusing on changes to specific registry values related to Microsoft Edge's phishing filter settings. This activity is significant because disabling the phishing filter can allow attackers to deceive users into visiting malicious websites without triggering browser warnings. If confirmed malicious, this could lead to users unknowingly accessing harmful sites, resulting in potential security incidents or data compromises.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_key_name = "*\\MicrosoftEdge\\PhishingFilter" Registry.registry_value_name IN ("EnabledV9", "PreventOverride") Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_overide_win_defender_phishing_filter_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that disable the Windows Defender phishing filter. It leverages data from the Endpoint.Registry
+  data model, focusing on changes to specific registry values related to Microsoft
+  Edge's phishing filter settings. This activity is significant because disabling
+  the phishing filter can allow attackers to deceive users into visiting malicious
+  websites without triggering browser warnings. If confirmed malicious, this could
+  lead to users unknowingly accessing harmful sites, resulting in potential security
+  incidents or data compromises.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_key_name =
+  "*\\MicrosoftEdge\\PhishingFilter" Registry.registry_value_name IN ("EnabledV9",
+  "PreventOverride") Registry.registry_value_data="0x00000000" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defense_overide_win_defender_phishing_filter_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml b/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml
index 08d64550c1..7ae79eb4b5 100644
--- a/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml
+++ b/detections/endpoint/windows_impair_defense_override_smartscreen_prompt.yml
@@ -16,10 +16,10 @@ description: The following analytic detects modifications to the Windows registr
   users to bypass security warnings. If confirmed malicious, this could lead to users
   inadvertently executing or accessing harmful content, increasing the risk of security
   incidents or system compromises.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE Registry.registry_path= "*\\Microsoft\\Edge\\PreventSmartScreenPromptOverride"
-  Registry.registry_value_data= "0x00000000" BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\Microsoft\\Edge\\PreventSmartScreenPromptOverride"
+  Registry.registry_value_data= "0x00000000" BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `windows_impair_defense_override_smartscreen_prompt_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
@@ -38,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -61,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml b/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml
index 508132648d..6922771d4b 100644
--- a/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml
+++ b/detections/endpoint/windows_impair_defense_set_win_defender_smart_screen_level_to_warn.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that set the Windows Defender SmartScreen level to "warn." This detection leverages data from the Endpoint.Registry data model, specifically monitoring changes to the ShellSmartScreenLevel registry value. This activity is significant because altering SmartScreen settings to "warn" can reduce immediate suspicion from users, allowing potentially malicious executables to run with just a warning prompt. If confirmed malicious, this could enable attackers to execute harmful files, increasing the risk of successful malware deployment and subsequent system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\System\\ShellSmartScreenLevel" Registry.registry_value_data="Warn" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defense_set_win_defender_smart_screen_level_to_warn_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+description: The following analytic detects modifications to the Windows registry
+  that set the Windows Defender SmartScreen level to "warn." This detection leverages
+  data from the Endpoint.Registry data model, specifically monitoring changes to the
+  ShellSmartScreenLevel registry value. This activity is significant because altering
+  SmartScreen settings to "warn" can reduce immediate suspicion from users, allowing
+  potentially malicious executables to run with just a warning prompt. If confirmed
+  malicious, this could enable attackers to execute harmful files, increasing the
+  risk of successful malware deployment and subsequent system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\System\\ShellSmartScreenLevel"
+  Registry.registry_value_data="Warn" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_impair_defense_set_win_defender_smart_screen_level_to_warn_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://x.com/malmoeb/status/1742604217989415386?s=20
 - https://github.com/undergroundwires/privacy.sexy
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable-windows-security-defender-features/windefender-bypas-2-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml b/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml
index c52613d08f..05d06a3522 100644
--- a/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml
+++ b/detections/endpoint/windows_impair_defenses_disable_av_autostart_via_registry.yml
@@ -7,9 +7,28 @@ data_source:
 - Sysmon EventID 13
 type: TTP
 status: production
-description: The following analytic detects modifications to the registry related to the disabling of autostart functionality for certain antivirus products, such as Kingsoft and Tencent. Malware like ValleyRAT may alter specific registry keys to prevent these security tools from launching automatically at startup, thereby weakening system defenses. By monitoring changes in the registry entries associated with antivirus autostart settings, this detection enables security analysts to identify attempts to disable protective software. Detecting these modifications early is critical for maintaining system integrity and preventing further compromise by malicious actors.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path IN("*\\kingsoft\\antivirus\\KAVReport\\*" , "*\\kingsoft\\antivirus\\KSetting\\*", "*\\kingsoft\\antivirus\\Windhunter\\*" ,"*\\Tencent\\QQPCMgr\\*") AND ((Registry.registry_value_name IN("autostart","kxesc", "WindhunterSwitch") AND Registry.registry_value_data = "0x00000000") OR (Registry.registry_value_name = "WindhunterLevel" AND Registry.registry_value_data = "0x00000004")) BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.registry_hive Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_av_autostart_via_registry_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709
+description: The following analytic detects modifications to the registry related
+  to the disabling of autostart functionality for certain antivirus products, such
+  as Kingsoft and Tencent. Malware like ValleyRAT may alter specific registry keys
+  to prevent these security tools from launching automatically at startup, thereby
+  weakening system defenses. By monitoring changes in the registry entries associated
+  with antivirus autostart settings, this detection enables security analysts to identify
+  attempts to disable protective software. Detecting these modifications early is
+  critical for maintaining system integrity and preventing further compromise by malicious
+  actors.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE Registry.registry_path IN("*\\kingsoft\\antivirus\\KAVReport\\*" , "*\\kingsoft\\antivirus\\KSetting\\*",
+  "*\\kingsoft\\antivirus\\Windhunter\\*" ,"*\\Tencent\\QQPCMgr\\*") AND ((Registry.registry_value_name
+  IN("autostart","kxesc", "WindhunterSwitch") AND Registry.registry_value_data = "0x00000000")
+  OR (Registry.registry_value_name = "WindhunterLevel" AND Registry.registry_value_data
+  = "0x00000004")) BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.registry_hive
+  Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_av_autostart_via_registry_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the official
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
 known_false_positives: unknown
 references:
 - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/kingsoft_reg/kingsoft_reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/kingsoft_reg/kingsoft_reg.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defenses_disable_hvci.yml b/detections/endpoint/windows_impair_defenses_disable_hvci.yml
index d318c8a7c3..a07a84e59c 100644
--- a/detections/endpoint/windows_impair_defenses_disable_hvci.yml
+++ b/detections/endpoint/windows_impair_defenses_disable_hvci.yml
@@ -8,10 +8,26 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects the disabling of Hypervisor-protected Code Integrity (HVCI) by monitoring changes in the Windows registry. It leverages data from the Endpoint datamodel, specifically focusing on registry paths and values related to HVCI settings. This activity is significant because HVCI helps protect the kernel and system processes from tampering by malicious code. If confirmed malicious, disabling HVCI could allow attackers to execute unsigned kernel-mode code, potentially leading to kernel-level rootkits or other severe security breaches.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\CurrentControlSet\\Control\\DeviceGuard\\Scenarios\\HypervisorEnforcedCodeIntegrity\\Enabled" Registry.registry_value_data="0x00000000"  by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action  Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_hvci_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives will be limited to administrative scripts disabling HVCI. Filter as needed.
+description: The following analytic detects the disabling of Hypervisor-protected
+  Code Integrity (HVCI) by monitoring changes in the Windows registry. It leverages
+  data from the Endpoint datamodel, specifically focusing on registry paths and values
+  related to HVCI settings. This activity is significant because HVCI helps protect
+  the kernel and system processes from tampering by malicious code. If confirmed malicious,
+  disabling HVCI could allow attackers to execute unsigned kernel-mode code, potentially
+  leading to kernel-level rootkits or other severe security breaches.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\CurrentControlSet\\Control\\DeviceGuard\\Scenarios\\HypervisorEnforcedCodeIntegrity\\Enabled"
+  Registry.registry_value_data="0x00000000"  by  Registry.registry_path Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid Registry.action  Registry.user
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_hvci_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives will be limited to administrative scripts disabling
+  HVCI. Filter as needed.
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.registry_value_name
-  - Registry.process_guid
-  - Registry.action
-  - Registry.user
-  - Registry.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/hvci_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/hvci_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml b/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml
index 078c0484bf..88639ae621 100644
--- a/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml
+++ b/detections/endpoint/windows_impair_defenses_disable_win_defender_auto_logging.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the disabling of Windows Defender logging by identifying changes to the Registry keys DefenderApiLogger or DefenderAuditLogger set to disable. It leverages data from the Endpoint.Registry datamodel to monitor specific registry paths and values. This activity is significant as it is commonly associated with Remote Access Trojan (RAT) malware attempting to evade detection. If confirmed malicious, this action could allow an attacker to conceal their activities, making it harder to detect further malicious actions and maintain persistence on the compromised endpoint.
+description: The following analytic detects the disabling of Windows Defender logging
+  by identifying changes to the Registry keys DefenderApiLogger or DefenderAuditLogger
+  set to disable. It leverages data from the Endpoint.Registry datamodel to monitor
+  specific registry paths and values. This activity is significant as it is commonly
+  associated with Remote Access Trojan (RAT) malware attempting to evade detection.
+  If confirmed malicious, this action could allow an attacker to conceal their activities,
+  making it harder to detect further malicious actions and maintain persistence on
+  the compromised endpoint.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path = "*WMI\\Autologger\\DefenderApiLogger\\Start" OR Registry.registry_path = "*WMI\\Autologger\\DefenderAuditLogger\\Start")  Registry.registry_value_data ="0x00000000" by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.dest Registry.user | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_win_defender_auto_logging_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path = "*WMI\\Autologger\\DefenderApiLogger\\Start"
+  OR Registry.registry_path = "*WMI\\Autologger\\DefenderAuditLogger\\Start")  Registry.registry_value_data
+  ="0x00000000" by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid Registry.action Registry.dest Registry.user | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_impair_defenses_disable_win_defender_auto_logging_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: It is unusual to turn this feature off a Windows system since
+  it is a default security control, although it is not rare for some policies to disable
+  it. Although no false positives have been identified, use the provided filter macro
+  to tune the search.
 references:
 - https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
 - https://app.any.run/tasks/45f5d114-91ea-486c-ab01-41c4093d2861/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_logging/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/disable_defender_logging/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_indicator_removal_via_rmdir.yml b/detections/endpoint/windows_indicator_removal_via_rmdir.yml
index 26bec1b218..64b100b4ee 100644
--- a/detections/endpoint/windows_indicator_removal_via_rmdir.yml
+++ b/detections/endpoint/windows_indicator_removal_via_rmdir.yml
@@ -9,9 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of the 'rmdir' command with '/s' and '/q' options to delete files and directory trees. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. This activity is significant as it may indicate malware attempting to remove traces or components during cleanup operations. If confirmed malicious, this behavior could allow attackers to eliminate forensic evidence, hinder incident response efforts, and maintain persistence by removing indicators of compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*rmdir*" Processes.process = "* /s *" Processes.process = "* /q *" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indicator_removal_via_rmdir_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic detects the execution of the 'rmdir' command with
+  '/s' and '/q' options to delete files and directory trees. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions and process metadata. This activity is significant as it may indicate
+  malware attempting to remove traces or components during cleanup operations. If
+  confirmed malicious, this behavior could allow attackers to eliminate forensic evidence,
+  hinder incident response efforts, and maintain persistence by removing indicators
+  of compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*rmdir*"
+  Processes.process = "* /s *" Processes.process = "* /q *" by Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.process_guid
+  Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_indicator_removal_via_rmdir_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: user and network administrator can execute this command.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,25 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/rmdir_delete_files_and_dir/rmdir.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/rmdir_delete_files_and_dir/rmdir.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml b/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml
index a7e0cc7c3f..4970f9b2e2 100644
--- a/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml
+++ b/detections/endpoint/windows_indirect_command_execution_via_forfiles.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Eric McGinnis, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of programs initiated by forfiles.exe. This command is typically used to run commands on multiple files, often within batch scripts. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where forfiles.exe is the parent process. This activity is significant because forfiles.exe can be exploited to bypass command line execution protections, making it a potential vector for malicious activity. If confirmed malicious, this could allow attackers to execute arbitrary commands, potentially leading to unauthorized access or further system compromise.
+description: The following analytic detects the execution of programs initiated by
+  forfiles.exe. This command is typically used to run commands on multiple files,
+  often within batch scripts. The detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events where forfiles.exe
+  is the parent process. This activity is significant because forfiles.exe can be
+  exploited to bypass command line execution protections, making it a potential vector
+  for malicious activity. If confirmed malicious, this could allow attackers to execute
+  arbitrary commands, potentially leading to unauthorized access or further system
+  compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*forfiles* /c *" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_forfiles_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legacy applications may be run using pcalua.exe. Similarly, forfiles.exe may be used in legitimate batch scripts.  Filter these results as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*forfiles*
+  /c *" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_forfiles_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legacy applications may be run using pcalua.exe. Similarly,
+  forfiles.exe may be used in legitimate batch scripts.  Filter these results as needed.
 references:
 - https://twitter.com/KyleHanslovan/status/912659279806640128
 - https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/forfiles
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml b/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml
index 5b9c524db7..65042484b5 100644
--- a/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml
+++ b/detections/endpoint/windows_indirect_command_execution_via_pcalua.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Eric McGinnis, Splunk
 status: production
 type: TTP
-description: The following analytic detects programs initiated by pcalua.exe, the Microsoft Windows Program Compatibility Assistant. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process information. While pcalua.exe can start legitimate programs, it is significant because attackers may use it to bypass command line execution protections. If confirmed malicious, this activity could allow attackers to execute arbitrary commands, potentially leading to unauthorized actions, privilege escalation, or persistence within the environment.
+description: The following analytic detects programs initiated by pcalua.exe, the
+  Microsoft Windows Program Compatibility Assistant. This detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process and parent
+  process information. While pcalua.exe can start legitimate programs, it is significant
+  because attackers may use it to bypass command line execution protections. If confirmed
+  malicious, this activity could allow attackers to execute arbitrary commands, potentially
+  leading to unauthorized actions, privilege escalation, or persistence within the
+  environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*pcalua* -a*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_pcalua_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some legacy applications may be run using pcalua.exe.  Filter these results as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*pcalua*
+  -a*" by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_pcalua_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some legacy applications may be run using pcalua.exe.  Filter
+  these results as needed.
 references:
 - https://twitter.com/KyleHanslovan/status/912659279806640128
 - https://lolbas-project.github.io/lolbas/Binaries/Pcalua/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1202/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml b/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml
index 2d6ef5dc4a..5853feb30f 100644
--- a/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml
+++ b/detections/endpoint/windows_indirect_command_execution_via_series_of_forfiles.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects excessive usage of the forfiles.exe process, which is often indicative of post-exploitation activities. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include process GUID, process name, and parent process. This activity is significant because forfiles.exe can be abused to execute commands on multiple files, a technique used by ransomware like Prestige. If confirmed malicious, this behavior could allow attackers to enumerate files, potentially leading to data exfiltration or further malicious actions.
+description: The following analytic detects excessive usage of the forfiles.exe process,
+  which is often indicative of post-exploitation activities. The detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs that include process GUID, process name, and parent process. This activity
+  is significant because forfiles.exe can be abused to execute commands on multiple
+  files, a technique used by ransomware like Prestige. If confirmed malicious, this
+  behavior could allow attackers to enumerate files, potentially leading to data exfiltration
+  or further malicious actions.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_guid) as process_guid  values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "forfiles.exe" OR Processes.original_file_name = "forfiles.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m | where count >=20 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_series_of_forfiles_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  values(Processes.process_guid) as process_guid  values(Processes.process_name) as
+  process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes
+  where Processes.process_name = "forfiles.exe" OR Processes.original_file_name =
+  "forfiles.exe" by Processes.parent_process_name Processes.parent_process Processes.dest
+  Processes.user _time span=1m | where count >=20 | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_indirect_command_execution_via_series_of_forfiles_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/forfiles
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_information_discovery_fsutil.yml b/detections/endpoint/windows_information_discovery_fsutil.yml
index 3fd12c63a5..f962ea7095 100644
--- a/detections/endpoint/windows_information_discovery_fsutil.yml
+++ b/detections/endpoint/windows_information_discovery_fsutil.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the execution of the Windows built-in tool FSUTIL with the FSINFO parameter to discover file system information. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. Monitoring this activity is significant because FSUTIL can be abused by adversaries to gather detailed information about the file system, aiding in further exploitation. If confirmed malicious, this activity could enable attackers to map the file system, identify valuable data, and plan subsequent actions such as privilege escalation or persistence.
+description: The following analytic identifies the execution of the Windows built-in
+  tool FSUTIL with the FSINFO parameter to discover file system information. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process execution logs that include command-line details. Monitoring this activity
+  is significant because FSUTIL can be abused by adversaries to gather detailed information
+  about the file system, aiding in further exploitation. If confirmed malicious, this
+  activity could enable attackers to map the file system, identify valuable data,
+  and plan subsequent actions such as privilege escalation or persistence.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="fsutil.exe" OR Processes.original_file_name = "fsutil.exe" AND Processes.process = "*fsinfo*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_information_discovery_fsutil_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="fsutil.exe"
+  OR Processes.original_file_name = "fsutil.exe" AND Processes.process = "*fsinfo*"
+  by Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_information_discovery_fsutil_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/fsutil
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,25 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_fsutil/fsutil-fsinfo-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_fsutil/fsutil-fsinfo-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml b/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml
index f01f30bc45..43d3b7ec2f 100644
--- a/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml
+++ b/detections/endpoint/windows_ingress_tool_transfer_using_explorer.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies instances where the Windows Explorer process (explorer.exe) is executed with a URL in its command line. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because adversaries, such as those using DCRat malware, may abuse explorer.exe to open URLs with the default browser, which is an uncommon and suspicious behavior. If confirmed malicious, this technique could allow attackers to download and execute malicious payloads, leading to potential system compromise and further malicious activities.
+description: The following analytic identifies instances where the Windows Explorer
+  process (explorer.exe) is executed with a URL in its command line. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  execution logs. This activity is significant because adversaries, such as those
+  using DCRat malware, may abuse explorer.exe to open URLs with the default browser,
+  which is an uncommon and suspicious behavior. If confirmed malicious, this technique
+  could allow attackers to download and execute malicious payloads, leading to potential
+  system compromise and further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = explorer.exe OR Processes.original_file_name = explorer.exe) AND NOT (Processes.parent_process_name IN("userinit.exe", "svchost.exe")) Processes.process IN ("* http://*", "* https://*") by  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ingress_tool_transfer_using_explorer_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present based on legitimate applications or third party utilities. Filter out any additional parent process names.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = explorer.exe
+  OR Processes.original_file_name = explorer.exe) AND NOT (Processes.parent_process_name
+  IN("userinit.exe", "svchost.exe")) Processes.process IN ("* http://*", "* https://*")
+  by  Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_ingress_tool_transfer_using_explorer_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present based on legitimate applications
+  or third party utilities. Filter out any additional parent process names.
 references:
 - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a remote payload.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to download a remote payload.
   mitre_attack_id:
   - T1105
   observable:
@@ -54,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_explorer_url/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_explorer_url/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_inprocserver32_new_outlook_form.yml b/detections/endpoint/windows_inprocserver32_new_outlook_form.yml
index 99c2dbe7eb..bb32b2a270 100644
--- a/detections/endpoint/windows_inprocserver32_new_outlook_form.yml
+++ b/detections/endpoint/windows_inprocserver32_new_outlook_form.yml
@@ -7,10 +7,25 @@ data_source:
 - Sysmon EventID 13
 type: Anomaly
 status: production
-description: The following analytic detects the creation or modification of registry keys associated with new Outlook form installations, potentially indicating exploitation of CVE-2024-21378. It leverages data from the Endpoint.Registry datamodel, focusing on registry paths involving InProcServer32 keys linked to Outlook forms. This activity is significant as it may signify an attempt to achieve authenticated remote code execution via malicious form objects. If confirmed malicious, this could allow an attacker to create arbitrary files and registry keys, leading to remote code execution and potential full system compromise.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\InProcServer32\\*" Registry.registry_value_data=*\\FORMS\\* by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.dest Registry.process_guid Registry.user | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_inprocserver32_new_outlook_form_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: False positives are possible if the organization adds new forms to Outlook via an automated method. Filter by name or path to reduce false positives.
+description: The following analytic detects the creation or modification of registry
+  keys associated with new Outlook form installations, potentially indicating exploitation
+  of CVE-2024-21378. It leverages data from the Endpoint.Registry datamodel, focusing
+  on registry paths involving InProcServer32 keys linked to Outlook forms. This activity
+  is significant as it may signify an attempt to achieve authenticated remote code
+  execution via malicious form objects. If confirmed malicious, this could allow an
+  attacker to create arbitrary files and registry keys, leading to remote code execution
+  and potential full system compromise.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  where Registry.registry_path="*\\InProcServer32\\*" Registry.registry_value_data=*\\FORMS\\*
+  by Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.dest Registry.process_guid Registry.user |
+  `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_inprocserver32_new_outlook_form_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: False positives are possible if the organization adds new forms
+  to Outlook via an automated method. Filter by name or path to reduce false positives.
 references:
 - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A registry key associated with a new Outlook form installation was created or modified. This could indicate exploitation of CVE-2024-21378 on $dest$.
+  message: A registry key associated with a new Outlook form installation was created
+    or modified. This could indicate exploitation of CVE-2024-21378 on $dest$.
   mitre_attack_id:
   - T1566
   - T1112
@@ -43,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_value_data
-  - Registry.dest
-  - Registry.process_guid
-  - Registry.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml b/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml
index 43abd6a2bc..d1071a922a 100644
--- a/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml
+++ b/detections/endpoint/windows_input_capture_using_credential_ui_dll.yml
@@ -5,12 +5,26 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects a process loading the credui.dll or wincredui.dll module. This detection leverages Sysmon EventCode 7 to identify instances where these DLLs are loaded by processes outside typical system directories. This activity is significant because adversaries often abuse these modules to create fake credential prompts or dump credentials, posing a risk of credential theft. If confirmed malicious, this activity could allow attackers to harvest user credentials, leading to unauthorized access and potential lateral movement within the network.
+description: The following analytic detects a process loading the credui.dll or wincredui.dll
+  module. This detection leverages Sysmon EventCode 7 to identify instances where
+  these DLLs are loaded by processes outside typical system directories. This activity
+  is significant because adversaries often abuse these modules to create fake credential
+  prompts or dump credentials, posing a risk of credential theft. If confirmed malicious,
+  this activity could allow attackers to harvest user credentials, leading to unauthorized
+  access and potential lateral movement within the network.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7  (ImageLoaded = "*\\credui.dll" AND OriginalFileName = "credui.dll") OR (ImageLoaded = "*\\wincredui.dll" AND OriginalFileName = "wincredui.dll") AND NOT(Image IN("*\\windows\\explorer.exe", "*\\windows\\system32\\*", "*\\windows\\sysWow64\\*", "*:\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded OriginalFileName dest EventCode Signed ProcessId ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_input_capture_using_credential_ui_dll_filter`'
-how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
-known_false_positives: this module can be loaded by a third party application. Filter is needed.
+search: '`sysmon` EventCode=7  (ImageLoaded = "*\\credui.dll" AND OriginalFileName
+  = "credui.dll") OR (ImageLoaded = "*\\wincredui.dll" AND OriginalFileName = "wincredui.dll")
+  AND NOT(Image IN("*\\windows\\explorer.exe", "*\\windows\\system32\\*", "*\\windows\\sysWow64\\*",
+  "*:\\program files*")) | stats count min(_time) as firstTime max(_time) as lastTime
+  by Image ImageLoaded OriginalFileName dest EventCode Signed ProcessId ProcessGuid
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_input_capture_using_credential_ui_dll_filter`'
+how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709
+  will add the ImageLoaded name to the process_name field, allowing this query to
+  work. Use as an example and implement for other products.
+known_false_positives: this module can be loaded by a third party application. Filter
+  is needed.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/api/wincred/nf-wincred-creduipromptforcredentialsa
 - https://github.com/redcanaryco/atomic-red-team/blob/f339e7da7d05f6057fdfcdd3742bfcf365fee2a9/atomics/T1056.002/T1056.002.md#atomic-test-2---powershell---prompt-user-for-password
@@ -33,19 +47,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_installutil_credential_theft.yml b/detections/endpoint/windows_installutil_credential_theft.yml
index b8862f660f..c044436433 100644
--- a/detections/endpoint/windows_installutil_credential_theft.yml
+++ b/detections/endpoint/windows_installutil_credential_theft.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Mauricio Velazo, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances where the Windows InstallUtil.exe binary loads `vaultcli.dll` and `Samlib.dll`. This detection leverages Sysmon EventCode 7 to identify these specific DLL loads. This activity is significant because it can indicate an attempt to execute code that bypasses application control and captures credentials using tools like Mimikatz. If confirmed malicious, this behavior could allow an attacker to steal credentials, potentially leading to unauthorized access and further compromise of the system.
+description: The following analytic detects instances where the Windows InstallUtil.exe
+  binary loads `vaultcli.dll` and `Samlib.dll`. This detection leverages Sysmon EventCode
+  7 to identify these specific DLL loads. This activity is significant because it
+  can indicate an attempt to execute code that bypasses application control and captures
+  credentials using tools like Mimikatz. If confirmed malicious, this behavior could
+  allow an attacker to steal credentials, potentially leading to unauthorized access
+  and further compromise of the system.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 process_name=installutil.exe loaded_file_path IN ("*\\samlib.dll", "*\\vaultcli.dll") | stats count min(_time) as firstTime max(_time) as lastTime by user_id, dest, process_name, loaded_file, loaded_file_path, original_file_name, process_guid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_credential_theft_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and module loads from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Typically, this will not trigger because, by its very nature, InstallUtil does not require credentials. Filter as needed.
+search: '`sysmon` EventCode=7 process_name=installutil.exe loaded_file_path IN ("*\\samlib.dll",
+  "*\\vaultcli.dll") | stats count min(_time) as firstTime max(_time) as lastTime
+  by user_id, dest, process_name, loaded_file, loaded_file_path, original_file_name,
+  process_guid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_installutil_credential_theft_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and module loads from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: Typically, this will not trigger because, by its very nature,
+  InstallUtil does not require credentials. Filter as needed.
 references:
 - https://gist.github.com/xorrior/bbac3919ca2aef8d924bdf3b16cce3d0
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of process name [$process_name$] loading a file [$loaded_file$] was identified on endpoint- [$dest$] to potentially capture credentials in memory.
+  message: An instance of process name [$process_name$] loading a file [$loaded_file$]
+    was identified on endpoint- [$dest$] to potentially capture credentials in memory.
   mitre_attack_id:
   - T1218.004
   - T1218
@@ -45,23 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_installutil_in_non_standard_path.yml b/detections/endpoint/windows_installutil_in_non_standard_path.yml
index 92ac115f28..d9ea21fd7e 100644
--- a/detections/endpoint/windows_installutil_in_non_standard_path.yml
+++ b/detections/endpoint/windows_installutil_in_non_standard_path.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of InstallUtil.exe from non-standard paths. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and original file names outside typical directories. This activity is significant because InstallUtil.exe is often used by attackers to execute malicious code or scripts. If confirmed malicious, this behavior could allow an attacker to bypass security controls, execute arbitrary code, and potentially gain unauthorized access or persist within the environment.
+description: The following analytic detects the execution of InstallUtil.exe from
+  non-standard paths. It leverages Endpoint Detection and Response (EDR) data, focusing
+  on process names and original file names outside typical directories. This activity
+  is significant because InstallUtil.exe is often used by attackers to execute malicious
+  code or scripts. If confirmed malicious, this behavior could allow an attacker to
+  bypass security controls, execute arbitrary code, and potentially gain unauthorized
+  access or persist within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where `process_installutil` NOT (Processes.process_path IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*", "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_in_non_standard_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering may be required. Certain utilities will run from non-standard paths based on the third-party application in use.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where `process_installutil` NOT (Processes.process_path
+  IN ("*\\Windows\\ADWS\\*","*\\Windows\\SysWOW64*", "*\\Windows\\system32*", "*\\Windows\\NetworkController\\*",
+  "*\\Windows\\SystemApps\\*", "*\\WinSxS\\*", "*\\Windows\\Microsoft.NET\\*")) by
+  Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.original_file_name Processes.process_id
+  Processes.parent_process_id Processes.process_hash | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_in_non_standard_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering may be required.
+  Certain utilities will run from non-standard paths based on the third-party application
+  in use.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml
 - https://attack.mitre.org/techniques/T1036/003/
@@ -24,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +67,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard path was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ from a non-standard
+    path was identified on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1036
   - T1036.003
@@ -66,23 +95,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon_installutil_path.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_installutil_remote_network_connection.yml b/detections/endpoint/windows_installutil_remote_network_connection.yml
index 3d00d85d70..16e550a8de 100644
--- a/detections/endpoint/windows_installutil_remote_network_connection.yml
+++ b/detections/endpoint/windows_installutil_remote_network_connection.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the Windows InstallUtil.exe binary making a remote network connection. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network telemetry. This activity is significant because InstallUtil.exe can be exploited to download and execute malicious code, bypassing application control mechanisms. If confirmed malicious, an attacker could achieve code execution, potentially leading to further system compromise, data exfiltration, or lateral movement within the network. Analysts should review the parent process, network connections, and any associated file modifications to determine the legitimacy of this activity.
+description: The following analytic detects the Windows InstallUtil.exe binary making
+  a remote network connection. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process and network telemetry. This activity is significant
+  because InstallUtil.exe can be exploited to download and execute malicious code,
+  bypassing application control mechanisms. If confirmed malicious, an attacker could
+  achieve code execution, potentially leading to further system compromise, data exfiltration,
+  or lateral movement within the network. Analysts should review the parent process,
+  network connections, and any associated file modifications to determine the legitimacy
+  of this activity.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_installutil` by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 | `windows_installutil_remote_network_connection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_installutil` by _time span=1h  Processes.process_id Processes.process_name
+  Processes.dest Processes.user Processes.process_path Processes.process Processes.parent_process_name
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly`
+  count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port !=
+  0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)`
+  | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path
+  process process_id dest_port C2 | `windows_installutil_remote_network_connection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives should be present as InstallUtil is
+  not typically used to download remote files. Filter as needed based on Developers
+  requirements.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md
 drilldown_searches:
@@ -19,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ generating a remote download.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ generating a remote download.
   mitre_attack_id:
   - T1218.004
   - T1218
@@ -54,26 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Ports.process_guid
-  - Ports.dest
-  - Ports.dest_port
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_installutil_uninstall_option.yml b/detections/endpoint/windows_installutil_uninstall_option.yml
index ee1de90a27..c4a947d7cf 100644
--- a/detections/endpoint/windows_installutil_uninstall_option.yml
+++ b/detections/endpoint/windows_installutil_uninstall_option.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the Windows InstallUtil.exe binary with the `/u` (uninstall) switch, which can execute code while bypassing application control. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because it can indicate an attempt to execute malicious code without administrative privileges. If confirmed malicious, an attacker could achieve code execution, potentially leading to further system compromise or persistence within the environment.
+description: The following analytic detects the use of the Windows InstallUtil.exe
+  binary with the `/u` (uninstall) switch, which can execute code while bypassing
+  application control. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names, parent processes, and command-line executions.
+  This activity is significant because it can indicate an attempt to execute malicious
+  code without administrative privileges. If confirmed malicious, an attacker could
+  achieve code execution, potentially leading to further system compromise or persistence
+  within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*/u*", "*uninstall*") NOT (Processes.process IN ("*C:\\WINDOWS\\CCM\\*")) NOT (Processes.parent_process_name IN ("Microsoft.SharePoint.Migration.ClientInstaller.exe")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_installutil_uninstall_option_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives should be present. Filter as needed by parent process or application.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process
+  IN ("*/u*", "*uninstall*") NOT (Processes.process IN ("*C:\\WINDOWS\\CCM\\*")) NOT
+  (Processes.parent_process_name IN ("Microsoft.SharePoint.Migration.ClientInstaller.exe"))
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_installutil_uninstall_option_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives should be present. Filter as needed
+  by parent process or application.
 references:
 - https://evi1cg.me/archives/AppLocker_Bypass_Techniques.html#menu_index_12
 - https://github.com/api0cradle/UltimateAppLockerByPassList/blob/master/md/Installutil.exe.md
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing an uninstall.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ performing an uninstall.
   mitre_attack_id:
   - T1218.004
   - T1218
@@ -58,23 +87,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_installutil_uninstall_option_with_network.yml b/detections/endpoint/windows_installutil_uninstall_option_with_network.yml
index ef859e2625..c559bee16c 100644
--- a/detections/endpoint/windows_installutil_uninstall_option_with_network.yml
+++ b/detections/endpoint/windows_installutil_uninstall_option_with_network.yml
@@ -5,12 +5,38 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the use of Windows InstallUtil.exe making a remote network connection using the `/u` (uninstall) switch. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process and network activity data. This behavior is significant as it may indicate an attempt to download and execute code while bypassing application control mechanisms. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies the use of Windows InstallUtil.exe
+  making a remote network connection using the `/u` (uninstall) switch. This detection
+  leverages Endpoint Detection and Response (EDR) telemetry, focusing on process and
+  network activity data. This behavior is significant as it may indicate an attempt
+  to download and execute code while bypassing application control mechanisms. If
+  confirmed malicious, this activity could allow an attacker to execute arbitrary
+  code, potentially leading to system compromise, data exfiltration, or further lateral
+  movement within the network.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*/u*", "*uninstall*") by _time span=1h  Processes.user Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest parent_process_name process_name process_path process process_id dest_port C2 | `windows_installutil_uninstall_option_with_network_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_installutil` Processes.process IN ("*/u*", "*uninstall*") by _time
+  span=1h  Processes.user Processes.process_id Processes.process_name Processes.dest
+  Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id
+  [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port != 0 by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port
+  | `drop_dm_object_name(All_Traffic)` | rename dest as C2 ] | table _time user dest
+  parent_process_name process_name process_path process process_id dest_port C2 |
+  `windows_installutil_uninstall_option_with_network_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives should be present as InstallUtil is
+  not typically used to download remote files. Filter as needed based on Developers
+  requirements.
 references:
 - https://evi1cg.me/archives/AppLocker_Bypass_Techniques.html#menu_index_12
 - https://github.com/api0cradle/UltimateAppLockerByPassList/blob/master/md/Installutil.exe.md
@@ -21,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ performing an uninstall.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ performing an uninstall.
   mitre_attack_id:
   - T1218.004
   - T1218
@@ -56,26 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Ports.process_guid
-  - Ports.dest
-  - Ports.dest_port
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_installutil_url_in_command_line.yml b/detections/endpoint/windows_installutil_url_in_command_line.yml
index d2bca66066..289f76ed56 100644
--- a/detections/endpoint/windows_installutil_url_in_command_line.yml
+++ b/detections/endpoint/windows_installutil_url_in_command_line.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of Windows InstallUtil.exe with an HTTP or HTTPS URL in the command line. This is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions containing URLs. This activity is significant as it may indicate an attempt to download and execute malicious code, potentially bypassing application control mechanisms. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or persistent access within the environment. Analysts should review the parent process, network connections, file modifications, and related processes for further investigation.
+description: The following analytic detects the use of Windows InstallUtil.exe with
+  an HTTP or HTTPS URL in the command line. This is identified through Endpoint Detection
+  and Response (EDR) telemetry, focusing on command-line executions containing URLs.
+  This activity is significant as it may indicate an attempt to download and execute
+  malicious code, potentially bypassing application control mechanisms. If confirmed
+  malicious, this could lead to unauthorized code execution, privilege escalation,
+  or persistent access within the environment. Analysts should review the parent process,
+  network connections, file modifications, and related processes for further investigation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process IN ("*http://*","*https://*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_installutil_url_in_command_line_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Limited false positives should be present as InstallUtil is not typically used to download remote files. Filter as needed based on Developers requirements.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_installutil` Processes.process
+  IN ("*http://*","*https://*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_installutil_url_in_command_line_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Limited false positives should be present as InstallUtil is
+  not typically used to download remote files. Filter as needed based on Developers
+  requirements.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.004/T1218.004.md
 - https://gist.github.com/DanielRTeixeira/0fd06ec8f041f34a32bf5623c6dd479d
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ passing a URL on the command-line.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ passing a URL on the command-line.
   mitre_attack_id:
   - T1218.004
   - T1218
@@ -57,23 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.004/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_iso_lnk_file_creation.yml b/detections/endpoint/windows_iso_lnk_file_creation.yml
index 0711935733..107775af9b 100644
--- a/detections/endpoint/windows_iso_lnk_file_creation.yml
+++ b/detections/endpoint/windows_iso_lnk_file_creation.yml
@@ -5,12 +5,29 @@ date: '2024-10-17'
 author: Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the creation of .iso.lnk files in the %USER%\AppData\Local\Temp\<random folder name>\ path, indicating that an ISO file has been mounted and accessed. This detection leverages the Endpoint.Filesystem data model, specifically monitoring file creation events in the Windows Recent folder. This activity is significant as it may indicate the delivery and execution of potentially malicious payloads via ISO files. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further system compromise.
+description: The following analytic detects the creation of .iso.lnk files in the
+  %USER%\AppData\Local\Temp\<random folder name>\ path, indicating that an ISO file
+  has been mounted and accessed. This detection leverages the Endpoint.Filesystem
+  data model, specifically monitoring file creation events in the Windows Recent folder.
+  This activity is significant as it may indicate the delivery and execution of potentially
+  malicious payloads via ISO files. If confirmed malicious, this could lead to unauthorized
+  code execution, data exfiltration, or further system compromise.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Microsoft\\Windows\\Recent\\*") Filesystem.file_name IN ("*.iso.lnk", "*.img.lnk", "*.vhd.lnk", "*vhdx.lnk") by Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iso_lnk_file_creation_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives may be high depending on the environment and consistent use of ISOs mounting. Restrict to servers, or filter out based on commonly used ISO names. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\Microsoft\\Windows\\Recent\\*")
+  Filesystem.file_name IN ("*.iso.lnk", "*.img.lnk", "*.vhd.lnk", "*vhdx.lnk") by
+  Filesystem.file_create_time Filesystem.process_id Filesystem.file_name Filesystem.file_path
+  Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_iso_lnk_file_creation_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives may be high depending on the environment and
+  consistent use of ISOs mounting. Restrict to servers, or filter out based on commonly
+  used ISO names. Filter as needed.
 references:
 - https://www.microsoft.com/security/blog/2021/05/27/new-sophisticated-email-based-attack-from-nobelium/
 - https://github.com/MHaggis/notes/blob/master/utilities/ISOBuilder.ps1
@@ -31,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 80
-  message: An ISO file was mounted on $dest$ and should be reviewed and filtered as needed.
+  message: An ISO file was mounted on $dest$ and should be reviewed and filtered as
+    needed.
   mitre_attack_id:
   - T1566.001
   - T1566
@@ -46,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.001/atomic_red_team/iso_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.001/atomic_red_team/iso_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_java_spawning_shells.yml b/detections/endpoint/windows_java_spawning_shells.yml
index 9268f06728..397c2381d1 100644
--- a/detections/endpoint/windows_java_spawning_shells.yml
+++ b/detections/endpoint/windows_java_spawning_shells.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies instances where java.exe or w3wp.exe spawns a Windows shell, such as cmd.exe or powershell.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as it may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). If confirmed malicious, attackers could execute arbitrary commands, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies instances where java.exe or w3wp.exe
+  spawns a Windows shell, such as cmd.exe or powershell.exe. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process and
+  parent process relationships. This activity is significant as it may indicate exploitation
+  attempts, such as those related to CVE-2021-44228 (Log4Shell). If confirmed malicious,
+  attackers could execute arbitrary commands, potentially leading to system compromise,
+  data exfiltration, or further lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java.exe OR Processes.parent_process_name=w3wp.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_java_spawning_shells_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on that.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java.exe
+  OR Processes.parent_process_name=w3wp.exe `windows_shells` by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_java_spawning_shells_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Filtering may be required on internal developer build systems
+  or classify assets as web facing and restrict the analytic based on that.
 references:
 - https://blog.netlab.360.com/ten-families-of-malicious-samples-are-spreading-using-the-log4j2-vulnerability-now/
 - https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72
@@ -30,7 +50,8 @@ tags:
   - CVE-2021-44228
   - CVE-2022-47966
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Windows shell, potentially indicative of exploitation.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ spawning a Windows shell, potentially indicative of exploitation.
   mitre_attack_id:
   - T1190
   - T1133
@@ -51,17 +72,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/endpoint/windows_kerberos_local_successful_logon.yml b/detections/endpoint/windows_kerberos_local_successful_logon.yml
index 07ccd15178..a273f57f76 100644
--- a/detections/endpoint/windows_kerberos_local_successful_logon.yml
+++ b/detections/endpoint/windows_kerberos_local_successful_logon.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a local successful authentication event on a Windows endpoint using the Kerberos package. It detects EventCode 4624 with LogonType 3 and source address 127.0.0.1, indicating a login to the built-in local Administrator account. This activity is significant as it may suggest a Kerberos relay attack, a method attackers use to escalate privileges. If confirmed malicious, this could allow an attacker to gain unauthorized access to sensitive systems, execute arbitrary code, or create new accounts in Active Directory, leading to potential system compromise.
+description: The following analytic identifies a local successful authentication event
+  on a Windows endpoint using the Kerberos package. It detects EventCode 4624 with
+  LogonType 3 and source address 127.0.0.1, indicating a login to the built-in local
+  Administrator account. This activity is significant as it may suggest a Kerberos
+  relay attack, a method attackers use to escalate privileges. If confirmed malicious,
+  this could allow an attacker to gain unauthorized access to sensitive systems, execute
+  arbitrary code, or create new accounts in Active Directory, leading to potential
+  system compromise.
 data_source:
 - Windows Event Log Security 4624
-search: '`wineventlog_security`  EventCode=4624 LogonType=3 AuthenticationPackageName=Kerberos action=success src=127.0.0.1 | stats count min(_time) as firstTime max(_time) as lastTime by dest, subject, action, SubjectLogonId, user, TargetUserName, src | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_kerberos_local_successful_logon_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4624 EventCode enabled. The Windows TA is also required.
-known_false_positives: False positives are possible, filtering may be required to restrict to workstations vs domain controllers. Filter as needed.
+search: '`wineventlog_security`  EventCode=4624 LogonType=3 AuthenticationPackageName=Kerberos
+  action=success src=127.0.0.1 | stats count min(_time) as firstTime max(_time) as
+  lastTime by dest, subject, action, SubjectLogonId, user, TargetUserName, src | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_kerberos_local_successful_logon_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4624 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: False positives are possible, filtering may be required to
+  restrict to workstations vs domain controllers. Filter as needed.
 references:
 - https://github.com/Dec0ne/KrbRelayUp
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A successful localhost Kerberos authentication event occurred on $dest$, possibly indicative of Kerberos relay attack.
+  message: A successful localhost Kerberos authentication event occurred on $dest$,
+    possibly indicative of Kerberos relay attack.
   mitre_attack_id:
   - T1558
   observable:
@@ -41,19 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - subject
-  - action
-  - Security_ID
-  - user
-  - TargetUserName
-  - src_ip
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_kerberos_local_successful_logon/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558/windows_kerberos_local_successful_logon/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_known_abused_dll_created.yml b/detections/endpoint/windows_known_abused_dll_created.yml
index 7cda1e80dd..de6cb09b96 100644
--- a/detections/endpoint/windows_known_abused_dll_created.yml
+++ b/detections/endpoint/windows_known_abused_dll_created.yml
@@ -5,12 +5,46 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic identifies the creation of Dynamic Link Libraries (DLLs) with a known history of exploitation in atypical locations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and filesystem events. This activity is significant as it may indicate DLL search order hijacking or sideloading, techniques used by attackers to execute arbitrary code, maintain persistence, or escalate privileges. If confirmed malicious, this activity could allow attackers to blend in with legitimate operations, posing a severe threat to system integrity and security.
+description: The following analytic identifies the creation of Dynamic Link Libraries
+  (DLLs) with a known history of exploitation in atypical locations. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process and
+  filesystem events. This activity is significant as it may indicate DLL search order
+  hijacking or sideloading, techniques used by attackers to execute arbitrary code,
+  maintain persistence, or escalate privileges. If confirmed malicious, this activity
+  could allow attackers to blend in with legitimate operations, posing a severe threat
+  to system integrity and security.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name!="unknown" Processes.process_name=* Processes.process_guid!=null by _time span=1h Processes.dest Processes.user Processes.process_guid Processes.process_name Processes.process Processes.parent_process Processes.parent_process_name | `drop_dm_object_name(Processes)` | join max=0 process_guid dest [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*") Filesystem.file_name="*.dll" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` | lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as desc | lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT islibrary as excluded | search islibrary = TRUE AND excluded != TRUE | stats latest(*) as * by dest process_guid ] | where isnotnull(file_name) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |  `windows_known_abused_dll_created_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` and `Filesystem` nodes of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This analytic may flag instances where DLLs are loaded by user mode programs for entirely legitimate and benign purposes. It is important for users to be aware that false positives are not only possible but likely, and that careful tuning of this analytic is necessary to distinguish between malicious activity and normal, everyday operations of applications. This may involve adjusting thresholds, whitelisting known good software, or incorporating additional context from other security tools and logs to reduce the rate of false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name!="unknown"
+  Processes.process_name=* Processes.process_guid!=null by _time span=1h Processes.dest
+  Processes.user Processes.process_guid Processes.process_name Processes.process Processes.parent_process
+  Processes.parent_process_name | `drop_dm_object_name(Processes)` | join max=0 process_guid
+  dest [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem
+  where Filesystem.file_path IN ("*\\users\\*","*\\Windows\Temp\\*","*\\programdata\\*")
+  Filesystem.file_name="*.dll" by _time span=1h Filesystem.dest Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)`
+  | lookup hijacklibs_loaded library AS file_name OUTPUT islibrary, ttp, comment as
+  desc | lookup hijacklibs_loaded library AS file_name excludes as file_path OUTPUT
+  islibrary as excluded | search islibrary = TRUE AND excluded != TRUE | stats latest(*)
+  as * by dest process_guid ] | where isnotnull(file_name) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` |  `windows_known_abused_dll_created_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` and `Filesystem`
+  nodes of the `Endpoint` data model. Use the Splunk Common Information Model (CIM)
+  to normalize the field names and speed up the data modeling process.
+known_false_positives: This analytic may flag instances where DLLs are loaded by user
+  mode programs for entirely legitimate and benign purposes. It is important for users
+  to be aware that false positives are not only possible but likely, and that careful
+  tuning of this analytic is necessary to distinguish between malicious activity and
+  normal, everyday operations of applications. This may involve adjusting thresholds,
+  whitelisting known good software, or incorporating additional context from other
+  security tools and logs to reduce the rate of false positives.
 references:
 - https://attack.mitre.org/techniques/T1574/002/
 - https://hijacklibs.net/api/
@@ -22,7 +56,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +71,8 @@ tags:
   asset_type: Endpoint
   confidence: 25
   impact: 40
-  message: The file [$file_name$] was written to an unusual location by [$process_name$] on [$dest$].
+  message: The file [$file_name$] was written to an unusual location by [$process_name$]
+    on [$dest$].
   mitre_attack_id:
   - T1574.001
   - T1574.002
@@ -58,24 +98,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.process_guid
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml b/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml
index 89832fc2db..d80b9a2432 100644
--- a/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml
+++ b/detections/endpoint/windows_known_abused_dll_loaded_suspiciously.yml
@@ -5,11 +5,29 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when DLLs with known abuse history are loaded from an unusual location. This activity may represent an attacker performing a DLL search order or sideload hijacking technique. These techniques are used to gain persistence as well as elevate privileges on the target system. This detection relies on Sysmon EID7 and is compatible with all Officla Sysmon TA versions.
+description: The following analytic detects when DLLs with known abuse history are
+  loaded from an unusual location. This activity may represent an attacker performing
+  a DLL search order or sideload hijacking technique. These techniques are used to
+  gain persistence as well as elevate privileges on the target system. This detection
+  relies on Sysmon EID7 and is compatible with all Officla Sysmon TA versions.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` ImageLoaded EventCode=7 NOT ImageLoaded IN ("*\\Program Files*","*\\system32\\*", "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*") | stats latest(ProcessGuid) as process_guid, count, min(_time) as firstTime, max(_time) as lastTime by User, Computer, Image, ImageLoaded | rename User as user, Computer as dest, Image as process, ImageLoaded as loaded_file | eval process_name = case(isnotnull(process),replace(process,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")), loaded_file_path = case(isnotnull(loaded_file), replace(loaded_file, "(:[\w\. ]+)", "")), loaded_file = case(isnotnull(loaded_file),replace(loaded_file,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")), user = case(NOT user IN ("-"), replace(user, "(.*)\\\(.+)$","\2")) | lookup hijacklibs_loaded library AS loaded_file OUTPUT islibrary comment as desc | lookup hijacklibs_loaded library AS loaded_file excludes as loaded_file_path OUTPUT islibrary as excluded | search islibrary = TRUE AND excluded = false | stats values(*) as * by dest, process_name, process, process_guid, loaded_file, loaded_file_path | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_known_abused_dll_loaded_suspiciously_filter`'
-how_to_implement: The following analytic requires Sysmon operational logs to be imported, with EID7 being mapped to the process_name field. Modify the sysmon macro as needed to match the sourcetype or add index.
+search: '`sysmon` ImageLoaded EventCode=7 NOT ImageLoaded IN ("*\\Program Files*","*\\system32\\*",
+  "*\\syswow64\\*","*\\winsxs\\*","*\\wbem\\*") | stats latest(ProcessGuid) as process_guid,
+  count, min(_time) as firstTime, max(_time) as lastTime by User, Computer, Image,
+  ImageLoaded | rename User as user, Computer as dest, Image as process, ImageLoaded
+  as loaded_file | eval process_name = case(isnotnull(process),replace(process,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")),
+  loaded_file_path = case(isnotnull(loaded_file), replace(loaded_file, "(:[\w\. ]+)",
+  "")), loaded_file = case(isnotnull(loaded_file),replace(loaded_file,"(.*\\\)(?=.*(\.\w*)$|(\w+)$)","")),
+  user = case(NOT user IN ("-"), replace(user, "(.*)\\\(.+)$","\2")) | lookup hijacklibs_loaded
+  library AS loaded_file OUTPUT islibrary comment as desc | lookup hijacklibs_loaded
+  library AS loaded_file excludes as loaded_file_path OUTPUT islibrary as excluded
+  | search islibrary = TRUE AND excluded = false | stats values(*) as * by dest, process_name,
+  process, process_guid, loaded_file, loaded_file_path | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_known_abused_dll_loaded_suspiciously_filter`'
+how_to_implement: The following analytic requires Sysmon operational logs to be imported,
+  with EID7 being mapped to the process_name field. Modify the sysmon macro as needed
+  to match the sourcetype or add index.
 known_false_positives: DLLs being loaded by user mode programs for legitimate reasons.
 references:
 - https://attack.mitre.org/techniques/T1574/002/
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,16 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - Computer
-  - ProcessGuid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/hijacklibs/hijacklibs_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml b/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml
index a78f4ff6f0..818a6f8f06 100644
--- a/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml
+++ b/detections/endpoint/windows_known_graphicalproton_loaded_modules.yml
@@ -7,9 +7,25 @@ status: production
 type: Anomaly
 data_source:
 - Sysmon EventID 7
-description: The following analytic detects the loading of DLL modules associated with the GraphicalProton backdoor implant, commonly used by SVR in targeted attacks. It leverages Sysmon EventCode 7 to identify specific DLLs loaded by processes. This activity is significant as it may indicate the presence of a sophisticated backdoor, warranting immediate investigation. If confirmed malicious, the attacker could gain persistent access to the compromised host, potentially leading to further exploitation and data exfiltration.
-search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\AclNumsInvertHost.dll", "*\\ModeBitmapNumericAnimate.dll", "*\\UnregisterAncestorAppendAuto.dll", "*\\DeregisterSeekUsers.dll", "*\\ScrollbarHandleGet.dll", "*\\PerformanceCaptionApi.dll", "*\\WowIcmpRemoveReg.dll", "*\\BlendMonitorStringBuild.dll", "*\\HandleFrequencyAll.dll", "*\\HardSwapColor.dll", "*\\LengthInMemoryActivate.dll", "*\\ParametersNamesPopup.dll", "*\\ModeFolderSignMove.dll", "*\\ChildPaletteConnected.dll", "*\\AddressResourcesSpec.dll") | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_known_graphicalproton_loaded_modules_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+description: The following analytic detects the loading of DLL modules associated
+  with the GraphicalProton backdoor implant, commonly used by SVR in targeted attacks.
+  It leverages Sysmon EventCode 7 to identify specific DLLs loaded by processes. This
+  activity is significant as it may indicate the presence of a sophisticated backdoor,
+  warranting immediate investigation. If confirmed malicious, the attacker could gain
+  persistent access to the compromised host, potentially leading to further exploitation
+  and data exfiltration.
+search: '`sysmon` EventCode=7 ImageLoaded IN ("*\\AclNumsInvertHost.dll", "*\\ModeBitmapNumericAnimate.dll",
+  "*\\UnregisterAncestorAppendAuto.dll", "*\\DeregisterSeekUsers.dll", "*\\ScrollbarHandleGet.dll",
+  "*\\PerformanceCaptionApi.dll", "*\\WowIcmpRemoveReg.dll", "*\\BlendMonitorStringBuild.dll",
+  "*\\HandleFrequencyAll.dll", "*\\HardSwapColor.dll", "*\\LengthInMemoryActivate.dll",
+  "*\\ParametersNamesPopup.dll", "*\\ModeFolderSignMove.dll", "*\\ChildPaletteConnected.dll",
+  "*\\AddressResourcesSpec.dll") | stats count min(_time) as firstTime max(_time)
+  as lastTime by Image ImageLoaded process_name dest EventCode Signed ProcessId Hashes
+  IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_known_graphicalproton_loaded_modules_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,21 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
-  - Hashes
-  - IMPHASH
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/svr_loaded_modules/loaded_module_svr.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/svr_loaded_modules/loaded_module_svr.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_krbrelayup_service_creation.yml b/detections/endpoint/windows_krbrelayup_service_creation.yml
index 1415ed8266..b7622b7716 100644
--- a/detections/endpoint/windows_krbrelayup_service_creation.yml
+++ b/detections/endpoint/windows_krbrelayup_service_creation.yml
@@ -5,12 +5,22 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a service with the default name "KrbSCM" associated with the KrbRelayUp tool. It leverages Windows System Event Logs, specifically EventCode 7045, to identify this activity. This behavior is significant as KrbRelayUp is a known tool used for privilege escalation attacks. If confirmed malicious, this activity could allow an attacker to escalate privileges, potentially gaining unauthorized access to sensitive systems and data.
+description: The following analytic detects the creation of a service with the default
+  name "KrbSCM" associated with the KrbRelayUp tool. It leverages Windows System Event
+  Logs, specifically EventCode 7045, to identify this activity. This behavior is significant
+  as KrbRelayUp is a known tool used for privilege escalation attacks. If confirmed
+  malicious, this activity could allow an attacker to escalate privileges, potentially
+  gaining unauthorized access to sensitive systems and data.
 data_source:
 - Windows Event Log System 7045
-search: '`wineventlog_system` EventCode=7045 ServiceName IN ("KrbSCM") | stats count min(_time) as firstTime max(_time) as lastTime by dest EventCode ImagePath ServiceName StartType ServiceType | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_krbrelayup_service_creation_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows System Event Logs with 7045 EventCode enabled. The Windows TA is also required.
-known_false_positives: False positives should be limited as this is specific to KrbRelayUp based attack. Filter as needed.
+search: '`wineventlog_system` EventCode=7045 ServiceName IN ("KrbSCM") | stats count
+  min(_time) as firstTime max(_time) as lastTime by dest EventCode ImagePath ServiceName
+  StartType ServiceType | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_krbrelayup_service_creation_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows System Event Logs with 7045 EventCode enabled. The Windows TA is also required.
+known_false_positives: False positives should be limited as this is specific to KrbRelayUp
+  based attack. Filter as needed.
 references:
 - https://github.com/Dec0ne/KrbRelayUp
 drilldown_searches:
@@ -19,7 +29,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Service_File_Name
-  - Service_Name
-  - Service_Start_Type
-  - Service_Type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/windows_krbrelayup_service_creation/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/windows_krbrelayup_service_creation/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml b/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml
index 0be5a1fa7c..d4f2976c19 100644
--- a/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml
+++ b/detections/endpoint/windows_large_number_of_computer_service_tickets_requested.yml
@@ -7,10 +7,24 @@ type: Anomaly
 status: production
 data_source:
 - Windows Event Log Security 4769
-description: The following analytic detects a high volume of Kerberos service ticket requests, specifically more than 30, from a single source within a 5-minute window. It leverages Event ID 4769, which logs when a Kerberos service ticket is requested, focusing on requests with computer names as the Service Name. This behavior is significant as it may indicate malicious activities such as lateral movement, malware staging, or reconnaissance. If confirmed malicious, an attacker could gain unauthorized access to multiple endpoints, potentially compromising the entire network.
-search: '`wineventlog_security` EventCode=4769 ServiceName="*$" TargetUserName!="*$" | bucket span=5m _time | stats dc(ServiceName) AS unique_targets values(ServiceName) as host_targets by _time, IpAddress, TargetUserName | where unique_targets > 30 | `windows_large_number_of_computer_service_tickets_requested_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
-known_false_positives: An single endpoint requesting a large number of kerberos service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems.
+description: The following analytic detects a high volume of Kerberos service ticket
+  requests, specifically more than 30, from a single source within a 5-minute window.
+  It leverages Event ID 4769, which logs when a Kerberos service ticket is requested,
+  focusing on requests with computer names as the Service Name. This behavior is significant
+  as it may indicate malicious activities such as lateral movement, malware staging,
+  or reconnaissance. If confirmed malicious, an attacker could gain unauthorized access
+  to multiple endpoints, potentially compromising the entire network.
+search: '`wineventlog_security` EventCode=4769 ServiceName="*$" TargetUserName!="*$"
+  | bucket span=5m _time | stats dc(ServiceName) AS unique_targets values(ServiceName)
+  as host_targets by _time, IpAddress, TargetUserName | where unique_targets > 30
+  | `windows_large_number_of_computer_service_tickets_requested_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+known_false_positives: An single endpoint requesting a large number of kerberos service
+  tickets is not common behavior. Possible false positive scenarios include but are
+  not limited to vulnerability scanners, administration systems and missconfigured
+  systems.
 references:
 - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/
 - https://attack.mitre.org/techniques/T1135/
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$IpAddress$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$IpAddress$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$IpAddress$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: A large number of kerberos computer service tickets were requested by $IpAddress$ within 5 minutes.
+  message: A large number of kerberos computer service tickets were requested by $IpAddress$
+    within 5 minutes.
   mitre_attack_id:
   - T1135
   - T1078
@@ -44,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ServiceName
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/large_number_computer_service_tickets/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/large_number_computer_service_tickets/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_lateral_tool_transfer_remcom.yml b/detections/endpoint/windows_lateral_tool_transfer_remcom.yml
index bed5dd0c11..0006b3a4b7 100644
--- a/detections/endpoint/windows_lateral_tool_transfer_remcom.yml
+++ b/detections/endpoint/windows_lateral_tool_transfer_remcom.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the execution of RemCom.exe, an open-source alternative to PsExec, used for lateral movement and remote command execution. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, original file names, and command-line arguments. This activity is significant as it indicates potential lateral movement within the network. If confirmed malicious, this could allow an attacker to execute commands remotely, potentially leading to further compromise and control over additional systems within the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=remcom.exe OR Processes.original_file_name=RemCom.exe) Processes.process="*\\*" Processes.process IN ("*/user:*", "*/pwd:*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lateral_tool_transfer_remcom_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present based on Administrative use. Filter as needed.
+description: The following analytic identifies the execution of RemCom.exe, an open-source
+  alternative to PsExec, used for lateral movement and remote command execution. It
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  names, original file names, and command-line arguments. This activity is significant
+  as it indicates potential lateral movement within the network. If confirmed malicious,
+  this could allow an attacker to execute commands remotely, potentially leading to
+  further compromise and control over additional systems within the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=remcom.exe
+  OR Processes.original_file_name=RemCom.exe) Processes.process="*\\*" Processes.process
+  IN ("*/user:*", "*/pwd:*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_lateral_tool_transfer_remcom_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present based on Administrative use.
+  Filter as needed.
 references:
 - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/
 - https://github.com/kavika13/RemCom
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to move laterally.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to move laterally.
   mitre_attack_id:
   - T1570
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1570/remcom/remcom_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1570/remcom/remcom_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_ldifde_directory_object_behavior.yml b/detections/endpoint/windows_ldifde_directory_object_behavior.yml
index 75ecdde2e4..f67596ab4b 100644
--- a/detections/endpoint/windows_ldifde_directory_object_behavior.yml
+++ b/detections/endpoint/windows_ldifde_directory_object_behavior.yml
@@ -9,9 +9,29 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the use of Ldifde.exe, a command-line utility for creating, modifying, or deleting LDAP directory objects. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution and command-line arguments. Monitoring Ldifde.exe is significant because it can be used by attackers to manipulate directory objects, potentially leading to unauthorized changes or data exfiltration. If confirmed malicious, this activity could allow an attacker to gain control over directory services, escalate privileges, or access sensitive information within the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ldifde.exe Processes.process IN ("*-i *", "*-f *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ldifde_directory_object_behavior_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies the use of Ldifde.exe, a command-line
+  utility for creating, modifying, or deleting LDAP directory objects. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  execution and command-line arguments. Monitoring Ldifde.exe is significant because
+  it can be used by attackers to manipulate directory objects, potentially leading
+  to unauthorized changes or data exfiltration. If confirmed malicious, this activity
+  could allow an attacker to gain control over directory services, escalate privileges,
+  or access sensitive information within the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ldifde.exe
+  Processes.process IN ("*-i *", "*-f *") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_ldifde_directory_object_behavior_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://lolbas-project.github.io/lolbas/Binaries/Ldifde/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +60,8 @@ tags:
   - 22cf8cb9-adb1-4e8c-80ca-7c723dfc8784
   confidence: 50
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing ldifde on a domain controller.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ utilizing ldifde on a domain controller.
   mitre_attack_id:
   - T1105
   - T1069.002
@@ -60,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/ldifde_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.002/AD_discovery/ldifde_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml b/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml
index c22da2f4ce..86ea74594f 100644
--- a/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml
+++ b/detections/endpoint/windows_linked_policies_in_adsi_discovery.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell Script Block Logging (EventCode=4104) to query Active Directory for domain organizational units. This detection leverages PowerShell operational logs to identify script blocks containing `[adsisearcher]`, `objectcategory=organizationalunit`, and `findAll()`. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gain situational awareness of the domain structure. If confirmed malicious, this could lead to further exploitation, such as privilege escalation or lateral movement within the network.
+description: The following analytic detects the use of the `[Adsisearcher]` type accelerator
+  in PowerShell Script Block Logging (EventCode=4104) to query Active Directory for
+  domain organizational units. This detection leverages PowerShell operational logs
+  to identify script blocks containing `[adsisearcher]`, `objectcategory=organizationalunit`,
+  and `findAll()`. This activity is significant as it indicates potential reconnaissance
+  efforts by adversaries to gain situational awareness of the domain structure. If
+  confirmed malicious, this could lead to further exploitation, such as privilege
+  escalation or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*objectcategory=organizationalunit*" ScriptBlockText = "*findAll()*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_linked_policies_in_adsi_discovery_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText
+  = "*objectcategory=organizationalunit*" ScriptBlockText = "*findAll()*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_linked_policies_in_adsi_discovery_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml2.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_local_administrator_credential_stuffing.yml b/detections/endpoint/windows_local_administrator_credential_stuffing.yml
index 2ce5c02e5d..050d890b1e 100644
--- a/detections/endpoint/windows_local_administrator_credential_stuffing.yml
+++ b/detections/endpoint/windows_local_administrator_credential_stuffing.yml
@@ -8,10 +8,24 @@ status: production
 data_source:
 - Windows Event Log Security 4624
 - Windows Event Log Security 4625
-description: The following analytic detects attempts to authenticate using the built-in local Administrator account across more than 30 endpoints within a 5-minute window. It leverages Windows Event Logs, specifically events 4625 and 4624, to identify this behavior. This activity is significant as it may indicate an adversary attempting to validate stolen local credentials across multiple hosts, potentially leading to privilege escalation. If confirmed malicious, this could allow the attacker to gain widespread access and control over numerous systems within the network, posing a severe security risk.
-search: '`wineventlog_security` EventCode=4625 OR EventCode=4624 Logon_Type=3 TargetUserName=Administrator | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets by _time, IpAddress, TargetUserName, EventCode | where unique_targets > 30 | `windows_local_administrator_credential_stuffing_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
-known_false_positives: Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed.
+description: The following analytic detects attempts to authenticate using the built-in
+  local Administrator account across more than 30 endpoints within a 5-minute window.
+  It leverages Windows Event Logs, specifically events 4625 and 4624, to identify
+  this behavior. This activity is significant as it may indicate an adversary attempting
+  to validate stolen local credentials across multiple hosts, potentially leading
+  to privilege escalation. If confirmed malicious, this could allow the attacker to
+  gain widespread access and control over numerous systems within the network, posing
+  a severe security risk.
+search: '`wineventlog_security` EventCode=4625 OR EventCode=4624 Logon_Type=3 TargetUserName=Administrator
+  | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as
+  host_targets by _time, IpAddress, TargetUserName, EventCode | where unique_targets
+  > 30 | `windows_local_administrator_credential_stuffing_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
+known_false_positives: Vulnerability scanners or system administration tools may also
+  trigger this detection. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1110/004/
 - https://attack.mitre.org/techniques/T1110/
@@ -25,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host_targets$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,17 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - TargetUserName
-  - Computer
-  - IpAddress
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.004/local_administrator_cred_stuffing/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.004/local_administrator_cred_stuffing/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml b/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml
index 52f7025132..cb6b2bc2ee 100644
--- a/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml
+++ b/detections/endpoint/windows_lolbas_executed_as_renamed_file.yml
@@ -5,14 +5,38 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies a LOLBAS process being executed where it's process name does not match it's original file name attribute. Processes that have been renamed and executed may be an indicator that an adversary is attempting to evade defenses or execute malicious code. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code.
+description: The following analytic identifies a LOLBAS process being executed where
+  it's process name does not match it's original file name attribute. Processes that
+  have been renamed and executed may be an indicator that an adversary is attempting
+  to evade defenses or execute malicious code. The LOLBAS project documents Windows
+  native binaries that can be abused by threat actors to perform tasks like executing
+  malicious code.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '|  tstats `security_content_summariesonly` latest(Processes.parent_process) as parent_process, latest(Processes.process) as process, latest(Processes.process_guid) as process_guid count, min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes where NOT Processes.original_file_name IN("-","unknown") AND NOT Processes.process_path IN ("*\\Program Files*","*\\PROGRA~*","*\\Windows\\System32\\*","*\\Windows\\Syswow64\\*") BY Processes.user Processes.dest Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process_path |`drop_dm_object_name(Processes)` | where NOT match(process_name, "(?i)".original_file_name) | lookup lolbas_file_path lolbas_file_name as original_file_name OUTPUT description as desc | search desc!="false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lolbas_executed_as_renamed_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A certain amount of false positives are likely with this detection. MSI based installers often trigger for SETUPAPL.dll and vendors will often copy system exectables to a different path for application usage.
+search: '|  tstats `security_content_summariesonly` latest(Processes.parent_process)
+  as parent_process, latest(Processes.process) as process, latest(Processes.process_guid)
+  as process_guid count, min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes
+  where NOT Processes.original_file_name IN("-","unknown") AND NOT Processes.process_path
+  IN ("*\\Program Files*","*\\PROGRA~*","*\\Windows\\System32\\*","*\\Windows\\Syswow64\\*")
+  BY Processes.user Processes.dest Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process_path |`drop_dm_object_name(Processes)`
+  | where NOT match(process_name, "(?i)".original_file_name) | lookup lolbas_file_path
+  lolbas_file_name as original_file_name OUTPUT description as desc | search desc!="false"
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lolbas_executed_as_renamed_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A certain amount of false positives are likely with this detection.
+  MSI based installers often trigger for SETUPAPL.dll and vendors will often copy
+  system exectables to a different path for application usage.
 references:
 - https://attack.mitre.org/techniques/T1036/
 - https://attack.mitre.org/techniques/T1036/003/
@@ -22,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 80
-  message: The file originally named $original_file_name$ was executed as $process_name$ on $dest$
+  message: The file originally named $original_file_name$ was executed as $process_name$
+    on $dest$
   mitre_attack_id:
   - T1036
   - T1036.003
@@ -55,17 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.user
-  - Processes.dest
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.original_file_name
-  - Processes.process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml b/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml
index 046806195f..1cbc99338c 100644
--- a/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml
+++ b/detections/endpoint/windows_lolbas_executed_outside_expected_path.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic identifies a LOLBAS process being executed outside of it's expected location. Processes being executed outside of expected locations may be an indicator that an adversary is attempting to evade defenses or execute malicious code. The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code.
+description: The following analytic identifies a LOLBAS process being executed outside
+  of it's expected location. Processes being executed outside of expected locations
+  may be an indicator that an adversary is attempting to evade defenses or execute
+  malicious code. The LOLBAS project documents Windows native binaries that can be
+  abused by threat actors to perform tasks like executing malicious code.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
-search: '|  tstats `security_content_summariesonly` latest(Processes.parent_process) as parent_process, latest(Processes.process) as process, latest(Processes.process_guid) as process_guid count, min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes where Processes.process != "unknown" AND NOT Processes.process_path IN ("*\\Program Files*","*\\PROGRA~*","*\\Windows\\System32\\*","*\\Windows\\Syswow64\\*") BY Processes.user Processes.dest Processes.parent_process_name Processes.process_name Processes.process_path |`drop_dm_object_name(Processes)` | lookup lolbas_file_path lolbas_file_name as process_name OUTPUT description as desc | lookup lolbas_file_path lolbas_file_name as process_name lolbas_file_path as process_path OUTPUT description as is_lolbas_path | search desc!="false" AND is_lolbas_path="false" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lolbas_executed_outside_expected_path_filter`'
-how_to_implement: To implement this search, you must ingest logs that contain the process name and process path, such as with Sysmon EID 1.
-known_false_positives: Vendors will often copy system exectables to a different path for application usage.
+search: '|  tstats `security_content_summariesonly` latest(Processes.parent_process)
+  as parent_process, latest(Processes.process) as process, latest(Processes.process_guid)
+  as process_guid count, min(_time) AS firstTime, max(_time) AS lastTime FROM datamodel=Endpoint.Processes
+  where Processes.process != "unknown" AND NOT Processes.process_path IN ("*\\Program
+  Files*","*\\PROGRA~*","*\\Windows\\System32\\*","*\\Windows\\Syswow64\\*") BY Processes.user
+  Processes.dest Processes.parent_process_name Processes.process_name Processes.process_path
+  |`drop_dm_object_name(Processes)` | lookup lolbas_file_path lolbas_file_name as
+  process_name OUTPUT description as desc | lookup lolbas_file_path lolbas_file_name
+  as process_name lolbas_file_path as process_path OUTPUT description as is_lolbas_path
+  | search desc!="false" AND is_lolbas_path="false" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_lolbas_executed_outside_expected_path_filter`'
+how_to_implement: To implement this search, you must ingest logs that contain the
+  process name and process path, such as with Sysmon EID 1.
+known_false_positives: Vendors will often copy system exectables to a different path
+  for application usage.
 references:
 - https://attack.mitre.org/techniques/T1036/
 - https://attack.mitre.org/techniques/T1036/005/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 80
-  message: The user $user$ executed a LOLBAS [$process_name$] from an unexpected location on $dest$
+  message: The user $user$ executed a LOLBAS [$process_name$] from an unexpected location
+    on $dest$
   mitre_attack_id:
   - T1036
   - T1036.005
@@ -54,17 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.user
-  - Processes.dest
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.original_file_name
-  - Processes.process_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/cmd_lolbas_usage/cmd_lolbas_usage.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml b/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml
index 8b58d9dc46..bf1eaab347 100644
--- a/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml
+++ b/detections/endpoint/windows_lsa_secrets_nolmhash_registry.yml
@@ -16,10 +16,10 @@ description: The following analytic detects modifications to the Windows registr
   is crucial as it can indicate attempts to weaken password storage security. If confirmed
   malicious, this could allow attackers to exploit weaker LM hashes, potentially leading
   to unauthorized access and credential theft.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\NoLMHash"
-  Registry.registry_value_data = 0x00000000) BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\NoLMHash"
+  Registry.registry_value_data = 0x00000000) BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_lsa_secrets_nolmhash_registry_filter`'
 how_to_implement: To successfully implement this search, you must be ingesting data
@@ -37,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -62,21 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.dest
-  - Registry.action
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml b/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml
index f8ceabfd72..ee66bdd29b 100644
--- a/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml
+++ b/detections/endpoint/windows_mail_protocol_in_non_common_process_path.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a Windows application establishing an SMTP connection from a non-common installation path. It leverages Sysmon EventCode 3 to identify processes not typically associated with email clients (e.g., Thunderbird, Outlook) making SMTP connections. This activity is significant as adversaries, including malware like AgentTesla, use such connections for Command and Control (C2) communication to exfiltrate stolen data. If confirmed malicious, this behavior could lead to unauthorized data exfiltration, including sensitive information like desktop screenshots, browser data, and system details, compromising the affected host.
+description: The following analytic detects a Windows application establishing an
+  SMTP connection from a non-common installation path. It leverages Sysmon EventCode
+  3 to identify processes not typically associated with email clients (e.g., Thunderbird,
+  Outlook) making SMTP connections. This activity is significant as adversaries, including
+  malware like AgentTesla, use such connections for Command and Control (C2) communication
+  to exfiltrate stolen data. If confirmed malicious, this behavior could lead to unauthorized
+  data exfiltration, including sensitive information like desktop screenshots, browser
+  data, and system details, compromising the affected host.
 data_source:
 - Sysmon EventID 3
-search: '`sysmon` EventCode=3 NOT(Image IN("*\\program files*", "*\\thunderbird.exe","*\\outlook.exe")) (DestinationPortName="smtp" OR DestinationPort=25 OR DestinationPort=587) | stats count min(_time) as firstTime max(_time) as lastTime by Image DestinationPort DestinationPortName DestinationHostname SourceHostname SourcePort SourcePortName Protocol DestinationIp dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mail_protocol_in_non_common_process_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 3 connection events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: third party application may use this network protocol as part of its feature. Filter is needed.
+search: '`sysmon` EventCode=3 NOT(Image IN("*\\program files*", "*\\thunderbird.exe","*\\outlook.exe"))
+  (DestinationPortName="smtp" OR DestinationPort=25 OR DestinationPort=587) | stats
+  count min(_time) as firstTime max(_time) as lastTime by Image DestinationPort DestinationPortName
+  DestinationHostname SourceHostname SourcePort SourcePortName Protocol DestinationIp
+  dest user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_mail_protocol_in_non_common_process_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and sysmon eventcode = 3 connection events from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: third party application may use this network protocol as part
+  of its feature. Filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla
 drilldown_searches:
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: a process $Image$ is having a SMTP connection to $DestinationHostname$ in $dest$
+  message: a process $Image$ is having a SMTP connection to $DestinationHostname$
+    in $dest$
   mitre_attack_id:
   - T1071.003
   - T1071
@@ -41,23 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - DestinationPort
-  - DestinationPortName
-  - DestinationHostname
-  - SourceHostname
-  - SourcePort
-  - SourcePortName
-  - Protocol
-  - DestinationIp
-  - dest
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_smtp/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_smtp/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_mark_of_the_web_bypass.yml b/detections/endpoint/windows_mark_of_the_web_bypass.yml
index 1dbb50b293..c12a9c0e66 100644
--- a/detections/endpoint/windows_mark_of_the_web_bypass.yml
+++ b/detections/endpoint/windows_mark_of_the_web_bypass.yml
@@ -7,9 +7,20 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 23
-description: The following analytic identifies a suspicious process that deletes the Mark-of-the-Web (MOTW) data stream. It leverages Sysmon EventCode 23 to detect when a file's Zone.Identifier stream is removed. This activity is significant because it is a common technique used by malware, such as Ave Maria RAT, to bypass security restrictions on files downloaded from the internet. If confirmed malicious, this behavior could allow an attacker to execute potentially harmful files without triggering security warnings, leading to further compromise of the system.
-search: '`sysmon` EventCode=23 TargetFilename = "*:Zone.Identifier" | stats  min(_time) as firstTime max(_time) as lastTime count by user EventCode Image TargetFilename ProcessID dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mark_of_the_web_bypass_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+description: The following analytic identifies a suspicious process that deletes the
+  Mark-of-the-Web (MOTW) data stream. It leverages Sysmon EventCode 23 to detect when
+  a file's Zone.Identifier stream is removed. This activity is significant because
+  it is a common technique used by malware, such as Ave Maria RAT, to bypass security
+  restrictions on files downloaded from the internet. If confirmed malicious, this
+  behavior could allow an attacker to execute potentially harmful files without triggering
+  security warnings, leading to further compromise of the system.
+search: '`sysmon` EventCode=23 TargetFilename = "*:Zone.Identifier" | stats  min(_time)
+  as firstTime max(_time) as lastTime count by user EventCode Image TargetFilename
+  ProcessID dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_mark_of_the_web_bypass_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the deleted target file name, process name and process id  from your endpoints.
+  If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1553/005/
@@ -20,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - TargetFilename
-  - dest
-  - user
-  - Image
-  - ProcessID
-  - _time
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.005/mark_of_the_web_bypass/possible-motw-deletion.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.005/mark_of_the_web_bypass/possible-motw-deletion.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_masquerading_explorer_as_child_process.yml b/detections/endpoint/windows_masquerading_explorer_as_child_process.yml
index 8f94cf3039..8c1e9f7d70 100644
--- a/detections/endpoint/windows_masquerading_explorer_as_child_process.yml
+++ b/detections/endpoint/windows_masquerading_explorer_as_child_process.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where explorer.exe is spawned by unusual parent processes such as cmd.exe, powershell.exe, or regsvr32.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because explorer.exe is typically initiated by userinit.exe, and deviations from this norm can indicate code injection or process masquerading attempts by malware like Qakbot. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, evade detection, and maintain persistence within the environment.
+description: The following analytic identifies instances where explorer.exe is spawned
+  by unusual parent processes such as cmd.exe, powershell.exe, or regsvr32.exe. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process and parent process relationships. This activity is significant because
+  explorer.exe is typically initiated by userinit.exe, and deviations from this norm
+  can indicate code injection or process masquerading attempts by malware like Qakbot.
+  If confirmed malicious, this behavior could allow attackers to execute arbitrary
+  code, evade detection, and maintain persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN("cmd.exe", "powershell.exe", "regsvr32.exe") AND Processes.process_name = "explorer.exe" AND Processes.process IN ("*\\explorer.exe") by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_masquerading_explorer_as_child_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN("cmd.exe", "powershell.exe", "regsvr32.exe") AND Processes.process_name = "explorer.exe"
+  AND Processes.process IN ("*\\explorer.exe") by Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process_id Processes.process_guid Processes.process
+  Processes.user Processes.dest Processes.parent_process_id | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_masquerading_explorer_as_child_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: explorer.exe hash a suspicious parent process $parent_process_name$ in $dest$
+  message: explorer.exe hash a suspicious parent process $parent_process_name$ in
+    $dest$
   mitre_attack_id:
   - T1574.002
   - T1574
@@ -43,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_masquerading_msdtc_process.yml b/detections/endpoint/windows_masquerading_msdtc_process.yml
index 07b9521ae1..ab7317e982 100644
--- a/detections/endpoint/windows_masquerading_msdtc_process.yml
+++ b/detections/endpoint/windows_masquerading_msdtc_process.yml
@@ -9,9 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the execution of msdtc.exe with specific command-line parameters (-a or -b), which are indicative of the PlugX malware. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because PlugX uses these parameters to masquerade its malicious operations within legitimate processes, making it harder to detect. If confirmed malicious, this behavior could allow attackers to gain unauthorized access, exfiltrate data, and conduct espionage, severely compromising the affected system.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "msdtc.exe" Processes.process = "*msdtc.exe*" Processes.process IN ("* -a*", "* -b*") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_masquerading_msdtc_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies the execution of msdtc.exe with specific
+  command-line parameters (-a or -b), which are indicative of the PlugX malware. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments. This activity is significant because
+  PlugX uses these parameters to masquerade its malicious operations within legitimate
+  processes, making it harder to detect. If confirmed malicious, this behavior could
+  allow attackers to gain unauthorized access, exfiltrate data, and conduct espionage,
+  severely compromising the affected system.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "msdtc.exe"
+  Processes.process = "*msdtc.exe*" Processes.process IN ("* -a*", "* -b*") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_masquerading_msdtc_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.plugx
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,23 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/msdtc_process_param/msdtc_a_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/msdtc_process_param/msdtc_a_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_mimikatz_binary_execution.yml b/detections/endpoint/windows_mimikatz_binary_execution.yml
index 27cbab9572..0aa6a839a3 100644
--- a/detections/endpoint/windows_mimikatz_binary_execution.yml
+++ b/detections/endpoint/windows_mimikatz_binary_execution.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise.
+description: The following analytic identifies the execution of the native mimikatz.exe
+  binary on Windows systems, including instances where the binary is renamed. It leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process names
+  and original file names. This activity is significant because Mimikatz is a widely
+  used tool for extracting authentication credentials, posing a severe security risk.
+  If confirmed malicious, this activity could allow attackers to obtain sensitive
+  credentials, escalate privileges, and move laterally within the network, leading
+  to potential data breaches and system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=mimikatz.exe OR Processes.original_file_name=mimikatz.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mimikatz_binary_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as this is directly looking for Mimikatz, the credential dumping utility.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=mimikatz.exe
+  OR Processes.original_file_name=mimikatz.exe) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_mimikatz_binary_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as this is directly looking
+  for Mimikatz, the credential dumping utility.
 references:
 - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf
 - https://www.varonis.com/blog/what-is-mimikatz
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting dump credentials.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting dump credentials.
   mitre_attack_id:
   - T1003
   observable:
@@ -61,23 +88,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/mimikatzwindows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/mimikatzwindows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml b/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml
index f679240907..b3ded53ede 100644
--- a/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml
+++ b/detections/endpoint/windows_mimikatz_crypto_export_file_extensions.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of files with extensions commonly associated with the Mimikatz Crypto module. It leverages the Endpoint.Filesystem data model to identify specific file names indicative of certificate export activities. This behavior is significant as it may indicate the use of Mimikatz to export cryptographic keys, which is a common tactic for credential theft. If confirmed malicious, this activity could allow an attacker to exfiltrate sensitive cryptographic material, potentially leading to unauthorized access and further compromise of the environment.
+description: The following analytic detects the creation of files with extensions
+  commonly associated with the Mimikatz Crypto module. It leverages the Endpoint.Filesystem
+  data model to identify specific file names indicative of certificate export activities.
+  This behavior is significant as it may indicate the use of Mimikatz to export cryptographic
+  keys, which is a common tactic for credential theft. If confirmed malicious, this
+  activity could allow an attacker to exfiltrate sensitive cryptographic material,
+  potentially leading to unauthorized access and further compromise of the environment.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.keyx.rsa.pvk","*sign.rsa.pvk","*sign.dsa.pvk","*dsa.ec.p8k","*dh.ec.p8k", "*.pfx", "*.der") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Filesystem)` | `windows_mimikatz_crypto_export_file_extensions_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives may be present and may need to be reviewed before this can be turned into a TTP. In addition, remove .pfx (standalone) if it's too much volume.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.keyx.rsa.pvk","*sign.rsa.pvk","*sign.dsa.pvk","*dsa.ec.p8k","*dh.ec.p8k",
+  "*.pfx", "*.der") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.file_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`
+  | `drop_dm_object_name(Filesystem)` | `windows_mimikatz_crypto_export_file_extensions_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives may be present and may need to be reviewed
+  before this can be turned into a TTP. In addition, remove .pfx (standalone) if it's
+  too much volume.
 references:
 - https://github.com/gentilkiwi/mimikatz/blob/master/mimikatz/modules/kuhl_m_crypto.c#L628-L645
 drilldown_searches:
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 40
-  message: Certificate file extensions realted to Mimikatz were identified on disk on $dest$.
+  message: Certificate file extensions realted to Mimikatz were identified on disk
+    on $dest$.
   mitre_attack_id:
   - T1649
   observable:
@@ -42,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certwrite_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/certwrite_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml b/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml
index dbfbf61c0d..8975354849 100644
--- a/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml
+++ b/detections/endpoint/windows_modify_registry_authenticationleveloverride.yml
@@ -8,10 +8,26 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry key "AuthenticationLevelOverride" within the Terminal Server Client settings. It leverages data from the Endpoint.Registry datamodel to identify changes where the registry value is set to 0x00000000. This activity is significant as it may indicate an attempt to override authentication levels for remote connections, a tactic used by DarkGate malware for malicious installations. If confirmed malicious, this could allow attackers to gain unauthorized remote access, potentially leading to data exfiltration or further system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal Server Client\\AuthenticationLevelOverride"  Registry.registry_value_data = 0x00000000 by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_authenticationleveloverride_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+description: The following analytic detects modifications to the Windows registry
+  key "AuthenticationLevelOverride" within the Terminal Server Client settings. It
+  leverages data from the Endpoint.Registry datamodel to identify changes where the
+  registry value is set to 0x00000000. This activity is significant as it may indicate
+  an attempt to override authentication levels for remote connections, a tactic used
+  by DarkGate malware for malicious installations. If confirmed malicious, this could
+  allow attackers to gain unauthorized remote access, potentially leading to data
+  exfiltration or further system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal
+  Server Client\\AuthenticationLevelOverride"  Registry.registry_value_data = 0x00000000
+  by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_authenticationleveloverride_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/AuthenticationLevelOverride/auth_sys.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/AuthenticationLevelOverride/auth_sys.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_auto_minor_updates.yml b/detections/endpoint/windows_modify_registry_auto_minor_updates.yml
index 6a1ab4d7b9..930d1facbf 100644
--- a/detections/endpoint/windows_modify_registry_auto_minor_updates.yml
+++ b/detections/endpoint/windows_modify_registry_auto_minor_updates.yml
@@ -8,10 +8,26 @@ type: Hunting
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic identifies a suspicious modification to the Windows auto update configuration registry. It detects changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" with a value of "0x00000000". This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to bypass detection and deploy additional payloads. If confirmed malicious, this modification could allow attackers to evade defenses, potentially leading to further system compromise and exploitation of zero-day vulnerabilities.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates" AND Registry.registry_value_data="0x00000000" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_minor_updates_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic identifies a suspicious modification to the Windows
+  auto update configuration registry. It detects changes to the registry path 
+  "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates"
+  with a value of "0x00000000". This activity is significant as it is commonly used
+  by adversaries, including malware like RedLine Stealer, to bypass detection and
+  deploy additional payloads. If confirmed malicious, this modification could allow
+  attackers to evade defenses, potentially leading to further system compromise and
+  exploitation of zero-day vulnerabilities.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AutoInstallMinorUpdates"
+  AND Registry.registry_value_data="0x00000000" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_minor_updates_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 tags:
@@ -32,19 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_auto_update_notif.yml b/detections/endpoint/windows_modify_registry_auto_update_notif.yml
index dc3f179d72..21da9cd8d9 100644
--- a/detections/endpoint/windows_modify_registry_auto_update_notif.yml
+++ b/detections/endpoint/windows_modify_registry_auto_update_notif.yml
@@ -8,10 +8,26 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious modification to the Windows registry that changes the auto-update notification setting to "Notify before download." This detection leverages data from the Endpoint.Registry data model, focusing on specific registry paths and values. This activity is significant because it is a known technique used by adversaries, including malware like RedLine Stealer, to evade detection and potentially deploy additional payloads. If confirmed malicious, this modification could allow attackers to bypass security measures, maintain persistence, and exploit vulnerabilities on the target host.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AUOptions" AND Registry.registry_value_data="0x00000002" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_update_notif_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious modification to the Windows
+  registry that changes the auto-update notification setting to "Notify before download."
+  This detection leverages data from the Endpoint.Registry data model, focusing on
+  specific registry paths and values. This activity is significant because it is a
+  known technique used by adversaries, including malware like RedLine Stealer, to
+  evade detection and potentially deploy additional payloads. If confirmed malicious,
+  this modification could allow attackers to bypass security measures, maintain persistence,
+  and exploit vulnerabilities on the target host.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\AUOptions"
+  AND Registry.registry_value_data="0x00000002" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_auto_update_notif_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_configure_bitlocker.yml b/detections/endpoint/windows_modify_registry_configure_bitlocker.yml
index 9fae1a6567..8d50210e1f 100644
--- a/detections/endpoint/windows_modify_registry_configure_bitlocker.yml
+++ b/detections/endpoint/windows_modify_registry_configure_bitlocker.yml
@@ -7,10 +7,29 @@ data_source:
 - Sysmon EventID 13
 type: TTP
 status: production
-description: This analytic is developed to detect suspicious registry modifications targeting BitLocker settings. The malware ShrinkLocker alters various registry keys to change how BitLocker handles encryption, potentially bypassing TPM requirements, enabling BitLocker without TPM, and enforcing specific startup key and PIN configurations. Such modifications can weaken system security, making it easier for unauthorized access and data breaches. Detecting these changes is crucial for maintaining robust encryption and data protection.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*" Registry.registry_value_name IN("EnableBDEWithNoTPM", "EnableNonTPM", "UseAdvancedStartup") Registry.registry_value_data = 0x00000001) OR (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*" Registry.registry_value_name IN("UsePIN", "UsePartialEncryptionKey", "UseTPM", "UseTPMKey", "UseTPMKeyPIN", "UseTPMPIN") Registry.registry_value_data = 0x00000002) by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_configure_bitlocker_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: This analytic is developed to detect suspicious registry modifications
+  targeting BitLocker settings. The malware ShrinkLocker alters various registry keys
+  to change how BitLocker handles encryption, potentially bypassing TPM requirements,
+  enabling BitLocker without TPM, and enforcing specific startup key and PIN configurations.
+  Such modifications can weaken system security, making it easier for unauthorized
+  access and data breaches. Detecting these changes is crucial for maintaining robust
+  encryption and data protection.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*"
+  Registry.registry_value_name IN("EnableBDEWithNoTPM", "EnableNonTPM", "UseAdvancedStartup")
+  Registry.registry_value_data = 0x00000001) OR (Registry.registry_path= "*\\Policies\\Microsoft\\FVE\\*"
+  Registry.registry_value_name IN("UsePIN", "UsePartialEncryptionKey", "UseTPM", "UseTPMKey",
+  "UseTPMKeyPIN", "UseTPMPIN") Registry.registry_value_data = 0x00000002) by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_configure_bitlocker_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,21 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.dest
-  - Registry.action
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/bitlocker_registry_setting//fve-reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/bitlocker_registry_setting//fve-reg.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_default_icon_setting.yml b/detections/endpoint/windows_modify_registry_default_icon_setting.yml
index 5b829356a6..0e0d1e2827 100644
--- a/detections/endpoint/windows_modify_registry_default_icon_setting.yml
+++ b/detections/endpoint/windows_modify_registry_default_icon_setting.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious modifications to the Windows registry's default icon settings, a technique associated with Lockbit ransomware. It leverages data from the Endpoint Registry data model, focusing on changes to registry paths under "*HKCR\\*\\defaultIcon\\(Default)*". This activity is significant as it is uncommon for normal users to modify these settings, and such changes can indicate ransomware infection or other malware. If confirmed malicious, this could lead to system defacement and signal a broader ransomware attack, potentially compromising sensitive data and system integrity.
+description: The following analytic detects suspicious modifications to the Windows
+  registry's default icon settings, a technique associated with Lockbit ransomware.
+  It leverages data from the Endpoint Registry data model, focusing on changes to
+  registry paths under "*HKCR\\*\\defaultIcon\\(Default)*". This activity is significant
+  as it is uncommon for normal users to modify these settings, and such changes can
+  indicate ransomware infection or other malware. If confirmed malicious, this could
+  lead to system defacement and signal a broader ransomware attack, potentially compromising
+  sensitive data and system integrity.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\defaultIcon\\(Default)*" Registry.registry_path = "*HKCR\\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.action Registry.dest  Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_modify_registry_default_icon_setting_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path
+  ="*\\defaultIcon\\(Default)*" Registry.registry_path = "*HKCR\\*" by Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.action Registry.dest  Registry.user
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`
+  | `windows_modify_registry_default_icon_setting_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
 known_false_positives: unknown
 references:
 - https://blogs.vmware.com/security/2022/10/lockbit-3-0-also-known-as-lockbit-black.html
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A suspicious registry modification to change the default icon association of windows to ransomware was detected on endpoint $dest$ by user $user$.
+  message: A suspicious registry modification to change the default icon association
+    of windows to ransomware was detected on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1112
   observable:
@@ -46,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lockbit_ransomware/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/lockbit_ransomware/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml b/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml
index 9083841810..46005f65df 100644
--- a/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml
+++ b/detections/endpoint/windows_modify_registry_delete_firewall_rules.yml
@@ -7,10 +7,23 @@ data_source:
 - Sysmon EventID 12
 type: TTP
 status: production
-description: The following analytic detects a potential deletion of firewall rules, indicating a possible security breach or unauthorized access attempt. It identifies actions where firewall rules are removed using commands like netsh advfirewall firewall delete rule, which can expose the network to external threats by disabling critical security measures. Monitoring these activities helps maintain network integrity and prevent malicious attacks.
-search: '`sysmon` EventCode=12 TargetObject = "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" EventType=DeleteValue |  rename Computer as dest |  rename User as user |  stats count min(_time) as firstTime max(_time) as lastTime by EventCode EventType TargetObject Image user dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_delete_firewall_rules_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
-known_false_positives: network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered.
+description: The following analytic detects a potential deletion of firewall rules,
+  indicating a possible security breach or unauthorized access attempt. It identifies
+  actions where firewall rules are removed using commands like netsh advfirewall firewall
+  delete rule, which can expose the network to external threats by disabling critical
+  security measures. Monitoring these activities helps maintain network integrity
+  and prevent malicious attacks.
+search: '`sysmon` EventCode=12 TargetObject = "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*"
+  EventType=DeleteValue |  rename Computer as dest |  rename User as user |  stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode EventType TargetObject
+  Image user dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_delete_firewall_rules_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
+known_false_positives: network admin may add/remove/modify public inbound firewall
+  rule that may cause this rule to be triggered.
 references:
 - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - EventType
-  - TargetObject
-  - Image
-  - user
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disable_rdp.yml b/detections/endpoint/windows_modify_registry_disable_rdp.yml
index f2238da3ad..ccc3d6d0d0 100644
--- a/detections/endpoint/windows_modify_registry_disable_rdp.yml
+++ b/detections/endpoint/windows_modify_registry_disable_rdp.yml
@@ -7,10 +7,25 @@ data_source:
 - Sysmon EventID 13
 type: Anomaly
 status: production
-description: This analytic is developed to detect suspicious registry modifications that disable Remote Desktop Protocol (RDP) by altering the "fDenyTSConnections" key. Changing this key's value to 1 prevents remote connections, which can disrupt remote management and access. Such modifications could indicate an attempt to hinder remote administration or isolate the system from remote intervention, potentially signifying malicious activity.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_rdp_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: This analytic is developed to detect suspicious registry modifications
+  that disable Remote Desktop Protocol (RDP) by altering the "fDenyTSConnections"
+  key. Changing this key's value to 1 prevents remote connections, which can disrupt
+  remote management and access. Such modifications could indicate an attempt to hinder
+  remote administration or isolate the system from remote intervention, potentially
+  signifying malicious activity.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal
+  Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_rdp_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,21 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.dest
-  - Registry.action
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_rdp//fdenytsconnection-reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disable_rdp//fdenytsconnection-reg.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml b/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml
index 5634f8ede1..ef33a91b9a 100644
--- a/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml
+++ b/detections/endpoint/windows_modify_registry_disable_restricted_admin.yml
@@ -15,10 +15,10 @@ description: The following analytic detects modifications to the Windows registr
   can disable a security feature that limits credential exposure during remote connections.
   If confirmed malicious, an attacker could weaken security controls, increasing the
   risk of credential theft and unauthorized access to sensitive systems.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DisableRestrictedAdmin"
-  Registry.registry_value_data = 0x00000000) BY Registry.dest Registry.user
-  Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DisableRestrictedAdmin"
+  Registry.registry_value_data = 0x00000000) BY Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_restricted_admin_filter`'
 how_to_implement: To successfully implement this search, you must be ingesting data
@@ -36,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -61,21 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.dest
-  - Registry.action
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.004/NoLMHash/lsa-reg-settings-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml b/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml
index 3d3cece5fa..31a1ba3b11 100644
--- a/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml
+++ b/detections/endpoint/windows_modify_registry_disable_toast_notifications.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects modifications to the Windows registry that disable toast notifications. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" with a value set to "0x00000000". This activity is significant because disabling toast notifications can prevent users from receiving critical system and application updates, which adversaries like Azorult exploit for defense evasion. If confirmed malicious, this action could allow attackers to operate undetected, leading to prolonged persistence and potential further compromise of the system.
+description: The following analytic detects modifications to the Windows registry
+  that disable toast notifications. It leverages data from the Endpoint.Registry datamodel,
+  specifically monitoring changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*"
+  with a value set to "0x00000000". This activity is significant because disabling
+  toast notifications can prevent users from receiving critical system and application
+  updates, which adversaries like Azorult exploit for defense evasion. If confirmed
+  malicious, this action could allow attackers to operate undetected, leading to prolonged
+  persistence and potential further compromise of the system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_toast_notifications_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\PushNotifications\\ToastEnabled*"
+  Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_disable_toast_notifications_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml b/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml
index 6d6b745b77..c405775bac 100644
--- a/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml
+++ b/detections/endpoint/windows_modify_registry_disable_win_defender_raw_write_notif.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects modifications to the Windows registry that disable the Windows Defender raw write notification feature. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the registry path associated with Windows Defender's real-time protection settings. This activity is significant because disabling raw write notifications can allow malware, such as Azorult, to bypass Windows Defender's behavior monitoring, potentially leading to undetected malicious activities. If confirmed malicious, this could enable attackers to execute code, persist in the environment, and access sensitive information without detection.
+description: The following analytic detects modifications to the Windows registry
+  that disable the Windows Defender raw write notification feature. It leverages data
+  from the Endpoint.Registry datamodel, specifically monitoring changes to the registry
+  path associated with Windows Defender's real-time protection settings. This activity
+  is significant because disabling raw write notifications can allow malware, such
+  as Azorult, to bypass Windows Defender's behavior monitoring, potentially leading
+  to undetected malicious activities. If confirmed malicious, this could enable attackers
+  to execute code, persist in the environment, and access sensitive information without
+  detection.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\Real-Time Protection\\DisableRawWriteNotification*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_win_defender_raw_write_notif_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\Real-Time Protection\\DisableRawWriteNotification*" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_win_defender_raw_write_notif_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive. Filter as needed.
 references:
 - https://admx.help/?Category=SystemCenterEndpointProtection&Policy=Microsoft.Policies.Antimalware::real-time_protection_disablerawwritenotification
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The registry for raw write notification settings was modified to disable in $dest$.
+  message: The registry for raw write notification settings was modified to disable
+    in $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -43,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml b/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml
index 0b190757b0..db8437cdcf 100644
--- a/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml
+++ b/detections/endpoint/windows_modify_registry_disable_windefender_notifications.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious registry modification aimed at disabling Windows Defender notifications. It leverages data from the Endpoint.Registry data model, specifically looking for changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender Security Center\\Notifications\\DisableNotifications" with a value of "0x00000001". This activity is significant as it indicates an attempt to evade detection by disabling security alerts, a technique used by adversaries and malware like RedLine Stealer. If confirmed malicious, this could allow attackers to operate undetected, increasing the risk of further compromise and data exfiltration.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender Security Center\\Notifications\\DisableNotifications" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_disable_windefender_notifications_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious registry modification aimed
+  at disabling Windows Defender notifications. It leverages data from the Endpoint.Registry
+  data model, specifically looking for changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows
+  Defender Security Center\\Notifications\\DisableNotifications" with a value of "0x00000001".
+  This activity is significant as it indicates an attempt to evade detection by disabling
+  security alerts, a technique used by adversaries and malware like RedLine Stealer.
+  If confirmed malicious, this could allow attackers to operate undetected, increasing
+  the risk of further compromise and data exfiltration.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows
+  Defender Security Center\\Notifications\\DisableNotifications" AND Registry.registry_value_data="0x00000001"
+  by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_modify_registry_disable_windefender_notifications_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml b/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml
index 30347855fb..f696f503b2 100644
--- a/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml
+++ b/detections/endpoint/windows_modify_registry_disable_windows_security_center_notif.yml
@@ -5,13 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects modifications to the Windows registry aimed at disabling Windows Security Center notifications. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the registry path "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" with a value of "0x00000000". This activity is significant as it can indicate an attempt by adversaries or malware, such as Azorult, to evade defenses by suppressing critical update notifications. If confirmed malicious, this could allow attackers to persist undetected, potentially leading to further exploitation and compromise of the host system.
+description: The following analytic detects modifications to the Windows registry
+  aimed at disabling Windows Security Center notifications. It leverages data from
+  the Endpoint.Registry datamodel, specifically monitoring changes to the registry
+  path "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" with
+  a value of "0x00000000". This activity is significant as it can indicate an attempt
+  by adversaries or malware, such as Azorult, to evade defenses by suppressing critical
+  update notifications. If confirmed malicious, this could allow attackers to persist
+  undetected, potentially leading to further exploitation and compromise of the host
+  system.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disable_windows_security_center_notif_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\ImmersiveShell\\UseActionCenterExperience*"
+  Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_disable_windows_security_center_notif_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: the registry for security center notification settings was modified to disable mode in $dest$
+  message: the registry for security center notification settings was modified to
+    disable mode in $dest$
   mitre_attack_id:
   - T1112
   observable:
@@ -43,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml b/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml
index f85e9d3ddc..127ca4f161 100644
--- a/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml
+++ b/detections/endpoint/windows_modify_registry_disableremotedesktopantialias.yml
@@ -8,10 +8,26 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry key "DisableRemoteDesktopAntiAlias" with a value set to 0x00000001. This detection leverages data from the Endpoint datamodel, specifically monitoring changes in the Registry node. This activity is significant as it may indicate the presence of DarkGate malware, which alters this registry setting to enhance its remote desktop capabilities. If confirmed malicious, this modification could allow an attacker to maintain persistence and control over the compromised host, potentially leading to further exploitation and data exfiltration.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal Services\\DisableRemoteDesktopAntiAlias"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disableremotedesktopantialias_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+description: The following analytic detects modifications to the Windows registry
+  key "DisableRemoteDesktopAntiAlias" with a value set to 0x00000001. This detection
+  leverages data from the Endpoint datamodel, specifically monitoring changes in the
+  Registry node. This activity is significant as it may indicate the presence of DarkGate
+  malware, which alters this registry setting to enhance its remote desktop capabilities.
+  If confirmed malicious, this modification could allow an attacker to maintain persistence
+  and control over the compromised host, potentially leading to further exploitation
+  and data exfiltration.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal
+  Services\\DisableRemoteDesktopAntiAlias"  Registry.registry_value_data = 0x00000001
+  by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disableremotedesktopantialias_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: the registry for remote desktop settings was modified to be DisableRemoteDesktopAntiAlias on $dest$.
+  message: the registry for remote desktop settings was modified to be DisableRemoteDesktopAntiAlias
+    on $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -41,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/DisableRemoteDesktopAntiAlias/disable_remote_alias.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/DisableRemoteDesktopAntiAlias/disable_remote_alias.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml b/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml
index ff5eb01d40..897ca5a2c7 100644
--- a/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml
+++ b/detections/endpoint/windows_modify_registry_disablesecuritysettings.yml
@@ -8,10 +8,26 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry that disable security settings for Terminal Services. It leverages the Endpoint data model, specifically monitoring changes to the registry path associated with Terminal Services security settings. This activity is significant because altering these settings can weaken the security posture of Remote Desktop Services, potentially allowing unauthorized remote access. If confirmed malicious, such modifications could enable attackers to gain persistent remote access to the system, facilitating further exploitation and data exfiltration.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal Services\\DisableSecuritySettings"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disablesecuritysettings_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+description: The following analytic detects modifications to the Windows registry
+  that disable security settings for Terminal Services. It leverages the Endpoint
+  data model, specifically monitoring changes to the registry path associated with
+  Terminal Services security settings. This activity is significant because altering
+  these settings can weaken the security posture of Remote Desktop Services, potentially
+  allowing unauthorized remote access. If confirmed malicious, such modifications
+  could enable attackers to gain persistent remote access to the system, facilitating
+  further exploitation and data exfiltration.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Terminal
+  Services\\DisableSecuritySettings"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disablesecuritysettings_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: the registry for terminal services settings was modified to disable security settings on $dest$.
+  message: the registry for terminal services settings was modified to disable security
+    settings on $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -42,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disablesecuritysetting.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/disablesecuritysetting.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml b/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml
index 5847248521..9834c69b40 100644
--- a/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml
+++ b/detections/endpoint/windows_modify_registry_disabling_wer_settings.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications in the Windows registry to disable Windows Error Reporting (WER) settings. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to registry paths related to WER with a value set to "0x00000001". This activity is significant as adversaries may disable WER to suppress error notifications, hiding the presence of malicious activities. If confirmed malicious, this could allow attackers to operate undetected, potentially leading to prolonged persistence and further exploitation within the environment.
+description: The following analytic detects modifications in the Windows registry
+  to disable Windows Error Reporting (WER) settings. It leverages data from the Endpoint.Registry
+  datamodel, specifically monitoring changes to registry paths related to WER with
+  a value set to "0x00000001". This activity is significant as adversaries may disable
+  WER to suppress error notifications, hiding the presence of malicious activities.
+  If confirmed malicious, this could allow attackers to operate undetected, potentially
+  leading to prolonged persistence and further exploitation within the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\disable*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disabling_wer_settings_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\Windows
+  Error Reporting\\disable*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_disabling_wer_settings_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_disallow_windows_app.yml b/detections/endpoint/windows_modify_registry_disallow_windows_app.yml
index a81c008df3..7b3dd212af 100644
--- a/detections/endpoint/windows_modify_registry_disallow_windows_app.yml
+++ b/detections/endpoint/windows_modify_registry_disallow_windows_app.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications to the Windows registry aimed at preventing the execution of specific computer programs. It leverages data from the Endpoint.Registry datamodel, focusing on changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" with a value of "0x00000001". This activity is significant as it can indicate an attempt to disable security tools, a tactic used by malware like Azorult. If confirmed malicious, this could allow an attacker to evade detection and maintain persistence on the compromised host.
+description: The following analytic detects modifications to the Windows registry
+  aimed at preventing the execution of specific computer programs. It leverages data
+  from the Endpoint.Registry datamodel, focusing on changes to the registry path 
+  "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*"
+  with a value of "0x00000001". This activity is significant as it can indicate an
+  attempt to disable security tools, a tactic used by malware like Azorult. If confirmed
+  malicious, this could allow an attacker to evade detection and maintain persistence
+  on the compromised host.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disallow_windows_app_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*"
+  Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_disallow_windows_app_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive. Filter as needed.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml b/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml
index ff43d2f38f..a48df5810b 100644
--- a/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml
+++ b/detections/endpoint/windows_modify_registry_do_not_connect_to_win_update.yml
@@ -8,10 +8,27 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious modification to the Windows registry that disables automatic updates. It leverages data from the Endpoint datamodel, specifically monitoring changes to the registry path "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" with a value of "0x00000001". This activity is significant as it can be used by adversaries, including malware like RedLine Stealer, to evade detection and prevent the system from receiving critical updates. If confirmed malicious, this could allow attackers to exploit vulnerabilities, persist in the environment, and potentially deploy additional payloads.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_do_not_connect_to_win_update_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious modification to the Windows
+  registry that disables automatic updates. It leverages data from the Endpoint datamodel,
+  specifically monitoring changes to the registry path 
+  "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations"
+  with a value of "0x00000001". This activity is significant as it can be used by
+  adversaries, including malware like RedLine Stealer, to evade detection and prevent
+  the system from receiving critical updates. If confirmed malicious, this could allow
+  attackers to exploit vulnerabilities, persist in the environment, and potentially
+  deploy additional payloads.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\DoNotConnectToWindowsUpdateInternetLocations"
+  AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_do_not_connect_to_win_update_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 - https://admx.help/?Category=Windows_10_2016&Policy=Microsoft.Policies.WindowsUpdate::DoNotConnectToWindowsUpdateInternetLocations
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_dontshowui.yml b/detections/endpoint/windows_modify_registry_dontshowui.yml
index 3c89ac5074..f2bfded9da 100644
--- a/detections/endpoint/windows_modify_registry_dontshowui.yml
+++ b/detections/endpoint/windows_modify_registry_dontshowui.yml
@@ -8,10 +8,26 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows Error Reporting registry key "DontShowUI" to suppress error reporting dialogs. It leverages data from the Endpoint datamodel's Registry node to identify changes where the registry value is set to 0x00000001. This activity is significant as it is commonly associated with DarkGate malware, which uses this modification to avoid detection during its installation. If confirmed malicious, this behavior could allow attackers to maintain a low profile, avoiding user alerts and potentially enabling further malicious activities without user intervention.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\DontShowUI"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_dontshowui_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+description: The following analytic detects modifications to the Windows Error Reporting
+  registry key "DontShowUI" to suppress error reporting dialogs. It leverages data
+  from the Endpoint datamodel's Registry node to identify changes where the registry
+  value is set to 0x00000001. This activity is significant as it is commonly associated
+  with DarkGate malware, which uses this modification to avoid detection during its
+  installation. If confirmed malicious, this behavior could allow attackers to maintain
+  a low profile, avoiding user alerts and potentially enabling further malicious activities
+  without user intervention.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows\\Windows
+  Error Reporting\\DontShowUI"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_dontshowui_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/wer_dontshowui/dontshowui_sys.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/wer_dontshowui/dontshowui_sys.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml b/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml
index bba5f81a96..15c53d77d8 100644
--- a/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml
+++ b/detections/endpoint/windows_modify_registry_enablelinkedconnections.yml
@@ -17,8 +17,8 @@ description: The following analytic detects a suspicious modification to the Win
   administrator-level privileges, a technique often abused by malware like BlackByte
   ransomware. If confirmed malicious, this could lead to unauthorized access to sensitive
   network resources, escalating the attacker's privileges.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLinkedConnections"
   Registry.registry_value_data = "0x00000001") BY _time span=1h Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
@@ -37,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,7 +53,8 @@ tags:
   - 4f4e2f9f-6209-4fcf-9b15-3b7455706f5b
   confidence: 70
   impact: 70
-  message: A registry modification in Windows EnableLinkedConnections configuration on $dest$
+  message: A registry modification in Windows EnableLinkedConnections configuration
+    on $dest$
   mitre_attack_id:
   - T1112
   observable:
@@ -60,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/enablelinkedconnections/blackbyte_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/enablelinkedconnections/blackbyte_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_longpathsenabled.yml b/detections/endpoint/windows_modify_registry_longpathsenabled.yml
index 380179cf3b..6bf2f071f0 100644
--- a/detections/endpoint/windows_modify_registry_longpathsenabled.yml
+++ b/detections/endpoint/windows_modify_registry_longpathsenabled.yml
@@ -16,11 +16,11 @@ description: The following analytic detects a modification to the Windows regist
   path limitations, potentially aiding in evasion techniques. If confirmed malicious,
   this modification could facilitate the execution of long-path payloads, aiding in
   persistence and further system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\CurrentControlSet\\Control\\FileSystem\\LongPathsEnabled"
-  Registry.registry_value_data = "0x00000001") BY Registry.registry_path
-  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
-  Registry.process_guid Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\CurrentControlSet\\Control\\FileSystem\\LongPathsEnabled"
+  Registry.registry_value_data = "0x00000001") BY Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `windows_modify_registry_longpathsenabled_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
@@ -36,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -59,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/longpathsenabled/longpath_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/blackbyte/longpathsenabled/longpath_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml b/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml
index 8eac5deb3f..746f5c09b0 100644
--- a/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml
+++ b/detections/endpoint/windows_modify_registry_maxconnectionperserver.yml
@@ -8,10 +8,27 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic identifies a suspicious modification of the Windows registry setting for max connections per server. It detects changes to specific registry paths using data from the Endpoint.Registry datamodel. This activity is significant because altering this setting can be exploited by attackers to increase the number of concurrent connections to a remote server, potentially facilitating DDoS attacks or enabling more effective lateral movement within a compromised network. If confirmed malicious, this could lead to network disruption or further compromise of additional systems.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\\MaxConnectionsPerServer*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet Settings\\MaxConnectionsPer1_0Server*")  Registry.registry_value_data = "0x0000000a" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_maxconnectionperserver_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic identifies a suspicious modification of the Windows
+  registry setting for max connections per server. It detects changes to specific
+  registry paths using data from the Endpoint.Registry datamodel. This activity is
+  significant because altering this setting can be exploited by attackers to increase
+  the number of concurrent connections to a remote server, potentially facilitating
+  DDoS attacks or enabling more effective lateral movement within a compromised network.
+  If confirmed malicious, this could lead to network disruption or further compromise
+  of additional systems.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet
+  Settings\\MaxConnectionsPerServer*" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Internet
+  Settings\\MaxConnectionsPer1_0Server*")  Registry.registry_value_data = "0x0000000a"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_maxconnectionperserver_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://asec.ahnlab.com/en/17692/
 - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer.
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/maxconnectionperserver/registry_event.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/maxconnectionperserver/registry_event.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml b/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml
index 3655d323d5..362cdb5005 100644
--- a/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml
+++ b/detections/endpoint/windows_modify_registry_no_auto_reboot_with_logon_user.yml
@@ -8,10 +8,26 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious modification to the Windows registry that disables automatic reboot with a logged-on user. This detection leverages the Endpoint data model to identify changes to the registry path `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoRebootWithLoggedOnUsers` with a value of `0x00000001`. This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to evade detection and maintain persistence. If confirmed malicious, this could allow attackers to bypass security measures and deploy additional payloads without interruption.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoRebootWithLoggedOnUsers" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_reboot_with_logon_user_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious modification to the Windows
+  registry that disables automatic reboot with a logged-on user. This detection leverages
+  the Endpoint data model to identify changes to the registry path 
+  `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoRebootWithLoggedOnUsers`
+  with a value of `0x00000001`. This activity is significant as it is commonly used
+  by adversaries, including malware like RedLine Stealer, to evade detection and maintain
+  persistence. If confirmed malicious, this could allow attackers to bypass security
+  measures and deploy additional payloads without interruption.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoRebootWithLoggedOnUsers"
+  AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_reboot_with_logon_user_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_no_auto_update.yml b/detections/endpoint/windows_modify_registry_no_auto_update.yml
index f8527b91ba..c74d49970f 100644
--- a/detections/endpoint/windows_modify_registry_no_auto_update.yml
+++ b/detections/endpoint/windows_modify_registry_no_auto_update.yml
@@ -8,10 +8,25 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic identifies a suspicious modification to the Windows registry that disables automatic updates. It detects changes to the registry path `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoUpdate` with a value of `0x00000001`. This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to evade detection and maintain persistence. If confirmed malicious, this could allow attackers to bypass security updates, leaving the system vulnerable to further exploitation and potential zero-day attacks.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoUpdate" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_update_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic identifies a suspicious modification to the Windows
+  registry that disables automatic updates. It detects changes to the registry path
+  `SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU\NoAutoUpdate` with a value
+  of `0x00000001`. This activity is significant as it is commonly used by adversaries,
+  including malware like RedLine Stealer, to evade detection and maintain persistence.
+  If confirmed malicious, this could allow attackers to bypass security updates, leaving
+  the system vulnerable to further exploitation and potential zero-day attacks.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\NoAutoUpdate"
+  AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_no_auto_update_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 drilldown_searches:
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml b/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml
index 33e0f6e757..63a6623609 100644
--- a/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml
+++ b/detections/endpoint/windows_modify_registry_nochangingwallpaper.yml
@@ -15,8 +15,8 @@ description: The following analytic detects modifications to the Windows registr
   to enforce a malicious wallpaper, thereby limiting user control over system settings.
   If confirmed malicious, this registry change could indicate a ransomware infection,
   leading to further system compromise and user disruption.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\ActiveDesktop\\NoChangingWallPaper"
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\ActiveDesktop\\NoChangingWallPaper"
   Registry.registry_value_data = 1) BY Registry.dest Registry.user Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
@@ -35,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: the registry settings was modified to disable changing of wallpaper on $dest$.
+  message: the registry settings was modified to disable changing of wallpaper on
+    $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -56,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/no_changing_wallpaper/NoChangingWallPaper.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/no_changing_wallpaper/NoChangingWallPaper.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml b/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml
index 6450260caf..b16ae0536f 100644
--- a/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml
+++ b/detections/endpoint/windows_modify_registry_on_smart_card_group_policy.yml
@@ -7,10 +7,24 @@ data_source:
 - Sysmon EventID 13
 type: Anomaly
 status: production
-description: This analytic is developed to detect suspicious registry modifications targeting the "scforceoption" key. Altering this key enforces smart card login for all users, potentially disrupting normal access methods. Unauthorized changes to this setting could indicate an attempt to restrict access or force a specific authentication method, possibly signifying malicious intent to manipulate system security protocols.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\System\\scforceoption*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_on_smart_card_group_policy_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: This analytic is developed to detect suspicious registry modifications
+  targeting the "scforceoption" key. Altering this key enforces smart card login for
+  all users, potentially disrupting normal access methods. Unauthorized changes to
+  this setting could indicate an attempt to restrict access or force a specific authentication
+  method, possibly signifying malicious intent to manipulate system security protocols.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows\\CurrentVersion\\Policies\\System\\scforceoption*"
+  Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user
+  Registry.registry_path Registry.registry_value_data Registry.action Registry.dest
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_on_smart_card_group_policy_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://www.bleepingcomputer.com/news/security/new-shrinklocker-ransomware-uses-bitlocker-to-encrypt-your-files/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A registry modification in Windows Smart Card Group Policy registry settings on $dest$
+  message: A registry modification in Windows Smart Card Group Policy registry settings
+    on $dest$
   mitre_attack_id:
   - T1112
   observable:
@@ -40,21 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.dest
-  - Registry.action
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/smart_card_group_policy/scforceoption-reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/smart_card_group_policy/scforceoption-reg.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_proxyenable.yml b/detections/endpoint/windows_modify_registry_proxyenable.yml
index 89f0769849..34a77c5b08 100644
--- a/detections/endpoint/windows_modify_registry_proxyenable.yml
+++ b/detections/endpoint/windows_modify_registry_proxyenable.yml
@@ -8,10 +8,26 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry key "ProxyEnable" to enable proxy settings. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "Internet Settings\ProxyEnable" registry path. This activity is significant as it is commonly exploited by malware and adversaries to establish proxy communication, potentially connecting to malicious Command and Control (C2) servers. If confirmed malicious, this could allow attackers to redirect network traffic through a proxy, facilitating unauthorized communication and data exfiltration, thereby compromising the security of the affected host.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet Settings\\ProxyEnable"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_proxyenable_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+description: The following analytic detects modifications to the Windows registry
+  key "ProxyEnable" to enable proxy settings. It leverages data from the Endpoint.Registry
+  datamodel, specifically monitoring changes to the "Internet Settings\ProxyEnable"
+  registry path. This activity is significant as it is commonly exploited by malware
+  and adversaries to establish proxy communication, potentially connecting to malicious
+  Command and Control (C2) servers. If confirmed malicious, this could allow attackers
+  to redirect network traffic through a proxy, facilitating unauthorized communication
+  and data exfiltration, thereby compromising the security of the affected host.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet
+  Settings\\ProxyEnable"  Registry.registry_value_data = 0x00000001 by  Registry.registry_path
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_proxyenable_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_enable/proxyenable.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_enable/proxyenable.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_proxyserver.yml b/detections/endpoint/windows_modify_registry_proxyserver.yml
index 75819fcf58..15fc81a220 100644
--- a/detections/endpoint/windows_modify_registry_proxyserver.yml
+++ b/detections/endpoint/windows_modify_registry_proxyserver.yml
@@ -8,10 +8,25 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows registry key for setting up a proxy server. It leverages data from the Endpoint.Registry datamodel, focusing on changes to the "Internet Settings\\ProxyServer" registry path. This activity is significant as it can indicate malware or adversaries configuring a proxy to facilitate unauthorized communication with Command and Control (C2) servers. If confirmed malicious, this could allow attackers to establish persistent, covert channels for data exfiltration or further exploitation of the compromised host.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet Settings\\ProxyServer" by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_proxyserver_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
+description: The following analytic detects modifications to the Windows registry
+  key for setting up a proxy server. It leverages data from the Endpoint.Registry
+  datamodel, focusing on changes to the "Internet Settings\\ProxyServer" registry
+  path. This activity is significant as it can indicate malware or adversaries configuring
+  a proxy to facilitate unauthorized communication with Command and Control (C2) servers.
+  If confirmed malicious, this could allow attackers to establish persistent, covert
+  channels for data exfiltration or further exploitation of the compromised host.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path = "*\\Internet
+  Settings\\ProxyServer" by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid Registry.action Registry.user Registry.dest | `drop_dm_object_name(Registry)`
+  | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_proxyserver_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive, however is not common. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.darkgate
 drilldown_searches:
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_server/ProxyServer_sys.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/proxy_server/ProxyServer_sys.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml b/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml
index cd0e4667fa..422e1b5bc5 100644
--- a/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml
+++ b/detections/endpoint/windows_modify_registry_qakbot_binary_data_registry.yml
@@ -5,12 +5,43 @@ date: '2024-09-30'
 author: Teoderick Contreras, Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the creation of a suspicious registry entry by Qakbot malware, characterized by 8 random registry value names with encrypted binary data. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on registry modifications under the "SOFTWARE\\Microsoft\\" path by processes like explorer.exe. This activity is significant as it indicates potential Qakbot infection, which uses the registry to store malicious code or configuration data. If confirmed malicious, this could allow attackers to maintain persistence and execute arbitrary code on the compromised system.
+description: The following analytic detects the creation of a suspicious registry
+  entry by Qakbot malware, characterized by 8 random registry value names with encrypted
+  binary data. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on registry modifications under the "SOFTWARE\\Microsoft\\"
+  path by processes like explorer.exe. This activity is significant as it indicates
+  potential Qakbot infection, which uses the registry to store malicious code or configuration
+  data. If confirmed malicious, this could allow attackers to maintain persistence
+  and execute arbitrary code on the compromised system.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count dc(registry_value_name) as registry_value_name_count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\*" AND Registry.registry_value_data = "Binary Data" by _time span=1m Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.process_id Registry.registry_key_name | `drop_dm_object_name(Registry)` | eval registry_key_name_len = len(registry_key_name) | eval registry_value_name_len = len(registry_value_name) | regex registry_value_name="^[0-9a-fA-F]{8}" | where registry_key_name_len < 80 AND registry_value_name_len == 8 | join process_guid, _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name IN ("explorer.exe", "wermgr.exe","dxdiag.exe", "OneDriveSetup.exe", "mobsync.exe", "msra.exe", "xwizard.exe") by _time span=1m Processes.process_id Processes.process_name Processes.process Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid Processes.process_path | `drop_dm_object_name(Processes)` ] | stats min(_time) as firstTime max(_time) as lastTime values(registry_value_name) as registry_value_name dc(registry_value_name) as registry_value_name_count values(registry_key_name) by dest process_guid process_name parent_process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where registry_value_name_count >= 5 | `windows_modify_registry_qakbot_binary_data_registry_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count dc(registry_value_name) as
+  registry_value_name_count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\*"
+  AND Registry.registry_value_data = "Binary Data" by _time span=1m Registry.dest
+  Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid Registry.process_id Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | eval registry_key_name_len = len(registry_key_name) | eval registry_value_name_len
+  = len(registry_value_name) | regex registry_value_name="^[0-9a-fA-F]{8}" | where
+  registry_key_name_len < 80 AND registry_value_name_len == 8 | join process_guid,
+  _time [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name IN ("explorer.exe", "wermgr.exe","dxdiag.exe", "OneDriveSetup.exe",
+  "mobsync.exe", "msra.exe", "xwizard.exe") by _time span=1m Processes.process_id
+  Processes.process_name Processes.process Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid Processes.process_path | `drop_dm_object_name(Processes)`
+  ] | stats min(_time) as firstTime max(_time) as lastTime values(registry_value_name)
+  as registry_value_name dc(registry_value_name) as registry_value_name_count values(registry_key_name)
+  by dest process_guid process_name parent_process_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | where registry_value_name_count >= 5 | `windows_modify_registry_qakbot_binary_data_registry_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/decrypting-qakbots-encrypted-registry-keys/
@@ -20,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,27 +77,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - user
-  - parent_process_name
-  - parent_process
-  - process_name
-  - process_path
-  - process
-  - proc_guid
-  - registry_path
-  - registry_value_name
-  - registry_value_data
-  - process_id
-  - registry_key_name
-  - registry_key_name_len
-  - registry_value_name_len
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot2/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_reg_restore.yml b/detections/endpoint/windows_modify_registry_reg_restore.yml
index 5e41c14136..8ebb743893 100644
--- a/detections/endpoint/windows_modify_registry_reg_restore.yml
+++ b/detections/endpoint/windows_modify_registry_reg_restore.yml
@@ -5,14 +5,35 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of reg.exe with the "restore" parameter, indicating an attempt to restore registry backup data on a host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate post-exploitation actions, such as those performed by tools like winpeas, which use "reg save" and "reg restore" to manipulate registry settings. If confirmed malicious, this could allow an attacker to revert registry changes, potentially bypassing security controls and maintaining persistence.
+description: The following analytic detects the execution of reg.exe with the "restore"
+  parameter, indicating an attempt to restore registry backup data on a host. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process execution logs and command-line arguments. This activity is significant
+  as it may indicate post-exploitation actions, such as those performed by tools like
+  winpeas, which use "reg save" and "reg restore" to manipulate registry settings.
+  If confirmed malicious, this could allow an attacker to revert registry changes,
+  potentially bypassing security controls and maintaining persistence.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process = "* restore *" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_reg_restore_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network administrator can use this command tool to backup registry before updates or modifying critical registries.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process
+  = "* restore *" by Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process
+  Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_reg_restore_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network administrator can use this command tool to backup registry
+  before updates or modifying critical registries.
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser
 - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS
@@ -40,25 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml b/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml
index 42905ed1c3..16ad1cefe6 100644
--- a/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml
+++ b/detections/endpoint/windows_modify_registry_regedit_silent_reg_import.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the modification of the Windows registry using the regedit.exe application with the silent mode parameter. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because the silent mode allows registry changes without user confirmation, which can be exploited by adversaries to import malicious registry settings. If confirmed malicious, this could enable attackers to persist in the environment, escalate privileges, or manipulate system configurations, leading to potential system compromise.
+description: The following analytic detects the modification of the Windows registry
+  using the regedit.exe application with the silent mode parameter. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  command-line executions. This activity is significant because the silent mode allows
+  registry changes without user confirmation, which can be exploited by adversaries
+  to import malicious registry settings. If confirmed malicious, this could enable
+  attackers to persist in the environment, escalate privileges, or manipulate system
+  configurations, leading to potential system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="regedit.exe" OR Processes.original_file_name="regedit.exe") AND Processes.process="* /s *" AND Processes.process="*.reg*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_regedit_silent_reg_import_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may execute this command that may cause some false positive. Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name="regedit.exe" OR Processes.original_file_name="regedit.exe")
+  AND Processes.process="* /s *" AND Processes.process="*.reg*" by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_regedit_silent_reg_import_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may execute this command that may cause some
+  false positive. Filter as needed.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 - https://www.techtarget.com/searchwindowsserver/tip/Command-line-options-for-Regeditexe
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: The regedit app was executed with silet mode parameter to import .reg file on $dest$.
+  message: The regedit app was executed with silet mode parameter to import .reg file
+    on $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -43,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_risk_behavior.yml b/detections/endpoint/windows_modify_registry_risk_behavior.yml
index d23f603a4b..c4c02dff24 100644
--- a/detections/endpoint/windows_modify_registry_risk_behavior.yml
+++ b/detections/endpoint/windows_modify_registry_risk_behavior.yml
@@ -6,10 +6,33 @@ author: Teoderick Contreras, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies instances where three or more distinct registry modification events associated with MITRE ATT&CK Technique T1112 are detected. It leverages data from the Risk data model in Splunk, focusing on registry-related sources and MITRE technique annotations. This activity is significant because multiple registry modifications can indicate an attempt to persist, hide malicious configurations, or erase forensic evidence. If confirmed malicious, this behavior could allow attackers to maintain persistent access, execute malicious code, and evade detection, posing a severe threat to the integrity and security of the affected host.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*registry*") All_Risk.annotations.mitre_attack.mitre_technique_id IN ("*T1112*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 3 | `windows_modify_registry_risk_behavior_filter`'
-how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
-known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
+description: The following analytic identifies instances where three or more distinct
+  registry modification events associated with MITRE ATT&CK Technique T1112 are detected.
+  It leverages data from the Risk data model in Splunk, focusing on registry-related
+  sources and MITRE technique annotations. This activity is significant because multiple
+  registry modifications can indicate an attempt to persist, hide malicious configurations,
+  or erase forensic evidence. If confirmed malicious, this behavior could allow attackers
+  to maintain persistent access, execute malicious code, and evade detection, posing
+  a severe threat to the integrity and security of the affected host.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*registry*")
+  All_Risk.annotations.mitre_attack.mitre_technique_id IN ("*T1112*") by All_Risk.risk_object
+  All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 3 | `windows_modify_registry_risk_behavior_filter`'
+how_to_implement: Splunk Enterprise Security is required to utilize this correlation.
+  In addition, modify the source_count value to your environment. In our testing,
+  a count of 4 or 5 was decent in a lab, but the number may need to be increased base
+  on internal testing. In addition, based on false positives, modify any analytics
+  to be anomaly and lower or increase risk based on organization importance.
+known_false_positives: False positives will be present based on many factors. Tune
+  the correlation as needed to reduce too many triggers.
 references:
 - https://www.splunk.com/en_us/blog/security/do-not-cross-the-redline-stealer-detections-and-analysis.html
 - https://www.splunk.com/en_us/blog/security/asyncrat-crusade-detections-and-defense.html
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/windows_mod_reg_risk_behavior/modify_reg_risk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/windows_mod_reg_risk_behavior/modify_reg_risk.log
     source: mod_reg
     sourcetype: stash
diff --git a/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml b/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml
index 6b576fb8e1..5a16e802d3 100644
--- a/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml
+++ b/detections/endpoint/windows_modify_registry_suppress_win_defender_notif.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects modifications in the Windows registry to suppress Windows Defender notifications. It leverages data from the Endpoint.Registry datamodel, specifically targeting changes to the "Notification_Suppress" registry value. This activity is significant because adversaries, including those deploying Azorult malware, use this technique to bypass Windows Defender and disable critical notifications. If confirmed malicious, this behavior could allow attackers to evade detection, maintain persistence, and execute further malicious activities without alerting the user or security tools.
+description: The following analytic detects modifications in the Windows registry
+  to suppress Windows Defender notifications. It leverages data from the Endpoint.Registry
+  datamodel, specifically targeting changes to the "Notification_Suppress" registry
+  value. This activity is significant because adversaries, including those deploying
+  Azorult malware, use this technique to bypass Windows Defender and disable critical
+  notifications. If confirmed malicious, this behavior could allow attackers to evade
+  detection, maintain persistence, and execute further malicious activities without
+  alerting the user or security tools.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows Defender\\UX Configuration\\Notification_Suppress*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_suppress_win_defender_notif_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Windows
+  Defender\\UX Configuration\\Notification_Suppress*" Registry.registry_value_data="0x00000001"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_suppress_win_defender_notif_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: the registry for suppresing windows fdefender notification settings was modified to disabled in $dest$
+  message: the registry for suppresing windows fdefender notification settings was
+    modified to disabled in $dest$
   mitre_attack_id:
   - T1112
   observable:
@@ -43,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_tamper_protection.yml b/detections/endpoint/windows_modify_registry_tamper_protection.yml
index e22923391e..54557ce857 100644
--- a/detections/endpoint/windows_modify_registry_tamper_protection.yml
+++ b/detections/endpoint/windows_modify_registry_tamper_protection.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious modification to the Windows Defender Tamper Protection registry setting. It leverages data from the Endpoint datamodel, specifically targeting changes where the registry path is set to disable Tamper Protection. This activity is significant because disabling Tamper Protection can allow adversaries to make further undetected changes to Windows Defender settings, potentially leading to reduced security on the system. If confirmed malicious, this could enable attackers to evade detection, persist in the environment, and execute further malicious activities without interference from Windows Defender.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows Defender\\Features\\TamperProtection" AND Registry.registry_value_data="0x00000000" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_tamper_protection_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious modification to the Windows
+  Defender Tamper Protection registry setting. It leverages data from the Endpoint
+  datamodel, specifically targeting changes where the registry path is set to disable
+  Tamper Protection. This activity is significant because disabling Tamper Protection
+  can allow adversaries to make further undetected changes to Windows Defender settings,
+  potentially leading to reduced security on the system. If confirmed malicious, this
+  could enable attackers to evade detection, persist in the environment, and execute
+  further malicious activities without interference from Windows Defender.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Microsoft\\Windows
+  Defender\\Features\\TamperProtection" AND Registry.registry_value_data="0x00000000"
+  by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_modify_registry_tamper_protection_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml b/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml
index 2ad1587a20..3312e70d09 100644
--- a/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml
+++ b/detections/endpoint/windows_modify_registry_to_add_or_modify_firewall_rule.yml
@@ -9,18 +9,18 @@ data_source:
 - Sysmon EventID 14
 type: Anomaly
 status: production
-description: The following analytic detects a potential addition or modification of firewall rules, 
-  signaling possible configuration changes or security policy adjustments. It tracks commands such as 
-  netsh advfirewall firewall add rule and netsh advfirewall firewall set rule, which may indicate attempts 
-  to alter network access controls. Monitoring these actions ensures the integrity of firewall settings and 
-  helps prevent unauthorized network access.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*"  Registry.action = modified
-  BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid  Registry.dest Registry.user Registry.action
-  | `drop_dm_object_name(Registry)`
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `windows_modify_registry_to_add_or_modify_firewall_rule_filter`'
+description: The following analytic detects a potential addition or modification of
+  firewall rules, signaling possible configuration changes or security policy adjustments.
+  It tracks commands such as netsh advfirewall firewall add rule and netsh advfirewall
+  firewall set rule, which may indicate attempts to alter network access controls.
+  Monitoring these actions ensures the integrity of firewall settings and helps prevent
+  unauthorized network access.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*"  Registry.action
+  = modified BY Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid  Registry.dest Registry.user
+  Registry.action | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_to_add_or_modify_firewall_rule_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   logs with the registry value name, registry path, and registry value data from your
   endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
@@ -35,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -61,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/firewall_modify_delete/firewall_mod_delete.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml b/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml
index e6e8315080..f611a5b797 100644
--- a/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml
+++ b/detections/endpoint/windows_modify_registry_updateserviceurlalternate.yml
@@ -8,10 +8,26 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious modification to the Windows Update configuration registry key, specifically targeting the UpdateServiceUrlAlternate setting. It leverages data from the Endpoint.Registry datamodel to identify changes to this registry path. This activity is significant because adversaries, including malware like RedLine Stealer, exploit this technique to bypass detection and deploy additional payloads. If confirmed malicious, this modification could allow attackers to redirect update services, potentially leading to the execution of malicious code, further system compromise, and persistent evasion of security defenses.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\UpdateServiceUrlAlternate" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_updateserviceurlalternate_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious modification to the Windows
+  Update configuration registry key, specifically targeting the UpdateServiceUrlAlternate
+  setting. It leverages data from the Endpoint.Registry datamodel to identify changes
+  to this registry path. This activity is significant because adversaries, including
+  malware like RedLine Stealer, exploit this technique to bypass detection and deploy
+  additional payloads. If confirmed malicious, this modification could allow attackers
+  to redirect update services, potentially leading to the execution of malicious code,
+  further system compromise, and persistent evasion of security defenses.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\UpdateServiceUrlAlternate"
+  by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_modify_registry_updateserviceurlalternate_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_usewuserver.yml b/detections/endpoint/windows_modify_registry_usewuserver.yml
index 74ae2317fb..089a7e23ab 100644
--- a/detections/endpoint/windows_modify_registry_usewuserver.yml
+++ b/detections/endpoint/windows_modify_registry_usewuserver.yml
@@ -8,10 +8,26 @@ type: Hunting
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects a suspicious modification to the Windows Update configuration registry key "UseWUServer." It leverages data from the Endpoint.Registry data model to identify changes where the registry value is set to "0x00000001." This activity is significant because it is commonly used by adversaries, including malware like RedLine Stealer, to bypass detection mechanisms and potentially exploit zero-day vulnerabilities. If confirmed malicious, this modification could allow attackers to evade defenses, persist on the target host, and deploy additional malicious payloads.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\UseWUServer" AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_usewuserver_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects a suspicious modification to the Windows
+  Update configuration registry key "UseWUServer." It leverages data from the Endpoint.Registry
+  data model to identify changes where the registry value is set to "0x00000001."
+  This activity is significant because it is commonly used by adversaries, including
+  malware like RedLine Stealer, to bypass detection mechanisms and potentially exploit
+  zero-day vulnerabilities. If confirmed malicious, this modification could allow
+  attackers to evade defenses, persist on the target host, and deploy additional malicious
+  payloads.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\AU\\UseWUServer"
+  AND Registry.registry_value_data="0x00000001" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_usewuserver_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 tags:
@@ -32,19 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_utilize_progids.yml b/detections/endpoint/windows_modify_registry_utilize_progids.yml
index 6c13459ebf..6c75b1abfc 100644
--- a/detections/endpoint/windows_modify_registry_utilize_progids.yml
+++ b/detections/endpoint/windows_modify_registry_utilize_progids.yml
@@ -7,9 +7,24 @@ data_source:
 - Sysmon EventID 13
 type: Anomaly
 status: production
-description: The following analytic detects modifications to the Windows Registry specifically targeting Programmatic Identifier associations to bypass User Account Control (UAC) Windows OS feature. ValleyRAT may create or alter registry entries to targetted progIDs like `.pwn` files with malicious processes, allowing it to execute harmful scripts or commands when these files are opened. By monitoring for unusual changes in registry keys linked to ProgIDs, this detection enables security analysts to identify potential threats like ValleyRAT execution attempts. Early detection of these modifications helps mitigate unauthorized execution and prevents further exploitation of the system.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\ms-settings\\CurVer\\(Default)" BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_utilize_progids_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709
+description: The following analytic detects modifications to the Windows Registry
+  specifically targeting Programmatic Identifier associations to bypass User Account
+  Control (UAC) Windows OS feature. ValleyRAT may create or alter registry entries
+  to targetted progIDs like `.pwn` files with malicious processes, allowing it to
+  execute harmful scripts or commands when these files are opened. By monitoring for
+  unusual changes in registry keys linked to ProgIDs, this detection enables security
+  analysts to identify potential threats like ValleyRAT execution attempts. Early
+  detection of these modifications helps mitigate unauthorized execution and prevents
+  further exploitation of the system.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE Registry.registry_path= "*\\ms-settings\\CurVer\\(Default)" BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_utilize_progids_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the official
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
 known_false_positives: unknown
 references:
 - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml b/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml
index b350303fe0..250e9f77b7 100644
--- a/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml
+++ b/detections/endpoint/windows_modify_registry_valleyrat_c2_config.yml
@@ -8,9 +8,26 @@ data_source:
 - Sysmon EventID 13
 type: TTP
 status: production
-description: "The following analytic detects modifications to theregistry related to ValleyRAT C2 configuration. Specifically,  it monitors changes in registry keys where ValleyRAT saves the IP address and port information of its command-and-control (C2) server. This activity is a key indicator of ValleyRAT attempting to establish persistent communication with its C2 infrastructure. By identifying these unauthorized registry modifications, security analysts can quickly detect malicious configurations and investigate the associated threats. Early detection of these changes helps prevent further exploitation and limits the malware\u2019s ability to exfiltrate data or control infected systems."
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*\\Console\\IpDateInfo" AND Registry.registry_value_data="Binary Data") OR (Registry.registry_path= "*\\Console\\SelfPath" AND Registry.registry_value_data="*.exe") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.registry_hive Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_valleyrat_c2_config_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the official Sysmon TA. https://splunkbase.splunk.com/app/5709
+description: The following analytic detects modifications to theregistry related to
+  ValleyRAT C2 configuration. Specifically,  it monitors changes in registry keys
+  where ValleyRAT saves the IP address and port information of its command-and-control
+  (C2) server. This activity is a key indicator of ValleyRAT attempting to establish
+  persistent communication with its C2 infrastructure. By identifying these unauthorized
+  registry modifications, security analysts can quickly detect malicious configurations
+  and investigate the associated threats. Early detection of these changes helps prevent
+  further exploitation and limits the malware’s ability to exfiltrate data or control
+  infected systems.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path= "*\\Console\\IpDateInfo" AND Registry.registry_value_data="Binary
+  Data") OR (Registry.registry_path= "*\\Console\\SelfPath" AND Registry.registry_value_data="*.exe")
+  BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.registry_hive
+  Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_registry_valleyrat_c2_config_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the official
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
 known_false_positives: unknown
 references:
 - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/valleyrat_c2_reg2/valleyrat_c2_reg2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/valleyrat_c2_reg2/valleyrat_c2_reg2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml b/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml
index 1fc7a9678b..bb84a879db 100644
--- a/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml
+++ b/detections/endpoint/windows_modify_registry_valleyrat_pwn_reg_entry.yml
@@ -7,9 +7,24 @@ data_source:
 - Sysmon EventID 13
 type: TTP
 status: production
-description: The following analytic detects modifications to the Windows Registry specifically targeting `.pwn` file associations related to the ValleyRAT malware. ValleyRAT may create or alter registry entries to associate `.pwn` files with malicious processes, allowing it to execute harmful scripts or commands when these files are opened. By monitoring for unusual changes in registry keys linked to `.pwn` extensions, this detection enables security analysts to identify potential ValleyRAT infection attempts. Early detection of these modifications helps mitigate unauthorized execution and prevents further exploitation of the system.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path= "*.pwn\\Shell\\Open\\command" OR Registry.registry_value_data = ".pwn") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_valleyrat_pwn_reg_entry_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
+description: The following analytic detects modifications to the Windows Registry
+  specifically targeting `.pwn` file associations related to the ValleyRAT malware.
+  ValleyRAT may create or alter registry entries to associate `.pwn` files with malicious
+  processes, allowing it to execute harmful scripts or commands when these files are
+  opened. By monitoring for unusual changes in registry keys linked to `.pwn` extensions,
+  this detection enables security analysts to identify potential ValleyRAT infection
+  attempts. Early detection of these modifications helps mitigate unauthorized execution
+  and prevents further exploitation of the system.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE (Registry.registry_path= "*.pwn\\Shell\\Open\\command" OR Registry.registry_value_data
+  = ".pwn") BY Registry.dest Registry.user Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.process_guid
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_modify_registry_valleyrat_pwn_reg_entry_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
 known_false_positives: unknown
 references:
 - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/pwn_reg/pwn_reg.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml b/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml
index bca2b01f24..80b4c5cac2 100644
--- a/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml
+++ b/detections/endpoint/windows_modify_registry_with_md5_reg_key_name.yml
@@ -8,9 +8,28 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects potentially malicious registry modifications characterized by MD5-like registry key names. It leverages the Endpoint data model to identify registry entries under the SOFTWARE path with 32-character hexadecimal names, a technique often used by NjRAT malware for fileless storage of keylogs and .DLL plugins. This activity is significant as it can indicate the presence of NjRAT or similar malware, which can lead to unauthorized data access and persistent threats within the environment. If confirmed malicious, attackers could maintain persistence and exfiltrate sensitive information.
-search: '| tstats `security_content_summariesonly`  count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | eval dropped_reg_path = split(registry_path, "\\") | eval dropped_reg_path_split_count = mvcount(dropped_reg_path) | eval validation_result= if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5") | where validation_result = "md5" AND dropped_reg_path_split_count <= 5 | table dest user registry_path registry_value_name registry_value_data registry_key_name reg_key_name dropped_reg_path_split_count validation_result | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_with_md5_reg_key_name_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+description: The following analytic detects potentially malicious registry modifications
+  characterized by MD5-like registry key names. It leverages the Endpoint data model
+  to identify registry entries under the SOFTWARE path with 32-character hexadecimal
+  names, a technique often used by NjRAT malware for fileless storage of keylogs and
+  .DLL plugins. This activity is significant as it can indicate the presence of NjRAT
+  or similar malware, which can lead to unauthorized data access and persistent threats
+  within the environment. If confirmed malicious, attackers could maintain persistence
+  and exfiltrate sensitive information.
+search: '| tstats `security_content_summariesonly`  count min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path
+  = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.dest
+  Registry.user Registry.registry_path Registry.registry_value_name Registry.registry_value_data
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | eval dropped_reg_path
+  = split(registry_path, "\\") | eval dropped_reg_path_split_count = mvcount(dropped_reg_path)
+  | eval validation_result= if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5")
+  | where validation_result = "md5" AND dropped_reg_path_split_count <= 5 | table
+  dest user registry_path registry_value_name registry_value_data registry_key_name
+  reg_key_name dropped_reg_path_split_count validation_result | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_modify_registry_with_md5_reg_key_name_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
@@ -20,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/njrat_md5_registry_entry/njrat_reg_binary.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/njrat_md5_registry_entry/njrat_reg_binary.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_wuserver.yml b/detections/endpoint/windows_modify_registry_wuserver.yml
index 9308bdb542..325b972a4c 100644
--- a/detections/endpoint/windows_modify_registry_wuserver.yml
+++ b/detections/endpoint/windows_modify_registry_wuserver.yml
@@ -8,10 +8,26 @@ type: Hunting
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects suspicious modifications to the Windows Update Server (WUServer) registry settings. It leverages data from the Endpoint.Registry data model to identify changes in the registry path associated with Windows Update configurations. This activity is significant because adversaries, including malware like RedLine Stealer, exploit this technique to bypass detection and deploy additional payloads. If confirmed malicious, this registry modification could allow attackers to evade defenses, potentially leading to further system compromise and persistent unauthorized access.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUServer" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_wuserver_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic detects suspicious modifications to the Windows
+  Update Server (WUServer) registry settings. It leverages data from the Endpoint.Registry
+  data model to identify changes in the registry path associated with Windows Update
+  configurations. This activity is significant because adversaries, including malware
+  like RedLine Stealer, exploit this technique to bypass detection and deploy additional
+  payloads. If confirmed malicious, this registry modification could allow attackers
+  to evade defenses, potentially leading to further system compromise and persistent
+  unauthorized access.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUServer"
+  by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_modify_registry_wuserver_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 tags:
@@ -32,19 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_registry_wustatusserver.yml b/detections/endpoint/windows_modify_registry_wustatusserver.yml
index 88f8b15fb1..4921e5f47a 100644
--- a/detections/endpoint/windows_modify_registry_wustatusserver.yml
+++ b/detections/endpoint/windows_modify_registry_wustatusserver.yml
@@ -8,10 +8,26 @@ type: Hunting
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic identifies suspicious modifications to the Windows Update configuration registry, specifically targeting the WUStatusServer key. It leverages data from the Endpoint datamodel to detect changes in the registry path associated with Windows Update settings. This activity is significant as it is commonly used by adversaries, including malware like RedLine Stealer, to bypass detection and deploy additional payloads. If confirmed malicious, this modification could allow attackers to evade defenses, potentially leading to further system compromise and persistent unauthorized access.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUStatusServer" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_wustatusserver_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+description: The following analytic identifies suspicious modifications to the Windows
+  Update configuration registry, specifically targeting the WUStatusServer key. It
+  leverages data from the Endpoint datamodel to detect changes in the registry path
+  associated with Windows Update settings. This activity is significant as it is commonly
+  used by adversaries, including malware like RedLine Stealer, to bypass detection
+  and deploy additional payloads. If confirmed malicious, this modification could
+  allow attackers to evade defenses, potentially leading to further system compromise
+  and persistent unauthorized access.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\SOFTWARE\\Policies\\Microsoft\\Windows\\WindowsUpdate\\WUStatusServer"
+  by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.registry_key_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_modify_registry_wustatusserver_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://learn.microsoft.com/de-de/security-updates/windowsupdateservices/18127499
 tags:
@@ -32,19 +48,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/modify_registry/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml b/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml
index 5f88330a74..3aefead4ec 100644
--- a/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml
+++ b/detections/endpoint/windows_modify_show_compress_color_and_info_tip_registry.yml
@@ -5,14 +5,21 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects suspicious modifications to the Windows registry keys related to file compression color and information tips. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "ShowCompColor" and "ShowInfoTip" values under the "Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced" path. This activity is significant as it was observed in the Hermetic Wiper malware, indicating potential malicious intent to alter file attributes and user interface elements. If confirmed malicious, this could signify an attempt to manipulate file visibility and deceive users, potentially aiding in further malicious activities.
+description: The following analytic detects suspicious modifications to the Windows
+  registry keys related to file compression color and information tips. It leverages
+  data from the Endpoint.Registry data model, specifically monitoring changes to the
+  "ShowCompColor" and "ShowInfoTip" values under the "Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced"
+  path. This activity is significant as it was observed in the Hermetic Wiper malware,
+  indicating potential malicious intent to alter file attributes and user interface
+  elements. If confirmed malicious, this could signify an attempt to manipulate file
+  visibility and deceive users, potentially aiding in further malicious activities.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path = "*\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced*"
-  AND Registry.registry_value_name  IN("ShowCompColor", "ShowInfoTip")) BY 
-  Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path = "*\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced*"
+  AND Registry.registry_value_name  IN("ShowCompColor", "ShowInfoTip")) BY Registry.dest
+  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
   Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`
   | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_modify_show_compress_color_and_info_tip_registry_filter`'
@@ -29,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,18 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/globalfolderoptions_reg/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/globalfolderoptions_reg/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml b/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml
index ffe0b056b4..8ab4a24815 100644
--- a/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml
+++ b/detections/endpoint/windows_modify_system_firewall_with_notable_process_path.yml
@@ -9,10 +9,36 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects suspicious modifications to system firewall rules, specifically allowing execution of applications from notable and potentially malicious file paths. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving firewall rule changes. This activity is significant as it may indicate an adversary attempting to bypass firewall restrictions to execute malicious files. If confirmed malicious, this could allow attackers to execute unauthorized code, potentially leading to further system compromise, data exfiltration, or persistence within the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*" Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*" Processes.process IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*","*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*", "*\\temp\\*", "*\\PerfLogs\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_system_firewall_with_notable_process_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: A network operator or systems administrator may utilize an automated or manual execution of this firewall rule that may generate false positives. Filter as needed.
+description: The following analytic detects suspicious modifications to system firewall
+  rules, specifically allowing execution of applications from notable and potentially
+  malicious file paths. This detection leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on command-line executions involving firewall rule
+  changes. This activity is significant as it may indicate an adversary attempting
+  to bypass firewall restrictions to execute malicious files. If confirmed malicious,
+  this could allow attackers to execute unauthorized code, potentially leading to
+  further system compromise, data exfiltration, or persistence within the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*firewall*"
+  Processes.process = "*allow*" Processes.process = "*add*" Processes.process = "*ENABLE*"
+  Processes.process IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*",
+  "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*",
+  "*\\Users\\Default\\*","*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*",
+  "*\\temp\\*", "*\\PerfLogs\\*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_modify_system_firewall_with_notable_process_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: A network operator or systems administrator may utilize an
+  automated or manual execution of this firewall rule that may generate false positives.
+  Filter as needed.
 references:
 - https://www.splunk.com/en_us/blog/security/more-than-just-a-rat-unveiling-njrat-s-mbr-wiping-capabilities.html
 drilldown_searches:
@@ -21,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,20 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_add_firewall_rule/njrat_firewall_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/njrat_add_firewall_rule/njrat_firewall_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml b/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml
index 732ad64dbe..51af72168e 100644
--- a/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml
+++ b/detections/endpoint/windows_mof_event_triggered_execution_via_wmi.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of MOFComp.exe loading a MOF file, often triggered by cmd.exe or powershell.exe, or from unusual paths like User Profile directories. It leverages Endpoint Detection and Response (EDR) data, focusing on process names, parent processes, and command-line executions. This activity is significant as it may indicate an attacker using WMI for persistence or lateral movement. If confirmed malicious, it could allow the attacker to execute arbitrary code, maintain persistence, or escalate privileges within the environment.
+description: The following analytic detects the execution of MOFComp.exe loading a
+  MOF file, often triggered by cmd.exe or powershell.exe, or from unusual paths like
+  User Profile directories. It leverages Endpoint Detection and Response (EDR) data,
+  focusing on process names, parent processes, and command-line executions. This activity
+  is significant as it may indicate an attacker using WMI for persistence or lateral
+  movement. If confirmed malicious, it could allow the attacker to execute arbitrary
+  code, maintain persistence, or escalate privileges within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name IN ("cmd.exe", "powershell.exe") Processes.process_name=mofcomp.exe) OR (Processes.process_name=mofcomp.exe Processes.process IN ("*\\AppData\\Local\\*","*\\Users\\Public\\*")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mof_event_triggered_execution_via_wmi_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present from automation based applications (SCCM), filtering may be required. In addition, break the query out based on volume of usage. Filter process names or file paths.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name
+  IN ("cmd.exe", "powershell.exe") Processes.process_name=mofcomp.exe) OR (Processes.process_name=mofcomp.exe
+  Processes.process IN ("*\\AppData\\Local\\*","*\\Users\\Public\\*")) by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mof_event_triggered_execution_via_wmi_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present from automation based applications
+  (SCCM), filtering may be required. In addition, break the query out based on volume
+  of usage. Filter process names or file paths.
 references:
 - https://attack.mitre.org/techniques/T1546/003/
 - https://thedfirreport.com/2022/07/11/select-xmrig-from-sqlserver/
@@ -25,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ loading a MOF file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ loading a MOF file.
   mitre_attack_id:
   - T1546.003
   observable:
@@ -58,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/mofcomp.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/mofcomp.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_moveit_transfer_writing_aspx.yml b/detections/endpoint/windows_moveit_transfer_writing_aspx.yml
index 313c0dbed1..dd21ed1582 100644
--- a/detections/endpoint/windows_moveit_transfer_writing_aspx.yml
+++ b/detections/endpoint/windows_moveit_transfer_writing_aspx.yml
@@ -7,10 +7,32 @@ status: experimental
 type: TTP
 data_source:
 - Sysmon EventID 11
-description: The following analytic detects the creation of new ASPX files in the MOVEit Transfer application's "wwwroot" directory. It leverages endpoint data on process and filesystem activity to identify processes responsible for creating these files. This activity is significant as it may indicate exploitation of a critical zero-day vulnerability in MOVEit Transfer, used by threat actors to install malicious ASPX files. If confirmed malicious, this could lead to exfiltration of sensitive data, including user credentials and file metadata, posing a severe risk to the organization's security.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\MOVEitTransfer\\wwwroot\\*") Filesystem.file_name IN("*.aspx", "*.ashx", "*.asp*") OR Filesystem.file_name IN ("human2.aspx","_human2.aspx") by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` | fields _time dest file_create_time file_name file_path process_name process_path process] | dedup file_create_time | table dest file_create_time, file_name, file_path, process_name | `windows_moveit_transfer_writing_aspx_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
-known_false_positives: The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
+description: The following analytic detects the creation of new ASPX files in the
+  MOVEit Transfer application's "wwwroot" directory. It leverages endpoint data on
+  process and filesystem activity to identify processes responsible for creating these
+  files. This activity is significant as it may indicate exploitation of a critical
+  zero-day vulnerability in MOVEit Transfer, used by threat actors to install malicious
+  ASPX files. If confirmed malicious, this could lead to exfiltration of sensitive
+  data, including user credentials and file metadata, posing a severe risk to the
+  organization's security.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name
+  Processes.dest | `drop_dm_object_name(Processes)` | join process_guid, _time [|
+  tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\MOVEitTransfer\\wwwroot\\*")
+  Filesystem.file_name IN("*.aspx", "*.ashx", "*.asp*") OR Filesystem.file_name IN
+  ("human2.aspx","_human2.aspx") by _time span=1h Filesystem.dest Filesystem.file_create_time
+  Filesystem.file_name Filesystem.file_path | `drop_dm_object_name(Filesystem)` |
+  fields _time dest file_create_time file_name file_path process_name process_path
+  process] | dedup file_create_time | table dest file_create_time, file_name, file_path,
+  process_name | `windows_moveit_transfer_writing_aspx_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem`
+  node.
+known_false_positives: The query is structured in a way that `action` (read, create)
+  is not defined. Review the results of this query, filter, and tune as necessary.
+  It may be necessary to generate this query specific to your endpoint product.
 references:
 - https://community.progress.com/s/article/MOVEit-Transfer-Critical-Vulnerability-31May2023
 - https://www.reddit.com/r/sysadmin/comments/13wxuej/critical_vulnerability_moveit_file_transfer/
@@ -37,21 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_hash
-  - Filesystem.user
-  - Filesystem.file_create_time
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/moveit_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/moveit_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml b/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml
index d3cd100602..72c579af2f 100644
--- a/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml
+++ b/detections/endpoint/windows_msexchange_management_mailbox_cmdlet_usage.yml
@@ -5,14 +5,28 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies suspicious Cmdlet usage in Exchange Management logs, focusing on commands like New-MailboxExportRequest and New-ManagementRoleAssignment. It leverages EventCode 1 and specific Message patterns to detect potential ProxyShell and ProxyNotShell abuse. This activity is significant as it may indicate unauthorized access or manipulation of mailboxes and roles, which are critical for maintaining email security. If confirmed malicious, attackers could export mailbox data, assign new roles, or search mailboxes, leading to data breaches and privilege escalation.
+description: The following analytic identifies suspicious Cmdlet usage in Exchange
+  Management logs, focusing on commands like New-MailboxExportRequest and New-ManagementRoleAssignment.
+  It leverages EventCode 1 and specific Message patterns to detect potential ProxyShell
+  and ProxyNotShell abuse. This activity is significant as it may indicate unauthorized
+  access or manipulation of mailboxes and roles, which are critical for maintaining
+  email security. If confirmed malicious, attackers could export mailbox data, assign
+  new roles, or search mailboxes, leading to data breaches and privilege escalation.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`msexchange_management` EventCode=1 Message IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "*Search-Mailbox*") | stats count min(_time) as firstTime max(_time) as lastTime by host Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename host AS dest | `windows_msexchange_management_mailbox_cmdlet_usage_filter`'
-how_to_implement: The following analytic requires collecting the Exchange Management logs via a input. An example inputs is here https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178. We used multiline as the XML format of the logs will require props/transforms. Multiline gives us everything we need in Message for now. Update the macro with your correct sourcetype.
-known_false_positives: False positives may be present when an Administrator utilizes the cmdlets in the query. Filter or monitor as needed.
+search: '`msexchange_management` EventCode=1 Message IN ("*New-MailboxExportRequest*",
+  "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "*Search-Mailbox*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by host Message | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | rename host AS dest | `windows_msexchange_management_mailbox_cmdlet_usage_filter`'
+how_to_implement: The following analytic requires collecting the Exchange Management
+  logs via a input. An example inputs is here https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178.
+  We used multiline as the XML format of the logs will require props/transforms. Multiline
+  gives us everything we need in Message for now. Update the macro with your correct
+  sourcetype.
+known_false_positives: False positives may be present when an Administrator utilizes
+  the cmdlets in the query. Filter or monitor as needed.
 references:
 - https://gist.github.com/MHaggis/f66f1d608ea046efb9157020cd34c178
 drilldown_searches:
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 40
-  message: Cmdlets related to ProxyShell and ProxyNotShell have been identified on $dest$.
+  message: Cmdlets related to ProxyShell and ProxyNotShell have been identified on
+    $dest$.
   mitre_attack_id:
   - T1059
   - T1059.001
@@ -45,14 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Message
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/msexchangemanagement.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/exchange/msexchangemanagement.log
     source: WinEventLog:MSExchange Management
     sourcetype: MSExchange:management
diff --git a/detections/endpoint/windows_mshta_execution_in_registry.yml b/detections/endpoint/windows_mshta_execution_in_registry.yml
index 62e657f797..06a592b7e8 100644
--- a/detections/endpoint/windows_mshta_execution_in_registry.yml
+++ b/detections/endpoint/windows_mshta_execution_in_registry.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of mshta.exe via registry entries to run malicious scripts. It leverages registry activity logs to identify entries containing "mshta," "javascript," "vbscript," or "WScript.Shell." This behavior is significant as it indicates potential fileless malware, such as Kovter, which uses encoded scripts in the registry to persist and execute without files. If confirmed malicious, this activity could allow attackers to maintain persistence, execute arbitrary code, and evade traditional file-based detection methods, posing a significant threat to system integrity and security.
+description: The following analytic detects the execution of mshta.exe via registry
+  entries to run malicious scripts. It leverages registry activity logs to identify
+  entries containing "mshta," "javascript," "vbscript," or "WScript.Shell." This behavior
+  is significant as it indicates potential fileless malware, such as Kovter, which
+  uses encoded scripts in the registry to persist and execute without files. If confirmed
+  malicious, this activity could allow attackers to maintain persistence, execute
+  arbitrary code, and evade traditional file-based detection methods, posing a significant
+  threat to system integrity and security.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_value_data = "*mshta*" OR Registry.registry_value_data IN ("*javascript:*", "*vbscript:*","*WScript.Shell*") by Registry.registry_key_name Registry.registry_path Registry.registry_value_data Registry.action Registry.dest Registry.user| `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mshta_execution_in_registry_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_value_data
+  = "*mshta*" OR Registry.registry_value_data IN ("*javascript:*", "*vbscript:*","*WScript.Shell*")
+  by Registry.registry_key_name Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest Registry.user| `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_mshta_execution_in_registry_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records registry activity from your hosts to populate the endpoint data model
+  in the registry node. This is typically populated via endpoint detection-and-response
+  product, such as Carbon Black or endpoint data sources, such as Sysmon. The data
+  used for this search is typically generated via logs that report reads and writes
+  to the registry.
 known_false_positives: unknown
 references:
 - https://redcanary.com/threat-detection-report/techniques/mshta/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.dest
-  - Registry.registry_value_data
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/mshta_in_registry/sysmon3.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/mshta_in_registry/sysmon3.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml b/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml
index 61fa3e8b2e..ac88a2fd17 100644
--- a/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml
+++ b/detections/endpoint/windows_mshta_writing_to_world_writable_path.yml
@@ -7,10 +7,38 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic identifies instances of `mshta.exe` writing files to world-writable directories. It leverages Sysmon EventCode 11 logs to detect file write operations by `mshta.exe` to directories like `C:\Windows\Tasks` and `C:\Windows\Temp`. This activity is significant as it often indicates an attempt to establish persistence or execute malicious code, deviating from the utility's legitimate use. If confirmed malicious, this behavior could lead to the execution of multi-stage payloads, potentially resulting in full system compromise and unauthorized access to sensitive information.
-search: '`sysmon` EventCode=11 Image="*\\mshta.exe" TargetFilename IN ("*\\Windows\\Tasks\\*", "*\\Windows\\Temp\\*", "*\\Windows\\tracing\\*", "*\\Windows\\PLA\\Reports\\*", "*\\Windows\\PLA\\Rules\\*", "*\\Windows\\PLA\\Templates\\*", "*\\Windows\\PLA\\Reports\\en-US\\*", "*\\Windows\\PLA\\Rules\\en-US\\*", "*\\Windows\\Registration\\CRMLog\\*", "*\\Windows\\System32\\Tasks\\*", "*\\Windows\\System32\\Com\\dmp\\*", "*\\Windows\\System32\\LogFiles\\WMI\\*", "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", "*\\Windows\\System32\\spool\\PRINTERS\\*", "*\\Windows\\System32\\spool\\SERVERS\\*", "*\\Windows\\System32\\spool\\drivers\\color\\*", "*\\Windows\\System32\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\*", "*\\Windows\\SysWOW64\\Com\\dmp\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\System\\*") | rename Computer as dest, User as user | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, Image, TargetFilename | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_mshta_writing_to_world_writable_path_filter`'
-how_to_implement: The analytic is designed to be run against Sysmon event logs collected from endpoints. The analytic requires the Sysmon event logs to be ingested into Splunk. The search focuses on EventCode 11 where the Image is `mshta.exe` and the TargetFilename is within world-writable directories such as `C:\Windows\Tasks`, `C:\Windows\Temp`, and others. The detection is designed to catch the initial file write operation by `mshta.exe` to these locations, which is indicative of an attempt to establish persistence or execute malicious code. The analytic can be modified to include additional world-writable directories as needed.
-known_false_positives: False positives may occur if legitimate processes are writing to world-writable directories. It is recommended to investigate the context of the file write operation to determine if it is malicious or not. Modify the search to include additional known good paths for `mshta.exe` to reduce false positives.
+description: The following analytic identifies instances of `mshta.exe` writing files
+  to world-writable directories. It leverages Sysmon EventCode 11 logs to detect file
+  write operations by `mshta.exe` to directories like `C:\Windows\Tasks` and `C:\Windows\Temp`.
+  This activity is significant as it often indicates an attempt to establish persistence
+  or execute malicious code, deviating from the utility's legitimate use. If confirmed
+  malicious, this behavior could lead to the execution of multi-stage payloads, potentially
+  resulting in full system compromise and unauthorized access to sensitive information.
+search: '`sysmon` EventCode=11 Image="*\\mshta.exe" TargetFilename IN ("*\\Windows\\Tasks\\*",
+  "*\\Windows\\Temp\\*", "*\\Windows\\tracing\\*", "*\\Windows\\PLA\\Reports\\*",
+  "*\\Windows\\PLA\\Rules\\*", "*\\Windows\\PLA\\Templates\\*", "*\\Windows\\PLA\\Reports\\en-US\\*",
+  "*\\Windows\\PLA\\Rules\\en-US\\*", "*\\Windows\\Registration\\CRMLog\\*", "*\\Windows\\System32\\Tasks\\*",
+  "*\\Windows\\System32\\Com\\dmp\\*", "*\\Windows\\System32\\LogFiles\\WMI\\*", "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*",
+  "*\\Windows\\System32\\spool\\PRINTERS\\*", "*\\Windows\\System32\\spool\\SERVERS\\*",
+  "*\\Windows\\System32\\spool\\drivers\\color\\*", "*\\Windows\\System32\\Tasks\\Microsoft\\Windows\\RemoteApp
+  and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\*", "*\\Windows\\SysWOW64\\Com\\dmp\\*",
+  "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\RemoteApp
+  and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\System\\*")
+  | rename Computer as dest, User as user | stats count min(_time) as firstTime max(_time)
+  as lastTime by dest, user, Image, TargetFilename | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_mshta_writing_to_world_writable_path_filter`'
+how_to_implement: The analytic is designed to be run against Sysmon event logs collected
+  from endpoints. The analytic requires the Sysmon event logs to be ingested into
+  Splunk. The search focuses on EventCode 11 where the Image is `mshta.exe` and the
+  TargetFilename is within world-writable directories such as `C:\Windows\Tasks`,
+  `C:\Windows\Temp`, and others. The detection is designed to catch the initial file
+  write operation by `mshta.exe` to these locations, which is indicative of an attempt
+  to establish persistence or execute malicious code. The analytic can be modified
+  to include additional world-writable directories as needed.
+known_false_positives: False positives may occur if legitimate processes are writing
+  to world-writable directories. It is recommended to investigate the context of the
+  file write operation to determine if it is malicious or not. Modify the search to
+  include additional known good paths for `mshta.exe` to reduce false positives.
 references:
 - https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties
 - https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader
@@ -20,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,16 +83,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - dest
-  - user
-  - Image
-  - TargetFilename
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
diff --git a/detections/endpoint/windows_msiexec_dllregisterserver.yml b/detections/endpoint/windows_msiexec_dllregisterserver.yml
index 898055478c..ae0fb58c37 100644
--- a/detections/endpoint/windows_msiexec_dllregisterserver.yml
+++ b/detections/endpoint/windows_msiexec_dllregisterserver.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of msiexec.exe with the /y switch parameter, which enables the loading of DLLRegisterServer. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line arguments and parent-child process relationships. This activity is significant because it can indicate an attempt to register malicious DLLs, potentially leading to code execution or persistence on the system. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic detects the execution of msiexec.exe with the
+  /y switch parameter, which enables the loading of DLLRegisterServer. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  command-line arguments and parent-child process relationships. This activity is
+  significant because it can indicate an attempt to register malicious DLLs, potentially
+  leading to code execution or persistence on the system. If confirmed malicious,
+  this could allow an attacker to execute arbitrary code, escalate privileges, or
+  maintain persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process IN ("*/y*", "*-y*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_dllregisterserver_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This analytic will need to be tuned for your environment based on legitimate usage of msiexec.exe. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process
+  IN ("*/y*", "*-y*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_msiexec_dllregisterserver_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This analytic will need to be tuned for your environment based
+  on legitimate usage of msiexec.exe. Filter as needed.
 references:
 - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to register a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to register a file.
   mitre_attack_id:
   - T1218.007
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml b/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml
index d800a27288..9ee35857d3 100644
--- a/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml
+++ b/detections/endpoint/windows_msiexec_hidewindow_rundll32_execution.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of the msiexec.exe process with the /HideWindow and rundll32 command-line parameters. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because it is a known tactic used by malware like QakBot to mask malicious operations under legitimate system processes. If confirmed malicious, this behavior could allow an attacker to download additional payloads, execute malicious code, or establish communication with remote servers, thereby evading detection and maintaining persistence.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = msiexec.exe  Processes.process = "* /HideWindow *" Processes.process = "* rundll32*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_hidewindow_rundll32_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Other possible 3rd party msi software installers use this technique as part of its installation process.
+description: The following analytic detects the execution of the msiexec.exe process
+  with the /HideWindow and rundll32 command-line parameters. It leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process creation events
+  and command-line arguments. This activity is significant because it is a known tactic
+  used by malware like QakBot to mask malicious operations under legitimate system
+  processes. If confirmed malicious, this behavior could allow an attacker to download
+  additional payloads, execute malicious code, or establish communication with remote
+  servers, thereby evading detection and maintaining persistence.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  = msiexec.exe  Processes.process = "* /HideWindow *" Processes.process = "* rundll32*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_msiexec_hidewindow_rundll32_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Other possible 3rd party msi software installers use this technique
+  as part of its installation process.
 references:
 - https://twitter.com/Max_Mal_/status/1736392741758611607
 - https://twitter.com/1ZRR4H/status/1735944522075386332
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: a msiexec parent process with /hidewindow rundll32 process commandline in $dest$
+  message: a msiexec parent process with /hidewindow rundll32 process commandline
+    in $dest$
   mitre_attack_id:
   - T1218.007
   - T1218
@@ -44,24 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/msiexec-hidewindow-rundll32/hidewndw-rundll32.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/msiexec-hidewindow-rundll32/hidewndw-rundll32.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msiexec_remote_download.yml b/detections/endpoint/windows_msiexec_remote_download.yml
index 224c1014a3..b0190b65b1 100644
--- a/detections/endpoint/windows_msiexec_remote_download.yml
+++ b/detections/endpoint/windows_msiexec_remote_download.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of msiexec.exe with an HTTP or HTTPS URL in the command line, indicating a remote file download attempt. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant as it may indicate an attempt to download and execute potentially malicious software from a remote server. If confirmed malicious, this could lead to unauthorized code execution, system compromise, or further malware deployment within the network.
+description: The following analytic detects the use of msiexec.exe with an HTTP or
+  HTTPS URL in the command line, indicating a remote file download attempt. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  execution logs that include command-line details. This activity is significant as
+  it may indicate an attempt to download and execute potentially malicious software
+  from a remote server. If confirmed malicious, this could lead to unauthorized code
+  execution, system compromise, or further malware deployment within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process IN ("*http://*", "*https://*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_remote_download_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present, filter by destination or parent process as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process
+  IN ("*http://*", "*https://*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_msiexec_remote_download_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present, filter by destination or parent
+  process as needed.
 references:
 - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download a remote file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to download a remote file.
   mitre_attack_id:
   - T1218.007
   observable:
@@ -55,23 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msiexec_spawn_discovery_command.yml b/detections/endpoint/windows_msiexec_spawn_discovery_command.yml
index c847ac59fc..fbb8974374 100644
--- a/detections/endpoint/windows_msiexec_spawn_discovery_command.yml
+++ b/detections/endpoint/windows_msiexec_spawn_discovery_command.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects MSIExec spawning multiple discovery commands, such as Cmd.exe or PowerShell.exe. This behavior is identified using data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where MSIExec is the parent process. This activity is significant because MSIExec typically does not spawn child processes other than itself, making this behavior highly suspicious. If confirmed malicious, an attacker could use these discovery commands to gather system information, potentially leading to further exploitation or lateral movement within the network.
+description: The following analytic detects MSIExec spawning multiple discovery commands,
+  such as Cmd.exe or PowerShell.exe. This behavior is identified using data from Endpoint
+  Detection and Response (EDR) agents, focusing on process creation events where MSIExec
+  is the parent process. This activity is significant because MSIExec typically does
+  not spawn child processes other than itself, making this behavior highly suspicious.
+  If confirmed malicious, an attacker could use these discovery commands to gather
+  system information, potentially leading to further exploitation or lateral movement
+  within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe Processes.process_name IN ("powershell.exe","cmd.exe", "nltest.exe","ipconfig.exe","systeminfo.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_spawn_discovery_command_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present with MSIExec spawning Cmd or PowerShell. Filtering will be needed. In addition, add other known discovery processes to enhance query.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe
+  Processes.process_name IN ("powershell.exe","cmd.exe", "nltest.exe","ipconfig.exe","systeminfo.exe")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_msiexec_spawn_discovery_command_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present with MSIExec spawning Cmd or
+  PowerShell. Filtering will be needed. In addition, add other known discovery processes
+  to enhance query.
 references:
 - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md
@@ -22,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ running different discovery commands.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ running different discovery commands.
   mitre_attack_id:
   - T1218.007
   observable:
@@ -55,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msiexec_spawn_windbg.yml b/detections/endpoint/windows_msiexec_spawn_windbg.yml
index 2a0e652d46..322e645480 100644
--- a/detections/endpoint/windows_msiexec_spawn_windbg.yml
+++ b/detections/endpoint/windows_msiexec_spawn_windbg.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the unusual behavior of MSIExec spawning WinDBG. It detects this activity by analyzing endpoint telemetry data, specifically looking for instances where 'msiexec.exe' is the parent process of 'windbg.exe'. This behavior is significant as it may indicate an attempt to debug or tamper with system processes, which is uncommon in typical user activity and could signify malicious intent. If confirmed malicious, this activity could allow an attacker to manipulate or inspect running processes, potentially leading to privilege escalation or persistence within the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe Processes.process_name=windbg.exe by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process_path Processes.parent_process Processes.process_name Processes.process_path Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_msiexec_spawn_windbg_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will only be present if the MSIExec process legitimately spawns WinDBG. Filter as needed.
+description: The following analytic identifies the unusual behavior of MSIExec spawning
+  WinDBG. It detects this activity by analyzing endpoint telemetry data, specifically
+  looking for instances where 'msiexec.exe' is the parent process of 'windbg.exe'.
+  This behavior is significant as it may indicate an attempt to debug or tamper with
+  system processes, which is uncommon in typical user activity and could signify malicious
+  intent. If confirmed malicious, this activity could allow an attacker to manipulate
+  or inspect running processes, potentially leading to privilege escalation or persistence
+  within the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msiexec.exe
+  Processes.process_name=windbg.exe by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process_path Processes.parent_process Processes.process_name Processes.process_path
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_msiexec_spawn_windbg_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will only be present if the MSIExec process
+  legitimately spawns WinDBG. Filter as needed.
 references:
 - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   atomic_guid: []
   confidence: 100
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1218.007
   observable:
@@ -55,21 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process_path
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_path
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windbg_msiexec.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windbg_msiexec.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml b/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml
index e811c30ece..d6565d530e 100644
--- a/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml
+++ b/detections/endpoint/windows_msiexec_unregister_dllregisterserver.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of msiexec.exe with the /z switch parameter, which is used to unload DLLRegisterServer. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs, including command-line arguments. This activity is significant because unloading DLLRegisterServer can be indicative of an attempt to deregister a DLL, potentially disrupting legitimate services or hiding malicious activity. If confirmed malicious, this could allow an attacker to disable security controls, evade detection, or disrupt system functionality, leading to further compromise of the environment.
+description: The following analytic detects the use of msiexec.exe with the /z switch
+  parameter, which is used to unload DLLRegisterServer. This detection leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process execution
+  logs, including command-line arguments. This activity is significant because unloading
+  DLLRegisterServer can be indicative of an attempt to deregister a DLL, potentially
+  disrupting legitimate services or hiding malicious activity. If confirmed malicious,
+  this could allow an attacker to disable security controls, evade detection, or disrupt
+  system functionality, leading to further compromise of the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process IN ("*/z*", "*-z*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_msiexec_unregister_dllregisterserver_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This analytic will need to be tuned for your environment based on legitimate usage of msiexec.exe. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_msiexec` Processes.process
+  IN ("*/z*", "*-z*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_msiexec_unregister_dllregisterserver_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This analytic will need to be tuned for your environment based
+  on legitimate usage of msiexec.exe. Filter as needed.
 references:
 - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.007/T1218.007.md
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to unregister a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to unregister a file.
   mitre_attack_id:
   - T1218.007
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_msiexec_with_network_connections.yml b/detections/endpoint/windows_msiexec_with_network_connections.yml
index 51a895408b..88849793e3 100644
--- a/detections/endpoint/windows_msiexec_with_network_connections.yml
+++ b/detections/endpoint/windows_msiexec_with_network_connections.yml
@@ -5,11 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects MSIExec making network connections over ports 443 or 80. This behavior is identified by correlating process creation events from Endpoint Detection and Response (EDR) agents with network traffic logs. Typically, MSIExec does not perform network communication to the internet, making this activity unusual and potentially indicative of malicious behavior. If confirmed malicious, an attacker could be using MSIExec to download or communicate with external servers, potentially leading to data exfiltration, command and control (C2) communication, or further malware deployment.
+description: The following analytic detects MSIExec making network connections over
+  ports 443 or 80. This behavior is identified by correlating process creation events
+  from Endpoint Detection and Response (EDR) agents with network traffic logs. Typically,
+  MSIExec does not perform network communication to the internet, making this activity
+  unusual and potentially indicative of malicious behavior. If confirmed malicious,
+  an attacker could be using MSIExec to download or communicate with external servers,
+  potentially leading to data exfiltration, command and control (C2) communication,
+  or further malware deployment.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where `process_msiexec` by _time Processes.user Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port IN ("80","443") by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port All_Traffic.dest_ip | `drop_dm_object_name(All_Traffic)` ] | table _time user dest parent_process_name process_name process_path process process_id dest_port dest_ip | `windows_msiexec_with_network_connections_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where `process_msiexec` by _time Processes.user Processes.process_id Processes.process_name
+  Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port IN ("80","443") by All_Traffic.process_id All_Traffic.dest
+  All_Traffic.dest_port All_Traffic.dest_ip | `drop_dm_object_name(All_Traffic)` ]
+  | table _time user dest parent_process_name process_name process_path process process_id
+  dest_port dest_ip | `windows_msiexec_with_network_connections_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives will be present and filtering is required.
 references:
 - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/
@@ -20,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 70
-  message: An instance of $process_name$ was identified on endpoint $dest$ contacting a remote destination $dest_ip$
+  message: An instance of $process_name$ was identified on endpoint $dest$ contacting
+    a remote destination $dest_ip$
   mitre_attack_id:
   - T1218.007
   observable:
@@ -53,22 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
-  - Processes.process_path
-  - Processes.process
-  - Processes.parent_process_name
-  - All_Traffic.process_id
-  - All_Traffic.dest
-  - All_Traffic.dest_port
-  - All_Traffic.dest_ip
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.007/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_multi_hop_proxy_tor_website_query.yml b/detections/endpoint/windows_multi_hop_proxy_tor_website_query.yml
index 9dd498e44a..0cd70aff7a 100644
--- a/detections/endpoint/windows_multi_hop_proxy_tor_website_query.yml
+++ b/detections/endpoint/windows_multi_hop_proxy_tor_website_query.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies DNS queries to known TOR proxy websites, such as "*.torproject.org" and "www.theonionrouter.com". It leverages Sysmon EventCode 22 to detect these queries by monitoring DNS query events from endpoints. This activity is significant because adversaries often use TOR proxies to disguise the source of their malicious traffic, making it harder to trace their actions. If confirmed malicious, this behavior could indicate an attempt to obfuscate network traffic, potentially allowing attackers to exfiltrate data or communicate with command and control servers undetected.
+description: The following analytic identifies DNS queries to known TOR proxy websites,
+  such as "*.torproject.org" and "www.theonionrouter.com". It leverages Sysmon EventCode
+  22 to detect these queries by monitoring DNS query events from endpoints. This activity
+  is significant because adversaries often use TOR proxies to disguise the source
+  of their malicious traffic, making it harder to trace their actions. If confirmed
+  malicious, this behavior could indicate an attempt to obfuscate network traffic,
+  potentially allowing attackers to exfiltrate data or communicate with command and
+  control servers undetected.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode=22 QueryName IN ("*.torproject.org", "www.theonionrouter.com") | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus ProcessId  Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_multi_hop_proxy_tor_website_query_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and sysmon eventcode = 22 dns query events from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: third party application may use this proxies if allowed in production environment. Filter is needed.
+search: '`sysmon` EventCode=22 QueryName IN ("*.torproject.org", "www.theonionrouter.com")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName
+  QueryStatus ProcessId  Computer | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_multi_hop_proxy_tor_website_query_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and sysmon eventcode = 22 dns query events from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: third party application may use this proxies if allowed in
+  production environment. Filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.agent_tesla
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: a process $Image$ is having a dns query in a tor domain $QueryName$ in $dest$
+  message: a process $Image$ is having a dns query in a tor domain $QueryName$ in
+    $dest$
   mitre_attack_id:
   - T1071.003
   - T1071
@@ -41,17 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - QueryName
-  - QueryStatus
-  - ProcessId
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_tor_dns_query/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/agent_tesla/agent_tesla_tor_dns_query/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_multiple_account_passwords_changed.yml b/detections/endpoint/windows_multiple_account_passwords_changed.yml
index 0725f0115f..ae251d3fc8 100644
--- a/detections/endpoint/windows_multiple_account_passwords_changed.yml
+++ b/detections/endpoint/windows_multiple_account_passwords_changed.yml
@@ -7,10 +7,24 @@ data_source:
 - Windows Event Log Security 4724
 type: TTP
 status: production
-description: The following analytic detects instances where more than five unique Windows account passwords are changed within a 10-minute interval. It leverages Event Code 4724 from the Windows Security Event Log, using the wineventlog_security dataset to monitor and count distinct TargetUserName values. This behavior is significant as rapid password changes across multiple accounts are unusual and may indicate unauthorized access or internal compromise. If confirmed malicious, this activity could lead to widespread account compromise, unauthorized access to sensitive information, and potential disruption of services.
-search: '`wineventlog_security` EventCode=4724 status=success | bucket span=10m _time | stats count dc(user) as unique_users values(user) as user by EventCode signature _time src_user SubjectDomainName TargetDomainName Logon_ID | where unique_users > 5 | `windows_multiple_account_passwords_changed_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events with the Windows TA. The Advanced Security Audit policy setting `Audit User Account Management` within `Account Management` needs to be enabled.
-known_false_positives: Service accounts may be responsible for the creation, deletion or modification of accounts for legitimate purposes. Filter as needed.
+description: The following analytic detects instances where more than five unique
+  Windows account passwords are changed within a 10-minute interval. It leverages
+  Event Code 4724 from the Windows Security Event Log, using the wineventlog_security
+  dataset to monitor and count distinct TargetUserName values. This behavior is significant
+  as rapid password changes across multiple accounts are unusual and may indicate
+  unauthorized access or internal compromise. If confirmed malicious, this activity
+  could lead to widespread account compromise, unauthorized access to sensitive information,
+  and potential disruption of services.
+search: '`wineventlog_security` EventCode=4724 status=success | bucket span=10m _time
+  | stats count dc(user) as unique_users values(user) as user by EventCode signature
+  _time src_user SubjectDomainName TargetDomainName Logon_ID | where unique_users
+  > 5 | `windows_multiple_account_passwords_changed_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events with the Windows TA. The Advanced Security Audit policy
+  setting `Audit User Account Management` within `Account Management` needs to be
+  enabled.
+known_false_positives: Service accounts may be responsible for the creation, deletion
+  or modification of accounts for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 40
-  message: User $src_user$ changed the passwords of multiple accounts in a short period of time.
+  message: User $src_user$ changed the passwords of multiple accounts in a short period
+    of time.
   mitre_attack_id:
   - T1098
   - T1078
@@ -41,21 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - success
-  - TargetUserName
-  - SubjectUserName
-  - src_user
-  - SubjectDomainName
-  - TargetDomainName
-  - Logon_ID
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_passwords_changed/windows_multiple_passwords_changed.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_passwords_changed/windows_multiple_passwords_changed.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_multiple_accounts_deleted.yml b/detections/endpoint/windows_multiple_accounts_deleted.yml
index 1912a78a0d..518a141a7f 100644
--- a/detections/endpoint/windows_multiple_accounts_deleted.yml
+++ b/detections/endpoint/windows_multiple_accounts_deleted.yml
@@ -7,10 +7,23 @@ data_source:
 - Windows Event Log Security 4726
 type: TTP
 status: production
-description: The following analytic detects the deletion of more than five unique Windows accounts within a 10-minute period, using Event Code 4726 from the Windows Security Event Log. It leverages the `wineventlog_security` dataset, segmenting data into 10-minute intervals to identify suspicious account deletions. This activity is significant as it may indicate an attacker attempting to erase traces of their actions. If confirmed malicious, this could lead to unauthorized access removal, hindering incident response and forensic investigations.
-search: '`wineventlog_security` EventCode=4726 status=success | bucket span=10m _time | stats count dc(user) as unique_users values(user) as user by EventCode signature _time src_user SubjectDomainName TargetDomainName Logon_ID | where unique_users > 5 | `windows_multiple_accounts_deleted_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events with the Windows TA. The Advanced Security Audit policy setting `Audit User Account Management` within `Account Management` needs to be enabled.
-known_false_positives: Service accounts may be responsible for the creation, deletion or modification of accounts for legitimate purposes. Filter as needed.
+description: The following analytic detects the deletion of more than five unique
+  Windows accounts within a 10-minute period, using Event Code 4726 from the Windows
+  Security Event Log. It leverages the `wineventlog_security` dataset, segmenting
+  data into 10-minute intervals to identify suspicious account deletions. This activity
+  is significant as it may indicate an attacker attempting to erase traces of their
+  actions. If confirmed malicious, this could lead to unauthorized access removal,
+  hindering incident response and forensic investigations.
+search: '`wineventlog_security` EventCode=4726 status=success | bucket span=10m _time
+  | stats count dc(user) as unique_users values(user) as user by EventCode signature
+  _time src_user SubjectDomainName TargetDomainName Logon_ID | where unique_users
+  > 5 | `windows_multiple_accounts_deleted_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events with the Windows TA. The Advanced Security Audit policy
+  setting `Audit User Account Management` within `Account Management` needs to be
+  enabled.
+known_false_positives: Service accounts may be responsible for the creation, deletion
+  or modification of accounts for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,21 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - success
-  - TargetUserName
-  - SubjectUserName
-  - src_user
-  - SubjectDomainName
-  - TargetDomainName
-  - Logon_ID
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_deleted/windows_multiple_accounts_deleted.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_deleted/windows_multiple_accounts_deleted.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_multiple_accounts_disabled.yml b/detections/endpoint/windows_multiple_accounts_disabled.yml
index 0a1b1bb0b5..2bde39f130 100644
--- a/detections/endpoint/windows_multiple_accounts_disabled.yml
+++ b/detections/endpoint/windows_multiple_accounts_disabled.yml
@@ -7,10 +7,24 @@ data_source:
 - Windows Event Log Security 4725
 type: TTP
 status: production
-description: The following analytic identifies instances where more than five unique Windows accounts are disabled within a 10-minute window, as indicated by Event Code 4725 in the Windows Security Event Log. It leverages the wineventlog_security dataset, grouping data into 10-minute segments and tracking the count and distinct count of TargetUserName. This behavior is significant as it may indicate internal policy breaches or an external attacker's attempt to disrupt operations. If confirmed malicious, this activity could lead to widespread account lockouts, hindering user access and potentially disrupting business operations.
-search: '`wineventlog_security` EventCode=4725 status=success | bucket span=10m _time | stats count dc(user) as unique_users values(user) as user by EventCode signature _time src_user SubjectDomainName TargetDomainName Logon_ID | where unique_users > 5 | `windows_multiple_accounts_disabled_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events with the Windows TA. The Advanced Security Audit policy setting `Audit User Account Management` within `Account Management` needs to be enabled.
-known_false_positives: Service accounts may be responsible for the creation, deletion or modification of accounts for legitimate purposes. Filter as needed.
+description: The following analytic identifies instances where more than five unique
+  Windows accounts are disabled within a 10-minute window, as indicated by Event Code
+  4725 in the Windows Security Event Log. It leverages the wineventlog_security dataset,
+  grouping data into 10-minute segments and tracking the count and distinct count
+  of TargetUserName. This behavior is significant as it may indicate internal policy
+  breaches or an external attacker's attempt to disrupt operations. If confirmed malicious,
+  this activity could lead to widespread account lockouts, hindering user access and
+  potentially disrupting business operations.
+search: '`wineventlog_security` EventCode=4725 status=success | bucket span=10m _time
+  | stats count dc(user) as unique_users values(user) as user by EventCode signature
+  _time src_user SubjectDomainName TargetDomainName Logon_ID | where unique_users
+  > 5 | `windows_multiple_accounts_disabled_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events with the Windows TA. The Advanced Security Audit policy
+  setting `Audit User Account Management` within `Account Management` needs to be
+  enabled.
+known_false_positives: Service accounts may be responsible for the creation, deletion
+  or modification of accounts for legitimate purposes. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1098/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,21 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - success
-  - TargetUserName
-  - SubjectUserName
-  - src_user
-  - SubjectDomainName
-  - TargetDomainName
-  - Logon_ID
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_disabled/windows_multiple_accounts_disabled.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/windows_multiple_accounts_disabled/windows_multiple_accounts_disabled.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml b/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml
index 0a09976e33..0649999440 100644
--- a/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml
+++ b/detections/endpoint/windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4768
 date: '2024-09-30'
-description: The following analytic detects a single source endpoint failing to authenticate with 30 unique disabled domain users using the Kerberos protocol within 5 minutes. It leverages Windows Security Event 4768, focusing on failure code `0x12`, indicating revoked credentials. This activity is significant as it may indicate a Password Spraying attack targeting disabled accounts, a tactic used by adversaries to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access or privilege escalation within the Active Directory environment, posing a severe security risk.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+description: The following analytic detects a single source endpoint failing to authenticate
+  with 30 unique disabled domain users using the Kerberos protocol within 5 minutes.
+  It leverages Windows Security Event 4768, focusing on failure code `0x12`, indicating
+  revoked credentials. This activity is significant as it may indicate a Password
+  Spraying attack targeting disabled accounts, a tactic used by adversaries to gain
+  initial access or elevate privileges. If confirmed malicious, this could lead to
+  unauthorized access or privilege escalation within the Active Directory environment,
+  posing a severe security risk.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 id: 98f22d82-9d62-11eb-9fcf-acde48001122
-known_false_positives: A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems.
+known_false_positives: A host failing to authenticate with multiple disabled domain
+  users is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners, multi-user systems missconfigured
+  systems.
 name: Windows Multiple Disabled Users Failed To Authenticate Wth Kerberos
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -15,10 +27,17 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos_filter`'
+search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket
+  span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_disabled_users_failed_to_authenticate_wth_kerberos_filter`'
 status: production
 tags:
   analytic_story:
@@ -45,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml b/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml
index 77743c3b54..741a817d43 100644
--- a/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml
+++ b/detections/endpoint/windows_multiple_invalid_users_fail_to_authenticate_using_kerberos.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4768
 date: '2024-09-30'
-description: The following analytic identifies a source endpoint failing to authenticate with 30 unique invalid domain users using the Kerberos protocol. This detection leverages EventCode 4768, specifically looking for failure code 0x6, indicating the user is not found in the Kerberos database. This activity is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access or privilege escalation within the Active Directory environment, posing a significant security risk.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a source endpoint failing to authenticate
+  with 30 unique invalid domain users using the Kerberos protocol. This detection
+  leverages EventCode 4768, specifically looking for failure code 0x6, indicating
+  the user is not found in the Kerberos database. This activity is significant as
+  it may indicate a Password Spraying attack, where an adversary attempts to gain
+  initial access or elevate privileges. If confirmed malicious, this could lead to
+  unauthorized access or privilege escalation within the Active Directory environment,
+  posing a significant security risk.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 id: 001266a6-9d5b-11eb-829b-acde48001122
-known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems.
+known_false_positives: A host failing to authenticate with multiple invalid domain
+  users is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners, multi-user systems and missconfigured
+  systems.
 name: Windows Multiple Invalid Users Fail To Authenticate Using Kerberos
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -15,10 +27,17 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_invalid_users_fail_to_authenticate_using_kerberos_filter`'
+search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket
+  span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_invalid_users_fail_to_authenticate_using_kerberos_filter`'
 status: production
 tags:
   analytic_story:
@@ -45,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml b/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml
index 647fb170c7..64b3890f15 100644
--- a/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml
+++ b/detections/endpoint/windows_multiple_invalid_users_failed_to_authenticate_using_ntlm.yml
@@ -2,10 +2,23 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4776
 date: '2024-09-30'
-description: The following analytic detects a single source endpoint failing to authenticate with 30 unique invalid users using the NTLM protocol. It leverages EventCode 4776 from Domain Controller logs, focusing on error code 0xC0000064, which indicates non-existent usernames. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information within the Active Directory environment.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled.
+description: The following analytic detects a single source endpoint failing to authenticate
+  with 30 unique invalid users using the NTLM protocol. It leverages EventCode 4776
+  from Domain Controller logs, focusing on error code 0xC0000064, which indicates
+  non-existent usernames. This behavior is significant as it may indicate a Password
+  Spraying attack, where an adversary attempts to gain initial access or elevate privileges.
+  If confirmed malicious, this activity could lead to unauthorized access, privilege
+  escalation, and potential compromise of sensitive information within the Active
+  Directory environment.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events. The Advanced Security Audit policy setting `Audit Credential
+  Validation' within `Account Logon` needs to be enabled.
 id: 57ad5a64-9df7-11eb-a290-acde48001122
-known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
+known_false_positives: A host failing to authenticate with multiple invalid domain
+  users is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners and missconfigured systems.
+  If this detection triggers on a host other than a Domain Controller, the behavior
+  could represent a password spraying attack against the host's local accounts.
 name: Windows Multiple Invalid Users Failed To Authenticate Using NTLM
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +30,17 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Workstation$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | where unique_accounts > 30 | `windows_multiple_invalid_users_failed_to_authenticate_using_ntlm_filter`'
+search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064
+  | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, Workstation | where unique_accounts > 30 | `windows_multiple_invalid_users_failed_to_authenticate_using_ntlm_filter`'
 status: production
 tags:
   analytic_story:
@@ -42,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetUserName
-  - Workstation
-  - Status
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml b/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml
index 1044ff0777..4989e357e9 100644
--- a/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml
+++ b/detections/endpoint/windows_multiple_ntlm_null_domain_authentications.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device
+description: The following analytic detects when a device is the target of numerous
+  NTLM authentications using a null domain. This activity generally results when an
+  attacker attempts to brute force, password spray, or otherwise authenticate to a
+  domain joined Windows device from a non-domain device. This activity may also generate
+  a large number of EventID 4776 events in tandem, however these events will not indicate
+  the attacker or target device
 data_source:
 - NTLM Operational 8004,8005,8006
-search: '`ntlm_audit` EventCode IN (8004,8005,8006) DomainName=NULL UserName!=NULL | eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications``` | `windows_multiple_ntlm_null_domain_authentications_filter` | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user))) as unique_count dc(eval(upper(src))) as src_count by dest | eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006.
-known_false_positives: Applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.
+search: '`ntlm_audit` EventCode IN (8004,8005,8006) DomainName=NULL UserName!=NULL
+  | eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading
+  \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM
+  alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy
+  for certain applications``` | `windows_multiple_ntlm_null_domain_authentications_filter`
+  | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user)))
+  as unique_count dc(eval(upper(src))) as src_count by dest | eventstats avg(unique_count)
+  as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3)
+  ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique,
+  1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+how_to_implement: The following analytic requires that NTLM Operational logs to be
+  imported from the environment Domain Controllers. This requires configuration of
+  specific auditing settings, see Microsoft references for further guidance. This
+  analytic is specific to EventID 8004~8006.
+known_false_positives: Applications that deal with non-domain joined authentications.
+  Recommend adjusting the upperBound_unique eval for tailoring the correlation to
+  your environment, running with a 24hr search window will smooth out some statistical
+  noise.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191
@@ -23,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 75
-  message: The device [$dest$] was the target of $count$ NTLM authentications from $src_count$ sources using $unique_count$ unique user accounts.
+  message: The device [$dest$] was the target of $count$ NTLM authentications from
+    $src_count$ sources using $unique_count$ unique user accounts.
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -45,16 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - DomainName
-  - Security
-  - WorkstationName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
     source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml b/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml
index d8751ad551..cf98780ad3 100644
--- a/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml
+++ b/detections/endpoint/windows_multiple_users_fail_to_authenticate_wth_explicitcredentials.yml
@@ -2,10 +2,24 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4648
 date: '2024-09-30'
-description: The following analytic identifies a source user failing to authenticate with 30 unique users using explicit credentials on a host. It leverages Windows Event 4648, which is generated when a process attempts an account logon by explicitly specifying account credentials. This detection is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
+description: The following analytic identifies a source user failing to authenticate
+  with 30 unique users using explicit credentials on a host. It leverages Windows
+  Event 4648, which is generated when a process attempts an account logon by explicitly
+  specifying account credentials. This detection is significant as it may indicate
+  a Password Spraying attack, where an adversary attempts to gain initial access or
+  elevate privileges within an Active Directory environment. If confirmed malicious,
+  this activity could lead to unauthorized access, privilege escalation, and potential
+  compromise of sensitive information.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
 id: e61918fa-9ca4-11eb-836c-acde48001122
-known_false_positives: A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc.
+known_false_positives: A source user failing attempting to authenticate multiple users
+  on a host is not a common behavior for regular systems. Some applications, however,
+  may exhibit this behavior in which case sets of users hosts can be added to an allow
+  list. Possible false positive scenarios include systems where several users connect
+  to like Mail servers, identity providers, remote desktop services, Citrix, etc.
 name: Windows Multiple Users Fail To Authenticate Wth ExplicitCredentials
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +31,18 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) as  tried_account by _time, Computer, Caller_User_Name | where unique_accounts > 30 | `windows_multiple_users_fail_to_authenticate_wth_explicitcredentials_filter`'
+search: '`wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$
+  | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name)
+  as  tried_account by _time, Computer, Caller_User_Name | where unique_accounts >
+  30 | `windows_multiple_users_fail_to_authenticate_wth_explicitcredentials_filter`'
 status: production
 tags:
   analytic_story:
@@ -43,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Target_User_Name
-  - Caller_User_Name
-  - Computer
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml
index 37c84e3485..4424e46a60 100644
--- a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml
+++ b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_host_using_ntlm.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4776
 date: '2024-09-30'
-description: The following analytic identifies a single source endpoint failing to authenticate with 30 unique valid users using the NTLM protocol. It leverages EventCode 4776 from Domain Controller logs, focusing on error code 0xC000006A, which indicates a bad password. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access to sensitive information or further compromise of the Active Directory environment.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a single source endpoint failing to
+  authenticate with 30 unique valid users using the NTLM protocol. It leverages EventCode
+  4776 from Domain Controller logs, focusing on error code 0xC000006A, which indicates
+  a bad password. This behavior is significant as it may indicate a Password Spraying
+  attack, where an adversary attempts to gain initial access or elevate privileges.
+  If confirmed malicious, this activity could lead to unauthorized access to sensitive
+  information or further compromise of the Active Directory environment.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events. The Advanced Security Audit policy setting `Audit Credential
+  Validation` within `Account Logon` needs to be enabled.
 id: 7ed272a4-9c77-11eb-af22-acde48001122
-known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
+known_false_positives: A host failing to authenticate with multiple valid domain users
+  is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners and missconfigured systems.
+  If this detection triggers on a host other than a Domain Controller, the behavior
+  could represent a password spraying attack against the host's local accounts.
 name: Windows Multiple Users Failed To Authenticate From Host Using NTLM
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +29,17 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Workstation$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_from_host_using_ntlm_filter`'
+search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xC000006A
+  | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, Workstation | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_from_host_using_ntlm_filter`'
 status: production
 tags:
   analytic_story:
@@ -42,16 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - Workstation
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml
index 0435bec6ea..657f80dbee 100644
--- a/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml
+++ b/detections/endpoint/windows_multiple_users_failed_to_authenticate_from_process.yml
@@ -2,10 +2,21 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4625
 date: '2024-09-30'
-description: The following analytic detects a source process failing to authenticate with 30 unique users, indicating a potential Password Spraying attack. It leverages Windows Event 4625 with Logon Type 2, collected from domain controllers, member servers, and workstations. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or further compromise of the network, posing a severe security risk.
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
+description: The following analytic detects a source process failing to authenticate
+  with 30 unique users, indicating a potential Password Spraying attack. It leverages
+  Windows Event 4625 with Logon Type 2, collected from domain controllers, member
+  servers, and workstations. This activity is significant as it may represent an adversary
+  attempting to gain initial access or elevate privileges within an Active Directory
+  environment. If confirmed malicious, this could lead to unauthorized access, privilege
+  escalation, or further compromise of the network, posing a severe security risk.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers aas well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
 id: 9015385a-9c84-11eb-bef2-acde48001122
-known_false_positives: A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
+known_false_positives: A process failing to authenticate with multiple users is not
+  a common behavior for legitimate user sessions. Possible false positive scenarios
+  include but are not limited to vulnerability scanners and missconfigured systems.
 name: Windows Multiple Users Failed To Authenticate From Process
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -18,10 +29,18 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, ProcessName, SubjectUserName, Computer | rename Computer as dest | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_from_process_filter`'
+search: '`wineventlog_security` EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket
+  span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, ProcessName, SubjectUserName, Computer | rename Computer
+  as dest | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_from_process_filter`'
 status: production
 tags:
   analytic_story:
@@ -44,18 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - ProcessName
-  - SubjectUserName
-  - TargetUserName
-  - Computer
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml b/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml
index 77ac17a331..a21216154c 100644
--- a/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml
+++ b/detections/endpoint/windows_multiple_users_failed_to_authenticate_using_kerberos.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4771
 date: '2024-09-30'
-description: The following analytic identifies a single source endpoint failing to authenticate with 30 unique users using the Kerberos protocol. It leverages EventCode 4771 with Status 0x18, indicating wrong password attempts, and aggregates these events over a 5-minute window. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges in an Active Directory environment. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a single source endpoint failing to
+  authenticate with 30 unique users using the Kerberos protocol. It leverages EventCode
+  4771 with Status 0x18, indicating wrong password attempts, and aggregates these
+  events over a 5-minute window. This behavior is significant as it may indicate a
+  Password Spraying attack, where an adversary attempts to gain initial access or
+  elevate privileges in an Active Directory environment. If confirmed malicious, this
+  activity could lead to unauthorized access, privilege escalation, and potential
+  compromise of sensitive information.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 id: 3a91a212-98a9-11eb-b86a-acde48001122
-known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms.
+known_false_positives: A host failing to authenticate with multiple valid domain users
+  is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners, missconfigured systems and
+  multi-user systems like Citrix farms.
 name: Windows Multiple Users Failed To Authenticate Using Kerberos
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +29,17 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_using_kerberos_filter`'
+search: '`wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 |
+  bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, IpAddress | where unique_accounts > 30 | `windows_multiple_users_failed_to_authenticate_using_kerberos_filter`'
 status: production
 tags:
   analytic_story:
@@ -47,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml b/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml
index ee2caeeada..53cf6532bb 100644
--- a/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml
+++ b/detections/endpoint/windows_multiple_users_remotely_failed_to_authenticate_from_host.yml
@@ -2,10 +2,23 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4625
 date: '2024-09-30'
-description: The following analytic identifies a source host failing to authenticate against a remote host with 30 unique users. It leverages Windows Event 4625 with Logon Type 3, indicating remote authentication attempts. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges in an Active Directory environment. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information. This detection is crucial for real-time security monitoring and threat hunting.
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
+description: The following analytic identifies a source host failing to authenticate
+  against a remote host with 30 unique users. It leverages Windows Event 4625 with
+  Logon Type 3, indicating remote authentication attempts. This behavior is significant
+  as it may indicate a Password Spraying attack, where an adversary attempts to gain
+  initial access or elevate privileges in an Active Directory environment. If confirmed
+  malicious, this activity could lead to unauthorized access, privilege escalation,
+  and potential compromise of sensitive information. This detection is crucial for
+  real-time security monitoring and threat hunting.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
 id: 80f9d53e-9ca1-11eb-b0d6-acde48001122
-known_false_positives: A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc.
+known_false_positives: A host failing to authenticate with multiple valid users against
+  a remote host is not a common behavior for legitimate systems. Possible false positive
+  scenarios include but are not limited to vulnerability scanners, remote administration
+  tools, missconfigyred systems, etc.
 name: Windows Multiple Users Remotely Failed To Authenticate From Host
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -18,10 +31,18 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress, Computer | rename Computer as dest| where unique_accounts > 30 | `windows_multiple_users_remotely_failed_to_authenticate_from_host_filter`'
+search: '`wineventlog_security` EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket
+  span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, IpAddress, Computer | rename Computer as dest| where
+  unique_accounts > 30 | `windows_multiple_users_remotely_failed_to_authenticate_from_host_filter`'
 status: production
 tags:
   analytic_story:
@@ -43,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - TargetUserName
-  - Computer
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_network_share_interaction_with_net.yml b/detections/endpoint/windows_network_share_interaction_with_net.yml
index 1648caaf92..c881b8d716 100644
--- a/detections/endpoint/windows_network_share_interaction_with_net.yml
+++ b/detections/endpoint/windows_network_share_interaction_with_net.yml
@@ -7,9 +7,27 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 1
-description: This analytic detects network share discovery and collection activities performed on Windows systems using the Net command. Attackers often use network share discovery to identify accessible shared resources within a network, which can be a precursor to privilege escalation or data exfiltration. By monitoring Windows Event Logs for the usage of the Net command to list and interact with network shares, this detection helps identify potential reconnaissance and collection activities.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.user_category) as user_category values(Processes.user_bunit) as user_bunit  FROM datamodel=Endpoint.Processes WHERE (Processes.process_name="net.exe" OR Processes.process_name="net1.exe" OR Processes.orig_process_name="net.exe" OR Processes.orig_process_name="net1.exe") BY Processes.user Processes.dest Processes.process_exec Processes.parent_process_exec Processes.process Processes.parent_process | `drop_dm_object_name(Processes)` | regex process="net[\s\.ex1]+view|net[\s\.ex1]+share|net[\s\.ex1]+use\s" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_network_share_interaction_with_net_filter`'
-how_to_implement: The detection is based on data originating from either Endpoint Detection and Response (EDR) telemetry or EventCode 4688 with process command line logging enabled. These sources provide security-related telemetry from the endpoints. To implement this search, you must ingest logs that contain the process name, parent process, and complete command-line executions. These logs must be mapped to the Splunk Common Information Model (CIM) to normalize the field names capture the data within the datamodel schema.
+description: This analytic detects network share discovery and collection activities
+  performed on Windows systems using the Net command. Attackers often use network
+  share discovery to identify accessible shared resources within a network, which
+  can be a precursor to privilege escalation or data exfiltration. By monitoring Windows
+  Event Logs for the usage of the Net command to list and interact with network shares,
+  this detection helps identify potential reconnaissance and collection activities.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Processes.user_category) as user_category values(Processes.user_bunit)
+  as user_bunit  FROM datamodel=Endpoint.Processes WHERE (Processes.process_name="net.exe"
+  OR Processes.process_name="net1.exe" OR Processes.orig_process_name="net.exe" OR
+  Processes.orig_process_name="net1.exe") BY Processes.user Processes.dest Processes.process_exec
+  Processes.parent_process_exec Processes.process Processes.parent_process | `drop_dm_object_name(Processes)`
+  | regex process="net[\s\.ex1]+view|net[\s\.ex1]+share|net[\s\.ex1]+use\s" | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_network_share_interaction_with_net_filter`'
+how_to_implement: The detection is based on data originating from either Endpoint
+  Detection and Response (EDR) telemetry or EventCode 4688 with process command line
+  logging enabled. These sources provide security-related telemetry from the endpoints.
+  To implement this search, you must ingest logs that contain the process name, parent
+  process, and complete command-line executions. These logs must be mapped to the
+  Splunk Common Information Model (CIM) to normalize the field names capture the data
+  within the datamodel schema.
 known_false_positives: Unknown
 references:
 - https://attack.mitre.org/techniques/T1135/
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,18 +55,11 @@ tags:
   - ab39a04f-0c93-4540-9ff2-83f862c385ae
   confidence: 100
   impact: 20
-  message: User $user$ leveraged net.exe on $dest$ to interact with network shares, executed by parent process $parent_process$
+  message: User $user$ leveraged net.exe on $dest$ to interact with network shares,
+    executed by parent process $parent_process$
   mitre_attack_id:
   - T1135
   - T1039
-  required_fields:
-  - Processes.process_name
-  - Processes.user
-  - Processes.dest
-  - Processes.process_exec
-  - Processes.parent_process_exec
-  - Processes.process
-  - Processes.parent_process
   observable:
   - name: dest
     type: Hostname
@@ -61,6 +77,7 @@ tags:
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/net_share/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_new_inprocserver32_added.yml b/detections/endpoint/windows_new_inprocserver32_added.yml
index 46d1dcaf20..26207d8f20 100644
--- a/detections/endpoint/windows_new_inprocserver32_added.yml
+++ b/detections/endpoint/windows_new_inprocserver32_added.yml
@@ -7,10 +7,24 @@ data_source:
 - Sysmon EventID 13
 type: Hunting
 status: production
-description: The following analytic detects the addition of new InProcServer32 registry keys on Windows endpoints. It leverages data from the Endpoint.Registry datamodel to identify changes in registry paths associated with InProcServer32. This activity is significant because malware often uses this mechanism to achieve persistence or execute malicious code by registering a new InProcServer32 key pointing to a harmful DLL. If confirmed malicious, this could allow an attacker to persist in the environment or execute arbitrary code, posing a significant threat to system integrity and security.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\InProcServer32\\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.dest Registry.process_guid Registry.user | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_new_inprocserver32_added_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-known_false_positives: False positives are expected. Filtering will be needed to properly reduce legitimate applications from the results.
+description: The following analytic detects the addition of new InProcServer32 registry
+  keys on Windows endpoints. It leverages data from the Endpoint.Registry datamodel
+  to identify changes in registry paths associated with InProcServer32. This activity
+  is significant because malware often uses this mechanism to achieve persistence
+  or execute malicious code by registering a new InProcServer32 key pointing to a
+  harmful DLL. If confirmed malicious, this could allow an attacker to persist in
+  the environment or execute arbitrary code, posing a significant threat to system
+  integrity and security.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  where Registry.registry_path="*\\InProcServer32\\*" by Registry.registry_path Registry.registry_key_name
+  Registry.registry_value_name Registry.registry_value_data Registry.dest Registry.process_guid
+  Registry.user | `drop_dm_object_name(Registry)` |`security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_new_inprocserver32_added_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node.
+known_false_positives: False positives are expected. Filtering will be needed to properly
+  reduce legitimate applications from the results.
 references:
 - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/
 tags:
@@ -19,7 +33,8 @@ tags:
   asset_type: Endpoint
   confidence: 20
   impact: 10
-  message: A new InProcServer32 registry key was added to a Windows endpoint. This could indicate suspicious or malicious activity on the $dest$ .
+  message: A new InProcServer32 registry key was added to a Windows endpoint. This
+    could indicate suspicious or malicious activity on the $dest$ .
   mitre_attack_id:
   - T1112
   observable:
@@ -31,20 +46,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_value_data
-  - Registry.dest
-  - Registry.process_guid
-  - Registry.user
   security_domain: endpoint
   cve:
   - CVE-2024-21378
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/cve-2024-21378/inprocserver32_windows-sysmon.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
diff --git a/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml b/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml
index ab9ef91549..aa2ea45e3c 100644
--- a/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml
+++ b/detections/endpoint/windows_ngrok_reverse_proxy_usage.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of ngrok.exe on a Windows operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because while ngrok is a legitimate tool for creating secure tunnels, it is increasingly used by adversaries to bypass network defenses and establish reverse proxies. If confirmed malicious, this could allow attackers to exfiltrate data, maintain persistence, or facilitate further attacks by tunneling traffic through the compromised system.
+description: The following analytic detects the execution of ngrok.exe on a Windows
+  operating system. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line arguments. This activity is significant
+  because while ngrok is a legitimate tool for creating secure tunnels, it is increasingly
+  used by adversaries to bypass network defenses and establish reverse proxies. If
+  confirmed malicious, this could allow attackers to exfiltrate data, maintain persistence,
+  or facilitate further attacks by tunneling traffic through the compromised system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok.exe Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*", "*tcp*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_ngrok_reverse_proxy_usage_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=ngrok.exe
+  Processes.process IN ("*start*", "*--config*","*http*","*authtoken*", "*http*",
+  "*tcp*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_ngrok_reverse_proxy_usage_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present based on organizations that
+  allow the use of Ngrok. Filter or monitor as needed.
 references:
 - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 50
-  message: A reverse proxy was identified spawning from $parent_process_name$ -  $process_name$ on endpoint $dest$ by user $user$.
+  message: A reverse proxy was identified spawning from $parent_process_name$ -  $process_name$
+    on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1572
   - T1090
@@ -58,22 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_nirsoft_advancedrun.yml b/detections/endpoint/windows_nirsoft_advancedrun.yml
index 6e88bc4ebb..dfa8fed2d4 100644
--- a/detections/endpoint/windows_nirsoft_advancedrun.yml
+++ b/detections/endpoint/windows_nirsoft_advancedrun.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of AdvancedRun.exe, a tool with capabilities similar to remote administration programs like PsExec. It identifies the process by its name or original file name and flags common command-line arguments. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. Monitoring this activity is crucial as AdvancedRun can be used for remote code execution and configuration-based automation. If malicious, this could allow attackers to execute arbitrary commands, escalate privileges, or maintain persistence within the environment.
+description: The following analytic detects the execution of AdvancedRun.exe, a tool
+  with capabilities similar to remote administration programs like PsExec. It identifies
+  the process by its name or original file name and flags common command-line arguments.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process and command-line telemetry. Monitoring this activity is crucial
+  as AdvancedRun can be used for remote code execution and configuration-based automation.
+  If malicious, this could allow attackers to execute arbitrary commands, escalate
+  privileges, or maintain persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=advancedrun.exe OR Processes.original_file_name=advancedrun.exe) Processes.process IN ("*EXEFilename*","*/cfg*","*RunAs*", "*WindowState*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_nirsoft_advancedrun_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as it is specific to AdvancedRun. Filter as needed based on legitimate usage.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=advancedrun.exe
+  OR Processes.original_file_name=advancedrun.exe) Processes.process IN ("*EXEFilename*","*/cfg*","*RunAs*",
+  "*WindowState*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.original_file_name Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `windows_nirsoft_advancedrun_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as it is specific to AdvancedRun.
+  Filter as needed based on legitimate usage.
 references:
 - http://www.nirsoft.net/utils/advanced_run.html
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 60
-  message: An instance of advancedrun.exe, $process_name$, was spawned by $parent_process_name$ on $dest$ by $user$.
+  message: An instance of advancedrun.exe, $process_name$, was spawned by $parent_process_name$
+    on $dest$ by $user$.
   mitre_attack_id:
   - T1588.002
   observable:
@@ -58,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_nirsoft_utilities.yml b/detections/endpoint/windows_nirsoft_utilities.yml
index 257de4fc78..be0b85bbfe 100644
--- a/detections/endpoint/windows_nirsoft_utilities.yml
+++ b/detections/endpoint/windows_nirsoft_utilities.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of commonly used NirSoft utilities on Windows systems. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution details such as process name, parent process, and command-line arguments. This activity is significant for a SOC because NirSoft utilities, while legitimate, can be used by adversaries for malicious purposes like credential theft or system reconnaissance. If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further system compromise.
+description: The following analytic identifies the execution of commonly used NirSoft
+  utilities on Windows systems. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution details such as process name, parent
+  process, and command-line arguments. This activity is significant for a SOC because
+  NirSoft utilities, while legitimate, can be used by adversaries for malicious purposes
+  like credential theft or system reconnaissance. If confirmed malicious, this activity
+  could lead to unauthorized access, data exfiltration, or further system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_path Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `is_nirsoft_software_macro` | `windows_nirsoft_utilities_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present. Filtering may be required before setting to alert.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.original_file_name Processes.process_path
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `is_nirsoft_software_macro`
+  | `windows_nirsoft_utilities_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present. Filtering may be required before
+  setting to alert.
 references:
 - https://www.cisa.gov/uscert/ncas/alerts/TA18-201A
 - http://www.nirsoft.net/
@@ -24,7 +44,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ related to NiRSoft software usage.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ related to NiRSoft software usage.
   mitre_attack_id:
   - T1588.002
   observable:
@@ -48,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1588.002/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml b/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml
index 15408c13d8..a71ef0f57e 100644
--- a/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml
+++ b/detections/endpoint/windows_njrat_fileless_storage_via_registry.yml
@@ -8,9 +8,25 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects suspicious registry modifications indicative of NjRat's fileless storage technique. It leverages the Endpoint.Registry data model to identify specific registry paths and values commonly used by NjRat for keylogging and executing DLL plugins. This activity is significant as it helps evade traditional file-based detection systems, making it crucial for SOC analysts to monitor. If confirmed malicious, this behavior could allow attackers to persist on the host, execute arbitrary code, and capture sensitive keystrokes, leading to potential data breaches and further system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\[kl]" OR  Registry.registry_value_data IN ("*[ENTER]*", "*[TAP]*", "*[Back]*") by Registry.dest Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_njrat_fileless_storage_via_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
+description: The following analytic detects suspicious registry modifications indicative
+  of NjRat's fileless storage technique. It leverages the Endpoint.Registry data model
+  to identify specific registry paths and values commonly used by NjRat for keylogging
+  and executing DLL plugins. This activity is significant as it helps evade traditional
+  file-based detection systems, making it crucial for SOC analysts to monitor. If
+  confirmed malicious, this behavior could allow attackers to persist on the host,
+  execute arbitrary code, and capture sensitive keystrokes, leading to potential data
+  breaches and further system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\[kl]"
+  OR  Registry.registry_value_data IN ("*[ENTER]*", "*[TAP]*", "*[Back]*") by Registry.dest
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_key_name
+  Registry.registry_value_name | `drop_dm_object_name(Registry)` | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `windows_njrat_fileless_storage_via_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Processes` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,19 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027.011/njrat_fileless_registry_entry/njrat_registry.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027.011/njrat_fileless_registry_entry/njrat_registry.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml b/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml
index 0cad750a0d..af2e61fe25 100644
--- a/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml
+++ b/detections/endpoint/windows_non_discord_app_access_discord_leveldb.yml
@@ -7,9 +7,22 @@ data_source:
 - Windows Event Log Security 4663
 type: Anomaly
 status: production
-description: The following analytic detects non-Discord applications accessing the Discord LevelDB database. It leverages Windows Security Event logs, specifically event code 4663, to identify file access attempts to the LevelDB directory by processes other than Discord. This activity is significant as it may indicate attempts to steal Discord credentials or access sensitive user data. If confirmed malicious, this could lead to unauthorized access to user profiles, messages, and other critical information, potentially compromising the security and privacy of the affected users.
-search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\discord\\Local Storage\\leveldb*") AND process_name != *\\discord.exe AND NOT (process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_non_discord_app_access_discord_leveldb_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+description: The following analytic detects non-Discord applications accessing the
+  Discord LevelDB database. It leverages Windows Security Event logs, specifically
+  event code 4663, to identify file access attempts to the LevelDB directory by processes
+  other than Discord. This activity is significant as it may indicate attempts to
+  steal Discord credentials or access sensitive user data. If confirmed malicious,
+  this could lead to unauthorized access to user profiles, messages, and other critical
+  information, potentially compromising the security and privacy of the affected users.
+search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\discord\\Local
+  Storage\\leveldb*") AND process_name != *\\discord.exe AND NOT (process_path IN
+  ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*"))
+  | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name
+  object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_non_discord_app_access_discord_leveldb_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: A non-discord process $process_name$ accessing discord "leveldb" file on $dest$
+  message: A non-discord process $process_name$ accessing discord "leveldb" file on
+    $dest$
   mitre_attack_id:
   - T1012
   observable:
@@ -40,19 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_non_system_account_targeting_lsass.yml b/detections/endpoint/windows_non_system_account_targeting_lsass.yml
index ba916222be..bb7e0a2656 100644
--- a/detections/endpoint/windows_non_system_account_targeting_lsass.yml
+++ b/detections/endpoint/windows_non_system_account_targeting_lsass.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies non-SYSTEM accounts requesting access to lsass.exe. This detection leverages Sysmon EventCode 10 logs to monitor access attempts to the Local Security Authority Subsystem Service (lsass.exe) by non-SYSTEM users. This activity is significant as it may indicate credential dumping attempts or unauthorized access to sensitive credentials. If confirmed malicious, an attacker could potentially extract credentials from memory, leading to privilege escalation or lateral movement within the network. Immediate investigation is required to determine the legitimacy of the access request and to mitigate any potential threats.
+description: The following analytic identifies non-SYSTEM accounts requesting access
+  to lsass.exe. This detection leverages Sysmon EventCode 10 logs to monitor access
+  attempts to the Local Security Authority Subsystem Service (lsass.exe) by non-SYSTEM
+  users. This activity is significant as it may indicate credential dumping attempts
+  or unauthorized access to sensitive credentials. If confirmed malicious, an attacker
+  could potentially extract credentials from memory, leading to privilege escalation
+  or lateral movement within the network. Immediate investigation is required to determine
+  the legitimacy of the access request and to mitigate any potential threats.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe NOT (SourceUser="NT AUTHORITY\\*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, parent_process_path ,parent_process_id, TargetImage, GrantedAccess, SourceUser, TargetUser | rename TargetUser as user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_non_system_account_targeting_lsass_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
-known_false_positives: False positives will occur based on legitimate application requests, filter based on source image as needed.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe NOT (SourceUser="NT AUTHORITY\\*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name,
+  parent_process_path ,parent_process_id, TargetImage, GrantedAccess, SourceUser,
+  TargetUser | rename TargetUser as user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `windows_non_system_account_targeting_lsass_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
+known_false_positives: False positives will occur based on legitimate application
+  requests, filter based on source image as needed.
 references:
 - https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service
 - https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +53,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A process, $parent_process_path$, has loaded $TargetImage$ that are typically related to credential dumping on $dest$. Review for further details.
+  message: A process, $parent_process_path$, has loaded $TargetImage$ that are typically
+    related to credential dumping on $dest$. Review for further details.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -54,19 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - TargetImage
-  - GrantedAccess
-  - SourceImage
-  - SourceProcessId
-  - SourceUser
-  - TargetUser
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_odbcconf_hunting.yml b/detections/endpoint/windows_odbcconf_hunting.yml
index 49786edbf8..8c40fc96b3 100644
--- a/detections/endpoint/windows_odbcconf_hunting.yml
+++ b/detections/endpoint/windows_odbcconf_hunting.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of Odbcconf.exe within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the process name is Odbcconf.exe. This activity is significant because Odbcconf.exe can be used by attackers to execute arbitrary commands or load malicious DLLs, potentially leading to code execution or persistence. If confirmed malicious, this behavior could allow an attacker to maintain access to the system, execute further malicious activities, or escalate privileges, posing a significant threat to the environment.
+description: The following analytic identifies the execution of Odbcconf.exe within
+  the environment. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process creation events where the process name is Odbcconf.exe. This
+  activity is significant because Odbcconf.exe can be used by attackers to execute
+  arbitrary commands or load malicious DLLs, potentially leading to code execution
+  or persistence. If confirmed malicious, this behavior could allow an attacker to
+  maintain access to the system, execute further malicious activities, or escalate
+  privileges, posing a significant threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_hunting_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present as this is meant to assist with filtering and tuning.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_hunting_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present as this is meant to assist
+  with filtering and tuning.
 references:
 - https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html
 - https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw
@@ -22,7 +42,8 @@ tags:
   asset_type: Endpoint
   confidence: 20
   impact: 30
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to circumvent controls.
   mitre_attack_id:
   - T1218.008
   observable:
@@ -46,23 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_odbcconf_load_dll.yml b/detections/endpoint/windows_odbcconf_load_dll.yml
index 55f1bb8926..065ec800f0 100644
--- a/detections/endpoint/windows_odbcconf_load_dll.yml
+++ b/detections/endpoint/windows_odbcconf_load_dll.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of odbcconf.exe with the regsvr action to load a DLL. This is identified by monitoring command-line arguments in process creation logs from Endpoint Detection and Response (EDR) agents. This activity is significant as it may indicate an attempt to execute arbitrary code via DLL loading, a common technique used in various attack vectors. If confirmed malicious, this could allow an attacker to execute code with the privileges of the odbcconf.exe process, potentially leading to system compromise or further lateral movement.
+description: The following analytic detects the execution of odbcconf.exe with the
+  regsvr action to load a DLL. This is identified by monitoring command-line arguments
+  in process creation logs from Endpoint Detection and Response (EDR) agents. This
+  activity is significant as it may indicate an attempt to execute arbitrary code
+  via DLL loading, a common technique used in various attack vectors. If confirmed
+  malicious, this could allow an attacker to execute code with the privileges of the
+  odbcconf.exe process, potentially leading to system compromise or further lateral
+  movement.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe Processes.process IN ("*/a *",  "*-a*") Processes.process="*regsvr*"  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_load_dll_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering may need to occur based on legitimate application usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe
+  Processes.process IN ("*/a *",  "*-a*") Processes.process="*regsvr*"  by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_load_dll_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering may need to occur
+  based on legitimate application usage. Filter as needed.
 references:
 - https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html
 - https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to circumvent controls.
   mitre_attack_id:
   - T1218.008
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-regsvr.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_odbcconf_load_response_file.yml b/detections/endpoint/windows_odbcconf_load_response_file.yml
index 2d35b25a16..3f67e2d393 100644
--- a/detections/endpoint/windows_odbcconf_load_response_file.yml
+++ b/detections/endpoint/windows_odbcconf_load_response_file.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of odbcconf.exe with a response file, which may contain commands to load a DLL (REGSVR) or other instructions. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it may indicate an attempt to execute arbitrary code or load malicious DLLs, potentially leading to unauthorized actions. If confirmed malicious, this could allow an attacker to gain code execution, escalate privileges, or establish persistence within the environment.
+description: The following analytic detects the execution of odbcconf.exe with a response
+  file, which may contain commands to load a DLL (REGSVR) or other instructions. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments. This activity is significant as it
+  may indicate an attempt to execute arbitrary code or load malicious DLLs, potentially
+  leading to unauthorized actions. If confirmed malicious, this could allow an attacker
+  to gain code execution, escalate privileges, or establish persistence within the
+  environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe Processes.process IN ("*-f *","*/f *") Processes.process="*.rsp*"  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_load_response_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present and filtering may need to occur based on legitimate application usage. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=odbcconf.exe
+  Processes.process IN ("*-f *","*/f *") Processes.process="*.rsp*"  by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_odbcconf_load_response_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present and filtering may need to occur
+  based on legitimate application usage. Filter as needed.
 references:
 - https://strontic.github.io/xcyclopedia/library/odbcconf.exe-07FBA12552331355C103999806627314.html
 - https://twitter.com/redcanary/status/1541838407894171650?s=20&t=kp3WBPtfnyA3xW7D7wx0uw
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to circumvent controls.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to circumvent controls.
   mitre_attack_id:
   - T1218.008
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-rsp.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.008/atomic_red_team/windows-sysmon-odbc-rsp.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_office_product_spawning_msdt.yml b/detections/endpoint/windows_office_product_spawning_msdt.yml
index b813c355bd..91b7028c08 100644
--- a/detections/endpoint/windows_office_product_spawning_msdt.yml
+++ b/detections/endpoint/windows_office_product_spawning_msdt.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a Microsoft Office product spawning the Windows msdt.exe process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where Office applications are the parent process. This activity is significant as it may indicate an attempt to exploit protocol handlers to bypass security controls, even if macros are disabled. If confirmed malicious, this behavior could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects a Microsoft Office product spawning the
+  Windows msdt.exe process. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events where Office applications
+  are the parent process. This activity is significant as it may indicate an attempt
+  to exploit protocol handlers to bypass security controls, even if macros are disabled.
+  If confirmed malicious, this behavior could allow an attacker to execute arbitrary
+  code, potentially leading to system compromise, data exfiltration, or further lateral
+  movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","outlook.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe") Processes.process_name=msdt.exe by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_office_product_spawning_msdt_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("winword.exe","excel.exe","powerpnt.exe","outlook.exe","mspub.exe","visio.exe","onenote.exe","onenotem.exe","onenoteviewer.exe","onenoteim.exe","msaccess.exe")
+  Processes.process_name=msdt.exe by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_office_product_spawning_msdt_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives should be limited, however filter as needed.
 references:
 - https://isc.sans.edu/diary/rss/28694
@@ -27,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +65,8 @@ tags:
   cve:
   - CVE-2022-30190
   impact: 100
-  message: Office parent process $parent_process_name$ has spawned a child process $process_name$ on host $dest$.
+  message: Office parent process $parent_process_name$ has spawned a child process
+    $process_name$ on host $dest$.
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -64,23 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/msdt.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_outlook_webview_registry_modification.yml b/detections/endpoint/windows_outlook_webview_registry_modification.yml
index d44fffbed0..821d49abd1 100644
--- a/detections/endpoint/windows_outlook_webview_registry_modification.yml
+++ b/detections/endpoint/windows_outlook_webview_registry_modification.yml
@@ -7,10 +7,34 @@ data_source:
 - Sysmon EventID 13
 type: Anomaly
 status: production
-description: The following analytic identifies modifications to specific Outlook registry values related to WebView and Today features. It detects when a URL is set in these registry locations, which could indicate attempts to manipulate Outlook's web-based components. The analytic focuses on changes to the "URL" value within Outlook's WebView and Today registry paths. This activity is significant as it may represent an attacker's effort to redirect Outlook's web content or inject malicious URLs. If successful, this technique could lead to phishing attempts, data theft, or serve as a stepping stone for further compromise of the user's email client and potentially sensitive information.
-search: '| tstats `security_content_summariesonly` count values(Registry.registry_value_name) as registry_value_name values(Registry.registry_value_data) as registry_value_data min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\WebView\\*" OR Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\Today") AND Registry.registry_value_name="URL" by Registry.dest, Registry.user, Registry.registry_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_outlook_webview_registry_modification_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may occur if legitimate Outlook processes are modified.
+description: The following analytic identifies modifications to specific Outlook registry
+  values related to WebView and Today features. It detects when a URL is set in these
+  registry locations, which could indicate attempts to manipulate Outlook's web-based
+  components. The analytic focuses on changes to the "URL" value within Outlook's
+  WebView and Today registry paths. This activity is significant as it may represent
+  an attacker's effort to redirect Outlook's web content or inject malicious URLs.
+  If successful, this technique could lead to phishing attempts, data theft, or serve
+  as a stepping stone for further compromise of the user's email client and potentially
+  sensitive information.
+search: '| tstats `security_content_summariesonly` count values(Registry.registry_value_name)
+  as registry_value_name values(Registry.registry_value_data) as registry_value_data
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry
+  where (Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\WebView\\*"
+  OR Registry.registry_path="*\\Software\\Microsoft\\Office\\*\\Outlook\\Today") AND
+  Registry.registry_value_name="URL" by Registry.dest, Registry.user, Registry.registry_path
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`
+  | `windows_outlook_webview_registry_modification_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may occur if legitimate Outlook processes are
+  modified.
 references:
 - https://gist.github.com/MHaggis/c6318acde2e2f691b550e3a491f49ff1
 - https://github.com/trustedsec/specula/wiki
@@ -20,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,17 +70,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_value_name
-  - Registry.registry_value_data
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon-webview.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon-webview.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_papercut_ng_spawn_shell.yml b/detections/endpoint/windows_papercut_ng_spawn_shell.yml
index be303bfda2..74b54e69cf 100644
--- a/detections/endpoint/windows_papercut_ng_spawn_shell.yml
+++ b/detections/endpoint/windows_papercut_ng_spawn_shell.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects instances where the PaperCut NG application (pc-app.exe) spawns a Windows shell, such as cmd.exe or PowerShell. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is pc-app.exe. This activity is significant as it may indicate an attacker attempting to gain unauthorized access or execute malicious commands on the system. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, or further compromise of the affected environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=pc-app.exe `process_cmd` OR `process_powershell` OR Processes.process_name=java.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_papercut_ng_spawn_shell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present, but most likely not. Filter as needed.
+description: The following analytic detects instances where the PaperCut NG application
+  (pc-app.exe) spawns a Windows shell, such as cmd.exe or PowerShell. This behavior
+  is identified using Endpoint Detection and Response (EDR) telemetry, focusing on
+  process creation events where the parent process is pc-app.exe. This activity is
+  significant as it may indicate an attacker attempting to gain unauthorized access
+  or execute malicious commands on the system. If confirmed malicious, this could
+  lead to unauthorized code execution, privilege escalation, or further compromise
+  of the affected environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=pc-app.exe
+  `process_cmd` OR `process_powershell` OR Processes.process_name=java.exe by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_papercut_ng_spawn_shell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present, but most likely not. Filter
+  as needed.
 references:
 - https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability
 - https://www.papercut.com/kb/Main/PO-1216-and-PO-1219
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   atomic_guid: []
   confidence: 90
   impact: 100
-  message: The PaperCut NG application has spawned a shell $process_name$ on endpoint $dest$ by $user$.
+  message: The PaperCut NG application has spawned a shell $process_name$ on endpoint
+    $dest$ by $user$.
   mitre_attack_id:
   - T1059
   - T1190
@@ -54,23 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-app-spawn_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-app-spawn_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml b/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml
index 506499ab2a..f16ef4243d 100644
--- a/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml
+++ b/detections/endpoint/windows_parent_pid_spoofing_with_explorer.yml
@@ -9,9 +9,28 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies a suspicious `explorer.exe` process with the `/root` command-line parameter. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process and command-line data. The presence of `/root` in `explorer.exe` is significant as it may indicate parent process spoofing, a technique used by malware to evade detection. If confirmed malicious, this activity could allow an attacker to operate undetected, potentially leading to unauthorized access, privilege escalation, or persistent threats within the environment.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*explorer.exe*" Processes.process="*/root,*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_parent_pid_spoofing_with_explorer_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies a suspicious `explorer.exe` process
+  with the `/root` command-line parameter. This detection leverages Endpoint Detection
+  and Response (EDR) telemetry, focusing on process and command-line data. The presence
+  of `/root` in `explorer.exe` is significant as it may indicate parent process spoofing,
+  a technique used by malware to evade detection. If confirmed malicious, this activity
+  could allow an attacker to operate undetected, potentially leading to unauthorized
+  access, privilege escalation, or persistent threats within the environment.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*explorer.exe*"
+  Processes.process="*/root,*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_parent_pid_spoofing_with_explorer_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://x.com/CyberRaiju/status/1273597319322058752?s=20
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134/explorer_root_proc_cmdline/explorer_root.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1134/explorer_root_proc_cmdline/explorer_root.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_password_managers_discovery.yml b/detections/endpoint/windows_password_managers_discovery.yml
index 3a2c4abeed..a0a3b390dc 100644
--- a/detections/endpoint/windows_password_managers_discovery.yml
+++ b/detections/endpoint/windows_password_managers_discovery.yml
@@ -5,13 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies command-line activity that searches for files related to password manager software, such as "*.kdbx*" and "*credential*". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant because attackers often target password manager databases to extract stored credentials, which can be used for further exploitation. If confirmed malicious, this behavior could lead to unauthorized access to sensitive information, enabling attackers to escalate privileges, move laterally, or exfiltrate critical data.
+description: The following analytic identifies command-line activity that searches
+  for files related to password manager software, such as "*.kdbx*" and "*credential*".
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process execution logs. This activity is significant because attackers often target
+  password manager databases to extract stored credentials, which can be used for
+  further exploitation. If confirmed malicious, this behavior could lead to unauthorized
+  access to sensitive information, enabling attackers to escalate privileges, move
+  laterally, or exfiltrate critical data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *" OR  Processes.process = "*findstr*" AND Processes.process IN ( "*.kdbx*", "*credential*", "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*", "*.htpasswd*", "*Ntds.dit*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_password_managers_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *"
+  OR  Processes.process = "*findstr*" AND Processes.process IN ( "*.kdbx*", "*credential*",
+  "*key3.db*","*pass*", "*cred*", "*key4.db*", "*accessTokens*", "*access_tokens*",
+  "*.htpasswd*", "*Ntds.dit*") by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_password_managers_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1555/005/
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: a process with commandline $process$ that can retrieve information related to password manager databases in $dest$
+  message: a process with commandline $process$ that can retrieve information related
+    to password manager databases in $dest$
   mitre_attack_id:
   - T1555.005
   observable:
@@ -45,25 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd_db/dir-db-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_pwd_db/dir-db-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml b/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml
index 575566dfd8..4b8ea7d2a9 100644
--- a/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml
+++ b/detections/endpoint/windows_phishing_outlook_drop_dll_in_form_dir.yml
@@ -7,9 +7,27 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic detects the creation of a DLL file by an outlook.exe process in the AppData\Local\Microsoft\FORMS directory. This detection leverages data from the Endpoint.Processes and Endpoint.Filesystem datamodels, focusing on process and file creation events. This activity is significant as it may indicate an attempt to exploit CVE-2024-21378, where a custom MAPI form loads a potentially malicious DLL. If confirmed malicious, this could allow an attacker to execute arbitrary code, leading to further system compromise or data exfiltration.
-search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe by _time span=1h Processes.process_id Processes.process_name Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid, _time [ | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name ="*.dll" Filesystem.file_path = "*\\AppData\\Local\\Microsoft\\FORMS\\IPM*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.process_guid | `drop_dm_object_name(Filesystem)` | fields file_name file_path process_name process_path process dest file_create_time _time process_guid] | `windows_phishing_outlook_drop_dll_in_form_dir_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
+description: The following analytic detects the creation of a DLL file by an outlook.exe
+  process in the AppData\Local\Microsoft\FORMS directory. This detection leverages
+  data from the Endpoint.Processes and Endpoint.Filesystem datamodels, focusing on
+  process and file creation events. This activity is significant as it may indicate
+  an attempt to exploit CVE-2024-21378, where a custom MAPI form loads a potentially
+  malicious DLL. If confirmed malicious, this could allow an attacker to execute arbitrary
+  code, leading to further system compromise or data exfiltration.
+search: '| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes
+  where Processes.process_name=outlook.exe by _time span=1h Processes.process_id Processes.process_name
+  Processes.process Processes.dest Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid, _time [ | tstats `security_content_summariesonly` count min(_time)
+  as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name
+  ="*.dll" Filesystem.file_path = "*\\AppData\\Local\\Microsoft\\FORMS\\IPM*" by _time
+  span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path
+  Filesystem.process_guid | `drop_dm_object_name(Filesystem)` | fields file_name file_path
+  process_name process_path process dest file_create_time _time process_guid] | `windows_phishing_outlook_drop_dll_in_form_dir_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
 known_false_positives: unknown
 references:
 - https://www.netspi.com/blog/technical/red-team-operations/microsoft-outlook-remote-code-execution-cve-2024-21378/
@@ -19,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,21 +63,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - file_create_time
-  - file_name
-  - file_path
-  - process_name
-  - process_path
-  - process
   security_domain: endpoint
   cve:
   - CVE-2024-21378
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/outlook_dropped_dll/outlook_phishing_form_dll.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/outlook_dropped_dll/outlook_phishing_form_dll.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml b/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml
index 205085f79b..2ea30cc895 100644
--- a/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml
+++ b/detections/endpoint/windows_phishing_pdf_file_executes_url_link.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious PDF viewer processes spawning browser application child processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process names. This activity is significant as it may indicate a PDF spear-phishing attempt where a malicious URL link is executed, leading to potential payload download. If confirmed malicious, this could allow attackers to execute code, escalate privileges, or persist in the environment by exploiting the user's browser to connect to a malicious site.
+description: The following analytic detects suspicious PDF viewer processes spawning
+  browser application child processes. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process and parent process names. This activity
+  is significant as it may indicate a PDF spear-phishing attempt where a malicious
+  URL link is executed, leading to potential payload download. If confirmed malicious,
+  this could allow attackers to execute code, escalate privileges, or persist in the
+  environment by exploiting the user's browser to connect to a malicious site.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name  IN ("AcroRd32.exe", "FoxitPDFReader.exe") Processes.process_name IN ("firefox.exe", "chrome.exe", "iexplore.exe") by Processes.user Processes.parent_process_name Processes.process_name  Processes.parent_process Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_pdf_file_executes_url_link_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives in PDF file opened PDF Viewer having legitimate URL link, however filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name  IN
+  ("AcroRd32.exe", "FoxitPDFReader.exe") Processes.process_name IN ("firefox.exe",
+  "chrome.exe", "iexplore.exe") by Processes.user Processes.parent_process_name Processes.process_name  Processes.parent_process
+  Processes.process Processes.process_id Processes.dest |`drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_pdf_file_executes_url_link_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives in PDF file opened PDF Viewer having legitimate
+  URL link, however filter as needed.
 references:
 - https://twitter.com/pr0xylife/status/1615382907446767616?s=20
 drilldown_searches:
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: a pdf file opened in pdf viewer process $parent_process_name$ has a child process of a browser $process_name$ in $dest$
+  message: a pdf file opened in pdf viewer process $parent_process_name$ has a child
+    process of a browser $process_name$ in $dest$
   mitre_attack_id:
   - T1566.001
   - T1566
@@ -44,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/phishing_pdf_uri/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/phishing_pdf_uri/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml b/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml
index 0ab17abaa3..f9577b5e55 100644
--- a/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml
+++ b/detections/endpoint/windows_phishing_recent_iso_exec_registry.yml
@@ -5,13 +5,31 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the creation of registry artifacts when an ISO container is opened, clicked, or mounted on a Windows operating system. It leverages data from the Endpoint.Registry data model, specifically monitoring registry keys related to recent ISO or IMG file executions. This activity is significant as adversaries increasingly use container-based phishing campaigns to bypass macro-based document execution controls. If confirmed malicious, this behavior could indicate an initial access attempt, potentially leading to further exploitation, persistence, or data exfiltration within the environment.
+description: The following analytic detects the creation of registry artifacts when
+  an ISO container is opened, clicked, or mounted on a Windows operating system. It
+  leverages data from the Endpoint.Registry data model, specifically monitoring registry
+  keys related to recent ISO or IMG file executions. This activity is significant
+  as adversaries increasingly use container-based phishing campaigns to bypass macro-based
+  document execution controls. If confirmed malicious, this behavior could indicate
+  an initial access attempt, potentially leading to further exploitation, persistence,
+  or data exfiltration within the environment.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_key_name= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.iso" OR Registry.registry_key_name= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.img" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_phishing_recent_iso_exec_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives may be high depending on the environment and consistent use of ISOs. Restrict to servers, or filter out based on commonly used ISO names. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_key_name= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.iso"
+  OR Registry.registry_key_name= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\RecentDocs\\.img"
+  by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_phishing_recent_iso_exec_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives may be high depending on the environment and
+  consistent use of ISOs. Restrict to servers, or filter out based on commonly used
+  ISO names. Filter as needed.
 references:
 - https://www.microsoft.com/security/blog/2021/05/27/new-sophisticated-email-based-attack-from-nobelium/
 - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/
@@ -30,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 50
-  message: An ISO file was mounted on $dest$ and should be reviewed and filtered as needed.
+  message: An ISO file was mounted on $dest$ and should be reviewed and filtered as
+    needed.
   mitre_attack_id:
   - T1566.001
   - T1566
@@ -43,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.action
-  - Registry.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_possible_credential_dumping.yml b/detections/endpoint/windows_possible_credential_dumping.yml
index 6f0b1d9ced..a6fc1dc5ef 100644
--- a/detections/endpoint/windows_possible_credential_dumping.yml
+++ b/detections/endpoint/windows_possible_credential_dumping.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects potential credential dumping by identifying specific GrantedAccess permission requests and CallTrace DLLs targeting the LSASS process. It leverages Sysmon EventCode 10 logs, focusing on access requests to lsass.exe and call traces involving debug and native API DLLs like dbgcore.dll, dbghelp.dll, and ntdll.dll. This activity is significant as credential dumping can lead to unauthorized access to sensitive credentials. If confirmed malicious, attackers could gain elevated privileges and persist within the environment, posing a severe security risk.
+description: The following analytic detects potential credential dumping by identifying
+  specific GrantedAccess permission requests and CallTrace DLLs targeting the LSASS
+  process. It leverages Sysmon EventCode 10 logs, focusing on access requests to lsass.exe
+  and call traces involving debug and native API DLLs like dbgcore.dll, dbghelp.dll,
+  and ntdll.dll. This activity is significant as credential dumping can lead to unauthorized
+  access to sensitive credentials. If confirmed malicious, attackers could gain elevated
+  privileges and persist within the environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=*\\lsass.exe granted_access IN ("0x01000", "0x1010", "0x1038", "0x40", "0x1400", "0x1fffff", "0x1410", "0x143a", "0x1438", "0x1000") CallTrace IN ("*dbgcore.dll*", "*dbghelp.dll*", "*ntdll.dll*", "*kernelbase.dll*", "*kernel32.dll*") NOT SourceUser IN ("NT AUTHORITY\\SYSTEM", "NT AUTHORITY\\NETWORK SERVICE") | stats count min(_time) as firstTime max(_time) as lastTime by dest, SourceImage, GrantedAccess, TargetImage, SourceProcessId, SourceUser, TargetUser | rename SourceUser as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_possible_credential_dumping_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
-known_false_positives: False positives will occur based on GrantedAccess 0x1010 and 0x1400, filter based on source image as needed or remove them. Concern is Cobalt Strike usage of Mimikatz will generate 0x1010 initially, but later be caught.
+search: '`sysmon` EventCode=10 TargetImage=*\\lsass.exe granted_access IN ("0x01000",
+  "0x1010", "0x1038", "0x40", "0x1400", "0x1fffff", "0x1410", "0x143a", "0x1438",
+  "0x1000") CallTrace IN ("*dbgcore.dll*", "*dbghelp.dll*", "*ntdll.dll*", "*kernelbase.dll*",
+  "*kernel32.dll*") NOT SourceUser IN ("NT AUTHORITY\\SYSTEM", "NT AUTHORITY\\NETWORK
+  SERVICE") | stats count min(_time) as firstTime max(_time) as lastTime by dest,
+  SourceImage, GrantedAccess, TargetImage, SourceProcessId, SourceUser, TargetUser
+  | rename SourceUser as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_possible_credential_dumping_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA. Enabling EventCode 10 TargetProcess lsass.exe is required.
+known_false_positives: False positives will occur based on GrantedAccess 0x1010 and
+  0x1400, filter based on source image as needed or remove them. Concern is Cobalt
+  Strike usage of Mimikatz will generate 0x1010 initially, but later be caught.
 references:
 - https://en.wikipedia.org/wiki/Local_Security_Authority_Subsystem_Service
 - https://docs.microsoft.com/en-us/windows/win32/api/minidumpapiset/nf-minidumpapiset-minidumpwritedump
@@ -24,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -38,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A process, $SourceImage$, has loaded $TargetImage$ that are typically related to credential dumping on $dest$. Review for further details.
+  message: A process, $SourceImage$, has loaded $TargetImage$ that are typically related
+    to credential dumping on $dest$. Review for further details.
   mitre_attack_id:
   - T1003.001
   - T1003
@@ -59,19 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - TargetImage
-  - GrantedAccess
-  - SourceImage
-  - SourceProcessId
-  - SourceUser
-  - TargetUser
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon_creddump.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_post_exploitation_risk_behavior.yml b/detections/endpoint/windows_post_exploitation_risk_behavior.yml
index 03d498348d..a4fb3ef1dd 100644
--- a/detections/endpoint/windows_post_exploitation_risk_behavior.yml
+++ b/detections/endpoint/windows_post_exploitation_risk_behavior.yml
@@ -6,10 +6,33 @@ author: Teoderick Contreras, Splunk
 status: production
 type: Correlation
 data_source: []
-description: The following analytic identifies four or more distinct post-exploitation behaviors on a Windows system. It leverages data from the Risk data model in Splunk Enterprise Security, focusing on multiple risk events and their associated MITRE ATT&CK tactics and techniques. This activity is significant as it indicates potential malicious actions following an initial compromise, such as persistence, privilege escalation, or data exfiltration. If confirmed malicious, this behavior could allow attackers to maintain control, escalate privileges, and further exploit the compromised environment, leading to significant security breaches and data loss.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("*Windows Post-Exploitation*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where source_count >= 4 | `windows_post_exploitation_risk_behavior_filter`'
-how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
-known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
+description: The following analytic identifies four or more distinct post-exploitation
+  behaviors on a Windows system. It leverages data from the Risk data model in Splunk
+  Enterprise Security, focusing on multiple risk events and their associated MITRE
+  ATT&CK tactics and techniques. This activity is significant as it indicates potential
+  malicious actions following an initial compromise, such as persistence, privilege
+  escalation, or data exfiltration. If confirmed malicious, this behavior could allow
+  attackers to maintain control, escalate privileges, and further exploit the compromised
+  environment, leading to significant security breaches and data loss.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories
+  IN ("*Windows Post-Exploitation*") by All_Risk.risk_object All_Risk.risk_object_type
+  All_Risk.annotations.mitre_attack.mitre_tactic | `drop_dm_object_name(All_Risk)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where
+  source_count >= 4 | `windows_post_exploitation_risk_behavior_filter`'
+how_to_implement: Splunk Enterprise Security is required to utilize this correlation.
+  In addition, modify the source_count value to your environment. In our testing,
+  a count of 4 or 5 was decent in a lab, but the number may need to be increased base
+  on internal testing. In addition, based on false positives, modify any analytics
+  to be anomaly and lower or increase risk based on organization importance.
+known_false_positives: False positives will be present based on many factors. Tune
+  the correlation as needed to reduce too many triggers.
 references:
 - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS/winPEASbat
 drilldown_searches:
@@ -18,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An increase of Windows Post Exploitation behavior has been detected on $risk_object$
+  message: An increase of Windows Post Exploitation behavior has been detected on
+    $risk_object$
   mitre_attack_id:
   - T1012
   - T1049
@@ -46,17 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/windows_post_exploitation/windows_post_exploitation_risk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/windows_post_exploitation/windows_post_exploitation_risk.log
     source: wpe
     sourcetype: stash
diff --git a/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml b/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml
index e23d3af1de..3cdb95d502 100644
--- a/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml
+++ b/detections/endpoint/windows_powershell_add_module_to_global_assembly_cache.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the addition of a DLL to the Windows Global Assembly Cache (GAC) using PowerShell. It leverages PowerShell Script Block Logging to identify commands containing "system.enterpriseservices.internal.publish". This activity is significant because adding a DLL to the GAC allows it to be shared across multiple applications, potentially enabling an adversary to execute malicious code system-wide. If confirmed malicious, this could lead to widespread code execution, privilege escalation, and persistent access across the operating system, posing a severe security risk.
+description: The following analytic detects the addition of a DLL to the Windows Global
+  Assembly Cache (GAC) using PowerShell. It leverages PowerShell Script Block Logging
+  to identify commands containing "system.enterpriseservices.internal.publish". This
+  activity is significant because adding a DLL to the GAC allows it to be shared across
+  multiple applications, potentially enabling an adversary to execute malicious code
+  system-wide. If confirmed malicious, this could lead to widespread code execution,
+  privilege escalation, and persistent access across the operating system, posing
+  a severe security risk.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN("*system.enterpriseservices.internal.publish*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_add_module_to_global_assembly_cache_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives may be present based on developers or third party utilities adding items to the GAC.
+search: '`powershell` EventCode=4104 ScriptBlockText IN("*system.enterpriseservices.internal.publish*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_powershell_add_module_to_global_assembly_cache_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives may be present based on developers or third
+  party utilities adding items to the GAC.
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
 - https://www.microsoft.com/en-us/security/blog/2022/07/26/malicious-iis-extensions-quietly-open-persistent-backdoors-into-servers/
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: PowerShell was used to install a module to the Global Assembly Cache on $Computer$.
+  message: PowerShell was used to install a module to the Global Assembly Cache on
+    $Computer$.
   mitre_attack_id:
   - T1505
   - T1505.004
@@ -42,15 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_publish_powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/pwsh_publish_powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_cryptography_namespace.yml b/detections/endpoint/windows_powershell_cryptography_namespace.yml
index 7059d14f25..c33d667101 100644
--- a/detections/endpoint/windows_powershell_cryptography_namespace.yml
+++ b/detections/endpoint/windows_powershell_cryptography_namespace.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious PowerShell script execution involving the cryptography namespace via EventCode 4104. It leverages PowerShell Script Block Logging to identify scripts using cryptographic functions, excluding common hashes like SHA and MD5. This activity is significant as it is often associated with malware that decrypts or decodes additional malicious payloads. If confirmed malicious, this could allow an attacker to execute further code, escalate privileges, or establish persistence within the environment. Analysts should investigate the parent process, decrypted data, network connections, and the user executing the script.
+description: The following analytic detects suspicious PowerShell script execution
+  involving the cryptography namespace via EventCode 4104. It leverages PowerShell
+  Script Block Logging to identify scripts using cryptographic functions, excluding
+  common hashes like SHA and MD5. This activity is significant as it is often associated
+  with malware that decrypts or decodes additional malicious payloads. If confirmed
+  malicious, this could allow an attacker to execute further code, escalate privileges,
+  or establish persistence within the environment. Analysts should investigate the
+  parent process, decrypted data, network connections, and the user executing the
+  script.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*System.Security.Cryptography*" AND NOT(ScriptBlockText IN ("*SHA*", "*MD5*", "*DeriveBytes*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_cryptography_namespace_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*System.Security.Cryptography*"
+  AND NOT(ScriptBlockText IN ("*SHA*", "*MD5*", "*DeriveBytes*")) | stats count min(_time)
+  as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID
+  | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_cryptography_namespace_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: False positives should be limited. Filter as needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A suspicious powershell script contains cryptography command detected on host $dest$
+  message: A suspicious powershell script contains cryptography command detected on
+    host $dest$
   mitre_attack_id:
   - T1059.001
   - T1059
@@ -45,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCodes
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/asyncrat_crypto_pwh_namespace/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/asyncrat_crypto_pwh_namespace/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_disable_http_logging.yml b/detections/endpoint/windows_powershell_disable_http_logging.yml
index 835610e41a..cfda5d9aa8 100644
--- a/detections/endpoint/windows_powershell_disable_http_logging.yml
+++ b/detections/endpoint/windows_powershell_disable_http_logging.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `get-WebConfigurationProperty` and `Set-ItemProperty` commands in PowerShell to disable HTTP logging on Windows systems. This detection leverages PowerShell Script Block Logging, specifically looking for script blocks that reference HTTP logging properties and attempt to set them to "false" or "dontLog". Disabling HTTP logging is significant as it can be used by adversaries to cover their tracks and delete logs, hindering forensic investigations. If confirmed malicious, this activity could allow attackers to evade detection and persist in the environment undetected.
+description: The following analytic detects the use of `get-WebConfigurationProperty`
+  and `Set-ItemProperty` commands in PowerShell to disable HTTP logging on Windows
+  systems. This detection leverages PowerShell Script Block Logging, specifically
+  looking for script blocks that reference HTTP logging properties and attempt to
+  set them to "false" or "dontLog". Disabling HTTP logging is significant as it can
+  be used by adversaries to cover their tracks and delete logs, hindering forensic
+  investigations. If confirmed malicious, this activity could allow attackers to evade
+  detection and persist in the environment undetected.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN("*get-WebConfigurationProperty*","*Set-ItemProperty*") AND ScriptBlockText IN ("*httpLogging*","*Logfile.enabled*") AND ScriptBlockText IN ("*dontLog*", "*false*")   | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_disable_http_logging_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required.
+search: '`powershell` EventCode=4104 ScriptBlockText IN("*get-WebConfigurationProperty*","*Set-ItemProperty*")
+  AND ScriptBlockText IN ("*httpLogging*","*Logfile.enabled*") AND ScriptBlockText
+  IN ("*dontLog*", "*false*")   | stats count min(_time) as firstTime max(_time) as
+  lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_disable_http_logging_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: It is possible administrators or scripts may run these commands,
+  filtering may be required.
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
 - https://www.crowdstrike.com/wp-content/uploads/2022/05/crowdstrike-iceapple-a-novel-internet-information-services-post-exploitation-framework-1.pdf
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A PowerShell Cmdlet related to disable or modifying a IIS HTTP logging has occurred on $Computer$.
+  message: A PowerShell Cmdlet related to disable or modifying a IIS HTTP logging
+    has occurred on $Computer$.
   mitre_attack_id:
   - T1562
   - T1562.002
@@ -47,15 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_disable_http_logging_windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_disable_http_logging_windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_export_certificate.yml b/detections/endpoint/windows_powershell_export_certificate.yml
index e989086ba0..ec10001c11 100644
--- a/detections/endpoint/windows_powershell_export_certificate.yml
+++ b/detections/endpoint/windows_powershell_export_certificate.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the PowerShell Cmdlet `export-certificate` by leveraging Script Block Logging. This activity is significant as it may indicate an adversary attempting to exfiltrate certificates from the local Certificate Store on a Windows endpoint. Monitoring this behavior is crucial because stolen certificates can be used to impersonate users, decrypt sensitive data, or facilitate further attacks. If confirmed malicious, this activity could lead to unauthorized access to encrypted communications and sensitive information, posing a severe security risk.
+description: The following analytic detects the use of the PowerShell Cmdlet `export-certificate`
+  by leveraging Script Block Logging. This activity is significant as it may indicate
+  an adversary attempting to exfiltrate certificates from the local Certificate Store
+  on a Windows endpoint. Monitoring this behavior is crucial because stolen certificates
+  can be used to impersonate users, decrypt sensitive data, or facilitate further
+  attacks. If confirmed malicious, this activity could lead to unauthorized access
+  to encrypted communications and sensitive information, posing a severe security
+  risk.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*export-certificate*") | rename Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_export_certificate_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*export-certificate*") |
+  rename Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText dest user_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_export_certificate_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: It is possible administrators or scripts may run these commands,
+  filtering may be required.
 references:
 - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj
 - https://learn.microsoft.com/en-us/powershell/module/pki/export-certificate?view=windowsserver2022-ps
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A PowerShell Cmdlet related to exporting a Certificate was ran on $dest$, attempting to export a certificate.
+  message: A PowerShell Cmdlet related to exporting a Certificate was ran on $dest$,
+    attempting to export a certificate.
   mitre_attack_id:
   - T1552.004
   - T1552
@@ -43,15 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_certificate.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_certificate.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_export_pfxcertificate.yml b/detections/endpoint/windows_powershell_export_pfxcertificate.yml
index fa2ada4cff..20421ae56d 100644
--- a/detections/endpoint/windows_powershell_export_pfxcertificate.yml
+++ b/detections/endpoint/windows_powershell_export_pfxcertificate.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate` by leveraging Script Block Logging. This activity is significant as it may indicate an adversary attempting to exfiltrate certificates from the Windows Certificate Store. Monitoring this behavior is crucial for identifying potential certificate theft, which can lead to unauthorized access and impersonation attacks. If confirmed malicious, this activity could allow attackers to compromise secure communications, authenticate as legitimate users, and escalate their privileges within the network.
+description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate`
+  by leveraging Script Block Logging. This activity is significant as it may indicate
+  an adversary attempting to exfiltrate certificates from the Windows Certificate
+  Store. Monitoring this behavior is crucial for identifying potential certificate
+  theft, which can lead to unauthorized access and impersonation attacks. If confirmed
+  malicious, this activity could allow attackers to compromise secure communications,
+  authenticate as legitimate users, and escalate their privileges within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*export-pfxcertificate*") | rename Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText dest user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_export_pfxcertificate_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*export-pfxcertificate*")
+  | rename Computer as dest  | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText dest user_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_export_pfxcertificate_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: It is possible administrators or scripts may run these commands,
+  filtering may be required.
 references:
 - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj
 - https://learn.microsoft.com/en-us/powershell/module/pki/export-pfxcertificate?view=windowsserver2022-ps
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: A PowerShell Cmdlet related to exporting a PFX Certificate was ran on $dest$, attempting to export a certificate.
+  message: A PowerShell Cmdlet related to exporting a PFX Certificate was ran on $dest$,
+    attempting to export a certificate.
   mitre_attack_id:
   - T1552.004
   - T1552
@@ -43,15 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_pfxcertificate.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4104_export_pfxcertificate.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml b/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml
index fc8fba71b7..a315d8e452 100644
--- a/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml
+++ b/detections/endpoint/windows_powershell_get_ciminstance_remote_computer.yml
@@ -7,10 +7,23 @@ type: Anomaly
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the use of the Get-CimInstance cmdlet with the -ComputerName parameter, indicating an attempt to retrieve information from a remote computer. It leverages PowerShell Script Block Logging to identify this specific command execution. This activity is significant as it may indicate unauthorized remote access or information gathering by an attacker. If confirmed malicious, this could allow the attacker to collect sensitive data from remote systems, potentially leading to further exploitation or lateral movement within the network.
-search: '`powershell` EventCode=4104 ScriptBlockText="*get-ciminstance*" AND ScriptBlockText="*computername*"  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_get_ciminstance_remote_computer_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: This is meant to be a low risk RBA anomaly analytic or to be used for hunting. Enable this with a low risk score and let it generate risk in the risk index.
+description: The following analytic detects the use of the Get-CimInstance cmdlet
+  with the -ComputerName parameter, indicating an attempt to retrieve information
+  from a remote computer. It leverages PowerShell Script Block Logging to identify
+  this specific command execution. This activity is significant as it may indicate
+  unauthorized remote access or information gathering by an attacker. If confirmed
+  malicious, this could allow the attacker to collect sensitive data from remote systems,
+  potentially leading to further exploitation or lateral movement within the network.
+search: '`powershell` EventCode=4104 ScriptBlockText="*get-ciminstance*" AND ScriptBlockText="*computername*"  |
+  stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_powershell_get_ciminstance_remote_computer_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: This is meant to be a low risk RBA anomaly analytic or to be
+  used for hunting. Enable this with a low risk score and let it generate risk in
+  the risk index.
 references:
 - https://learn.microsoft.com/en-us/powershell/module/cimcmdlets/get-ciminstance?view=powershell-7.3
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +46,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: A PowerShell Cmdlet Get-CIMInstnace was ran on $Computer$, attempting to connect to a remote host.
+  message: A PowerShell Cmdlet Get-CIMInstnace was ran on $Computer$, attempting to
+    connect to a remote host.
   mitre_attack_id:
   - T1059.001
   observable:
@@ -40,15 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/get_ciminstance_windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/get_ciminstance_windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml b/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml
index 6116e39e97..0f9f1eef6a 100644
--- a/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml
+++ b/detections/endpoint/windows_powershell_iis_components_webglobalmodule_usage.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the usage of PowerShell Cmdlets - New-WebGlobalModule, Enable-WebGlobalModule, and Set-WebGlobalModule, which are used to create, enable, or modify IIS Modules. This detection leverages PowerShell Script Block Logging, specifically monitoring EventCode 4104 for these cmdlets. This activity is significant as adversaries may use these lesser-known cmdlets to manipulate IIS configurations, similar to AppCmd.exe, potentially bypassing traditional defenses. If confirmed malicious, this could allow attackers to persist in the environment, manipulate web server behavior, or escalate privileges.
+description: The following analytic detects the usage of PowerShell Cmdlets - New-WebGlobalModule,
+  Enable-WebGlobalModule, and Set-WebGlobalModule, which are used to create, enable,
+  or modify IIS Modules. This detection leverages PowerShell Script Block Logging,
+  specifically monitoring EventCode 4104 for these cmdlets. This activity is significant
+  as adversaries may use these lesser-known cmdlets to manipulate IIS configurations,
+  similar to AppCmd.exe, potentially bypassing traditional defenses. If confirmed
+  malicious, this could allow attackers to persist in the environment, manipulate
+  web server behavior, or escalate privileges.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText IN("*New-WebGlobalModule*","*Enable-WebGlobalModule*","*Set-WebGlobalModule*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  | `windows_powershell_iis_components_webglobalmodule_usage_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required.
+search: '`powershell` EventCode=4104 ScriptBlockText IN("*New-WebGlobalModule*","*Enable-WebGlobalModule*","*Set-WebGlobalModule*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`  |
+  `windows_powershell_iis_components_webglobalmodule_usage_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: It is possible administrators or scripts may run these commands,
+  filtering may be required.
 references:
 - https://learn.microsoft.com/en-us/powershell/module/webadministration/new-webglobalmodule?view=windowsserver2022-ps
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
@@ -24,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A PowerShell Cmdlet related to enabling, creating or modifying a IIS module has occurred on $Computer$.
+  message: A PowerShell Cmdlet related to enabling, creating or modifying a IIS module
+    has occurred on $Computer$.
   mitre_attack_id:
   - T1505
   - T1505.004
@@ -46,15 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/4104_windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_import_applocker_policy.yml b/detections/endpoint/windows_powershell_import_applocker_policy.yml
index 71dd579476..a7a8192d54 100644
--- a/detections/endpoint/windows_powershell_import_applocker_policy.yml
+++ b/detections/endpoint/windows_powershell_import_applocker_policy.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the import of Windows PowerShell Applocker cmdlets, specifically identifying the use of "Import-Module Applocker" and "Set-AppLockerPolicy" with an XML policy. It leverages PowerShell Script Block Logging (EventCode 4104) to capture and analyze script block text. This activity is significant as it may indicate an attempt to enforce restrictive Applocker policies, potentially used by malware like Azorult to disable antivirus products. If confirmed malicious, this could allow an attacker to bypass security controls, leading to further system compromise and persistence.
+description: The following analytic detects the import of Windows PowerShell Applocker
+  cmdlets, specifically identifying the use of "Import-Module Applocker" and "Set-AppLockerPolicy"
+  with an XML policy. It leverages PowerShell Script Block Logging (EventCode 4104)
+  to capture and analyze script block text. This activity is significant as it may
+  indicate an attempt to enforce restrictive Applocker policies, potentially used
+  by malware like Azorult to disable antivirus products. If confirmed malicious, this
+  could allow an attacker to bypass security controls, leading to further system compromise
+  and persistence.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText="*Import-Module Applocker*" ScriptBlockText="*Set-AppLockerPolicy *" ScriptBlockText="* -XMLPolicy *" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_import_applocker_policy_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: administrators may execute this command that may cause some false positive.
+search: '`powershell` EventCode=4104 ScriptBlockText="*Import-Module Applocker*" ScriptBlockText="*Set-AppLockerPolicy
+  *" ScriptBlockText="* -XMLPolicy *" | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest
+  | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_powershell_import_applocker_policy_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: administrators may execute this command that may cause some
+  false positive.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -28,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A PowerShell script contains Import Applocker Policy command $ScriptBlockText$ with EventCode $EventCode$ on host $dest$
+  message: A PowerShell script contains Import Applocker Policy command $ScriptBlockText$
+    with EventCode $EventCode$ on host $dest$
   mitre_attack_id:
   - T1059.001
   - T1059
@@ -47,15 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/import_applocker_policy/windows-powershell-xml2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/import_applocker_policy/windows-powershell-xml2.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_remotesigned_file.yml b/detections/endpoint/windows_powershell_remotesigned_file.yml
index c963cdbd0f..d624a7e825 100644
--- a/detections/endpoint/windows_powershell_remotesigned_file.yml
+++ b/detections/endpoint/windows_powershell_remotesigned_file.yml
@@ -9,10 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the use of the "remotesigned" execution policy for PowerShell scripts. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions containing "remotesigned" and "-File". This activity is significant because the "remotesigned" policy allows locally created scripts to run without restrictions, posing a potential security risk. If confirmed malicious, an attacker could execute unauthorized scripts, leading to code execution, privilege escalation, or persistence within the environment.
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="* remotesigned *" Processes.process="* -File *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_remotesigned_file_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible administrators or scripts may run these commands, filtering may be required.
+description: The following analytic identifies the use of the "remotesigned" execution
+  policy for PowerShell scripts. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on command-line executions containing "remotesigned" and
+  "-File". This activity is significant because the "remotesigned" policy allows locally
+  created scripts to run without restrictions, posing a potential security risk. If
+  confirmed malicious, an attacker could execute unauthorized scripts, leading to
+  code execution, privilege escalation, or persistence within the environment.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_powershell` Processes.process="*
+  remotesigned *" Processes.process="* -File *" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_remotesigned_file_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible administrators or scripts may run these commands,
+  filtering may be required.
 references:
 - https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.security/set-executionpolicy?view=powershell-7.3
 drilldown_searches:
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.original_file_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_remotesigned/remotesigned_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_remotesigned/remotesigned_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_scheduletask.yml b/detections/endpoint/windows_powershell_scheduletask.yml
index 484a751315..01f92eff68 100644
--- a/detections/endpoint/windows_powershell_scheduletask.yml
+++ b/detections/endpoint/windows_powershell_scheduletask.yml
@@ -7,20 +7,42 @@ status: production
 type: Anomaly
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects potential malicious activities involving PowerShell's task scheduling cmdlets. It leverages PowerShell Script Block Logging (EventCode 4104) to identify unusual or suspicious use of cmdlets like 'New-ScheduledTask' and 'Set-ScheduledTask'. This activity is significant as attackers often use these cmdlets for persistence and remote execution of malicious code. If confirmed malicious, this could allow attackers to maintain access, deliver additional payloads, or execute ransomware, leading to data theft or other severe impacts. Immediate investigation and mitigation are crucial to prevent further compromise.
-search: '`powershell` EventCode=4104 ScriptBlockText IN ("*New-ScheduledTask*", "*New-ScheduledTaskAction*", "*New-ScheduledTaskSettingsSet*", "*New-ScheduledTaskTrigger*", "*Register-ClusteredScheduledTask*", "*Register-ScheduledTask*", "*Set-ClusteredScheduledTask*", "*Set-ScheduledTask*", "*Start-ScheduledTask*", "*Enable-ScheduledTask*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_scheduletask_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: Benign administrative tasks can also trigger alerts, necessitating a firm understanding of the typical system behavior and precise tuning of the analytic to reduce false positives.
+description: The following analytic detects potential malicious activities involving
+  PowerShell's task scheduling cmdlets. It leverages PowerShell Script Block Logging
+  (EventCode 4104) to identify unusual or suspicious use of cmdlets like 'New-ScheduledTask'
+  and 'Set-ScheduledTask'. This activity is significant as attackers often use these
+  cmdlets for persistence and remote execution of malicious code. If confirmed malicious,
+  this could allow attackers to maintain access, deliver additional payloads, or execute
+  ransomware, leading to data theft or other severe impacts. Immediate investigation
+  and mitigation are crucial to prevent further compromise.
+search: '`powershell` EventCode=4104 ScriptBlockText IN ("*New-ScheduledTask*", "*New-ScheduledTaskAction*",
+  "*New-ScheduledTaskSettingsSet*", "*New-ScheduledTaskTrigger*", "*Register-ClusteredScheduledTask*",
+  "*Register-ScheduledTask*", "*Set-ClusteredScheduledTask*", "*Set-ScheduledTask*",
+  "*Start-ScheduledTask*", "*Enable-ScheduledTask*") | stats count min(_time) as firstTime
+  max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_scheduletask_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: Benign administrative tasks can also trigger alerts, necessitating
+  a firm understanding of the typical system behavior and precise tuning of the analytic
+  to reduce false positives.
 references:
 - https://learn.microsoft.com/en-us/powershell/module/scheduledtasks/?view=windowsserver2022-ps
 - https://thedfirreport.com/2023/06/12/a-truly-graceful-wipe-out/
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$user_id$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" user_id = "$user_id$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" user_id =
+    "$user_id$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$user_id$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$user_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$user_id$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   - af9fd58f-c4ac-4bf2-a9ba-224b71ff25fd
   confidence: 50
   impact: 50
-  message: The PowerShell cmdlets related to task creation, modification and start occurred on $Computer$ by $user_id$.
+  message: The PowerShell cmdlets related to task creation, modification and start
+    occurred on $Computer$ by $user_id$.
   mitre_attack_id:
   - T1053.005
   - T1059.001
@@ -49,15 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/pwsh_scheduledtask.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/pwsh_scheduledtask.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml b/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml
index 93f2ab14c4..f80e5c77ac 100644
--- a/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml
+++ b/detections/endpoint/windows_powershell_wmi_win32_scheduledjob.yml
@@ -7,10 +7,26 @@ type: TTP
 status: production
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the use of the Win32_ScheduledJob WMI class via PowerShell script block logging. This class, which manages scheduled tasks, is disabled by default due to security concerns and must be explicitly enabled through registry modifications. The detection leverages PowerShell event code 4104 and script block text analysis. Monitoring this activity is crucial as it may indicate malicious intent, especially if the class was enabled by an attacker. If confirmed malicious, this could allow attackers to persist in the environment by creating scheduled tasks.
-search: '`powershell` EventCode=4104 ScriptBlockText="*win32_scheduledjob*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_wmi_win32_scheduledjob_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
-known_false_positives: False positives may be present based on legacy applications or utilities. Win32_ScheduledJob uses the Remote Procedure Call (RPC) protocol to create scheduled tasks on remote computers. It uses the DCOM (Distributed Component Object Model) infrastructure to establish a connection with the remote computer and invoke the necessary methods. The RPC service needs to be running on both the local and remote computers for the communication to take place.
+description: The following analytic detects the use of the Win32_ScheduledJob WMI
+  class via PowerShell script block logging. This class, which manages scheduled tasks,
+  is disabled by default due to security concerns and must be explicitly enabled through
+  registry modifications. The detection leverages PowerShell event code 4104 and script
+  block text analysis. Monitoring this activity is crucial as it may indicate malicious
+  intent, especially if the class was enabled by an attacker. If confirmed malicious,
+  this could allow attackers to persist in the environment by creating scheduled tasks.
+search: '`powershell` EventCode=4104 ScriptBlockText="*win32_scheduledjob*" | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powershell_wmi_win32_scheduledjob_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+known_false_positives: False positives may be present based on legacy applications
+  or utilities. Win32_ScheduledJob uses the Remote Procedure Call (RPC) protocol to
+  create scheduled tasks on remote computers. It uses the DCOM (Distributed Component
+  Object Model) infrastructure to establish a connection with the remote computer
+  and invoke the necessary methods. The RPC service needs to be running on both the
+  local and remote computers for the communication to take place.
 references:
 - https://securityonline.info/wmiexec-regout-get-outputdata-response-from-registry/
 - https://learn.microsoft.com/en-us/windows/win32/cimwin32prov/win32-scheduledjob
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 80
-  message: PowerShell attempting to create a task via WMI - Win32_ScheduledJob,  was ran on $dest$.
+  message: PowerShell attempting to create a task via WMI - Win32_ScheduledJob,  was
+    ran on $dest$.
   mitre_attack_id:
   - T1059.001
   - T1059
@@ -42,15 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - dest
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/win32_scheduledjob_windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/win32_scheduledjob_windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powersploit_gpp_discovery.yml b/detections/endpoint/windows_powersploit_gpp_discovery.yml
index 07d17c3ad2..ab6b87a362 100644
--- a/detections/endpoint/windows_powersploit_gpp_discovery.yml
+++ b/detections/endpoint/windows_powersploit_gpp_discovery.yml
@@ -7,9 +7,21 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of the Get-GPPPassword PowerShell cmdlet, which is used to search for unsecured credentials in Group Policy Preferences (GPP). This detection leverages PowerShell Script Block Logging to identify specific script block text associated with this cmdlet. Monitoring this activity is crucial as it can indicate an attempt to retrieve and decrypt stored credentials from SYSVOL, potentially leading to unauthorized access. If confirmed malicious, this activity could allow an attacker to escalate privileges or move laterally within the network by exploiting exposed credentials.
-search: '`powershell` EventCode=4104  (ScriptBlockText=Get-GPPPassword OR ScriptBlockText=Get-CachedGPPPassword) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powersploit_gpp_discovery_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+description: The following analytic detects the execution of the Get-GPPPassword PowerShell
+  cmdlet, which is used to search for unsecured credentials in Group Policy Preferences
+  (GPP). This detection leverages PowerShell Script Block Logging to identify specific
+  script block text associated with this cmdlet. Monitoring this activity is crucial
+  as it can indicate an attempt to retrieve and decrypt stored credentials from SYSVOL,
+  potentially leading to unauthorized access. If confirmed malicious, this activity
+  could allow an attacker to escalate privileges or move laterally within the network
+  by exploiting exposed credentials.
+search: '`powershell` EventCode=4104  (ScriptBlockText=Get-GPPPassword OR ScriptBlockText=Get-CachedGPPPassword)
+  | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_powersploit_gpp_discovery_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Unknown
 references:
 - https://attack.mitre.org/techniques/T1552/006/
@@ -20,11 +32,17 @@ references:
 - https://support.microsoft.com/en-us/topic/ms14-025-vulnerability-in-group-policy-preferences-could-allow-elevation-of-privilege-may-13-2014-60734e15-af79-26ca-ea53-8cd617073c30
 drilldown_searches:
 - name: View the detection results for - "$Computer$" and "$UserID$"
-  search: '%original_detection_search% | search  Computer = "$Computer$" UserID = "$UserID$"'
+  search: '%original_detection_search% | search  Computer = "$Computer$" UserID =
+    "$UserID$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$" and "$UserID$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$", "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$",
+    "$UserID$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/powershell_gpp_discovery/win-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552.006/powershell_gpp_discovery/win-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml b/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml
index 42e7f63546..7cf6c55172 100644
--- a/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml
+++ b/detections/endpoint/windows_powerview_ad_access_control_list_enumeration.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of PowerView PowerShell cmdlets `Get-ObjectAcl` or `Get-DomainObjectAcl`, which are used to enumerate Access Control List (ACL) permissions for Active Directory objects. It leverages Event ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior is significant as it may indicate an attempt to discover weak permissions in Active Directory, potentially leading to privilege escalation. If confirmed malicious, attackers could exploit these permissions to gain unauthorized access or escalate their privileges within the network.
-search: '`powershell` EventCode=4104  (ScriptBlockText=*get-objectacl* OR ScriptBlockText=*Get-DomainObjectAcl* ) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_ad_access_control_list_enumeration_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
-known_false_positives: Administrators may leverage PowerView for legitimate purposes, filter as needed.
+description: The following analytic detects the execution of PowerView PowerShell
+  cmdlets `Get-ObjectAcl` or `Get-DomainObjectAcl`, which are used to enumerate Access
+  Control List (ACL) permissions for Active Directory objects. It leverages Event
+  ID 4104 from PowerShell Script Block Logging to identify this activity. This behavior
+  is significant as it may indicate an attempt to discover weak permissions in Active
+  Directory, potentially leading to privilege escalation. If confirmed malicious,
+  attackers could exploit these permissions to gain unauthorized access or escalate
+  their privileges within the network.
+search: '`powershell` EventCode=4104  (ScriptBlockText=*get-objectacl* OR ScriptBlockText=*Get-DomainObjectAcl*
+  ) | stats count min(_time) as firstTime max(_time) as lastTime by Opcode Computer
+  UserID EventCode ScriptBlockText | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_powerview_ad_access_control_list_enumeration_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.=
+known_false_positives: Administrators may leverage PowerView for legitimate purposes,
+  filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1078/002/
 - https://medium.com/r3d-buck3t/enumerating-access-controls-in-active-directory-c06e2efa8b89
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Opcode
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/powerview_acl_enumeration/windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/powerview_acl_enumeration/windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml b/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml
index e4683a8ec6..91a1c83fa9 100644
--- a/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml
+++ b/detections/endpoint/windows_powerview_constrained_delegation_discovery.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerView commandlets to discover Windows endpoints with Kerberos Constrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific commandlets like `Get-DomainComputer` or `Get-NetComputer` with the `-TrustedToAuth` parameter. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to map out privileged delegation settings in Active Directory. If confirmed malicious, this could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network.
+description: The following analytic detects the use of PowerView commandlets to discover
+  Windows endpoints with Kerberos Constrained Delegation. It leverages PowerShell
+  Script Block Logging (EventCode=4104) to identify specific commandlets like `Get-DomainComputer`
+  or `Get-NetComputer` with the `-TrustedToAuth` parameter. This activity is significant
+  as it indicates potential reconnaissance efforts by adversaries or Red Teams to
+  map out privileged delegation settings in Active Directory. If confirmed malicious,
+  this could allow attackers to identify high-value targets for further exploitation,
+  potentially leading to privilege escalation or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-TrustedToAuth*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_constrained_delegation_discovery_filter`'
-how_to_implement: The following  analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators or power users may leverage PowerView for system management or troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR
+  ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-TrustedToAuth*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powerview_constrained_delegation_discovery_filter`'
+how_to_implement: The following  analytic requires PowerShell operational logs to
+  be imported. Modify the powershell macro as needed to match the sourcetype or add
+  index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators or power users may leverage PowerView for system
+  management or troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
 - https://adsecurity.org/?p=1667
@@ -24,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml b/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml
index fdfecae26b..86a7b16e91 100644
--- a/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml
+++ b/detections/endpoint/windows_powerview_kerberos_service_ticket_request.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainSPNTicket` commandlet, part of the PowerView tool, by leveraging PowerShell Script Block Logging (EventCode=4104). This commandlet requests Kerberos service tickets for specified service principal names (SPNs). Monitoring this activity is crucial as it can indicate attempts to perform Kerberoasting, a technique used to extract SPN account passwords via cracking tools like hashcat. If confirmed malicious, this activity could allow attackers to gain unauthorized access to sensitive accounts, potentially leading to privilege escalation and further network compromise.
+description: The following analytic detects the execution of the `Get-DomainSPNTicket`
+  commandlet, part of the PowerView tool, by leveraging PowerShell Script Block Logging
+  (EventCode=4104). This commandlet requests Kerberos service tickets for specified
+  service principal names (SPNs). Monitoring this activity is crucial as it can indicate
+  attempts to perform Kerberoasting, a technique used to extract SPN account passwords
+  via cracking tools like hashcat. If confirmed malicious, this activity could allow
+  attackers to gain unauthorized access to sensitive accounts, potentially leading
+  to privilege escalation and further network compromise.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText=*Get-DomainSPNTicket* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_kerberos_service_ticket_request_filter`'
-how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: False positive may include Administrators using PowerView for troubleshooting and management.
+search: '`powershell` EventCode=4104 ScriptBlockText=*Get-DomainSPNTicket* | stats
+  count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powerview_kerberos_service_ticket_request_filter`'
+how_to_implement: The following analytic requires PowerShell operational logs to be
+  imported. Modify the powershell macro as needed to match the sourcetype or add index.
+  This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: False positive may include Administrators using PowerView for
+  troubleshooting and management.
 references:
 - https://powersploit.readthedocs.io/en/latest/Recon/Get-DomainSPNTicket/
 - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/kerberoast
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 30
-  message: PowerView commandlets used for requesting SPN service ticket executed on $dest$
+  message: PowerView commandlets used for requesting SPN service ticket executed on
+    $dest$
   mitre_attack_id:
   - T1558
   - T1558.003
@@ -46,15 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Computer
-  - ScriptBlockText
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powerview_spn_discovery.yml b/detections/endpoint/windows_powerview_spn_discovery.yml
index bfa67f3d98..43c918375e 100644
--- a/detections/endpoint/windows_powerview_spn_discovery.yml
+++ b/detections/endpoint/windows_powerview_spn_discovery.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the `Get-DomainUser` or `Get-NetUser` PowerShell cmdlets with the `-SPN` parameter, indicating the use of PowerView for SPN discovery. It leverages PowerShell Script Block Logging (EventCode=4104) to identify these specific commands. This activity is significant as it suggests an attempt to enumerate domain accounts associated with Service Principal Names (SPNs), a common precursor to Kerberoasting attacks. If confirmed malicious, this could allow an attacker to identify and target accounts for credential theft, potentially leading to unauthorized access and privilege escalation within the network.
+description: The following analytic detects the execution of the `Get-DomainUser`
+  or `Get-NetUser` PowerShell cmdlets with the `-SPN` parameter, indicating the use
+  of PowerView for SPN discovery. It leverages PowerShell Script Block Logging (EventCode=4104)
+  to identify these specific commands. This activity is significant as it suggests
+  an attempt to enumerate domain accounts associated with Service Principal Names
+  (SPNs), a common precursor to Kerberoasting attacks. If confirmed malicious, this
+  could allow an attacker to identify and target accounts for credential theft, potentially
+  leading to unauthorized access and privilege escalation within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText =*Get-NetUser* OR ScriptBlockText=*Get-DomainUser*) ScriptBlockText= *-SPN* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_powerview_spn_discovery_filter`'
-how_to_implement: The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: False positive may include Administrators using PowerView for troubleshooting and management.
+search: '`powershell` EventCode=4104 (ScriptBlockText =*Get-NetUser* OR ScriptBlockText=*Get-DomainUser*)
+  ScriptBlockText= *-SPN* | stats count min(_time) as firstTime max(_time) as lastTime
+  by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename
+  UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_powerview_spn_discovery_filter`'
+how_to_implement: The following analytic requires PowerShell operational logs to be
+  imported. Modify the powershell macro as needed to match the sourcetype or add index.
+  This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: False positive may include Administrators using PowerView for
+  troubleshooting and management.
 references:
 - https://book.hacktricks.xyz/windows-hardening/active-directory-methodology/kerberoast
 - https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,15 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Computer
-  - ScriptBlockText
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview-2/windows-powershell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/powerview-2/windows-powershell.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml b/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml
index 68a2b0a8f4..0330d71e50 100644
--- a/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml
+++ b/detections/endpoint/windows_powerview_unconstrained_delegation_discovery.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of PowerView commandlets to discover Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific commands like `Get-DomainComputer` or `Get-NetComputer` with the `-Unconstrained` parameter. This activity is significant as it indicates potential reconnaissance efforts by adversaries or Red Teams to map out privileged delegation settings in Active Directory. If confirmed malicious, this could allow attackers to identify high-value targets for further exploitation, potentially leading to privilege escalation or lateral movement within the network.
+description: The following analytic detects the use of PowerView commandlets to discover
+  Windows endpoints with Kerberos Unconstrained Delegation. It leverages PowerShell
+  Script Block Logging (EventCode=4104) to identify specific commands like `Get-DomainComputer`
+  or `Get-NetComputer` with the `-Unconstrained` parameter. This activity is significant
+  as it indicates potential reconnaissance efforts by adversaries or Red Teams to
+  map out privileged delegation settings in Active Directory. If confirmed malicious,
+  this could allow attackers to identify high-value targets for further exploitation,
+  potentially leading to privilege escalation or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-Unconstrained*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_unconstrained_delegation_discovery_filter`'
-how_to_implement: The following  analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
-known_false_positives: Administrators or power users may leverage PowerView for system management or troubleshooting.
+search: '`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainComputer*" OR
+  ScriptBlockText = "*Get-NetComputer*") AND (ScriptBlockText = "*-Unconstrained*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_powerview_unconstrained_delegation_discovery_filter`'
+how_to_implement: The following  analytic requires PowerShell operational logs to
+  be imported. Modify the powershell macro as needed to match the sourcetype or add
+  index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+known_false_positives: Administrators or power users may leverage PowerView for system
+  management or troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1018/
 - https://adsecurity.org/?p=1667
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,16 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - UserID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/windows_powerview_constrained_delegation_discovery/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_private_keys_discovery.yml b/detections/endpoint/windows_private_keys_discovery.yml
index be43f85881..20b7a0c7f7 100644
--- a/detections/endpoint/windows_private_keys_discovery.yml
+++ b/detections/endpoint/windows_private_keys_discovery.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies processes that retrieve information related to private key files, often used by post-exploitation tools like winpeas. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions that search for private key certificates. This activity is significant as it indicates potential attempts to locate insecurely stored credentials, which adversaries can exploit for privilege escalation, persistence, or remote service authentication. If confirmed malicious, this behavior could allow attackers to access sensitive information, escalate privileges, or maintain persistence within the compromised environment.
+description: The following analytic identifies processes that retrieve information
+  related to private key files, often used by post-exploitation tools like winpeas.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on command-line executions that search for private key certificates. This
+  activity is significant as it indicates potential attempts to locate insecurely
+  stored credentials, which adversaries can exploit for privilege escalation, persistence,
+  or remote service authentication. If confirmed malicious, this behavior could allow
+  attackers to access sensitive information, escalate privileges, or maintain persistence
+  within the compromised environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *" OR  Processes.process = "*findstr*" AND Processes.process IN ( "*.rdg*", "*.gpg*", "*.pgp*", "*.p12*", "*.der*", "*.csr*", "*.cer*", "*.ovpn*", "*.key*",  "*.ppk*", "*.p12*", "*.pem*", "*.pfx*", "*.p7b*", "*.asc*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_private_keys_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*dir *"
+  OR  Processes.process = "*findstr*" AND Processes.process IN ( "*.rdg*", "*.gpg*",
+  "*.pgp*", "*.p12*", "*.der*", "*.csr*", "*.cer*", "*.ovpn*", "*.key*",  "*.ppk*",
+  "*.p12*", "*.pem*", "*.pfx*", "*.p7b*", "*.asc*") by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_private_keys_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://attack.mitre.org/techniques/T1552/004/
@@ -23,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: a process with commandline $process$ that can retrieve information related to private keys in $dest$
+  message: a process with commandline $process$ that can retrieve information related
+    to private keys in $dest$
   mitre_attack_id:
   - T1552.004
   - T1552
@@ -46,25 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_private_key/dir-private-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/winpeas_search_private_key/dir-private-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml b/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml
index df0e77c6b9..20841f8a99 100644
--- a/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml
+++ b/detections/endpoint/windows_privilege_escalation_suspicious_process_elevation.yml
@@ -5,14 +5,47 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a process running with low or medium integrity from a user account spawns an elevated process with high or system integrity in suspicious locations. This behavior is identified using process execution data from Windows process monitoring or Sysmon EventID 1. This activity is significant as it may indicate a threat actor successfully elevating privileges, which is a common tactic in advanced attacks. If confirmed malicious, this could allow the attacker to execute code with higher privileges, potentially leading to full system compromise and persistent access.
+description: The following analytic detects when a process running with low or medium
+  integrity from a user account spawns an elevated process with high or system integrity
+  in suspicious locations. This behavior is identified using process execution data
+  from Windows process monitoring or Sysmon EventID 1. This activity is significant
+  as it may indicate a threat actor successfully elevating privileges, which is a
+  common tactic in advanced attacks. If confirmed malicious, this could allow the
+  attacker to execute code with higher privileges, potentially leading to full system
+  compromise and persistent access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$") by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.parent_process, Processes.parent_process_name Processes.process_name Processes.process, Processes.process_path, Processes.process_guid, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | eval join_guid = process_guid, integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) | rename user as src_user, parent_process* as orig_parent_process*, process* as parent_process* | join max=0 dest join_guid  [| tstats `security_content_summariesonly` count max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_integrity_level IN ("system") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$")) OR (Processes.process_integrity_level IN ("high","system") AND (Processes.parent_process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") OR Processes.process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*"))) by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.process_name, Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) | rename parent_process_guid as join_guid ] | where elevated_integrity_level > integrity_level OR user != elevated_user | fields dest, user, src_user, parent_process_name, parent_process, parent_process_path, parent_process_guid, parent_process_integrity_level, parent_process_current_directory, process_name, process, process_path, process_guid, process_integrity_level, process_current_directory, orig_parent_process_name, orig_parent_process, orig_parent_process_guid, firstTime, lastTime, count  | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` |  `windows_privilege_escalation_suspicious_process_elevation_filter`'
-how_to_implement: Target environment must ingest process execution data sources such as Windows process monitoring and/or Sysmon EID 1.
-known_false_positives: False positives may be generated by administrators installing benign applications using run-as/elevation.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from
+  datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high")
+  NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$")
+  by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.parent_process,
+  Processes.parent_process_name Processes.process_name Processes.process, Processes.process_path,
+  Processes.process_guid, Processes.process_integrity_level, Processes.process_current_directory
+  | `drop_dm_object_name(Processes)` | eval join_guid = process_guid, integrity_level
+  = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)
+  | rename user as src_user, parent_process* as orig_parent_process*, process* as
+  parent_process* | join max=0 dest join_guid  [| tstats `security_content_summariesonly`
+  count max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_integrity_level
+  IN ("system") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$"))
+  OR (Processes.process_integrity_level IN ("high","system") AND (Processes.parent_process_path
+  IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") OR Processes.process_path
+  IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*"))) by Processes.dest,
+  Processes.user, Processes.parent_process_guid, Processes.process_name, Processes.process,
+  Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory
+  | `drop_dm_object_name(Processes)` | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)
+  | rename parent_process_guid as join_guid ] | where elevated_integrity_level > integrity_level
+  OR user != elevated_user | fields dest, user, src_user, parent_process_name, parent_process,
+  parent_process_path, parent_process_guid, parent_process_integrity_level, parent_process_current_directory,
+  process_name, process, process_path, process_guid, process_integrity_level, process_current_directory,
+  orig_parent_process_name, orig_parent_process, orig_parent_process_guid, firstTime,
+  lastTime, count  | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  |  `windows_privilege_escalation_suspicious_process_elevation_filter`'
+how_to_implement: Target environment must ingest process execution data sources such
+  as Windows process monitoring and/or Sysmon EID 1.
+known_false_positives: False positives may be generated by administrators installing
+  benign applications using run-as/elevation.
 references:
 - https://attack.mitre.org/techniques/T1068/
 - https://vuls.cert.org/confluence/display/Wiki/2021/06/21/Finding+Privilege+Escalation+Vulnerabilities+in+Windows+using+Process+Monitor
@@ -20,11 +53,17 @@ references:
 - https://atomicredteam.io/privilege-escalation/T1134.001/
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
-  search: '%original_detection_search% | search  dest = "$dest$" user = "$user$" src_user = "$src_user$"'
+  search: '%original_detection_search% | search  dest = "$dest$" user = "$user$" src_user
+    = "$src_user$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime
+    max(_time) as lastTime values(search_name) as "Search Name" values(risk_message)
+    as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +73,8 @@ tags:
   asset_type: Endpoint
   confidence: 40
   impact: 100
-  message: The user $src_user$ launched a process [$parent_process_name$] which spawned a suspicious elevated integrity process [$process_name$].
+  message: The user $src_user$ launched a process [$parent_process_name$] which spawned
+    a suspicious elevated integrity process [$process_name$].
   mitre_attack_id:
   - T1068
   - T1548
@@ -60,23 +100,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_guid
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.process_guid
-  - Processes.process_integrity_level
-  - Processes.process_current_directory
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml b/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml
index 4420cfe8f5..9ba4269c2b 100644
--- a/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml
+++ b/detections/endpoint/windows_privilege_escalation_system_process_without_system_parent.yml
@@ -5,13 +5,26 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects any system integrity level process spawned by a non-system account. It leverages Sysmon EventID 1, focusing on process integrity and parent user data. This behavior is significant as it often indicates successful privilege escalation to SYSTEM from a user-controlled process or service. If confirmed malicious, this activity could allow an attacker to gain full control over the system, execute arbitrary code, and potentially compromise the entire environment.
+description: The following analytic detects any system integrity level process spawned
+  by a non-system account. It leverages Sysmon EventID 1, focusing on process integrity
+  and parent user data. This behavior is significant as it often indicates successful
+  privilege escalation to SYSTEM from a user-controlled process or service. If confirmed
+  malicious, this activity could allow an attacker to gain full control over the system,
+  execute arbitrary code, and potentially compromise the entire environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '`sysmon` EventCode=1 IntegrityLevel="system" ParentUser=* NOT ParentUser IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","*DWM-*","*$","-") | eval src_user = replace(ParentUser,"^[^\\\]+\\\\","") | stats count min(_time) as firstTime max(_time) as lastTime values(process_name) as process_name values(process) as process, values(process_path) as process_path, values(process_current_directory) as process_current_directory values(parent_process) as parent_process by dest, user, src_user, parent_process_name, parent_process_guid | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `windows_privilege_escalation_system_process_without_system_parent_filter`'
-how_to_implement: Target environment must ingest sysmon data, specifically Event ID 1 with process integrity and parent user data.
+search: '`sysmon` EventCode=1 IntegrityLevel="system" ParentUser=* NOT ParentUser
+  IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","*DWM-*","*$","-") | eval src_user
+  = replace(ParentUser,"^[^\\\]+\\\\","") | stats count min(_time) as firstTime max(_time)
+  as lastTime values(process_name) as process_name values(process) as process, values(process_path)
+  as process_path, values(process_current_directory) as process_current_directory
+  values(parent_process) as parent_process by dest, user, src_user, parent_process_name,
+  parent_process_guid | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `windows_privilege_escalation_system_process_without_system_parent_filter`'
+how_to_implement: Target environment must ingest sysmon data, specifically Event ID
+  1 with process integrity and parent user data.
 known_false_positives: Unknown
 references:
 - https://attack.mitre.org/techniques/T1068/
@@ -24,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$src_user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$src_user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: The process [$process_name$] on $dest$ was launched with system level integrity by $src_user$.
+  message: The process [$process_name$] on $dest$ was launched with system level integrity
+    by $src_user$.
   mitre_attack_id:
   - T1068
   - T1548
@@ -56,18 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - user
-  - ParentUser
-  - parent_process_name
-  - parent_process_guid
-  - IntegrityLevel
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml b/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml
index d052618604..471407fefb 100644
--- a/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml
+++ b/detections/endpoint/windows_privilege_escalation_user_process_spawn_system_process.yml
@@ -5,13 +5,40 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a process with low, medium, or high integrity spawns a system integrity process from a user-controlled location. This behavior is indicative of privilege escalation attempts where attackers elevate their privileges to SYSTEM level from a user-controlled process or service. The detection leverages Sysmon data, specifically Event ID 15, to identify such transitions. Monitoring this activity is crucial as it can signify an attacker gaining SYSTEM-level access, potentially leading to full control over the affected system, unauthorized access to sensitive data, and further malicious activities.
+description: The following analytic detects when a process with low, medium, or high
+  integrity spawns a system integrity process from a user-controlled location. This
+  behavior is indicative of privilege escalation attempts where attackers elevate
+  their privileges to SYSTEM level from a user-controlled process or service. The
+  detection leverages Sysmon data, specifically Event ID 15, to identify such transitions.
+  Monitoring this activity is crucial as it can signify an attacker gaining SYSTEM-level
+  access, potentially leading to full control over the affected system, unauthorized
+  access to sensitive data, and further malicious activities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high") NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$") AND Processes.process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.parent_process, Processes.parent_process_name Processes.process_name Processes.process, Processes.process_path, Processes.process_guid, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | eval join_guid = process_guid | join max=0 dest join_guid  [| tstats `security_content_summariesonly` count max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("system") AND Processes.parent_process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*") by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.process_name, Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | rename parent_process_guid as join_guid, process* as system_process*, user as system_user ] | fields dest, user, parent_process, parent_process_name, parent_process_guid, process, process_name, process_guid, process_integrity_level,process_path, process_current_directory, system_process_name, system_process, system_process_path, system_process_integrity_level, system_process_current_directory, system_user, firstTime, lastTime, count | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `windows_privilege_escalation_user_process_spawn_system_process_filter`'
-how_to_implement: Target environment must ingest sysmon data, specifically Event ID 15.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime from
+  datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium","high")
+  NOT Processes.user IN ("*SYSTEM","*LOCAL SERVICE","*NETWORK SERVICE","DWM-*","*$")
+  AND Processes.process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*")
+  by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.parent_process,
+  Processes.parent_process_name Processes.process_name Processes.process, Processes.process_path,
+  Processes.process_guid, Processes.process_integrity_level, Processes.process_current_directory
+  | `drop_dm_object_name(Processes)` | eval join_guid = process_guid | join max=0
+  dest join_guid  [| tstats `security_content_summariesonly` count max(_time) as lastTime
+  from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("system")
+  AND Processes.parent_process_path IN ("*\\\\*","*\\Users\\*","*\\Temp\\*","*\\ProgramData\\*")
+  by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.process_name,
+  Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory
+  | `drop_dm_object_name(Processes)` | rename parent_process_guid as join_guid, process*
+  as system_process*, user as system_user ] | fields dest, user, parent_process, parent_process_name,
+  parent_process_guid, process, process_name, process_guid, process_integrity_level,process_path,
+  process_current_directory, system_process_name, system_process, system_process_path,
+  system_process_integrity_level, system_process_current_directory, system_user, firstTime,
+  lastTime, count | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `windows_privilege_escalation_user_process_spawn_system_process_filter`'
+how_to_implement: Target environment must ingest sysmon data, specifically Event ID
+  15.
 known_false_positives: Unknown
 references:
 - https://attack.mitre.org/techniques/T1068/
@@ -24,7 +51,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: The user $user$ launched a process [$process_name$] which spawned a system level integrity process [$system_process$].
+  message: The user $user$ launched a process [$process_name$] which spawned a system
+    level integrity process [$system_process$].
   mitre_attack_id:
   - T1068
   - T1548
@@ -56,18 +89,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - user
-  - ParentUser
-  - parent_process_name
-  - parent_process_guid
-  - IntegrityLevel
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/windows_escalation_behavior/windows_escalation_behavior_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_privileged_group_modification.yml b/detections/endpoint/windows_privileged_group_modification.yml
index 955620a92c..489581a27d 100644
--- a/detections/endpoint/windows_privileged_group_modification.yml
+++ b/detections/endpoint/windows_privileged_group_modification.yml
@@ -14,10 +14,36 @@ data_source:
 - Windows Event Log Security 4790
 type: TTP
 status: production
-description: This analytic detects modifications to privileged groups in Active Directory, including creation, deletion, and changes to various types of groups such as local, global, universal, and LDAP query groups. It specifically monitors for changes to high-privilege groups like "Administrators", "Domain Admins", "Enterprise Admins", and "ESX Admins", among others. This detection is particularly relevant in the context of potential exploitation of vulnerabilities like the VMware ESXi Active Directory Integration Authentication Bypass (CVE-2024-37085), where attackers may attempt to manipulate privileged groups to gain unauthorized access to systems.
-search: '`wineventlog_security` EventCode IN (4727,4731,4744,4749,4754,4759,4783,4790) TargetUserName IN ("Account Operators", "Administrators", "Admins DNS", "Backup Operators", "DnsAdmins", "Domain Admins", "Enterprise Admins", "Enterprise Key Admins", "ESX Admins", "ESXi Admins", "Group Policy Creator Owners", "Hyper-V Administrators", "Key Admins", "Print Operators", "Remote Desktop Users", "Remote Management Users", "Replicators", "Schema Admins", "Server Operators") | eval object_category=case( EventCode="4731", "Local Group (Security)", EventCode="4744", "Local Group (Distribution)", EventCode="4727", "Global Group (Security)", EventCode="4749", "Global Group (Distribution)", EventCode="4754", "Universal Group (Security)", EventCode="4759", "Universal Group (Distribution)", EventCode="4783", "Basic Application Group", EventCode="4790", "LDAP Query Group") | rename Computer as dest, result AS change_type, TargetUserName AS object, TargetSid AS object_path | stats count min(_time) as firstTime max(_time) as lastTime by EventCode src_user object_category object object_path dest change_type status | `windows_privileged_group_modification_filter`'
-how_to_implement: To successfully implement this search, ensure that Windows Security Event logging is enabled and being ingested into Splunk, particularly for event codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific events.
-known_false_positives: Legitimate administrators might create, delete, or modify an a privileged group for valid reasons. Verify that the group changes are authorized and part of normal administrative tasks. Consider the context of the action, such as the user performing it and any related activities.
+description: This analytic detects modifications to privileged groups in Active Directory,
+  including creation, deletion, and changes to various types of groups such as local,
+  global, universal, and LDAP query groups. It specifically monitors for changes to
+  high-privilege groups like "Administrators", "Domain Admins", "Enterprise Admins",
+  and "ESX Admins", among others. This detection is particularly relevant in the context
+  of potential exploitation of vulnerabilities like the VMware ESXi Active Directory
+  Integration Authentication Bypass (CVE-2024-37085), where attackers may attempt
+  to manipulate privileged groups to gain unauthorized access to systems.
+search: '`wineventlog_security` EventCode IN (4727,4731,4744,4749,4754,4759,4783,4790)
+  TargetUserName IN ("Account Operators", "Administrators", "Admins DNS", "Backup
+  Operators", "DnsAdmins", "Domain Admins", "Enterprise Admins", "Enterprise Key Admins",
+  "ESX Admins", "ESXi Admins", "Group Policy Creator Owners", "Hyper-V Administrators",
+  "Key Admins", "Print Operators", "Remote Desktop Users", "Remote Management Users",
+  "Replicators", "Schema Admins", "Server Operators") | eval object_category=case(
+  EventCode="4731", "Local Group (Security)", EventCode="4744", "Local Group (Distribution)",
+  EventCode="4727", "Global Group (Security)", EventCode="4749", "Global Group (Distribution)",
+  EventCode="4754", "Universal Group (Security)", EventCode="4759", "Universal Group
+  (Distribution)", EventCode="4783", "Basic Application Group", EventCode="4790",
+  "LDAP Query Group") | rename Computer as dest, result AS change_type, TargetUserName
+  AS object, TargetSid AS object_path | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode src_user object_category object object_path dest change_type
+  status | `windows_privileged_group_modification_filter`'
+how_to_implement: To successfully implement this search, ensure that Windows Security
+  Event logging is enabled and being ingested into Splunk, particularly for event
+  codes 4727, 4730, and 4737. Configure Group Policy settings to audit these specific
+  events.
+known_false_positives: Legitimate administrators might create, delete, or modify an
+  a privileged group for valid reasons. Verify that the group changes are authorized
+  and part of normal administrative tasks. Consider the context of the action, such
+  as the user performing it and any related activities.
 references:
 - https://nvd.nist.gov/vuln/detail/CVE-2024-37085
 - https://www.rapid7.com/blog/post/2024/07/30/vmware-esxi-cve-2024-37085-targeted-in-ransomware-campaigns/%5C
@@ -28,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +68,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: A privileged group modification was detected. Group "$object$" ($object_category$) was $change_type$ on $dest$ by user $src_user$.
+  message: A privileged group modification was detected. Group "$object$" ($object_category$)
+    was $change_type$ on $dest$ by user $src_user$.
   mitre_attack_id:
   - T1136.001
   - T1136.002
@@ -54,22 +86,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - src_user
-  - object_category
-  - TargetUserName
-  - TargetSid
-  - dest
-  - result
-  - status
-  - _time
   security_domain: endpoint
   cve:
   - CVE-2024-37085
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security-esxadmins.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_commandline_discovery.yml b/detections/endpoint/windows_process_commandline_discovery.yml
index a1531923d4..d3a759bf6a 100644
--- a/detections/endpoint/windows_process_commandline_discovery.yml
+++ b/detections/endpoint/windows_process_commandline_discovery.yml
@@ -9,10 +9,32 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the use of Windows Management Instrumentation Command-line (WMIC) to retrieve information about running processes, specifically targeting the command lines used to launch those processes. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on logs containing process details and command-line executions. This activity is significant as it may indicate suspicious behavior, such as a user or process gathering detailed process information, which is uncommon for non-technical users. If confirmed malicious, this could allow an attacker to gain insights into running processes, aiding in further exploitation or lateral movement.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process= "* process *" Processes.process= "* get commandline *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_commandline_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators or power users may use this command for troubleshooting. Filter as needed.
+description: The following analytic detects the use of Windows Management Instrumentation
+  Command-line (WMIC) to retrieve information about running processes, specifically
+  targeting the command lines used to launch those processes. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on logs containing
+  process details and command-line executions. This activity is significant as it
+  may indicate suspicious behavior, such as a user or process gathering detailed process
+  information, which is uncommon for non-technical users. If confirmed malicious,
+  this could allow an attacker to gain insights into running processes, aiding in
+  further exploitation or lateral movement.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process=
+  "* process *" Processes.process= "* get commandline *" by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_process_commandline_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators or power users may use this command for troubleshooting.
+  Filter as needed.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
 tags:
@@ -21,7 +43,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: Activity related to process commandline discovery detected on $dest$ using wmic.exe.
+  message: Activity related to process commandline discovery detected on $dest$ using
+    wmic.exe.
   mitre_attack_id:
   - T1057
   observable:
@@ -33,23 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1057/process_commandline_discovery/wmic-cmdline-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1057/process_commandline_discovery/wmic-cmdline-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml b/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml
index c726e6536f..54c4696bc1 100644
--- a/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml
+++ b/detections/endpoint/windows_process_injection_in_non_service_searchindexer.yml
@@ -9,9 +9,29 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies instances of the searchindexer.exe process that are not spawned by services.exe, indicating potential process injection. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes. This activity is significant because QakBot malware often uses a fake searchindexer.exe to evade detection and perform malicious actions such as data exfiltration and keystroke logging. If confirmed malicious, this activity could allow attackers to maintain persistence, steal sensitive information, and communicate with command and control servers.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name != services.exe Processes.process_name=searchindexer.exe by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_in_non_service_searchindexer_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic identifies instances of the searchindexer.exe
+  process that are not spawned by services.exe, indicating potential process injection.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and parent processes. This activity is significant because
+  QakBot malware often uses a fake searchindexer.exe to evade detection and perform
+  malicious actions such as data exfiltration and keystroke logging. If confirmed
+  malicious, this activity could allow attackers to maintain persistence, steal sensitive
+  information, and communicate with command and control servers.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  != services.exe Processes.process_name=searchindexer.exe by Processes.dest Processes.user
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_in_non_service_searchindexer_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://twitter.com/Max_Mal_/status/1736392741758611607
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,24 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/non-service-searchindexer/seaarch-indexer-non-service.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/non-service-searchindexer/seaarch-indexer-non-service.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_injection_into_notepad.yml b/detections/endpoint/windows_process_injection_into_notepad.yml
index bb87099e46..43e419a90a 100644
--- a/detections/endpoint/windows_process_injection_into_notepad.yml
+++ b/detections/endpoint/windows_process_injection_into_notepad.yml
@@ -7,10 +7,26 @@ type: Anomaly
 status: production
 data_source:
 - Sysmon EventID 10
-description: The following analytic detects process injection into Notepad.exe using Sysmon EventCode 10. It identifies suspicious GrantedAccess requests (0x40 and 0x1fffff) to Notepad.exe, excluding common system paths like System32, Syswow64, and Program Files. This behavior is often associated with the SliverC2 framework by BishopFox. Monitoring this activity is crucial as it may indicate an initial payload attempting to execute malicious code within Notepad.exe. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to privilege escalation or persistent access within the environment.
-search: '`sysmon` EventCode=10 TargetImage IN (*\\notepad.exe) NOT (SourceImage IN ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*")) GrantedAccess IN ("0x40","0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by dest SourceImage TargetImage GrantedAccess CallTrace | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_into_notepad_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: False positives may be present based on SourceImage paths. If removing the paths is important, realize svchost and many native binaries inject into notepad consistently. Restrict or tune as needed.
+description: The following analytic detects process injection into Notepad.exe using
+  Sysmon EventCode 10. It identifies suspicious GrantedAccess requests (0x40 and 0x1fffff)
+  to Notepad.exe, excluding common system paths like System32, Syswow64, and Program
+  Files. This behavior is often associated with the SliverC2 framework by BishopFox.
+  Monitoring this activity is crucial as it may indicate an initial payload attempting
+  to execute malicious code within Notepad.exe. If confirmed malicious, this could
+  allow attackers to execute arbitrary code, potentially leading to privilege escalation
+  or persistent access within the environment.
+search: '`sysmon` EventCode=10 TargetImage IN (*\\notepad.exe) NOT (SourceImage IN
+  ("*\\system32\\*","*\\syswow64\\*","*\\Program Files\\*")) GrantedAccess IN ("0x40","0x1fffff")
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest SourceImage
+  TargetImage GrantedAccess CallTrace | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_process_injection_into_notepad_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: False positives may be present based on SourceImage paths.
+  If removing the paths is important, realize svchost and many native binaries inject
+  into notepad consistently. Restrict or tune as needed.
 references:
 - https://dominicbreuker.com/post/learning_sliver_c2_08_implant_basics/
 - https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 40
-  message: An instance of $SourceImage$ injecting into $TargetImage$ was identified on endpoint $dest$.
+  message: An instance of $SourceImage$ injecting into $TargetImage$ was identified
+    on endpoint $dest$.
   mitre_attack_id:
   - T1055
   - T1055.002
@@ -50,17 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - SourceImage
-  - TargetImage
-  - GrantedAccess
-  - CallTrace
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/T1055_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/T1055_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml b/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml
index 95dcc06dc3..d09bb96e01 100644
--- a/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml
+++ b/detections/endpoint/windows_process_injection_of_wermgr_to_known_browser.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the suspicious remote thread execution of the wermgr.exe process into known browsers such as firefox.exe, chrome.exe, and others. It leverages Sysmon EventCode 8 logs to detect this behavior by monitoring SourceImage and TargetImage fields. This activity is significant because it is indicative of Qakbot malware, which injects malicious code into legitimate processes to steal information. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and exfiltrate sensitive data from the compromised host.
+description: The following analytic identifies the suspicious remote thread execution
+  of the wermgr.exe process into known browsers such as firefox.exe, chrome.exe, and
+  others. It leverages Sysmon EventCode 8 logs to detect this behavior by monitoring
+  SourceImage and TargetImage fields. This activity is significant because it is indicative
+  of Qakbot malware, which injects malicious code into legitimate processes to steal
+  information. If confirmed malicious, this activity could allow attackers to execute
+  arbitrary code, escalate privileges, and exfiltrate sensitive data from the compromised
+  host.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode=8 SourceImage = "*\\wermgr.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGuid SourceProcessId StartAddress StartFunction TargetProcessGuid TargetProcessId  EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_of_wermgr_to_known_browser_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=8 SourceImage = "*\\wermgr.exe" TargetImage IN ("*\\firefox.exe",
+  "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") | stats count min(_time)
+  as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGuid
+  SourceProcessId StartAddress StartFunction TargetProcessGuid TargetProcessId  EventCode
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_process_injection_of_wermgr_to_known_browser_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the SourceImage, TargetImage, and EventCode executions from your endpoints
+  related to create remote thread or injecting codes. If you are using Sysmon, you
+  must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://news.sophos.com/en-us/2022/03/10/qakbot-decoded/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: wermgr.exe process $SourceImage$ create a remote thread to a browser process $TargetImage$ in host $dest$
+  message: wermgr.exe process $SourceImage$ create a remote thread to a browser process
+    $TargetImage$ in host $dest$
   mitre_attack_id:
   - T1055.001
   - T1055
@@ -42,22 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - SourceProcessGuid
-  - SourceProcessId
-  - StartAddress
-  - StartFunction
-  - TargetProcessGuid
-  - TargetProcessId
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/remote_thread/sysmon_wermgr_remote.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/remote_thread/sysmon_wermgr_remote.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_injection_remote_thread.yml b/detections/endpoint/windows_process_injection_remote_thread.yml
index 22a70c7d21..ba3fcbfb4b 100644
--- a/detections/endpoint/windows_process_injection_remote_thread.yml
+++ b/detections/endpoint/windows_process_injection_remote_thread.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious remote thread execution in processes such as Taskmgr.exe, calc.exe, and notepad.exe, which may indicate process injection by malware like Qakbot. This detection leverages Sysmon EventCode 8 to identify remote thread creation in specific target processes. This activity is significant as it often signifies an attempt by malware to inject malicious code into legitimate processes, potentially leading to unauthorized code execution. If confirmed malicious, this could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence on the compromised host.
+description: The following analytic detects suspicious remote thread execution in
+  processes such as Taskmgr.exe, calc.exe, and notepad.exe, which may indicate process
+  injection by malware like Qakbot. This detection leverages Sysmon EventCode 8 to
+  identify remote thread creation in specific target processes. This activity is significant
+  as it often signifies an attempt by malware to inject malicious code into legitimate
+  processes, potentially leading to unauthorized code execution. If confirmed malicious,
+  this could allow attackers to execute arbitrary code, escalate privileges, or maintain
+  persistence on the compromised host.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode=8 TargetImage IN ("*\\Taskmgr.exe", "*\\calc.exe", "*\\notepad.exe", "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe", "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe","*\\cmd.exe", "*\\powershell.exe") | stats count min(_time) as firstTime max(_time) as lastTime by  TargetImage TargetProcessId SourceProcessId  EventCode StartAddress SourceImage dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_remote_thread_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts like remote thread EventCode=8 of sysmon. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=8 TargetImage IN ("*\\Taskmgr.exe", "*\\calc.exe", "*\\notepad.exe",
+  "*\\rdpclip.exe", "*\\explorer.exe", "*\\wermgr.exe", "*\\ping.exe", "*\\OneDriveSetup.exe",
+  "*\\dxdiag.exe", "*\\mobsync.exe", "*\\msra.exe", "*\\xwizard.exe","*\\cmd.exe",
+  "*\\powershell.exe") | stats count min(_time) as firstTime max(_time) as lastTime
+  by  TargetImage TargetProcessId SourceProcessId  EventCode StartAddress SourceImage
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_process_injection_remote_thread_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts like remote thread EventCode=8 of
+  sysmon. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon
+  TA.
 known_false_positives: unknown
 references:
 - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: process $SourceImage$ create a remote thread to process $TargetImage$ on host $dest$
+  message: process $SourceImage$ create a remote thread to process $TargetImage$ on
+    host $dest$
   mitre_attack_id:
   - T1055
   - T1055.002
@@ -48,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - TargetProcessId
-  - SourceProcessId
-  - StartAddress
-  - EventCode
-  - dest
-  - signature
-  - TargetProcessGuid
-  - SourceProcessGuid
-  - StartAddress
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr2/sysmon_wermgr2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr2/sysmon_wermgr2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_injection_wermgr_child_process.yml b/detections/endpoint/windows_process_injection_wermgr_child_process.yml
index 1040c1a33b..011f67e511 100644
--- a/detections/endpoint/windows_process_injection_wermgr_child_process.yml
+++ b/detections/endpoint/windows_process_injection_wermgr_child_process.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies a suspicious instance of wermgr.exe spawning a child process unrelated to error or fault handling. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process relationships and command-line executions. This activity is significant as it can indicate Qakbot malware, which injects malicious code into wermgr.exe to evade detection and execute malicious actions. If confirmed malicious, this behavior could allow an attacker to conduct reconnaissance, execute arbitrary code, and persist within the network, posing a severe security risk.
+description: The following analytic identifies a suspicious instance of wermgr.exe
+  spawning a child process unrelated to error or fault handling. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process relationships
+  and command-line executions. This activity is significant as it can indicate Qakbot
+  malware, which injects malicious code into wermgr.exe to evade detection and execute
+  malicious actions. If confirmed malicious, this behavior could allow an attacker
+  to conduct reconnaissance, execute arbitrary code, and persist within the network,
+  posing a severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" AND NOT (Processes.process_name IN ("WerFaultSecure.exe", "wermgr.exe", "WerFault.exe")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_wermgr_child_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  = "wermgr.exe" AND NOT (Processes.process_name IN ("WerFaultSecure.exe", "wermgr.exe",
+  "WerFault.exe")) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_process_injection_wermgr_child_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_wermgr/sysmon_wermgr.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_injection_with_public_source_path.yml b/detections/endpoint/windows_process_injection_with_public_source_path.yml
index 984ec740e7..0a8f1406a1 100644
--- a/detections/endpoint/windows_process_injection_with_public_source_path.yml
+++ b/detections/endpoint/windows_process_injection_with_public_source_path.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects a process from a non-standard file path on Windows attempting to create a remote thread in another process. This is identified using Sysmon EventCode 8, focusing on processes not originating from typical system directories. This behavior is significant as it often indicates process injection, a technique used by adversaries to evade detection or escalate privileges. If confirmed malicious, this activity could allow an attacker to execute arbitrary code within another process, potentially leading to unauthorized actions and further compromise of the system.
+description: The following analytic detects a process from a non-standard file path
+  on Windows attempting to create a remote thread in another process. This is identified
+  using Sysmon EventCode 8, focusing on processes not originating from typical system
+  directories. This behavior is significant as it often indicates process injection,
+  a technique used by adversaries to evade detection or escalate privileges. If confirmed
+  malicious, this activity could allow an attacker to execute arbitrary code within
+  another process, potentially leading to unauthorized actions and further compromise
+  of the system.
 data_source:
 - Sysmon EventID 8
-search: '`sysmon` EventCode=8 TargetImage = "*.exe" AND NOT(SourceImage IN("C:\\Windows\\*", "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage signature TargetProcessGuid SourceProcessGuid TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_injection_with_public_source_path_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Some security products or third party applications may utilize CreateRemoteThread, filter as needed before enabling as a notable.
+search: '`sysmon` EventCode=8 TargetImage = "*.exe" AND NOT(SourceImage IN("C:\\Windows\\*",
+  "C:\\Program File*", "%systemroot%\\*")) | stats count min(_time) as firstTime max(_time)
+  as lastTime by SourceImage TargetImage signature TargetProcessGuid SourceProcessGuid
+  TargetProcessId SourceProcessId StartAddress EventCode dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_process_injection_with_public_source_path_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting data
+  that records process activity from your hosts to populate the endpoint data model
+  in the processes node. If you are using Sysmon, you must have at least version 6.0.4
+  of the Sysmon TA.
+known_false_positives: Some security products or third party applications may utilize
+  CreateRemoteThread, filter as needed before enabling as a notable.
 references:
 - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/
 tags:
@@ -19,7 +34,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: process $SourceImage$ create a remote thread to process $TargetImage$ on host $dest$
+  message: process $SourceImage$ create a remote thread to process $TargetImage$ on
+    host $dest$
   mitre_attack_id:
   - T1055
   - T1055.002
@@ -40,23 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - TargetProcessId
-  - SourceProcessId
-  - StartAddress
-  - EventCode
-  - dest
-  - signature
-  - TargetProcessGuid
-  - SourceProcessGuid
-  - StartAddress
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/create_remote_thread/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/create_remote_thread/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_with_namedpipe_commandline.yml b/detections/endpoint/windows_process_with_namedpipe_commandline.yml
index e5b501c3db..67c683f361 100644
--- a/detections/endpoint/windows_process_with_namedpipe_commandline.yml
+++ b/detections/endpoint/windows_process_with_namedpipe_commandline.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects processes with command lines containing named pipes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This behavior is significant as it is often used by adversaries, such as those behind the Olympic Destroyer malware, for inter-process communication post-injection, aiding in defense evasion and privilege escalation. If confirmed malicious, this activity could allow attackers to maintain persistence, escalate privileges, or evade defenses, potentially leading to further compromise of the system.
+description: The following analytic detects processes with command lines containing
+  named pipes. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process command-line executions. This behavior is significant as it
+  is often used by adversaries, such as those behind the Olympic Destroyer malware,
+  for inter-process communication post-injection, aiding in defense evasion and privilege
+  escalation. If confirmed malicious, this activity could allow attackers to maintain
+  persistence, escalate privileges, or evade defenses, potentially leading to further
+  compromise of the system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\\\.\\pipe\\*" NOT (Processes.process_path IN ("*\\program files*")) by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_path Processes.process_guid Processes.parent_process_id Processes.dest Processes.user Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_with_namedpipe_commandline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Normal browser application may use this technique. Please update the filter macros to remove false positives.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\\\.\\pipe\\*"
+  NOT (Processes.process_path IN ("*\\program files*")) by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.original_file_name
+  Processes.process_id Processes.parent_process_path Processes.process_guid Processes.parent_process_id
+  Processes.dest Processes.user Processes.process_path | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_with_namedpipe_commandline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Normal browser application may use this technique. Please update
+  the filter macros to remove false positives.
 references:
 - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,24 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/olympic_destroyer/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/olympic_destroyer/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml b/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml
index 6fac7cf083..bca00db1d6 100644
--- a/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml
+++ b/detections/endpoint/windows_process_writing_file_to_world_writable_path.yml
@@ -6,10 +6,41 @@ author: Michael Haag, Splunk
 data_source: []
 type: Hunting
 status: production
-description: The following analytic identifies a process writing a .txt file to a world writable path. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on file creation events within specific directories. This activity is significant as adversaries often use such techniques to deliver payloads to a system, which is uncommon for legitimate processes. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a significant security risk.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name=*.txt Filesystem.file_path IN ("*\\Windows\\Tasks\\*", "*\\Windows\\Temp\\*", "*\\Windows\\tracing\\*", "*\\Windows\\PLA\\Reports\\*", "*\\Windows\\PLA\\Rules\\*", "*\\Windows\\PLA\\Templates\\*", "*\\Windows\\PLA\\Reports\\en-US\\*", "*\\Windows\\PLA\\Rules\\en-US\\*", "*\\Windows\\Registration\\CRMLog\\*", "*\\Windows\\System32\\Tasks\\*", "*\\Windows\\System32\\Com\\dmp\\*", "*\\Windows\\System32\\LogFiles\\WMI\\*", "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", "*\\Windows\\System32\\spool\\PRINTERS\\*", "*\\Windows\\System32\\spool\\SERVERS\\*", "*\\Windows\\System32\\spool\\drivers\\color\\*", "*\\Windows\\System32\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\*", "*\\Windows\\SysWOW64\\Com\\dmp\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\System\\*") by Filesystem.dest, Filesystem.user, Filesystem.file_name Filesystem.file_path | `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_process_writing_file_to_world_writable_path_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the file creation event, process name, file path and, file name. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Filesystem` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may occur if legitimate software writes to these paths. Modify the search to include additional file name extensions. To enhance it further, adding a join on Processes.process_name may assist with restricting the analytic to specific process names. Investigate the process and file to determine if it is malicious.
+description: The following analytic identifies a process writing a .txt file to a
+  world writable path. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on file creation events within specific directories. This
+  activity is significant as adversaries often use such techniques to deliver payloads
+  to a system, which is uncommon for legitimate processes. If confirmed malicious,
+  this behavior could allow attackers to execute arbitrary code, escalate privileges,
+  or maintain persistence within the environment, posing a significant security risk.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name=*.txt
+  Filesystem.file_path IN ("*\\Windows\\Tasks\\*", "*\\Windows\\Temp\\*", "*\\Windows\\tracing\\*",
+  "*\\Windows\\PLA\\Reports\\*", "*\\Windows\\PLA\\Rules\\*", "*\\Windows\\PLA\\Templates\\*",
+  "*\\Windows\\PLA\\Reports\\en-US\\*", "*\\Windows\\PLA\\Rules\\en-US\\*", "*\\Windows\\Registration\\CRMLog\\*",
+  "*\\Windows\\System32\\Tasks\\*", "*\\Windows\\System32\\Com\\dmp\\*", "*\\Windows\\System32\\LogFiles\\WMI\\*",
+  "*\\Windows\\System32\\Microsoft\\Crypto\\RSA\\MachineKeys\\*", "*\\Windows\\System32\\spool\\PRINTERS\\*",
+  "*\\Windows\\System32\\spool\\SERVERS\\*", "*\\Windows\\System32\\spool\\drivers\\color\\*",
+  "*\\Windows\\System32\\Tasks\\Microsoft\\Windows\\RemoteApp and Desktop Connections
+  Update\\*", "*\\Windows\\SysWOW64\\Tasks\\*", "*\\Windows\\SysWOW64\\Com\\dmp\\*",
+  "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\RemoteApp
+  and Desktop Connections Update\\*", "*\\Windows\\SysWOW64\\Tasks\\Microsoft\\Windows\\PLA\\System\\*")
+  by Filesystem.dest, Filesystem.user, Filesystem.file_name Filesystem.file_path |
+  `drop_dm_object_name("Filesystem")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_process_writing_file_to_world_writable_path_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the file creation event, process name, file path
+  and, file name. These logs must be processed using the appropriate Splunk Technology
+  Add-ons that are specific to the EDR product. The logs must also be mapped to the
+  `Filesystem` node of the `Endpoint` data model. Use the Splunk Common Information
+  Model (CIM) to normalize the field names and speed up the data modeling process.
+known_false_positives: False positives may occur if legitimate software writes to
+  these paths. Modify the search to include additional file name extensions. To enhance
+  it further, adding a join on Processes.process_name may assist with restricting
+  the analytic to specific process names. Investigate the process and file to determine
+  if it is malicious.
 references:
 - https://research.splunk.com/endpoint/efbcf8ee-bc75-47f1-8985-a5c638c4faf0/
 tags:
@@ -18,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A process wrote a file name- [$file_name$] to a world writable file path [$file_path$] on host- [$dest$].
+  message: A process wrote a file name- [$file_name$] to a world writable file path
+    [$file_path$] on host- [$dest$].
   mitre_attack_id:
   - T1218.005
   observable:
@@ -34,17 +66,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - user
-  - file_name
-  - file_path
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/mshta_tasks_windows-sysmon.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
diff --git a/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml b/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml
index 857043048c..cd73b2cafc 100644
--- a/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml
+++ b/detections/endpoint/windows_processes_killed_by_industroyer2_malware.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the termination of specific processes by the Industroyer2 malware. It leverages Sysmon EventCode 5 to identify when processes like "PServiceControl.exe" and "PService_PPD.exe" are killed. This activity is significant as it targets processes related to energy facility networks, indicating a potential attack on critical infrastructure. If confirmed malicious, this could lead to disruption of essential services, loss of control over energy systems, and significant operational impact. Immediate investigation is required to determine the cause and mitigate any potential threats.
+description: The following analytic detects the termination of specific processes
+  by the Industroyer2 malware. It leverages Sysmon EventCode 5 to identify when processes
+  like "PServiceControl.exe" and "PService_PPD.exe" are killed. This activity is significant
+  as it targets processes related to energy facility networks, indicating a potential
+  attack on critical infrastructure. If confirmed malicious, this could lead to disruption
+  of essential services, loss of control over energy systems, and significant operational
+  impact. Immediate investigation is required to determine the cause and mitigate
+  any potential threats.
 data_source:
 - Sysmon EventID 5
-search: '`sysmon` EventCode=5 process_name IN ("PServiceControl.exe", "PService_PPD.exe") | stats min(_time) as firstTime max(_time) as lastTime count by process_name process process_path process_guid process_id EventCode dest user_id | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_processes_killed_by_industroyer2_malware_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
-known_false_positives: False positives are possible if legitimate applications are allowed to terminate this process during testing or updates. Filter as needed based on paths that are used legitimately.
+search: '`sysmon` EventCode=5 process_name IN ("PServiceControl.exe", "PService_PPD.exe")
+  | stats min(_time) as firstTime max(_time) as lastTime count by process_name process
+  process_path process_guid process_id EventCode dest user_id | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `windows_processes_killed_by_industroyer2_malware_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: False positives are possible if legitimate applications are
+  allowed to terminate this process during testing or updates. Filter as needed based
+  on paths that are used legitimately.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,24 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/industroyer2/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/industroyer2/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_protocol_tunneling_with_plink.yml b/detections/endpoint/windows_protocol_tunneling_with_plink.yml
index 8d64d69244..49840d56ad 100644
--- a/detections/endpoint/windows_protocol_tunneling_with_plink.yml
+++ b/detections/endpoint/windows_protocol_tunneling_with_plink.yml
@@ -5,14 +5,40 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: This analytic detects the use of Plink (including renamed versions like pvhost.exe) for protocol tunneling, which may be used for egress or lateral movement within an organization. It identifies specific command-line options (-R, -L, -D, -l, -N, -P, -pw) commonly used for port forwarding and tunneling by analyzing process execution logs from Endpoint Detection and Response (EDR) agents. This activity is significant as it may indicate an attempt to bypass network security controls or establish unauthorized connections. If confirmed malicious, this could allow an attacker to exfiltrate data, move laterally across the network, or maintain persistent access, posing a severe threat to the organization's security. The detection covers both the original Plink executable and potential renamed versions, enhancing its ability to catch evasion attempts.
+description: This analytic detects the use of Plink (including renamed versions like
+  pvhost.exe) for protocol tunneling, which may be used for egress or lateral movement
+  within an organization. It identifies specific command-line options (-R, -L, -D,
+  -l, -N, -P, -pw) commonly used for port forwarding and tunneling by analyzing process
+  execution logs from Endpoint Detection and Response (EDR) agents. This activity
+  is significant as it may indicate an attempt to bypass network security controls
+  or establish unauthorized connections. If confirmed malicious, this could allow
+  an attacker to exfiltrate data, move laterally across the network, or maintain persistent
+  access, posing a severe threat to the organization's security. The detection covers
+  both the original Plink executable and potential renamed versions, enhancing its
+  ability to catch evasion attempts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=plink.exe OR Processes.process_name=pvhost.exe OR Processes.original_file_name=Plink) AND Processes.process IN ("*-R *", "*-L *", "*-D *", "*-l *", "*-N *", "*-P *", "*-pw *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_protocol_tunneling_with_plink_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if the organization allows for SSH tunneling outbound or internally. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=plink.exe
+  OR Processes.process_name=pvhost.exe OR Processes.original_file_name=Plink) AND
+  Processes.process IN ("*-R *", "*-L *", "*-D *", "*-l *", "*-N *", "*-P *", "*-pw
+  *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_protocol_tunneling_with_plink_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if the organization allows for
+  SSH tunneling outbound or internally. Filter as needed.
 references:
 - https://thedfirreport.com/2022/06/06/will-the-real-msiexec-please-stand-up-exploit-leads-to-data-exfiltration/
 - https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html
@@ -26,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to tunnel to a remote destination.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to tunnel to a remote destination.
   mitre_attack_id:
   - T1572
   - T1021.004
@@ -60,23 +92,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/plink/plink-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/plink/plink-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_proxy_via_netsh.yml b/detections/endpoint/windows_proxy_via_netsh.yml
index 699df0f775..3fb15002d5 100644
--- a/detections/endpoint/windows_proxy_via_netsh.yml
+++ b/detections/endpoint/windows_proxy_via_netsh.yml
@@ -9,10 +9,33 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the use of netsh.exe to configure a connection proxy, which can be leveraged for persistence by executing a helper DLL. It detects this activity by analyzing process creation events from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "portproxy" and "v4tov4" parameters. This activity is significant because it indicates potential unauthorized network configuration changes, which could be used to maintain persistence or redirect network traffic. If confirmed malicious, this could allow an attacker to maintain covert access or manipulate network communications, posing a significant security risk.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process = "* portproxy *" Processes.process = "* v4tov4 *" by Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_proxy_via_netsh_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.
+description: The following analytic identifies the use of netsh.exe to configure a
+  connection proxy, which can be leveraged for persistence by executing a helper DLL.
+  It detects this activity by analyzing process creation events from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions involving "portproxy"
+  and "v4tov4" parameters. This activity is significant because it indicates potential
+  unauthorized network configuration changes, which could be used to maintain persistence
+  or redirect network traffic. If confirmed malicious, this could allow an attacker
+  to maintain covert access or manipulate network communications, posing a significant
+  security risk.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_netsh` Processes.process
+  = "* portproxy *" Processes.process = "* v4tov4 *" by Processes.parent_process_name
+  Processes.parent_process Processes.original_file_name Processes.process_name Processes.process
+  Processes.user Processes.dest |`drop_dm_object_name("Processes")` |`security_content_ctime(firstTime)`
+  |`security_content_ctime(lastTime)` | `windows_proxy_via_netsh_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Some VPN applications are known to launch netsh.exe. Outside
+  of these instances, it is unusual for an executable to launch netsh.exe and run
+  commands.
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +60,8 @@ tags:
   - b8223ea9-4be2-44a6-b50a-9657a3d4e72a
   confidence: 70
   impact: 70
-  message: A process $process_name$ has launched netsh with command-line $process$ on $dest$.
+  message: A process $process_name$ has launched netsh with command-line $process$
+    on $dest$.
   mitre_attack_id:
   - T1090.001
   - T1090
@@ -49,18 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.user
-  - Processes.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_proxy_via_registry.yml b/detections/endpoint/windows_proxy_via_registry.yml
index c63ef7931c..900a447c7d 100644
--- a/detections/endpoint/windows_proxy_via_registry.yml
+++ b/detections/endpoint/windows_proxy_via_registry.yml
@@ -8,9 +8,24 @@ type: Anomaly
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects the modification of registry keys related to the Windows Proxy settings via netsh.exe. It leverages data from the Endpoint.Registry data model, focusing on changes to the registry path "*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*". This activity is significant because netsh.exe can be used to establish a persistent proxy, potentially allowing an attacker to execute a helper DLL whenever netsh.exe runs. If confirmed malicious, this could enable the attacker to maintain persistence, manipulate network configurations, and potentially exfiltrate data or further compromise the system.
-search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path ="*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.action Registry.dest  Registry.user | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_proxy_via_registry_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
+description: The following analytic detects the modification of registry keys related
+  to the Windows Proxy settings via netsh.exe. It leverages data from the Endpoint.Registry
+  data model, focusing on changes to the registry path "*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*".
+  This activity is significant because netsh.exe can be used to establish a persistent
+  proxy, potentially allowing an attacker to execute a helper DLL whenever netsh.exe
+  runs. If confirmed malicious, this could enable the attacker to maintain persistence,
+  manipulate network configurations, and potentially exfiltrate data or further compromise
+  the system.
+search: '| tstats `security_content_summariesonly` count  min(_time) as firstTime
+  max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path
+  ="*\\System\\CurrentControlSet\\Services\\PortProxy\\v4tov4\\tcp*" by Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.action Registry.dest  Registry.user
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`
+  | `windows_proxy_via_registry_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the registry value name, registry path, and registry value data from your
+  endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical
+  Sysmon TA. https://splunkbase.splunk.com/app/5709
 known_false_positives: unknown
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/05/24/volt-typhoon-targets-us-critical-infrastructure-with-living-off-the-land-techniques/
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,19 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.001/netsh_portproxy/volt_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_query_registry_browser_list_application.yml b/detections/endpoint/windows_query_registry_browser_list_application.yml
index 2ecbe4cf2e..834ed9b74c 100644
--- a/detections/endpoint/windows_query_registry_browser_list_application.yml
+++ b/detections/endpoint/windows_query_registry_browser_list_application.yml
@@ -7,10 +7,25 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log Security 4663
-description: The following analytic detects a suspicious process accessing the registry entries for default internet browsers. It leverages Windows Security Event logs, specifically event code 4663, to identify access attempts to these registry paths. This activity is significant because adversaries can exploit this registry key to gather information about installed browsers and their settings, potentially leading to the theft of sensitive data such as login credentials and browsing history. If confirmed malicious, this behavior could enable attackers to exfiltrate sensitive information and compromise user accounts.
-search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\SOFTWARE\\Clients\\StartMenuInternet\\*", "*\\SOFTWARE\\Clients\\StartMenuInternet\\*") AND NOT (process_path IN ("*:\\Windows\\System32\\*", "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_browser_list_application_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: uninstall application may access this registry to remove the entry of the target application. filter is needed.
+description: The following analytic detects a suspicious process accessing the registry
+  entries for default internet browsers. It leverages Windows Security Event logs,
+  specifically event code 4663, to identify access attempts to these registry paths.
+  This activity is significant because adversaries can exploit this registry key to
+  gather information about installed browsers and their settings, potentially leading
+  to the theft of sensitive data such as login credentials and browsing history. If
+  confirmed malicious, this behavior could enable attackers to exfiltrate sensitive
+  information and compromise user accounts.
+search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\SOFTWARE\\Clients\\StartMenuInternet\\*",
+  "*\\SOFTWARE\\Clients\\StartMenuInternet\\*") AND NOT (process_path IN ("*:\\Windows\\System32\\*",
+  "*:\\Windows\\SysWow64\\*", "*:\\Program Files*", "*:\\Windows\\*")) | stats count
+  min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path
+  process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_query_registry_browser_list_application_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: uninstall application may access this registry to remove the
+  entry of the target application. filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,19 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_list/ar3_4663_redline_reg.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/browser_list/ar3_4663_redline_reg.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_query_registry_reg_save.yml b/detections/endpoint/windows_query_registry_reg_save.yml
index f115c8e8f6..d3c1c2e2f8 100644
--- a/detections/endpoint/windows_query_registry_reg_save.yml
+++ b/detections/endpoint/windows_query_registry_reg_save.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the reg.exe process with the "save" parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant because threat actors often use the "reg save" command to dump credentials or test registry modification capabilities on compromised hosts. If confirmed malicious, this behavior could allow attackers to escalate privileges, persist in the environment, or access sensitive information stored in the registry.
+description: The following analytic detects the execution of the reg.exe process with
+  the "save" parameter. This detection leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on process execution logs and command-line arguments.
+  This activity is significant because threat actors often use the "reg save" command
+  to dump credentials or test registry modification capabilities on compromised hosts.
+  If confirmed malicious, this behavior could allow attackers to escalate privileges,
+  persist in the environment, or access sensitive information stored in the registry.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process = "* save *" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_reg_save_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network administrator can use this command tool to backup registry before updates or modifying critical registries.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where  `process_reg` AND Processes.process
+  = "* save *" by Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process
+  Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_reg_save_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network administrator can use this command tool to backup registry
+  before updates or modifying critical registries.
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser
 - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS
@@ -41,25 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_query_registry_uninstall_program_list.yml b/detections/endpoint/windows_query_registry_uninstall_program_list.yml
index 87f511e7bf..9bc556392a 100644
--- a/detections/endpoint/windows_query_registry_uninstall_program_list.yml
+++ b/detections/endpoint/windows_query_registry_uninstall_program_list.yml
@@ -7,10 +7,22 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log Security 4663
-description: The following analytic detects a suspicious query on the uninstall application list in the Windows OS registry. It leverages Windows Security Event logs, specifically event code 4663, to identify access to the "Uninstall" registry key. This activity is significant because adversaries or malware can exploit this key to gather information about installed applications, aiding in further attacks. If confirmed malicious, this behavior could allow attackers to map out installed software, potentially identifying vulnerabilities or software to exploit, leading to further system compromise.
-search: '`wineventlog_security` EventCode=4663 object_file_path="\\REGISTRY\\MACHINE\\SOFTWARE\\WOW6432Node\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*" | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_query_registry_uninstall_program_list_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
-known_false_positives: Uninstall application may access this registry to remove the entry of the target application. Filter is needed.
+description: The following analytic detects a suspicious query on the uninstall application
+  list in the Windows OS registry. It leverages Windows Security Event logs, specifically
+  event code 4663, to identify access to the "Uninstall" registry key. This activity
+  is significant because adversaries or malware can exploit this key to gather information
+  about installed applications, aiding in further attacks. If confirmed malicious,
+  this behavior could allow attackers to map out installed software, potentially identifying
+  vulnerabilities or software to exploit, leading to further system compromise.
+search: '`wineventlog_security` EventCode=4663 object_file_path="\\REGISTRY\\MACHINE\\SOFTWARE\\WOW6432Node\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name
+  object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_query_registry_uninstall_program_list_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+known_false_positives: Uninstall application may access this registry to remove the
+  entry of the target application. Filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,19 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/recon_registry/recon-reg-redline-security-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/recon_registry/recon-reg-redline-security-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_raccine_scheduled_task_deletion.yml b/detections/endpoint/windows_raccine_scheduled_task_deletion.yml
index 54a2e74dc9..05ad8286f1 100644
--- a/detections/endpoint/windows_raccine_scheduled_task_deletion.yml
+++ b/detections/endpoint/windows_raccine_scheduled_task_deletion.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the deletion of the Raccine Rules Updater scheduled task using the `schtasks.exe` command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because adversaries may delete this task to disable Raccine, a tool designed to prevent ransomware attacks. If confirmed malicious, this action could allow ransomware to execute without interference, leading to potential data encryption and loss.
+description: The following analytic identifies the deletion of the Raccine Rules Updater
+  scheduled task using the `schtasks.exe` command. This detection leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. This activity is significant because adversaries may delete this task
+  to disable Raccine, a tool designed to prevent ransomware attacks. If confirmed
+  malicious, this action could allow ransomware to execute without interference, leading
+  to potential data encryption and loss.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*delete*" AND Processes.process="*Raccine*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raccine_scheduled_task_deletion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe
+  Processes.process="*delete*" AND Processes.process="*Raccine*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raccine_scheduled_task_deletion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives should be limited, however filter as needed.
 references:
 - https://redcanary.com/blog/blackbyte-ransomware/
@@ -22,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user user$ attempting to disable Raccines scheduled task.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user user$ attempting to disable Raccines scheduled task.
   mitre_attack_id:
   - T1562.001
   observable:
@@ -55,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_raccine.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon_raccine.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml b/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml
index b43edb0b0c..5d5237313a 100644
--- a/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml
+++ b/detections/endpoint/windows_rapid_authentication_on_multiple_hosts.yml
@@ -7,10 +7,23 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 4624
-description: The following analytic detects a source computer authenticating to 30 or more remote endpoints within a 5-minute timespan using Event ID 4624. This behavior is identified by analyzing Windows Event Logs for LogonType 3 events and counting unique target computers. Such activity is significant as it may indicate lateral movement or network share enumeration by an adversary. If confirmed malicious, this could lead to unauthorized access to multiple systems, potentially compromising sensitive data and escalating privileges within the network.
-search: '`wineventlog_security` EventCode=4624 LogonType=3 TargetUserName!="ANONYMOUS LOGON" TargetUserName!="*$" | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as host_targets by _time, IpAddress, TargetUserName | where unique_targets > 30 | `windows_rapid_authentication_on_multiple_hosts_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
-known_false_positives: Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed.
+description: The following analytic detects a source computer authenticating to 30
+  or more remote endpoints within a 5-minute timespan using Event ID 4624. This behavior
+  is identified by analyzing Windows Event Logs for LogonType 3 events and counting
+  unique target computers. Such activity is significant as it may indicate lateral
+  movement or network share enumeration by an adversary. If confirmed malicious, this
+  could lead to unauthorized access to multiple systems, potentially compromising
+  sensitive data and escalating privileges within the network.
+search: '`wineventlog_security` EventCode=4624 LogonType=3 TargetUserName!="ANONYMOUS
+  LOGON" TargetUserName!="*$" | bucket span=5m _time | stats dc(Computer) AS unique_targets
+  values(Computer) as host_targets by _time, IpAddress, TargetUserName | where unique_targets
+  > 30 | `windows_rapid_authentication_on_multiple_hosts_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
+known_false_positives: Vulnerability scanners or system administration tools may also
+  trigger this detection. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1135/
 - https://thedfirreport.com/2023/01/23/sharefinder-how-threat-actors-discover-file-shares/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host_targets$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host_targets$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 60
-  message: The source computer with ip address $IpAddress$ authenticated to a large number of remote endpoints within 5 minutes.
+  message: The source computer with ip address $IpAddress$ authenticated to a large
+    number of remote endpoints within 5 minutes.
   mitre_attack_id:
   - T1003.002
   observable:
@@ -47,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - TargetUserName
-  - Computer
-  - IpAddress
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/rapid_authentication_multiple_hosts/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/rapid_authentication_multiple_hosts/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_rasautou_dll_execution.yml b/detections/endpoint/windows_rasautou_dll_execution.yml
index cd6eee3f7a..63339bbfcf 100644
--- a/detections/endpoint/windows_rasautou_dll_execution.yml
+++ b/detections/endpoint/windows_rasautou_dll_execution.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of an arbitrary DLL by the Windows Remote Auto Dialer (rasautou.exe). This behavior is identified by analyzing process creation events where rasautou.exe is executed with specific command-line arguments. This activity is significant because it leverages a Living Off The Land Binary (LOLBin) to execute potentially malicious code, bypassing traditional security controls. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise, privilege escalation, or persistent access within the environment.
+description: The following analytic detects the execution of an arbitrary DLL by the
+  Windows Remote Auto Dialer (rasautou.exe). This behavior is identified by analyzing
+  process creation events where rasautou.exe is executed with specific command-line
+  arguments. This activity is significant because it leverages a Living Off The Land
+  Binary (LOLBin) to execute potentially malicious code, bypassing traditional security
+  controls. If confirmed malicious, this technique could allow an attacker to execute
+  arbitrary code, potentially leading to system compromise, privilege escalation,
+  or persistent access within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rasautou.exe Processes.process="* -d *"AND Processes.process="* -p *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rasautou_dll_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be limited to applications that require Rasautou.exe to load a DLL from disk. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rasautou.exe
+  Processes.process="* -d *"AND Processes.process="* -p *" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rasautou_dll_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be limited to applications that require
+  Rasautou.exe to load a DLL from disk. Filter as needed.
 references:
 - https://github.com/mandiant/DueDLLigence
 - https://github.com/MHaggis/notes/blob/master/utilities/Invoke-SPLDLLigence.ps1
@@ -24,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to load a DLL in a suspicious manner.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ attempting to load a DLL in a suspicious manner.
   mitre_attack_id:
   - T1055.001
   - T1218
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055.001/rasautou/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055.001/rasautou/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml b/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml
index 3d14ab24ed..c0d5898f4c 100644
--- a/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml
+++ b/detections/endpoint/windows_raw_access_to_disk_volume_partition.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects suspicious raw access reads to the device disk partition of a host machine. It leverages Sysmon EventCode 9 logs to identify processes attempting to read or write to the boot sector, excluding legitimate system processes. This activity is significant as it is commonly associated with destructive actions by adversaries, such as wiping, encrypting, or overwriting the boot sector, as seen in attacks involving malware like HermeticWiper. If confirmed malicious, this behavior could lead to severe impacts, including system inoperability, data loss, or compromised boot integrity.
+description: The following analytic detects suspicious raw access reads to the device
+  disk partition of a host machine. It leverages Sysmon EventCode 9 logs to identify
+  processes attempting to read or write to the boot sector, excluding legitimate system
+  processes. This activity is significant as it is commonly associated with destructive
+  actions by adversaries, such as wiping, encrypting, or overwriting the boot sector,
+  as seen in attacks involving malware like HermeticWiper. If confirmed malicious,
+  this behavior could lead to severe impacts, including system inoperability, data
+  loss, or compromised boot integrity.
 data_source:
 - Sysmon EventID 9
-search: '`sysmon` EventCode=9 Device = \\Device\\HarddiskVolume* NOT (Image IN("*\\Windows\\System32\\*", "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by dest signature signature_id process_guid process_name process_path Device | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_disk_volume_partition_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the raw access read event (like sysmon eventcode 9), process name and process guid from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: This event is really notable but we found minimal number of normal application from system32 folder like svchost.exe accessing it too. In this case we used 'system32' and 'syswow64' path as a filter for this detection.
+search: '`sysmon` EventCode=9 Device = \\Device\\HarddiskVolume* NOT (Image IN("*\\Windows\\System32\\*",
+  "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as
+  lastTime by dest signature signature_id process_guid process_name process_path Device
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_disk_volume_partition_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the raw access read event (like sysmon eventcode 9), process name and
+  process guid from your endpoints. If you are using Sysmon, you must have at least
+  version 6.0.4 of the Sysmon TA.
+known_false_positives: This event is really notable but we found minimal number of
+  normal application from system32 folder like svchost.exe accessing it too. In this
+  case we used 'system32' and 'syswow64' path as a filter for this detection.
 references:
 - https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,21 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - signature
-  - signature_id
-  - process_guid
-  - process_name
-  - process_path
-  - Device
-  - EventCode
-  - Image
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/hermetic_wiper/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml b/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml
index 30b221146e..77b516ecd8 100644
--- a/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml
+++ b/detections/endpoint/windows_raw_access_to_master_boot_record_drive.yml
@@ -5,12 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects suspicious raw access reads to the drive containing the Master Boot Record (MBR). It leverages Sysmon EventCode 9 to identify processes attempting to read or write to the MBR sector, excluding legitimate system processes. This activity is significant because adversaries often target the MBR to wipe, encrypt, or overwrite it as part of their impact payload. If confirmed malicious, this could lead to system instability, data loss, or a complete system compromise, severely impacting the organization's operations.
+description: The following analytic detects suspicious raw access reads to the drive
+  containing the Master Boot Record (MBR). It leverages Sysmon EventCode 9 to identify
+  processes attempting to read or write to the MBR sector, excluding legitimate system
+  processes. This activity is significant because adversaries often target the MBR
+  to wipe, encrypt, or overwrite it as part of their impact payload. If confirmed
+  malicious, this could lead to system instability, data loss, or a complete system
+  compromise, severely impacting the organization's operations.
 data_source:
 - Sysmon EventID 9
-search: '`sysmon` EventCode=9 Device = \\Device\\Harddisk0\\DR0 NOT (Image IN("*\\Windows\\System32\\*", "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by Computer Image Device ProcessGuid ProcessId EventDescription EventCode | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_raw_access_to_master_boot_record_drive_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the raw access read event (like sysmon eventcode 9), process name and process guid from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: This event is really notable but we found minimal number of normal application from system32 folder like svchost.exe accessing it too. In this case we used 'system32' and 'syswow64' path as a filter for this detection.
+search: '`sysmon` EventCode=9 Device = \\Device\\Harddisk0\\DR0 NOT (Image IN("*\\Windows\\System32\\*",
+  "*\\Windows\\SysWOW64\\*")) | stats count min(_time) as firstTime max(_time) as
+  lastTime by Computer Image Device ProcessGuid ProcessId EventDescription EventCode
+  | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_raw_access_to_master_boot_record_drive_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the raw access read event (like sysmon eventcode 9), process name and
+  process guid from your endpoints. If you are using Sysmon, you must have at least
+  version 6.0.4 of the Sysmon TA.
+known_false_positives: This event is really notable but we found minimal number of
+  normal application from system32 folder like svchost.exe accessing it too. In this
+  case we used 'system32' and 'syswow64' path as a filter for this detection.
 references:
 - https://www.splunk.com/en_us/blog/security/threat-advisory-strt-ta02-destructive-software.html
 - https://www.crowdstrike.com/blog/technical-analysis-of-whispergate-malware/
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,19 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Image
-  - Device
-  - ProcessGuid
-  - ProcessId
-  - EventDescription
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1561.002/mbr_raw_access/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1561.002/mbr_raw_access/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_rdp_connection_successful.yml b/detections/endpoint/windows_rdp_connection_successful.yml
index 29e3f18ec3..5165cd7714 100644
--- a/detections/endpoint/windows_rdp_connection_successful.yml
+++ b/detections/endpoint/windows_rdp_connection_successful.yml
@@ -7,10 +7,21 @@ status: production
 type: Hunting
 data_source:
 - Windows Event Log RemoteConnectionManager 1149
-description: The following analytic detects successful Remote Desktop Protocol (RDP) connections by monitoring EventCode 1149 from the Windows TerminalServices RemoteConnectionManager Operational log. This detection is significant as successful RDP connections can indicate remote access to a system, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network. Monitoring successful RDP connections is crucial for identifying potential security breaches and mitigating risks promptly.
-search: '`remoteconnectionmanager` EventCode=1149 | stats count min(_time) as firstTime max(_time) as lastTime by Computer, user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename Computer as dest | `windows_rdp_connection_successful_filter`'
-how_to_implement: The following analyic requires the WIndows TerminalServices RemoteConnectionManager Operational log to be enabled and ingested into Splunk. For the inputs, review https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706.
-known_false_positives: False positives will be present, filter as needed or restrict to critical assets on the perimeter.
+description: The following analytic detects successful Remote Desktop Protocol (RDP)
+  connections by monitoring EventCode 1149 from the Windows TerminalServices RemoteConnectionManager
+  Operational log. This detection is significant as successful RDP connections can
+  indicate remote access to a system, which may be leveraged by attackers to control
+  or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized
+  access, data theft, or further lateral movement within the network. Monitoring successful
+  RDP connections is crucial for identifying potential security breaches and mitigating
+  risks promptly.
+search: '`remoteconnectionmanager` EventCode=1149 | stats count min(_time) as firstTime
+  max(_time) as lastTime by Computer, user_id | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | rename Computer as dest | `windows_rdp_connection_successful_filter`'
+how_to_implement: The following analyic requires the WIndows TerminalServices RemoteConnectionManager
+  Operational log to be enabled and ingested into Splunk. For the inputs, review https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706.
+known_false_positives: False positives will be present, filter as needed or restrict
+  to critical assets on the perimeter.
 references:
 - https://gist.github.com/MHaggis/138c6bf563bacbda4a2524f089773706
 - https://doublepulsar.com/rdp-hijacking-how-to-hijack-rds-and-remoteapp-sessions-transparently-to-move-through-an-da2a1e73a5f6
@@ -34,16 +45,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - ComputerName
-  - Source_Network_Address
-  - User
-  - Message
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/windows_rdp_connection_successful/windows-xml.log
-    source: WinEventLog:Microsoft-Windows-TerminalServices-RemoteConnectionManager/Operational
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/windows_rdp_connection_successful/windows-xml.log
+    source: 
+      WinEventLog:Microsoft-Windows-TerminalServices-RemoteConnectionManager/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_registry_bootexecute_modification.yml b/detections/endpoint/windows_registry_bootexecute_modification.yml
index 834e089c62..3008b1697e 100644
--- a/detections/endpoint/windows_registry_bootexecute_modification.yml
+++ b/detections/endpoint/windows_registry_bootexecute_modification.yml
@@ -16,8 +16,8 @@ description: The following analytic detects modifications to the BootExecute reg
   to achieve persistence, load malicious code, or tamper with the boot process. If
   confirmed malicious, this could allow an attacker to maintain persistence, execute
   arbitrary code at boot, or disrupt system operations.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE Registry.registry_path="HKLM\\System\\CurrentControlSet\\Control\\Session
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE Registry.registry_path="HKLM\\System\\CurrentControlSet\\Control\\Session
   Manager\\BootExecute" BY Registry.dest Registry.registry_path Registry.registry_key_name
   Registry.registry_value_name Registry.registry_value_data Registry.process_guid,
   Registry.action | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
@@ -36,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,7 +51,8 @@ tags:
   atomic_guid: []
   confidence: 100
   impact: 100
-  message: The Registry BootExecute value was modified on $dest$ and should be reviewed immediately.
+  message: The Registry BootExecute value was modified on $dest$ and should be reviewed
+    immediately.
   mitre_attack_id:
   - T1542
   - T1547.001
@@ -59,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.registry_value_data
-  - Registry.process_guid
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/bootexecute-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/bootexecute-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_registry_certificate_added.yml b/detections/endpoint/windows_registry_certificate_added.yml
index e0071bd669..e4a1d95628 100644
--- a/detections/endpoint/windows_registry_certificate_added.yml
+++ b/detections/endpoint/windows_registry_certificate_added.yml
@@ -5,15 +5,22 @@ date: '2024-11-14'
 author: Michael Haag, Teodeerick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the installation of a root CA certificate by monitoring specific registry paths for SetValue events. It leverages data from the Endpoint datamodel, focusing on registry paths containing "certificates" and registry values named "Blob." This activity is significant because unauthorized root CA certificates can compromise the integrity of encrypted communications and facilitate man-in-the-middle attacks. If confirmed malicious, this could allow an attacker to intercept, decrypt, or manipulate sensitive data, leading to severe security breaches.
+description: The following analytic detects the installation of a root CA certificate
+  by monitoring specific registry paths for SetValue events. It leverages data from
+  the Endpoint datamodel, focusing on registry paths containing "certificates" and
+  registry values named "Blob." This activity is significant because unauthorized
+  root CA certificates can compromise the integrity of encrypted communications and
+  facilitate man-in-the-middle attacks. If confirmed malicious, this could allow an
+  attacker to intercept, decrypt, or manipulate sensitive data, leading to severe
+  security breaches.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  where Registry.registry_path IN ("*\\certificates\\*") AND Registry.registry_value_name="Blob"
-  by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name
-  Registry.process_guid Registry.registry_key_name Registry.registry_value_data |
-  `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\certificates\\*")
+  AND Registry.registry_value_name="Blob" by Registry.dest Registry.user Registry.registry_path
+  Registry.registry_value_name Registry.process_guid Registry.registry_key_name Registry.registry_value_data
+  | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_registry_certificate_added_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
@@ -32,7 +39,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -55,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1587.002/atomic_red_team/certblob_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1587.002/atomic_red_team/certblob_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_registry_delete_task_sd.yml b/detections/endpoint/windows_registry_delete_task_sd.yml
index 06112f5dc1..713c3a6e0f 100644
--- a/detections/endpoint/windows_registry_delete_task_sd.yml
+++ b/detections/endpoint/windows_registry_delete_task_sd.yml
@@ -5,15 +5,22 @@ date: '2024-11-14'
 author: Michael Haag, Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a process attempting to delete a scheduled task's Security Descriptor (SD) from the registry path of that task. It leverages the Endpoint.Registry data model to identify registry actions performed by the SYSTEM user, specifically targeting deletions or modifications of the SD value. This activity is significant as it may indicate an attempt to remove evidence of a scheduled task for defense evasion. If confirmed malicious, it suggests an attacker with privileged access trying to hide their tracks, potentially compromising system integrity and security. Immediate investigation is required.
+description: The following analytic detects a process attempting to delete a scheduled
+  task's Security Descriptor (SD) from the registry path of that task. It leverages
+  the Endpoint.Registry data model to identify registry actions performed by the SYSTEM
+  user, specifically targeting deletions or modifications of the SD value. This activity
+  is significant as it may indicate an attempt to remove evidence of a scheduled task
+  for defense evasion. If confirmed malicious, it suggests an attacker with privileged
+  access trying to hide their tracks, potentially compromising system integrity and
+  security. Immediate investigation is required.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  where Registry.registry_path IN ("*\\Schedule\\TaskCache\\Tree\\*") Registry.user="SYSTEM"
-  Registry.registry_value_name="SD" (Registry.action=Deleted OR Registry.action=modified)
-  by Registry.dest Registry.process_guid Registry.user Registry.registry_path
-  Registry.registry_value_name Registry.registry_key_name Registry.registry_value_data
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Schedule\\TaskCache\\Tree\\*")
+  Registry.user="SYSTEM" Registry.registry_value_name="SD" (Registry.action=Deleted
+  OR Registry.action=modified) by Registry.dest Registry.process_guid Registry.user
+  Registry.registry_path Registry.registry_value_name Registry.registry_key_name Registry.registry_value_data
   Registry.status Registry.action | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
   | `security_content_ctime(lastTime)` | `windows_registry_delete_task_sd_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
@@ -34,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -58,21 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/sd_delete_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/sd_delete_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml b/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml
index cb14cb3150..82d4a4f1bd 100644
--- a/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml
+++ b/detections/endpoint/windows_registry_modification_for_safe_mode_persistence.yml
@@ -5,12 +5,19 @@ date: '2024-11-14'
 author: Teoderick Contreras, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies modifications to the SafeBoot registry keys, specifically within the Minimal and Network paths. This detection leverages registry activity logs from endpoint data sources like Sysmon or EDR tools. Monitoring these keys is crucial as adversaries can use them to persist drivers or services in Safe Mode, with Network allowing network connections. If confirmed malicious, this activity could enable attackers to maintain persistence even in Safe Mode, potentially bypassing certain security measures and facilitating further malicious actions.
+description: The following analytic identifies modifications to the SafeBoot registry
+  keys, specifically within the Minimal and Network paths. This detection leverages
+  registry activity logs from endpoint data sources like Sysmon or EDR tools. Monitoring
+  these keys is crucial as adversaries can use them to persist drivers or services
+  in Safe Mode, with Network allowing network connections. If confirmed malicious,
+  this activity could enable attackers to maintain persistence even in Safe Mode,
+  potentially bypassing certain security measures and facilitating further malicious
+  actions.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  where Registry.registry_path IN ("*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Minimal\\*","*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Network\\*")
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Minimal\\*","*SYSTEM\\CurrentControlSet\\Control\\SafeBoot\\Network\\*")
   by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name
   Registry.process_guid Registry.registry_key_name Registry.registry_value_data |
   `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
@@ -34,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 60
-  message: Safeboot registry $registry_path$ was added or modified with a new value $registry_value_name$ on $dest$
+  message: Safeboot registry $registry_path$ was added or modified with a new value
+    $registry_value_name$ on $dest$
   mitre_attack_id:
   - T1547.001
   - T1547
@@ -58,16 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
-  - Registry.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_registry_payload_injection.yml b/detections/endpoint/windows_registry_payload_injection.yml
index dfab993f66..7bca4bdbbf 100644
--- a/detections/endpoint/windows_registry_payload_injection.yml
+++ b/detections/endpoint/windows_registry_payload_injection.yml
@@ -5,12 +5,39 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects suspiciously long data written to the Windows registry, a behavior often linked to fileless malware or persistence techniques. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on registry events with data lengths exceeding 512 characters. This activity is significant as it can indicate an attempt to evade traditional file-based defenses, making it crucial for SOC monitoring. If confirmed malicious, this technique could allow attackers to maintain persistence, execute code, or manipulate system configurations without leaving a conventional file footprint.
+description: The following analytic detects suspiciously long data written to the
+  Windows registry, a behavior often linked to fileless malware or persistence techniques.
+  It leverages Endpoint Detection and Response (EDR) telemetry, focusing on registry
+  events with data lengths exceeding 512 characters. This activity is significant
+  as it can indicate an attempt to evade traditional file-based defenses, making it
+  crucial for SOC monitoring. If confirmed malicious, this technique could allow attackers
+  to maintain persistence, execute code, or manipulate system configurations without
+  leaving a conventional file footprint.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid| `drop_dm_object_name(Processes)` | join max=0 dest process_guid [| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry where Registry.registry_value_data=* by _time span=1h Registry.dest Registry.registry_path Registry.registry_value_name Registry.process_guid Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)` | eval reg_data_len = len(registry_value_data) | where reg_data_len > 512] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_registry_payload_injection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid| `drop_dm_object_name(Processes)`
+  | join max=0 dest process_guid [| tstats `security_content_summariesonly` count
+  from datamodel=Endpoint.Registry where Registry.registry_value_data=* by _time span=1h
+  Registry.dest Registry.registry_path Registry.registry_value_name Registry.process_guid
+  Registry.registry_value_data Registry.registry_key_name | `drop_dm_object_name(Registry)`
+  | eval reg_data_len = len(registry_value_data) | where reg_data_len > 512] | fields
+  firstTime lastTime dest user parent_process_name parent_process process_name process_path
+  process registry_key_name registry_path registry_value_name registry_value_data
+  process_guid | where isnotnull(registry_value_data)| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `windows_registry_payload_injection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown, possible custom scripting.
 references:
 - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations
@@ -22,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +63,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 100
-  message: The process $process_name$ added a suspicious length of registry data on $dest$.
+  message: The process $process_name$ added a suspicious length of registry data on
+    $dest$.
   mitre_attack_id:
   - T1027
   - T1027.011
@@ -56,25 +89,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - user
-  - dest
-  - process_id
-  - process_name
-  - process
-  - process_path
-  - parent_process_name
-  - parent_process
-  - process_guid
-  - registry_path
-  - registry_value_name
-  - registry_value_data
-  - registry_key_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_registry_sip_provider_modification.yml b/detections/endpoint/windows_registry_sip_provider_modification.yml
index 3595c99506..4b07a29853 100644
--- a/detections/endpoint/windows_registry_sip_provider_modification.yml
+++ b/detections/endpoint/windows_registry_sip_provider_modification.yml
@@ -8,10 +8,29 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic detects modifications to the Windows Registry SIP Provider. It leverages Sysmon EventID 7 to monitor registry changes in paths and values related to Cryptography Providers and OID Encoding Types. This activity is significant as it may indicate an attempt to subvert trust controls, a common tactic for bypassing security measures and maintaining persistence. If confirmed malicious, an attacker could manipulate the system's cryptographic functions, potentially leading to unauthorized access, data theft, or other damaging outcomes. Review the modified registry paths and concurrent processes to identify the attack source.
-search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\SOFTWARE\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\Microsoft\\Cryptography\\OID\\EncodingType*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\OID\\EncodingType*") Registry.registry_value_name IN ("Dll","$DLL") by Registry.dest , Registry.user Registry.registry_value_name, Registry.registry_value_data | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`| `windows_registry_sip_provider_modification_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: Be aware of potential false positives - legitimate applications may cause benign activities to be flagged.
+description: The following analytic detects modifications to the Windows Registry
+  SIP Provider. It leverages Sysmon EventID 7 to monitor registry changes in paths
+  and values related to Cryptography Providers and OID Encoding Types. This activity
+  is significant as it may indicate an attempt to subvert trust controls, a common
+  tactic for bypassing security measures and maintaining persistence. If confirmed
+  malicious, an attacker could manipulate the system's cryptographic functions, potentially
+  leading to unauthorized access, data theft, or other damaging outcomes. Review the
+  modified registry paths and concurrent processes to identify the attack source.
+search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name)
+  as registry_key_name values(Registry.registry_path) as registry_path min(_time)
+  as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path
+  IN ("*\\SOFTWARE\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\Microsoft\\Cryptography\\OID\\EncodingType*",
+  "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\Providers\\*", "*\\SOFTWARE\\WOW6432Node\\Microsoft\\Cryptography\\OID\\EncodingType*")
+  Registry.registry_value_name IN ("Dll","$DLL") by Registry.dest , Registry.user
+  Registry.registry_value_name, Registry.registry_value_data | `security_content_ctime(lastTime)`
+  | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`| `windows_registry_sip_provider_modification_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: Be aware of potential false positives - legitimate applications
+  may cause benign activities to be flagged.
 references:
 - https://attack.mitre.org/techniques/T1553/003/
 - https://github.com/SigmaHQ/sigma/blob/master/rules/windows/registry/registry_set/registry_set_sip_persistence.yml
@@ -25,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,15 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_value_name
-  - Registry.registry_value_data
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_regsvr32_renamed_binary.yml b/detections/endpoint/windows_regsvr32_renamed_binary.yml
index 576e130884..c4d5d4fff2 100644
--- a/detections/endpoint/windows_regsvr32_renamed_binary.yml
+++ b/detections/endpoint/windows_regsvr32_renamed_binary.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where the regsvr32.exe binary has been renamed and executed. This detection leverages Endpoint Detection and Response (EDR) data, specifically focusing on the original filename metadata. Renaming regsvr32.exe is significant as it can be an evasion technique used by attackers to bypass security controls. If confirmed malicious, this activity could allow an attacker to execute arbitrary DLLs, potentially leading to code execution, privilege escalation, or persistence within the environment.
+description: The following analytic identifies instances where the regsvr32.exe binary
+  has been renamed and executed. This detection leverages Endpoint Detection and Response
+  (EDR) data, specifically focusing on the original filename metadata. Renaming regsvr32.exe
+  is significant as it can be an evasion technique used by attackers to bypass security
+  controls. If confirmed malicious, this activity could allow an attacker to execute
+  arbitrary DLLs, potentially leading to code execution, privilege escalation, or
+  persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != regsvr32.exe AND Processes.original_file_name=regsvr32.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_regsvr32_renamed_binary_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name != regsvr32.exe
+  AND Processes.original_file_name=regsvr32.exe by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_regsvr32_renamed_binary_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://twitter.com/pr0xylife/status/1585612370441031680?s=46&t=Dc3CJi4AnM-8rNoacLbScg
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_3/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/qakbot/qbot_3/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml b/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml
index 1cf755450f..2fc861d989 100644
--- a/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml
+++ b/detections/endpoint/windows_remote_access_software_brc4_loaded_dll.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the loading of four specific Windows DLLs (credui.dll, dbghelp.dll, samcli.dll, winhttp.dll) by a non-standard process. This detection leverages Sysmon EventCode 7 to monitor DLL load events and flags when all four DLLs are loaded within a short time frame. This activity is significant as it may indicate the presence of Brute Ratel C4, a sophisticated remote access tool used for credential dumping and other malicious activities. If confirmed malicious, this behavior could lead to unauthorized access, credential theft, and further compromise of the affected system.
+description: The following analytic identifies the loading of four specific Windows
+  DLLs (credui.dll, dbghelp.dll, samcli.dll, winhttp.dll) by a non-standard process.
+  This detection leverages Sysmon EventCode 7 to monitor DLL load events and flags
+  when all four DLLs are loaded within a short time frame. This activity is significant
+  as it may indicate the presence of Brute Ratel C4, a sophisticated remote access
+  tool used for credential dumping and other malicious activities. If confirmed malicious,
+  this behavior could lead to unauthorized access, credential theft, and further compromise
+  of the affected system.
 data_source:
 - Sysmon EventID 7
-search: '`sysmon` EventCode=7 |bin _time span=30s | eval BRC4_AnomalyLoadedDll=case(OriginalFileName=="credui.dll", 1, OriginalFileName=="DBGHELP.DLL", 1, OriginalFileName=="SAMCLI.DLL", 1, OriginalFileName=="winhttp.dll", 1, 1=1, 0) | eval BRC4_LoadedDllPath=case(match(ImageLoaded, "credui.dll"), 1, match(ImageLoaded, "dbghelp.dll"), 1, match(ImageLoaded, "samcli.dll"), 1, match(ImageLoaded, "winhttp.dll"), 1, 1=1, 0) | stats count min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as ImageLoaded values(OriginalFileName) as OriginalFileName dc(ImageLoaded) as ImageLoadedCount by Image  BRC4_LoadedDllPath BRC4_AnomalyLoadedDll dest EventCode Signed | where  ImageLoadedCount == 4 AND (BRC4_LoadedDllPath == 1 OR BRC4_AnomalyLoadedDll == 1) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_access_software_brc4_loaded_dll_filter`'
-how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709 will add the ImageLoaded name to the process_name field, allowing this query to work. Use as an example and implement for other products.
-known_false_positives: This module can be loaded by a third party application. Filter is needed.
+search: '`sysmon` EventCode=7 |bin _time span=30s | eval BRC4_AnomalyLoadedDll=case(OriginalFileName=="credui.dll",
+  1, OriginalFileName=="DBGHELP.DLL", 1, OriginalFileName=="SAMCLI.DLL", 1, OriginalFileName=="winhttp.dll",
+  1, 1=1, 0) | eval BRC4_LoadedDllPath=case(match(ImageLoaded, "credui.dll"), 1, match(ImageLoaded,
+  "dbghelp.dll"), 1, match(ImageLoaded, "samcli.dll"), 1, match(ImageLoaded, "winhttp.dll"),
+  1, 1=1, 0) | stats count min(_time) as firstTime max(_time) as lastTime values(ImageLoaded)
+  as ImageLoaded values(OriginalFileName) as OriginalFileName dc(ImageLoaded) as ImageLoadedCount
+  by Image  BRC4_LoadedDllPath BRC4_AnomalyLoadedDll dest EventCode Signed | where  ImageLoadedCount
+  == 4 AND (BRC4_LoadedDllPath == 1 OR BRC4_AnomalyLoadedDll == 1) | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_remote_access_software_brc4_loaded_dll_filter`'
+how_to_implement: The latest Sysmon TA 3.0 https://splunkbase.splunk.com/app/5709
+  will add the ImageLoaded name to the process_name field, allowing this query to
+  work. Use as an example and implement for other products.
+known_false_positives: This module can be loaded by a third party application. Filter
+  is needed.
 references:
 - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/
 - https://www.mdsec.co.uk/2022/08/part-3-how-i-met-your-beacon-brute-ratel/
@@ -25,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: a process $Image$ loaded several modules $ImageLoaded$ that might related to credential access on $dest$.
+  message: a process $Image$ loaded several modules $ImageLoaded$ that might related
+    to credential access on $dest$.
   mitre_attack_id:
   - T1219
   - T1003
@@ -47,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/iso_version_dll_campaign/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_access_software_hunt.yml b/detections/endpoint/windows_remote_access_software_hunt.yml
index 1e0f792183..7c554b9b52 100644
--- a/detections/endpoint/windows_remote_access_software_hunt.yml
+++ b/detections/endpoint/windows_remote_access_software_hunt.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the use of remote access software within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This detection is significant as unauthorized remote access tools can be used by adversaries to maintain persistent access to compromised systems. If confirmed malicious, this activity could allow attackers to remotely control systems, exfiltrate data, or further infiltrate the network. Review the identified software to ensure it is authorized and take action against any unauthorized utilities.
+description: The following analytic identifies the use of remote access software within
+  the environment. It leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process execution logs. This detection is significant as unauthorized
+  remote access tools can be used by adversaries to maintain persistent access to
+  compromised systems. If confirmed malicious, this activity could allow attackers
+  to remotely control systems, exfiltrate data, or further infiltrate the network.
+  Review the identified software to ensure it is authorized and take action against
+  any unauthorized utilities.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown by Processes.dest Processes.user Processes.process_name Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup remote_access_software remote_utility AS process_name OUTPUT isutility | search isutility = True | `windows_remote_access_software_hunt_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be found. Filter as needed and create higher fidelity analytics based off banned remote access software.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(Processes.process) as process values(Processes.parent_process)
+  as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown
+  Processes.user!=unknown by Processes.dest Processes.user Processes.process_name
+  Processes.process | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `drop_dm_object_name(Processes)` | lookup remote_access_software remote_utility
+  AS process_name OUTPUT isutility | search isutility = True | `windows_remote_access_software_hunt_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be found. Filter as needed and create
+  higher fidelity analytics based off banned remote access software.
 references:
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1219/T1219.md
@@ -41,22 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_access_software_rms_registry.yml b/detections/endpoint/windows_remote_access_software_rms_registry.yml
index 81cc21fc88..82b504df03 100644
--- a/detections/endpoint/windows_remote_access_software_rms_registry.yml
+++ b/detections/endpoint/windows_remote_access_software_rms_registry.yml
@@ -5,13 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation or modification of Windows registry entries related to the Remote Manipulator System (RMS) Remote Admin tool. It leverages data from the Endpoint.Registry datamodel, focusing on registry paths containing "SYSTEM\\Remote Manipulator System." This activity is significant because RMS, while legitimate, is often abused by adversaries, such as in the Azorult malware campaigns, to gain unauthorized remote access. If confirmed malicious, this could allow attackers to remotely control the targeted host, leading to potential data exfiltration, system manipulation, or further network compromise.
+description: The following analytic detects the creation or modification of Windows
+  registry entries related to the Remote Manipulator System (RMS) Remote Admin tool.
+  It leverages data from the Endpoint.Registry datamodel, focusing on registry paths
+  containing "SYSTEM\\Remote Manipulator System." This activity is significant because
+  RMS, while legitimate, is often abused by adversaries, such as in the Azorult malware
+  campaigns, to gain unauthorized remote access. If confirmed malicious, this could
+  allow attackers to remotely control the targeted host, leading to potential data
+  exfiltration, system manipulation, or further network compromise.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\Remote Manipulator System*" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_access_software_rms_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\Remote
+  Manipulator System*" by Registry.registry_key_name Registry.user Registry.registry_path
+  Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_access_software_rms_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.rms
@@ -21,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_assistance_spawning_process.yml b/detections/endpoint/windows_remote_assistance_spawning_process.yml
index f04b078277..6308be250d 100644
--- a/detections/endpoint/windows_remote_assistance_spawning_process.yml
+++ b/detections/endpoint/windows_remote_assistance_spawning_process.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects Microsoft Remote Assistance (msra.exe) spawning PowerShell.exe or cmd.exe as a child process. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where msra.exe is the parent process. This activity is significant because msra.exe typically does not spawn command-line interfaces, indicating potential process injection or misuse. If confirmed malicious, an attacker could use this technique to execute arbitrary commands, escalate privileges, or maintain persistence on the compromised system.
+description: The following analytic detects Microsoft Remote Assistance (msra.exe)
+  spawning PowerShell.exe or cmd.exe as a child process. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process creation
+  events where msra.exe is the parent process. This activity is significant because
+  msra.exe typically does not spawn command-line interfaces, indicating potential
+  process injection or misuse. If confirmed malicious, an attacker could use this
+  technique to execute arbitrary commands, escalate privileges, or maintain persistence
+  on the compromised system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msra.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_assistance_spawning_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, filter as needed. Add additional shells as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=msra.exe
+  `windows_shells` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_remote_assistance_spawning_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, filter as needed. Add additional
+  shells as needed.
 references:
 - https://thedfirreport.com/2022/02/07/qbot-likes-to-move-it-move-it/
 - https://app.any.run/tasks/ca1616de-89a1-4afc-a3e4-09d428df2420/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$, generating behavior not common with msra.exe.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$, generating behavior not common with msra.exe.
   mitre_attack_id:
   - T1055
   observable:
@@ -51,23 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/msra/msra-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/msra/msra-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_create_service.yml b/detections/endpoint/windows_remote_create_service.yml
index 6891a790d8..2fcb0977a6 100644
--- a/detections/endpoint/windows_remote_create_service.yml
+++ b/detections/endpoint/windows_remote_create_service.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the creation of a new service on a remote endpoint using sc.exe. It leverages data from Endpoint Detection and Response (EDR) agents, specifically monitoring for EventCode 7045, which indicates a new service creation. This activity is significant as it may indicate lateral movement or remote code execution attempts by an attacker. If confirmed malicious, this could allow the attacker to establish persistence, escalate privileges, or execute arbitrary code on the remote system, potentially leading to further compromise of the network.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process IN ("*create*")  Processes.process="*\\\\*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_create_service_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Note that false positives may occur, and filtering may be necessary, especially when it comes to remote service creation by administrators or software management utilities.
+description: The following analytic identifies the creation of a new service on a
+  remote endpoint using sc.exe. It leverages data from Endpoint Detection and Response
+  (EDR) agents, specifically monitoring for EventCode 7045, which indicates a new
+  service creation. This activity is significant as it may indicate lateral movement
+  or remote code execution attempts by an attacker. If confirmed malicious, this could
+  allow the attacker to establish persistence, escalate privileges, or execute arbitrary
+  code on the remote system, potentially leading to further compromise of the network.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe
+  Processes.process IN ("*create*")  Processes.process="*\\\\*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_create_service_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Note that false positives may occur, and filtering may be necessary,
+  especially when it comes to remote service creation by administrators or software
+  management utilities.
 references:
 - https://attack.mitre.org/techniques/T1543/003/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to create a remote service.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to create a remote service.
   mitre_attack_id:
   - T1543
   - T1543.003
@@ -57,23 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remote_service_create_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remote_service_create_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml b/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml
index 7bab51cc3f..04cbbc590a 100644
--- a/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml
+++ b/detections/endpoint/windows_remote_service_rdpwinst_tool_execution.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of the RDPWInst.exe tool, which is an RDP wrapper library used to enable remote desktop host support and concurrent RDP sessions. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, original file names, and specific command-line arguments. This activity is significant because adversaries can abuse this tool to establish unauthorized RDP connections, facilitating remote access and potential lateral movement within the network. If confirmed malicious, this could lead to unauthorized access, data exfiltration, and further compromise of the targeted host.
+description: The following analytic detects the execution of the RDPWInst.exe tool,
+  which is an RDP wrapper library used to enable remote desktop host support and concurrent
+  RDP sessions. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names, original file names, and specific command-line
+  arguments. This activity is significant because adversaries can abuse this tool
+  to establish unauthorized RDP connections, facilitating remote access and potential
+  lateral movement within the network. If confirmed malicious, this could lead to
+  unauthorized access, data exfiltration, and further compromise of the targeted host.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="RDPWInst.exe" OR Processes.original_file_name="RDPWInst.exe") AND Processes.process IN ("* -i*", "* -s*", "* -o*", "* -w*", "* -r*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_service_rdpwinst_tool_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This tool was designed for home usage and not commonly seen in production environment. Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name="RDPWInst.exe" OR Processes.original_file_name="RDPWInst.exe")
+  AND Processes.process IN ("* -i*", "* -s*", "* -o*", "* -w*", "* -r*") by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_service_rdpwinst_tool_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This tool was designed for home usage and not commonly seen
+  in production environment. Filter as needed.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml b/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml
index 3ce370191e..743443d00d 100644
--- a/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml
+++ b/detections/endpoint/windows_remote_services_allow_rdp_in_firewall.yml
@@ -5,14 +5,38 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects modifications to the Windows firewall to enable Remote Desktop Protocol (RDP) on a targeted machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "netsh.exe" to allow TCP port 3389. This activity is significant as it may indicate an adversary attempting to gain remote access to a compromised host, a common tactic for lateral movement. If confirmed malicious, this could allow attackers to remotely control the system, leading to potential data exfiltration or further network compromise.
+description: The following analytic detects modifications to the Windows firewall
+  to enable Remote Desktop Protocol (RDP) on a targeted machine. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  involving "netsh.exe" to allow TCP port 3389. This activity is significant as it
+  may indicate an adversary attempting to gain remote access to a compromised host,
+  a common tactic for lateral movement. If confirmed malicious, this could allow attackers
+  to remotely control the system, leading to potential data exfiltration or further
+  network compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "netsh.exe" OR Processes.original_file_name= "netsh.exe") AND Processes.process = "*firewall*" AND Processes.process = "*add*" AND Processes.process = "*protocol=TCP*" AND Processes.process = "*localport=3389*" AND Processes.process = "*action=allow*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_rdp_in_firewall_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as cmdline
+  values(Processes.parent_process_name) as parent_process values(Processes.process_name)
+  count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name = "netsh.exe" OR Processes.original_file_name= "netsh.exe")
+  AND Processes.process = "*firewall*" AND Processes.process = "*add*" AND Processes.process
+  = "*protocol=TCP*" AND Processes.process = "*localport=3389*" AND Processes.process
+  = "*action=allow*" by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_rdp_in_firewall_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 drilldown_searches:
@@ -21,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,20 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_services_allow_remote_assistance.yml b/detections/endpoint/windows_remote_services_allow_remote_assistance.yml
index 989ecbb1db..6a48b8c98c 100644
--- a/detections/endpoint/windows_remote_services_allow_remote_assistance.yml
+++ b/detections/endpoint/windows_remote_services_allow_remote_assistance.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects modifications in the Windows registry to enable remote desktop assistance on a targeted machine. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "Control\\Terminal Server\\fAllowToGetHelp" registry path. This activity is significant because enabling remote assistance via registry is uncommon and often associated with adversaries or malware like Azorult. If confirmed malicious, this could allow an attacker to remotely access and control the compromised host, leading to potential data exfiltration or further system compromise.
+description: The following analytic detects modifications in the Windows registry
+  to enable remote desktop assistance on a targeted machine. It leverages data from
+  the Endpoint.Registry datamodel, specifically monitoring changes to the "Control\\Terminal
+  Server\\fAllowToGetHelp" registry path. This activity is significant because enabling
+  remote assistance via registry is uncommon and often associated with adversaries
+  or malware like Azorult. If confirmed malicious, this could allow an attacker to
+  remotely access and control the compromised host, leading to potential data exfiltration
+  or further system compromise.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fAllowToGetHelp*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_allow_remote_assistance_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal
+  Server\\fAllowToGetHelp*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_remote_services_allow_remote_assistance_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://docs.microsoft.com/en-us/windows-hardware/customize/desktop/unattend/microsoft-windows-remoteassistance-exe-fallowtogethelp
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_remote_services_rdp_enable.yml b/detections/endpoint/windows_remote_services_rdp_enable.yml
index f1818abc01..bdf507211d 100644
--- a/detections/endpoint/windows_remote_services_rdp_enable.yml
+++ b/detections/endpoint/windows_remote_services_rdp_enable.yml
@@ -5,13 +5,29 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects modifications in the Windows registry to enable Remote Desktop Protocol (RDP) on a targeted machine. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to the "fDenyTSConnections" registry value. This activity is significant as enabling RDP via registry is uncommon and often associated with adversaries or malware attempting to gain remote access. If confirmed malicious, this could allow attackers to remotely control the compromised host, potentially leading to further exploitation and lateral movement within the network.
+description: The following analytic detects modifications in the Windows registry
+  to enable Remote Desktop Protocol (RDP) on a targeted machine. It leverages data
+  from the Endpoint.Registry datamodel, specifically monitoring changes to the "fDenyTSConnections"
+  registry value. This activity is significant as enabling RDP via registry is uncommon
+  and often associated with adversaries or malware attempting to gain remote access.
+  If confirmed malicious, this could allow attackers to remotely control the compromised
+  host, potentially leading to further exploitation and lateral movement within the
+  network.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_remote_services_rdp_enable_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: administrators may enable or disable this feature that may cause some false positive.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Control\\Terminal
+  Server\\fDenyTSConnections*" Registry.registry_value_data="0x00000000" by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.action
+  Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_remote_services_rdp_enable_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: administrators may enable or disable this feature that may
+  cause some false positive.
 references:
 - https://www.hybrid-analysis.com/sample/9d6611c2779316f1ef4b4a6edcfdfb5e770fe32b31ec2200df268c3bd236ed75?environmentId=100
 drilldown_searches:
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.action
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_replication_through_removable_media.yml b/detections/endpoint/windows_replication_through_removable_media.yml
index baf7a49a3a..9f26a22464 100644
--- a/detections/endpoint/windows_replication_through_removable_media.yml
+++ b/detections/endpoint/windows_replication_through_removable_media.yml
@@ -5,12 +5,32 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation or dropping of executable or script files in the root directory of a removable drive. It leverages data from the Endpoint.Filesystem datamodel, focusing on specific file types and their creation paths. This activity is significant as it may indicate an attempt to spread malware, such as ransomware, via removable media. If confirmed malicious, this behavior could lead to unauthorized code execution, lateral movement, or persistence within the network, potentially compromising sensitive data and systems.
+description: The following analytic detects the creation or dropping of executable
+  or script files in the root directory of a removable drive. It leverages data from
+  the Endpoint.Filesystem datamodel, focusing on specific file types and their creation
+  paths. This activity is significant as it may indicate an attempt to spread malware,
+  such as ransomware, via removable media. If confirmed malicious, this behavior could
+  lead to unauthorized code execution, lateral movement, or persistence within the
+  network, potentially compromising sensitive data and systems.
 data_source:
 - Sysmon EventID 11
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name = *.js  OR Filesystem.file_name= *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.user Filesystem.dest | `drop_dm_object_name(Filesystem)` | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) | where LIKE(root_drive, "%:") AND dropped_file_path_split_count = 2  AND root_drive!= "C:" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_replication_through_removable_media_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: Administrators may allow creation of script or exe in the paths specified. Filter as needed.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe
+  OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name
+  = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name
+  = *.js  OR Filesystem.file_name= *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name
+  = *.pif) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name
+  Filesystem.file_path Filesystem.user Filesystem.dest | `drop_dm_object_name(Filesystem)`
+  | eval dropped_file_path = split(file_path, "\\") | eval dropped_file_path_split_count
+  = mvcount(dropped_file_path) | eval root_drive = mvindex(dropped_file_path,0) |
+  where LIKE(root_drive, "%:") AND dropped_file_path_split_count = 2  AND root_drive!=
+  "C:" | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_replication_through_removable_media_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+known_false_positives: Administrators may allow creation of script or exe in the paths
+  specified. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1204/002/
 - https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia
@@ -20,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: executable or script $file_path$ was dropped in root drive $root_drive$ in $dest$
+  message: executable or script $file_path$ was dropped in root drive $root_drive$
+    in $dest$
   mitre_attack_id:
   - T1091
   observable:
@@ -47,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_path
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/spread_in_root_drives/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/spread_in_root_drives/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_root_domain_linked_policies_discovery.yml b/detections/endpoint/windows_root_domain_linked_policies_discovery.yml
index 0cad0ab741..6278031200 100644
--- a/detections/endpoint/windows_root_domain_linked_policies_discovery.yml
+++ b/detections/endpoint/windows_root_domain_linked_policies_discovery.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for root domain linked policies. It leverages PowerShell Script Block Logging (EventCode=4104) to identify this activity. This behavior is significant as it may indicate an attempt by adversaries or Red Teams to gain situational awareness and perform Active Directory Discovery. If confirmed malicious, this activity could allow attackers to map out domain policies, potentially aiding in further exploitation or lateral movement within the network.
+description: The following analytic detects the use of the `[Adsisearcher]` type accelerator
+  in PowerShell to query Active Directory for root domain linked policies. It leverages
+  PowerShell Script Block Logging (EventCode=4104) to identify this activity. This
+  behavior is significant as it may indicate an attempt by adversaries or Red Teams
+  to gain situational awareness and perform Active Directory Discovery. If confirmed
+  malicious, this activity could allow attackers to map out domain policies, potentially
+  aiding in further exploitation or lateral movement within the network.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText = "*.SearchRooT*" ScriptBlockText = "*.gplink*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_root_domain_linked_policies_discovery_filter`'
-how_to_implement: The following Hunting analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*[adsisearcher]*" ScriptBlockText
+  = "*.SearchRooT*" ScriptBlockText = "*.gplink*" | stats count min(_time) as firstTime
+  max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | rename Computer
+  as dest, user_id as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_root_domain_linked_policies_discovery_filter`'
+how_to_implement: The following Hunting analytic requires PowerShell operational logs
+  to be imported. Modify the powershell macro as needed to match the sourcetype or
+  add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://www.welivesecurity.com/2022/04/12/industroyer2-industroyer-reloaded/
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Windows PowerShell [Adsisearcher] was used user enumeration on endpoint $dest$
+  message: Windows PowerShell [Adsisearcher] was used user enumeration on endpoint
+    $dest$
   mitre_attack_id:
   - T1087.002
   - T1087
@@ -44,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - ScriptBlockText
-  - Computer
-  - user_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml1.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1087.002/adsi_discovery/windows-powershell-xml1.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml b/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml
index a0f391b919..ba3f44f963 100644
--- a/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml
+++ b/detections/endpoint/windows_rundll32_apply_user_settings_changes.yml
@@ -9,9 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of a suspicious rundll32 command line that updates user-specific system parameters, such as desktop backgrounds, display settings, and visual themes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "user32.dll,UpdatePerUserSystemParameters." This activity is significant as it is uncommon for legitimate purposes and has been observed in Rhysida Ransomware for defense evasion. If confirmed malicious, this could allow an attacker to disguise activities or make unauthorized system changes, potentially leading to persistent unauthorized access.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process= "*user32.dll,UpdatePerUserSystemParameters*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rundll32_apply_user_settings_changes_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic detects the execution of a suspicious rundll32
+  command line that updates user-specific system parameters, such as desktop backgrounds,
+  display settings, and visual themes. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on command-line executions involving "user32.dll,UpdatePerUserSystemParameters."
+  This activity is significant as it is uncommon for legitimate purposes and has been
+  observed in Rhysida Ransomware for defense evasion. If confirmed malicious, this
+  could allow an attacker to disguise activities or make unauthorized system changes,
+  potentially leading to persistent unauthorized access.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process= "*user32.dll,UpdatePerUserSystemParameters*" by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.parent_process_name |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_rundll32_apply_user_settings_changes_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-319a
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/update_per_user_system/rundll32_updateperusersystem.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/update_per_user_system/rundll32_updateperusersystem.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_rundll32_webdav_request.yml b/detections/endpoint/windows_rundll32_webdav_request.yml
index 7fdc333980..535c27bd24 100644
--- a/detections/endpoint/windows_rundll32_webdav_request.yml
+++ b/detections/endpoint/windows_rundll32_webdav_request.yml
@@ -9,10 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies the execution of rundll32.exe with command-line arguments loading davclnt.dll and the davsetcookie function to access a remote WebDAV instance. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an attempt to exploit CVE-2023-23397, a known vulnerability. If confirmed malicious, this could allow an attacker to execute remote code or exfiltrate data, posing a severe threat to the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*","*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rundll32_webdav_request_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present based on legitimate software, filtering may need to occur.
+description: The following analytic identifies the execution of rundll32.exe with
+  command-line arguments loading davclnt.dll and the davsetcookie function to access
+  a remote WebDAV instance. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line executions.
+  This activity is significant as it may indicate an attempt to exploit CVE-2023-23397,
+  a known vulnerability. If confirmed malicious, this could allow an attacker to execute
+  remote code or exfiltrate data, posing a severe threat to the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe
+  Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*","*\\windows\\syswow64\\davclnt.dll,*davsetcookie*")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_rundll32_webdav_request_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present based on legitimate software,
+  filtering may need to occur.
 references:
 - https://strontic.github.io/xcyclopedia/library/davclnt.dll-0EA3050E7CC710526E330C413C165DA0.html
 - https://twitter.com/ACEResponder/status/1636116096506818562?s=20
@@ -25,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +61,8 @@ tags:
   - CVE-2023-23397
   confidence: 60
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server.
   mitre_attack_id:
   - T1048.003
   observable:
@@ -60,23 +86,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml b/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml
index 2140021938..e0be560a53 100644
--- a/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml
+++ b/detections/endpoint/windows_rundll32_webdav_with_network_connection.yml
@@ -6,10 +6,36 @@ author: Michael Haag, Splunk
 type: TTP
 status: experimental
 data_source: []
-description: The following analytic detects the execution of rundll32.exe with command-line arguments loading davclnt.dll and the davsetcookie function to access a remote WebDav instance. It uses data from Endpoint Detection and Response (EDR) agents, correlating process execution and network traffic data. This activity is significant as it may indicate exploitation of CVE-2023-23397, a known vulnerability. If confirmed malicious, this could allow an attacker to establish unauthorized remote connections, potentially leading to data exfiltration or further network compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name=svchost.exe `process_rundll32` Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*", "*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") by host _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename dest as src | join host process_id [ | tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port!=0 NOT (All_Traffic.dest_ip IN (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16))  by host All_Traffic.process_id | `drop_dm_object_name(All_Traffic)`] | `windows_rundll32_webdav_with_network_connection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present based on legitimate software, filtering may need to occur.
+description: The following analytic detects the execution of rundll32.exe with command-line
+  arguments loading davclnt.dll and the davsetcookie function to access a remote WebDav
+  instance. It uses data from Endpoint Detection and Response (EDR) agents, correlating
+  process execution and network traffic data. This activity is significant as it may
+  indicate exploitation of CVE-2023-23397, a known vulnerability. If confirmed malicious,
+  this could allow an attacker to establish unauthorized remote connections, potentially
+  leading to data exfiltration or further network compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name=svchost.exe
+  `process_rundll32` Processes.process IN ("*\\windows\\system32\\davclnt.dll,*davsetcookie*",
+  "*\\windows\\syswow64\\davclnt.dll,*davsetcookie*") by host _time span=1h Processes.process_id
+  Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+  Processes.parent_process | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | rename dest as src | join host process_id
+  [ | tstats `security_content_summariesonly` count latest(All_Traffic.dest) as dest
+  latest(All_Traffic.dest_ip) as dest_ip latest(All_Traffic.dest_port) as dest_port
+  FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port!=0 NOT (All_Traffic.dest_ip
+  IN (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16))  by host All_Traffic.process_id
+  | `drop_dm_object_name(All_Traffic)`] | `windows_rundll32_webdav_with_network_connection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present based on legitimate software,
+  filtering may need to occur.
 references:
 - https://strontic.github.io/xcyclopedia/library/davclnt.dll-0EA3050E7CC710526E330C413C165DA0.html
 - https://twitter.com/ACEResponder/status/1636116096506818562?s=20
@@ -24,7 +50,8 @@ tags:
   - CVE-2023-23397
   confidence: 60
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to contact a remote WebDav server.
   mitre_attack_id:
   - T1048.003
   observable:
@@ -48,26 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - All_Traffic.dest_port
-  - All_Traffic.dest_ip
-  - All_Traffic.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/cve-2023-23397/webdav_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_scheduled_task_created_via_xml.yml b/detections/endpoint/windows_scheduled_task_created_via_xml.yml
index 4416a73c5b..b35db15f82 100644
--- a/detections/endpoint/windows_scheduled_task_created_via_xml.yml
+++ b/detections/endpoint/windows_scheduled_task_created_via_xml.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the creation of scheduled tasks in Windows using schtasks.exe with the -create flag and an XML parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it is a common technique for establishing persistence or achieving privilege escalation, often used by malware like Trickbot and Winter-Vivern. If confirmed malicious, this could allow attackers to maintain access, execute additional payloads, and potentially lead to data theft or ransomware deployment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process=*create* Processes.process="* /xml *" by Processes.user  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_guid Processes.process_id Processes.parent_process_guid Processes.dest | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_created_via_xml_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible scripts or administrators may trigger this analytic. Filter as needed based on parent process, application.
+description: The following analytic detects the creation of scheduled tasks in Windows
+  using schtasks.exe with the -create flag and an XML parameter. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  executions and process details. This activity is significant as it is a common technique
+  for establishing persistence or achieving privilege escalation, often used by malware
+  like Trickbot and Winter-Vivern. If confirmed malicious, this could allow attackers
+  to maintain access, execute additional payloads, and potentially lead to data theft
+  or ransomware deployment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe
+  Processes.process=*create* Processes.process="* /xml *" by Processes.user  Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process Processes.process_guid
+  Processes.process_id Processes.parent_process_guid Processes.dest | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_created_via_xml_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible scripts or administrators may trigger this analytic.
+  Filter as needed based on parent process, application.
 references:
 - https://twitter.com/_CERT_UA/status/1620781684257091584
 - https://cert.gov.ua/article/3761104
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A scheduled task process, $process_name$, with 'create' or 'delete' commands present in the command line.
+  message: A scheduled task process, $process_name$, with 'create' or 'delete' commands
+    present in the command line.
   mitre_attack_id:
   - T1053.005
   - T1053
@@ -51,21 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process
-  - Processes.dest
-  - Processes.user
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/scheduledtask/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/scheduledtask/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml b/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml
index e3e79527be..578d97d86a 100644
--- a/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml
+++ b/detections/endpoint/windows_scheduled_task_dll_module_loaded.yml
@@ -7,10 +7,26 @@ data_source:
 - Sysmon EventID 7
 type: TTP
 status: production
-description: The following analytic detects instances where the taskschd.dll is loaded by processes running in suspicious or writable directories. This activity is unusual, as legitimate processes that load taskschd.dll typically reside in protected system locations. Malware or threat actors may attempt to load this DLL from writable or non-standard directories to manipulate the Task Scheduler and execute malicious tasks. By identifying processes that load taskschd.dll in these unsafe locations, this detection helps security analysts flag potentially malicious activity and investigate further to prevent unauthorized system modifications.
-search: '`sysmon` EventCode=7 Image IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*", "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*", "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*", "\\Windows\\repair\\*", "*\\temp\\*", "*\\PerfLogs\\*") ImageLoaded = "*\\taskschd.dll" | stats min(_time) as firstTime max(_time) as lastTime count by user_id, dest, Image ,ImageLoaded, , OriginalFileName, ProcessGuid | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_dll_module_loaded_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Third party Legitimate application may load this task schedule dll module.
+description: The following analytic detects instances where the taskschd.dll is loaded
+  by processes running in suspicious or writable directories. This activity is unusual,
+  as legitimate processes that load taskschd.dll typically reside in protected system
+  locations. Malware or threat actors may attempt to load this DLL from writable or
+  non-standard directories to manipulate the Task Scheduler and execute malicious
+  tasks. By identifying processes that load taskschd.dll in these unsafe locations,
+  this detection helps security analysts flag potentially malicious activity and investigate
+  further to prevent unauthorized system modifications.
+search: '`sysmon` EventCode=7 Image IN ("*\\windows\\fonts\\*", "*\\windows\\temp\\*",
+  "*\\users\\public\\*", "*\\windows\\debug\\*", "*\\Users\\Administrator\\Music\\*",
+  "*\\Windows\\servicing\\*", "*\\Users\\Default\\*", "*Recycle.bin*", "*\\Windows\\Media\\*",
+  "\\Windows\\repair\\*", "*\\temp\\*", "*\\PerfLogs\\*") ImageLoaded = "*\\taskschd.dll"
+  | stats min(_time) as firstTime max(_time) as lastTime count by user_id, dest, Image
+  ,ImageLoaded, , OriginalFileName, ProcessGuid | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_scheduled_task_dll_module_loaded_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: Third party Legitimate application may load this task schedule
+  dll module.
 references:
 - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape
 - https://www.fortinet.com/blog/threat-research/valleyrat-campaign-targeting-chinese-speakers
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,19 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - process_name
-  - dest
-  - EventCode
-  - Signed
-  - ProcessId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/taskschd_dll/taskschd_dll.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/taskschd_dll/taskschd_dll.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml b/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml
index 6b99cdff6d..cdb048556c 100644
--- a/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml
+++ b/detections/endpoint/windows_scheduled_task_service_spawned_shell.yml
@@ -5,13 +5,37 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when the Task Scheduler service ("svchost.exe -k netsvcs -p -s Schedule") spawns common command line, scripting, or shell execution binaries such as "powershell.exe" or "cmd.exe". This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as attackers often abuse the Task Scheduler for execution and persistence, blending in with legitimate Windows operations. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, or escalate privileges within the environment.
+description: The following analytic detects when the Task Scheduler service ("svchost.exe
+  -k netsvcs -p -s Schedule") spawns common command line, scripting, or shell execution
+  binaries such as "powershell.exe" or "cmd.exe". This detection leverages data from
+  Endpoint Detection and Response (EDR) agents, focusing on process and parent process
+  relationships. This activity is significant as attackers often abuse the Task Scheduler
+  for execution and persistence, blending in with legitimate Windows operations. If
+  confirmed malicious, this could allow attackers to execute arbitrary code, maintain
+  persistence, or escalate privileges within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*\\system32\\svchost.exe*" AND Processes.parent_process="*-k*" AND Processes.parent_process= "*netsvcs*" AND Processes.parent_process="*-p*" AND Processes.parent_process="*-s*" AND Processes.parent_process="*Schedule*" Processes.process_name IN("powershell.exe", "wscript.exe", "cscript.exe", "cmd.exe", "sh.exe", "ksh.exe", "zsh.exe", "bash.exe", "scrcons.exe","pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_service_spawned_shell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*\\system32\\svchost.exe*"
+  AND Processes.parent_process="*-k*" AND Processes.parent_process= "*netsvcs*" AND
+  Processes.parent_process="*-p*" AND Processes.parent_process="*-s*" AND Processes.parent_process="*Schedule*"
+  Processes.process_name IN("powershell.exe", "wscript.exe", "cscript.exe", "cmd.exe",
+  "sh.exe", "ksh.exe", "zsh.exe", "bash.exe", "scrcons.exe","pwsh.exe") by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id Processes.parent_process_name |
+  `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_scheduled_task_service_spawned_shell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Unknown, possible custom scripting.
 references:
 - https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations
@@ -23,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 25
   impact: 80
-  message: A windows scheduled task spawned the shell application $process_name$ on $dest$.
+  message: A windows scheduled task spawned the shell application $process_name$ on
+    $dest$.
   mitre_attack_id:
   - T1053.005
   - T1059
@@ -53,20 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml b/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml
index fe964506a9..14f68acb56 100644
--- a/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml
+++ b/detections/endpoint/windows_scheduled_task_with_highest_privileges.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a new scheduled task with the highest execution privileges via Schtasks.exe. It leverages Endpoint Detection and Response (EDR) logs to monitor for specific command-line parameters ('/rl' and 'highest') in schtasks.exe executions. This activity is significant as it is commonly used in AsyncRAT attacks for persistence and privilege escalation. If confirmed malicious, this could allow an attacker to maintain persistent access and execute tasks with elevated privileges, potentially leading to unauthorized system access and data breaches.
+description: The following analytic detects the creation of a new scheduled task with
+  the highest execution privileges via Schtasks.exe. It leverages Endpoint Detection
+  and Response (EDR) logs to monitor for specific command-line parameters ('/rl' and
+  'highest') in schtasks.exe executions. This activity is significant as it is commonly
+  used in AsyncRAT attacks for persistence and privilege escalation. If confirmed
+  malicious, this could allow an attacker to maintain persistent access and execute
+  tasks with elevated privileges, potentially leading to unauthorized system access
+  and data breaches.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/rl *" Processes.process = "* highest *" by Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_task_with_highest_privileges_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may arise from legitimate applications that create tasks to run as SYSTEM. Therefore, it's recommended to adjust filters based on parent process or modify the query to include world writable paths for restriction.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe"
+  Processes.process = "*/rl *" Processes.process = "* highest *" by Processes.process_name
+  Processes.parent_process_name Processes.parent_process Processes.process Processes.process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_scheduled_task_with_highest_privileges_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may arise from legitimate applications that
+  create tasks to run as SYSTEM. Therefore, it's recommended to adjust filters based
+  on parent process or modify the query to include world writable paths for restriction.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: a $process_name$ creating a schedule task $process$ with highest run level privilege in $dest$
+  message: a $process_name$ creating a schedule task $process$ with highest run level
+    privilege in $dest$
   mitre_attack_id:
   - T1053
   - T1053.005
@@ -46,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/asyncrat_highest_priv_schtasks/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/asyncrat_highest_priv_schtasks/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml b/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml
index 0111b8b453..a68c37a13e 100644
--- a/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml
+++ b/detections/endpoint/windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr.yml
@@ -7,9 +7,23 @@ data_source:
 - Windows Security 4698
 type: TTP
 status: production
-description: The following analytic detects the creation or modification of Windows Scheduled Tasks related to CompMgmtLauncher or Eventvwr. These legitimate system utilities, used for launching the Computer Management Console and Event Viewer, can be abused by attackers to execute malicious payloads under the guise of normal system processes. By leveraging these tasks, adversaries can establish persistence or elevate privileges without raising suspicion. This detection helps security analysts identify unusual or unauthorized scheduled tasks involving these executables, allowing for timely investigation and remediation of potential threats.
-search: '`wineventlog_security` EventCode=4698 TaskContent = "*&lt;Command&gt;C:\\Windows\\System32\\CompMgmtLauncher.exe&lt;/Command&gt;*" OR TaskContent = "*&lt;Command&gt;C:\\Windows\\System32\\zh-CN\\eventvwr.msc&lt;/Command&gt;*" OR TaskContent = "*&lt;Command&gt;C:\\Windows\\System32\\eventvwr.msc&lt;/Command&gt;*" | stats count min(_time) as firstTime max(_time) as lastTime by dest action EventData_Xml TaskContent TaskName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well as the URL ToolBox application are also required.
+description: The following analytic detects the creation or modification of Windows
+  Scheduled Tasks related to CompMgmtLauncher or Eventvwr. These legitimate system
+  utilities, used for launching the Computer Management Console and Event Viewer,
+  can be abused by attackers to execute malicious payloads under the guise of normal
+  system processes. By leveraging these tasks, adversaries can establish persistence
+  or elevate privileges without raising suspicion. This detection helps security analysts
+  identify unusual or unauthorized scheduled tasks involving these executables, allowing
+  for timely investigation and remediation of potential threats.
+search: '`wineventlog_security` EventCode=4698 TaskContent = "*&lt;Command&gt;C:\\Windows\\System32\\CompMgmtLauncher.exe&lt;/Command&gt;*"
+  OR TaskContent = "*&lt;Command&gt;C:\\Windows\\System32\\zh-CN\\eventvwr.msc&lt;/Command&gt;*"
+  OR TaskContent = "*&lt;Command&gt;C:\\Windows\\System32\\eventvwr.msc&lt;/Command&gt;*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest action EventData_Xml
+  TaskContent TaskName | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_scheduled_tasks_for_compmgmtlauncher_or_eventvwr_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4698 EventCode enabled. The Windows TA as well
+  as the URL ToolBox application are also required.
 known_false_positives: unknown
 references:
 - https://www.proofpoint.com/us/blog/threat-insight/chinese-malware-appears-earnest-across-cybercrime-threat-landscape
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,16 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - dest
-  - action
-  - EventData_Xml
-  - TaskContent
-  - TaskName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/valleyrat_schedtask/valleyrat_schedtask.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053/valleyrat_schedtask/valleyrat_schedtask.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_schtasks_create_run_as_system.yml b/detections/endpoint/windows_schtasks_create_run_as_system.yml
index 7c2be6b736..e6d7c8fda4 100644
--- a/detections/endpoint/windows_schtasks_create_run_as_system.yml
+++ b/detections/endpoint/windows_schtasks_create_run_as_system.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a new scheduled task using Schtasks.exe to run as the SYSTEM user. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it often indicates an attempt to gain elevated privileges or maintain persistence within the environment. If confirmed malicious, an attacker could execute code with SYSTEM-level privileges, potentially leading to data theft, ransomware deployment, or further system compromise. Immediate investigation and mitigation are crucial to prevent further damage.
+description: The following analytic detects the creation of a new scheduled task using
+  Schtasks.exe to run as the SYSTEM user. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on command-line executions and process
+  details. This activity is significant as it often indicates an attempt to gain elevated
+  privileges or maintain persistence within the environment. If confirmed malicious,
+  an attacker could execute code with SYSTEM-level privileges, potentially leading
+  to data theft, ransomware deployment, or further system compromise. Immediate investigation
+  and mitigation are crucial to prevent further damage.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_schtasks` Processes.process="*/create *" AND Processes.process="*/ru *" AND Processes.process="*system*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_schtasks_create_run_as_system_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be limited to legitimate applications creating a task to run as SYSTEM. Filter as needed based on parent process, or modify the query to have world writeable paths to restrict it.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_schtasks` Processes.process="*/create
+  *" AND Processes.process="*/ru *" AND Processes.process="*system*" by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_schtasks_create_run_as_system_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be limited to legitimate applications
+  creating a task to run as SYSTEM. Filter as needed based on parent process, or modify
+  the query to have world writeable paths to restrict it.
 references:
 - https://pentestlab.blog/2019/11/04/persistence-scheduled-tasks/
 - https://www.ired.team/offensive-security/persistence/t1053-schtask
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 80
-  message: An $process_name$ was created on endpoint $dest$ attempting to spawn as SYSTEM.
+  message: An $process_name$ was created on endpoint $dest$ attempting to spawn as
+    SYSTEM.
   mitre_attack_id:
   - T1053.005
   - T1053
@@ -51,23 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_system/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_system/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_screen_capture_in_temp_folder.yml b/detections/endpoint/windows_screen_capture_in_temp_folder.yml
index 15b95a5a7d..bb3aca28e9 100644
--- a/detections/endpoint/windows_screen_capture_in_temp_folder.yml
+++ b/detections/endpoint/windows_screen_capture_in_temp_folder.yml
@@ -7,13 +7,19 @@ data_source:
 - Sysmon EventID 11
 type: TTP
 status: production
-description: The following analytic detects the creation of screen capture files by the Braodo stealer malware. This stealer is known to capture screenshots of the victim's desktop as part of its data theft activities. The detection focuses on identifying unusual screen capture activity, especially when images are saved in directories often used by malware, such as temporary or hidden folders. Monitoring for these files helps to quickly identify malicious screen capture attempts, allowing security teams to respond and mitigate potential information exposure before sensitive data is compromised.
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem 
-  where Filesystem.file_name IN ("screenshot.png", "screenshot.jpg","screenshot.bmp") Filesystem.file_path = "*\\temp\\*" 
-  by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time 
-  | `drop_dm_object_name(Filesystem)`
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
+description: The following analytic detects the creation of screen capture files by
+  the Braodo stealer malware. This stealer is known to capture screenshots of the
+  victim's desktop as part of its data theft activities. The detection focuses on
+  identifying unusual screen capture activity, especially when images are saved in
+  directories often used by malware, such as temporary or hidden folders. Monitoring
+  for these files helps to quickly identify malicious screen capture attempts, allowing
+  security teams to respond and mitigate potential information exposure before sensitive
+  data is compromised.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("screenshot.png",
+  "screenshot.jpg","screenshot.bmp") Filesystem.file_path = "*\\temp\\*" by _time
+  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time
+  | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
   | `windows_screen_capture_in_temp_folder_filter`'
 how_to_implement: To successfully implement this search you need to be ingesting information
   on process that include the name of the process responsible for the changes from
@@ -30,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.dest
-  - Filesystem.file_create_time
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1113/braodo_screenshot/braodo_screenshot.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1113/braodo_screenshot/braodo_screenshot.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_screen_capture_via_powershell.yml b/detections/endpoint/windows_screen_capture_via_powershell.yml
index 803b279ca4..58297985b8 100644
--- a/detections/endpoint/windows_screen_capture_via_powershell.yml
+++ b/detections/endpoint/windows_screen_capture_via_powershell.yml
@@ -7,9 +7,21 @@ status: production
 type: TTP
 data_source:
 - Powershell Script Block Logging 4104
-description: The following analytic detects the execution of a PowerShell script designed to capture screen images on a host. It leverages PowerShell Script Block Logging to identify specific script block text patterns associated with screen capture activities. This behavior is significant as it may indicate an attempt to exfiltrate sensitive information by capturing desktop screenshots. If confirmed malicious, this activity could allow an attacker to gather visual data from the compromised system, potentially leading to data breaches or further exploitation.
-search: '`powershell` EventCode=4104 ScriptBlockText = "*[Drawing.Graphics]::FromImage(*" AND ScriptBlockText = "*New-Object Drawing.Bitmap*" AND ScriptBlockText = "*.CopyFromScreen*" | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_screen_capture_via_powershell_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+description: The following analytic detects the execution of a PowerShell script designed
+  to capture screen images on a host. It leverages PowerShell Script Block Logging
+  to identify specific script block text patterns associated with screen capture activities.
+  This behavior is significant as it may indicate an attempt to exfiltrate sensitive
+  information by capturing desktop screenshots. If confirmed malicious, this activity
+  could allow an attacker to gather visual data from the compromised system, potentially
+  leading to data breaches or further exploitation.
+search: '`powershell` EventCode=4104 ScriptBlockText = "*[Drawing.Graphics]::FromImage(*"
+  AND ScriptBlockText = "*New-Object Drawing.Bitmap*" AND ScriptBlockText = "*.CopyFromScreen*"
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText
+  Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_screen_capture_via_powershell_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: unknown
 references:
 - https://twitter.com/_CERT_UA/status/1620781684257091584
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +49,8 @@ tags:
   - Source:Endpoint
   - Stage:Collection
   impact: 70
-  message: A PowerShell script was identified possibly performing screen captures on $Computer$.
+  message: A PowerShell script was identified possibly performing screen captures
+    on $Computer$.
   mitre_attack_id:
   - T1113
   observable:
@@ -44,16 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winter-vivern/pwh_exfiltration/windows-powershell-xml.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_security_account_manager_stopped.yml b/detections/endpoint/windows_security_account_manager_stopped.yml
index e4f1e135d4..409e756633 100644
--- a/detections/endpoint/windows_security_account_manager_stopped.yml
+++ b/detections/endpoint/windows_security_account_manager_stopped.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Rod Soto, Jose Hernandez, Splunk
 status: production
 type: TTP
-description: The following analytic detects the stopping of the Windows Security Account Manager (SAM) service via command-line, typically using the "net stop samss" command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because stopping the SAM service can disrupt authentication mechanisms and is often associated with ransomware attacks like Ryuk. If confirmed malicious, this action could lead to unauthorized access, privilege escalation, and potential system-wide compromise.
+description: The following analytic detects the stopping of the Windows Security Account
+  Manager (SAM) service via command-line, typically using the "net stop samss" command.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process names and command-line executions. This activity is significant
+  because stopping the SAM service can disrupt authentication mechanisms and is often
+  associated with ransomware attacks like Ryuk. If confirmed malicious, this action
+  could lead to unauthorized access, privilege escalation, and potential system-wide
+  compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY Processes.dest Processes.user Processes.process Processes.process_guid Processes.process_name | `drop_dm_object_name(Processes)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe"
+  "Processes.process"="*stop \"samss\"*") BY Processes.dest Processes.user Processes.process
+  Processes.process_guid Processes.process_name | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_security_account_manager_stopped_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: SAM is a critical windows service, stopping it would cause
+  major issues on an endpoint this makes false positive rare. AlthoughNo false positives
+  have been identified.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$dest$" and "$user$"
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: 'The Windows Security Account Manager (SAM) was stopped via cli by $user$ on $dest$ by this command: $process$'
+  message: 'The Windows Security Account Manager (SAM) was stopped via cli by $user$
+    on $dest$ by this command: $process$'
   mitre_attack_id:
   - T1489
   observable:
@@ -45,16 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_security_support_provider_reg_query.yml b/detections/endpoint/windows_security_support_provider_reg_query.yml
index ea81a315e0..f21e6531d1 100644
--- a/detections/endpoint/windows_security_support_provider_reg_query.yml
+++ b/detections/endpoint/windows_security_support_provider_reg_query.yml
@@ -5,13 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies command-line activity querying the registry for Security Support Providers (SSPs) related to Local Security Authority (LSA) protection and configuration. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on processes accessing specific LSA registry paths. Monitoring this activity is crucial as adversaries and post-exploitation tools like winpeas may use it to gather information on LSA protections, potentially leading to credential theft. If confirmed malicious, attackers could exploit this to scrape password hashes or plaintext passwords from memory, significantly compromising system security.
+description: The following analytic identifies command-line activity querying the
+  registry for Security Support Providers (SSPs) related to Local Security Authority
+  (LSA) protection and configuration. This detection leverages Endpoint Detection
+  and Response (EDR) telemetry, focusing on processes accessing specific LSA registry
+  paths. Monitoring this activity is crucial as adversaries and post-exploitation
+  tools like winpeas may use it to gather information on LSA protections, potentially
+  leading to credential theft. If confirmed malicious, attackers could exploit this
+  to scrape password hashes or plaintext passwords from memory, significantly compromising
+  system security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process = "* query *" AND Processes.process = "*\\SYSTEM\\CurrentControlSet\\Control\\LSA*" Processes.process IN ("*RunAsPPL*" , "*LsaCfgFlags*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_security_support_provider_reg_query_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_reg` AND Processes.process
+  = "* query *" AND Processes.process = "*\\SYSTEM\\CurrentControlSet\\Control\\LSA*"
+  Processes.process IN ("*RunAsPPL*" , "*LsaCfgFlags*") by Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.process_guid
+  Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_security_support_provider_reg_query_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://blog.netwrix.com/2022/01/11/understanding-lsa-protection/
@@ -23,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,25 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml b/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml
index 9a505de332..ec774951bc 100644
--- a/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml
+++ b/detections/endpoint/windows_server_software_component_gacutil_install_to_gac.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of GACUtil.exe to add a DLL into the Global Assembly Cache (GAC). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because adding a DLL to the GAC allows it to be called by any application, potentially enabling widespread code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code across the operating system, leading to privilege escalation or persistent access.
+description: The following analytic detects the use of GACUtil.exe to add a DLL into
+  the Global Assembly Cache (GAC). It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line executions. This activity
+  is significant because adding a DLL to the GAC allows it to be called by any application,
+  potentially enabling widespread code execution. If confirmed malicious, this could
+  allow an attacker to execute arbitrary code across the operating system, leading
+  to privilege escalation or persistent access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=gacutil.exe Processes.process IN ("*-i *","*/i *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_server_software_component_gacutil_install_to_gac_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if gacutil.exe is utilized day to day by developers. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=gacutil.exe
+  Processes.process IN ("*-i *","*/i *") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_server_software_component_gacutil_install_to_gac_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if gacutil.exe is utilized day
+  to day by developers. Filter as needed.
 references:
 - https://strontic.github.io/xcyclopedia/library/gacutil.exe-F2FE4DF74BD214EDDC1A658043828089.html
 - https://www.microsoft.com/en-us/security/blog/2022/12/12/iis-modules-the-evolution-of-web-shells-and-how-to-detect-them/
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +58,9 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to add a module to the global assembly cache.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to add a module to the global assembly
+    cache.
   mitre_attack_id:
   - T1505
   - T1505.004
@@ -58,23 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/gacutil_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.004/gacutil_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_create_kernel_mode_driver.yml b/detections/endpoint/windows_service_create_kernel_mode_driver.yml
index 5379f01a3e..6a61a3261f 100644
--- a/detections/endpoint/windows_service_create_kernel_mode_driver.yml
+++ b/detections/endpoint/windows_service_create_kernel_mode_driver.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of a new kernel mode driver using the sc.exe command. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. The activity is significant because adding a kernel driver is uncommon in regular operations and can indicate an attempt to gain low-level access to the system. If confirmed malicious, this could allow an attacker to execute code with high privileges, potentially compromising the entire system and evading traditional security measures.
+description: The following analytic identifies the creation of a new kernel mode driver
+  using the sc.exe command. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs that include command-line
+  details. The activity is significant because adding a kernel driver is uncommon
+  in regular operations and can indicate an attempt to gain low-level access to the
+  system. If confirmed malicious, this could allow an attacker to execute code with
+  high privileges, potentially compromising the entire system and evading traditional
+  security measures.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process="*kernel*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_kernel_mode_driver_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present based on common applications adding new drivers, however, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe
+  Processes.process="*kernel*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_service_create_kernel_mode_driver_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present based on common applications
+  adding new drivers, however, filter as needed.
 references:
 - https://www.aon.com/cyber-solutions/aon_cyber_labs/yours-truly-signed-av-driver-weaponizing-an-antivirus-driver/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 60
-  message: Service control, $process_name$, loaded a new kernel mode driver on $dest$ by $user$.
+  message: Service control, $process_name$, loaded a new kernel mode driver on $dest$
+    by $user$.
   mitre_attack_id:
   - T1543.003
   - T1543
@@ -49,21 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sc_kernel.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sc_kernel.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_create_remcomsvc.yml b/detections/endpoint/windows_service_create_remcomsvc.yml
index 9911ddb664..787d4d15ef 100644
--- a/detections/endpoint/windows_service_create_remcomsvc.yml
+++ b/detections/endpoint/windows_service_create_remcomsvc.yml
@@ -7,10 +7,22 @@ type: Anomaly
 status: production
 data_source:
 - Windows Event Log System 7045
-description: The following analytic detects the creation of the RemComSvc service on a Windows endpoint, typically indicating lateral movement using RemCom.exe. It leverages Windows EventCode 7045 from the System event log, specifically looking for the "RemCom Service" name. This activity is significant as it often signifies unauthorized lateral movement within the network, which is a common tactic used by attackers to spread malware or gain further access. If confirmed malicious, this could lead to unauthorized access to sensitive systems, data exfiltration, or further compromise of the network.
-search: '`wineventlog_system` EventCode=7045 ServiceName="RemCom Service" | stats count min(_time) as firstTime max(_time) as lastTime by dest ImagePath ServiceName ServiceType   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_remcomsvc_filter`'
-how_to_implement: To implement this analytic, the Windows EventCode 7045 will need to be logged. The Windows TA for Splunk is also recommended.
-known_false_positives: False positives may be present, filter as needed based on administrative activity.
+description: The following analytic detects the creation of the RemComSvc service
+  on a Windows endpoint, typically indicating lateral movement using RemCom.exe. It
+  leverages Windows EventCode 7045 from the System event log, specifically looking
+  for the "RemCom Service" name. This activity is significant as it often signifies
+  unauthorized lateral movement within the network, which is a common tactic used
+  by attackers to spread malware or gain further access. If confirmed malicious, this
+  could lead to unauthorized access to sensitive systems, data exfiltration, or further
+  compromise of the network.
+search: '`wineventlog_system` EventCode=7045 ServiceName="RemCom Service" | stats
+  count min(_time) as firstTime max(_time) as lastTime by dest ImagePath ServiceName
+  ServiceType   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_service_create_remcomsvc_filter`'
+how_to_implement: To implement this analytic, the Windows EventCode 7045 will need
+  to be logged. The Windows TA for Splunk is also recommended.
+known_false_positives: False positives may be present, filter as needed based on administrative
+  activity.
 references:
 - https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/
 - https://github.com/kavika13/RemCom
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - EventCode
-  - ImagePath
-  - ServiceName
-  - ServiceType
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remcom_windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/remcom_windows-system.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_create_sliverc2.yml b/detections/endpoint/windows_service_create_sliverc2.yml
index 745b1232e4..68bb3c2ef7 100644
--- a/detections/endpoint/windows_service_create_sliverc2.yml
+++ b/detections/endpoint/windows_service_create_sliverc2.yml
@@ -7,10 +7,21 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log System 7045
-description: The following analytic detects the creation of a Windows service named "Sliver" with the description "Sliver Implant," indicative of SliverC2 lateral movement using the PsExec module. It leverages Windows EventCode 7045 from the System Event log to identify this activity. This behavior is significant as it may indicate an adversary's attempt to establish persistence or execute commands remotely. If confirmed malicious, this activity could allow attackers to maintain control over the compromised system, execute arbitrary code, and further infiltrate the network.
-search: '`wineventlog_system` EventCode=7045 ServiceName="sliver" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_sliverc2_filter`'
-how_to_implement: To implement this analytic, the Windows EventCode 7045 will need to be logged from the System Event log. The Windows TA for Splunk is also recommended.
-known_false_positives: False positives should be limited, but if another service out there is named Sliver, filtering may be needed.
+description: The following analytic detects the creation of a Windows service named
+  "Sliver" with the description "Sliver Implant," indicative of SliverC2 lateral movement
+  using the PsExec module. It leverages Windows EventCode 7045 from the System Event
+  log to identify this activity. This behavior is significant as it may indicate an
+  adversary's attempt to establish persistence or execute commands remotely. If confirmed
+  malicious, this activity could allow attackers to maintain control over the compromised
+  system, execute arbitrary code, and further infiltrate the network.
+search: '`wineventlog_system` EventCode=7045 ServiceName="sliver" | stats count min(_time)
+  as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName
+  ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_service_create_sliverc2_filter`'
+how_to_implement: To implement this analytic, the Windows EventCode 7045 will need
+  to be logged from the System Event log. The Windows TA for Splunk is also recommended.
+known_false_positives: False positives should be limited, but if another service out
+  there is named Sliver, filtering may be needed.
 references:
 - https://github.com/BishopFox/sliver/blob/71f94928bf36c1557ea5fbeffa161b71116f56b2/client/command/exec/psexec.go#LL61C5-L61C16
 - https://www.microsoft.com/en-us/security/blog/2022/08/24/looking-for-the-sliver-lining-hunting-for-emerging-command-and-control-frameworks/
@@ -21,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,16 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - Computer
-  - ServiceName
-  - ImagePath
-  - ServiceType
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/sliver_windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/sliver/sliver_windows-system.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_create_with_tscon.yml b/detections/endpoint/windows_service_create_with_tscon.yml
index da66a29d6c..cfcd7c6d30 100644
--- a/detections/endpoint/windows_service_create_with_tscon.yml
+++ b/detections/endpoint/windows_service_create_with_tscon.yml
@@ -9,10 +9,40 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects potential RDP Hijacking attempts by identifying the creation of a Windows service using sc.exe with a binary path that includes tscon.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant as it indicates an attacker may be trying to hijack a disconnected RDP session, posing a risk of unauthorized access. If confirmed malicious, the attacker could gain control over an existing user session, leading to potential data theft or further system compromise.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe Processes.process="*/dest:rdp-tcp*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_create_with_tscon_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may arise in the RDP Hijacking analytic when legitimate administrators access remote sessions for maintenance or troubleshooting purposes. These activities might resemble an attacker''s attempt to hijack a disconnected session, leading to false alarms. To mitigate the risk of false positives and improve the overall security posture, organizations can implement Group Policy to automatically disconnect RDP sessions when they are complete. By enforcing this policy, administrators ensure that disconnected sessions are promptly terminated, reducing the window of opportunity for an attacker to hijack a session. Additionally, organizations can also implement access control mechanisms and monitor the behavior of privileged accounts to further enhance security and reduce the chances of false positives in RDP Hijacking detection.
+description: The following analytic detects potential RDP Hijacking attempts by identifying
+  the creation of a Windows service using sc.exe with a binary path that includes
+  tscon.exe. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on process creation events and command-line arguments. This activity
+  is significant as it indicates an attacker may be trying to hijack a disconnected
+  RDP session, posing a risk of unauthorized access. If confirmed malicious, the attacker
+  could gain control over an existing user session, leading to potential data theft
+  or further system compromise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sc.exe
+  Processes.process="*/dest:rdp-tcp*" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_service_create_with_tscon_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may arise in the RDP Hijacking analytic when
+  legitimate administrators access remote sessions for maintenance or troubleshooting
+  purposes. These activities might resemble an attacker''s attempt to hijack a disconnected
+  session, leading to false alarms. To mitigate the risk of false positives and improve
+  the overall security posture, organizations can implement Group Policy to automatically
+  disconnect RDP sessions when they are complete. By enforcing this policy, administrators
+  ensure that disconnected sessions are promptly terminated, reducing the window of
+  opportunity for an attacker to hijack a session. Additionally, organizations can
+  also implement access control mechanisms and monitor the behavior of privileged
+  accounts to further enhance security and reduce the chances of false positives in
+  RDP Hijacking detection.
 references:
 - https://doublepulsar.com/rdp-hijacking-how-to-hijack-rds-and-remoteapp-sessions-transparently-to-move-through-an-da2a1e73a5f6
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1563.002/T1563.002.md
@@ -22,7 +52,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +66,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to hijack a RDP session.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to hijack a RDP session.
   mitre_attack_id:
   - T1563.002
   - T1563
@@ -57,23 +93,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/rdphijack/tscon_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1563.002/rdphijack/tscon_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_created_with_suspicious_service_path.yml b/detections/endpoint/windows_service_created_with_suspicious_service_path.yml
index 070be95bd1..2c390871c1 100644
--- a/detections/endpoint/windows_service_created_with_suspicious_service_path.yml
+++ b/detections/endpoint/windows_service_created_with_suspicious_service_path.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a Windows Service with a binary path located in uncommon directories, using Windows Event ID 7045. It leverages logs from the `wineventlog_system` to identify services installed outside typical system directories. This activity is significant as adversaries, including those deploying Clop ransomware, often create malicious services for lateral movement, remote code execution, persistence, and execution. If confirmed malicious, this could allow attackers to maintain persistence, execute arbitrary code, and potentially escalate privileges, posing a severe threat to the environment.
+description: The following analytic detects the creation of a Windows Service with
+  a binary path located in uncommon directories, using Windows Event ID 7045. It leverages
+  logs from the `wineventlog_system` to identify services installed outside typical
+  system directories. This activity is significant as adversaries, including those
+  deploying Clop ransomware, often create malicious services for lateral movement,
+  remote code execution, persistence, and execution. If confirmed malicious, this
+  could allow attackers to maintain persistence, execute arbitrary code, and potentially
+  escalate privileges, posing a severe threat to the environment.
 data_source:
 - Windows Event Log System 7045
-search: '`wineventlog_system` EventCode=7045 ImagePath = "*.exe" NOT (ImagePath IN ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath ServiceName ServiceType StartType Computer UserID | rename Computer as dest| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_created_with_suspicious_service_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-known_false_positives: Legitimate applications may install services with uncommon services paths.
+search: '`wineventlog_system` EventCode=7045 ImagePath = "*.exe" NOT (ImagePath IN
+  ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*"))
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath
+  ServiceName ServiceType StartType Computer UserID | rename Computer as dest| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_service_created_with_suspicious_service_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints.
+known_false_positives: Legitimate applications may install services with uncommon
+  services paths.
 references:
 - https://www.mandiant.com/resources/fin11-email-campaigns-precursor-for-ransomware-data-theft
 - https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A service $ImagePath$ was created from a non-standard path using $ServiceName$ on $dest$
+  message: A service $ImagePath$ was created from a non-standard path using $ServiceName$
+    on $dest$
   mitre_attack_id:
   - T1569
   - T1569.002
@@ -53,18 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - Service_File_Name
-  - Service_Type
-  - _time
-  - Service_Name
-  - Service_Start_Type
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/windows_service_created_with_suspicious_service_path/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/windows_service_created_with_suspicious_service_path/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_created_within_public_path.yml b/detections/endpoint/windows_service_created_within_public_path.yml
index 92657f36ec..6d142e3850 100644
--- a/detections/endpoint/windows_service_created_within_public_path.yml
+++ b/detections/endpoint/windows_service_created_within_public_path.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of a Windows Service with its binary path located in public directories using Windows Event ID 7045. This detection leverages logs from the `wineventlog_system` data source, focusing on the `ImagePath` field to identify services installed outside standard system directories. This activity is significant as it may indicate the installation of a malicious service, often used by adversaries for lateral movement or remote code execution. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, or further compromise the system.
+description: The following analytic detects the creation of a Windows Service with
+  its binary path located in public directories using Windows Event ID 7045. This
+  detection leverages logs from the `wineventlog_system` data source, focusing on
+  the `ImagePath` field to identify services installed outside standard system directories.
+  This activity is significant as it may indicate the installation of a malicious
+  service, often used by adversaries for lateral movement or remote code execution.
+  If confirmed malicious, this could allow attackers to execute arbitrary code, maintain
+  persistence, or further compromise the system.
 data_source:
 - Windows Event Log System 7045
-search: '`wineventlog_system` EventCode=7045 ImagePath = "*.exe" NOT (ImagePath IN ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*")) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath ServiceName ServiceType StartType Computer UserID | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_created_within_public_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-known_false_positives: Legitimate applications may install services with uncommon services paths.
+search: '`wineventlog_system` EventCode=7045 ImagePath = "*.exe" NOT (ImagePath IN
+  ("*:\\Windows\\*", "*:\\Program File*", "*:\\Programdata\\*", "*%systemroot%\\*"))
+  | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ImagePath
+  ServiceName ServiceType StartType Computer UserID | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_service_created_within_public_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints.
+known_false_positives: Legitimate applications may install services with uncommon
+  services paths.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/services/service-control-manager
 - https://pentestlab.blog/2020/07/21/lateral-movement-services/
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - Service_File_Name
-  - Service_Type
-  - _time
-  - Service_Name
-  - Service_Start_Type
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/windows_service_created_with_suspicious_service_path/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/windows_service_created_with_suspicious_service_path/windows-xml.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_creation_on_remote_endpoint.yml b/detections/endpoint/windows_service_creation_on_remote_endpoint.yml
index 93ad06f826..e51ea344e0 100644
--- a/detections/endpoint/windows_service_creation_on_remote_endpoint.yml
+++ b/detections/endpoint/windows_service_creation_on_remote_endpoint.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of a Windows Service on a remote endpoint using `sc.exe`. It detects this activity by analyzing process execution logs from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments that include remote paths and service creation commands. This behavior is significant because adversaries often exploit the Service Control Manager for lateral movement and remote code execution. If confirmed malicious, this activity could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network.
+description: The following analytic identifies the creation of a Windows Service on
+  a remote endpoint using `sc.exe`. It detects this activity by analyzing process
+  execution logs from Endpoint Detection and Response (EDR) agents, focusing on command-line
+  arguments that include remote paths and service creation commands. This behavior
+  is significant because adversaries often exploit the Service Control Manager for
+  lateral movement and remote code execution. If confirmed malicious, this activity
+  could allow attackers to execute arbitrary code on remote systems, potentially leading
+  to further compromise and persistence within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*create* AND Processes.process=*binpath*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_creation_on_remote_endpoint_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may create Windows Services on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe
+  OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*create*
+  AND Processes.process=*binpath*) by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `windows_service_creation_on_remote_endpoint_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may create Windows Services on remote systems,
+  but this activity is usually limited to a small set of hosts or users.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/services/service-control-manager
 - https://docs.microsoft.com/en-us/windows/win32/services/controlling-a-service-using-sc
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_creation_using_registry_entry.yml b/detections/endpoint/windows_service_creation_using_registry_entry.yml
index 607fc19694..6e12d8c491 100644
--- a/detections/endpoint/windows_service_creation_using_registry_entry.yml
+++ b/detections/endpoint/windows_service_creation_using_registry_entry.yml
@@ -5,13 +5,19 @@ date: '2024-11-14'
 author: Teoderick Contreras, Splunk, Steven Dick
 status: production
 type: TTP
-description: The following analytic detects the modification of registry keys that define Windows services using reg.exe. This detection leverages Splunk to search for specific keywords in the registry path, value name, and value data fields. This activity is significant because it indicates potential unauthorized changes to service configurations, a common persistence technique used by attackers. If confirmed malicious, this could allow an attacker to maintain access, escalate privileges, or move laterally within the network, leading to data theft, ransomware, or other damaging outcomes.
+description: The following analytic detects the modification of registry keys that
+  define Windows services using reg.exe. This detection leverages Splunk to search
+  for specific keywords in the registry path, value name, and value data fields. This
+  activity is significant because it indicates potential unauthorized changes to service
+  configurations, a common persistence technique used by attackers. If confirmed malicious,
+  this could allow an attacker to maintain access, escalate privileges, or move laterally
+  within the network, leading to data theft, ransomware, or other damaging outcomes.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry 
-  WHERE (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services*" Registry.registry_value_name
-  = ImagePath) BY Registry.dest Registry.user Registry.registry_path
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services*"
+  Registry.registry_value_name = ImagePath) BY Registry.dest Registry.user Registry.registry_path
   Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
   Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data)
   | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_creation_using_registry_entry_filter`'
@@ -29,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +55,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A Windows Service was created on a endpoint from $dest$ using a registry entry
+  message: A Windows Service was created on a endpoint from $dest$ using a registry
+    entry
   mitre_attack_id:
   - T1574.011
   observable:
@@ -56,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_deletion_in_registry.yml b/detections/endpoint/windows_service_deletion_in_registry.yml
index 2191da8a71..d09d41dd81 100644
--- a/detections/endpoint/windows_service_deletion_in_registry.yml
+++ b/detections/endpoint/windows_service_deletion_in_registry.yml
@@ -5,13 +5,31 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the deletion of a service from the Windows Registry under CurrentControlSet\Services. It leverages data from the Endpoint.Registry datamodel, specifically monitoring registry paths and actions related to service deletion. This activity is significant as adversaries may delete services to evade detection and hinder incident response efforts. If confirmed malicious, this action could disrupt legitimate services, impair system functionality, and potentially allow attackers to maintain a lower profile within the environment, complicating detection and remediation efforts.
+description: The following analytic detects the deletion of a service from the Windows
+  Registry under CurrentControlSet\Services. It leverages data from the Endpoint.Registry
+  datamodel, specifically monitoring registry paths and actions related to service
+  deletion. This activity is significant as adversaries may delete services to evade
+  detection and hinder incident response efforts. If confirmed malicious, this action
+  could disrupt legitimate services, impair system functionality, and potentially
+  allow attackers to maintain a lower profile within the environment, complicating
+  detection and remediation efforts.
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\CurrentControlSet\\Services*" AND (Registry.action = deleted OR (Registry.registry_value_name = DeleteFlag AND Registry.registry_value_data = 0x00000001 AND Registry.action=modified)) by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_value_name Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_deletion_in_registry_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
-known_false_positives: This event can be seen when administrator delete a service or uninstall/reinstall a software that creates service entry, but it is still recommended to check this alert with high priority.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SYSTEM\\CurrentControlSet\\Services*"
+  AND (Registry.action = deleted OR (Registry.registry_value_name = DeleteFlag AND
+  Registry.registry_value_data = 0x00000001 AND Registry.action=modified)) by Registry.registry_key_name
+  Registry.user Registry.registry_path Registry.registry_value_data Registry.registry_value_name
+  Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_service_deletion_in_registry_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure
+  that this registry was included in your config files ex. sysmon config to be monitored.
+known_false_positives: This event can be seen when administrator delete a service
+  or uninstall/reinstall a software that creates service entry, but it is still recommended
+  to check this alert with high priority.
 references:
 - https://unit42.paloaltonetworks.com/brute-ratel-c4-tool/
 drilldown_searches:
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,24 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.user
-  - Registry.dest
-  - Registry.registry_value_name
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.dest
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/service_deletion/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/brute_ratel/service_deletion/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml b/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml
index c844d80d6b..a443ecd454 100644
--- a/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml
+++ b/detections/endpoint/windows_service_initiation_on_remote_endpoint.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of `sc.exe` with command-line arguments used to start a Windows Service on a remote endpoint. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant because adversaries may exploit the Service Control Manager for lateral movement and remote code execution. If confirmed malicious, this could allow attackers to execute arbitrary code on remote systems, potentially leading to further compromise and persistence within the network.
+description: The following analytic detects the execution of `sc.exe` with command-line
+  arguments used to start a Windows Service on a remote endpoint. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  command-line executions. This activity is significant because adversaries may exploit
+  the Service Control Manager for lateral movement and remote code execution. If confirmed
+  malicious, this could allow attackers to execute arbitrary code on remote systems,
+  potentially leading to further compromise and persistence within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*start*) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_initiation_on_remote_endpoint_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may start Windows Services on remote systems, but this activity is usually limited to a small set of hosts or users.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sc.exe
+  OR Processes.original_file_name=sc.exe) (Processes.process=*\\\\* AND Processes.process=*start*)
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_service_initiation_on_remote_endpoint_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may start Windows Services on remote systems,
+  but this activity is usually limited to a small set of hosts or users.
 references:
 - https://docs.microsoft.com/en-us/windows/win32/services/controlling-a-service-using-sc
 - https://attack.mitre.org/techniques/T1543/003/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/lateral_movement/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_stop_by_deletion.yml b/detections/endpoint/windows_service_stop_by_deletion.yml
index 8620249eac..f071e66c52 100644
--- a/detections/endpoint/windows_service_stop_by_deletion.yml
+++ b/detections/endpoint/windows_service_stop_by_deletion.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of `sc.exe` to delete a Windows service. It leverages Endpoint Detection and Response (EDR) data, focusing on process execution logs that capture command-line arguments. This activity is significant because adversaries often delete services to disable security mechanisms or critical system functions, aiding in evasion and persistence. If confirmed malicious, this action could lead to the termination of essential security services, allowing attackers to operate undetected and potentially escalate their privileges or maintain long-term access to the compromised system.
+description: The following analytic detects the use of `sc.exe` to delete a Windows
+  service. It leverages Endpoint Detection and Response (EDR) data, focusing on process
+  execution logs that capture command-line arguments. This activity is significant
+  because adversaries often delete services to disable security mechanisms or critical
+  system functions, aiding in evasion and persistence. If confirmed malicious, this
+  action could lead to the termination of essential security services, allowing attackers
+  to operate undetected and potentially escalate their privileges or maintain long-term
+  access to the compromised system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = sc.exe OR Processes.original_file_name = sc.exe) Processes.process="* delete *" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_by_deletion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: It is possible administrative scripts may start/stop/delete services. Filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name = sc.exe OR Processes.original_file_name = sc.exe)
+  Processes.process="* delete *" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_service_stop_by_deletion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: It is possible administrative scripts may start/stop/delete
+  services. Filter as needed.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 - https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +60,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to delete a service.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ attempting to delete a service.
   mitre_attack_id:
   - T1489
   observable:
@@ -45,20 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.process_name
-  - Processes.process
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_id
-  - Processes.parent_process_id
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_stop_via_net__and_sc_application.yml b/detections/endpoint/windows_service_stop_via_net__and_sc_application.yml
index bdceeee061..161721aeec 100644
--- a/detections/endpoint/windows_service_stop_via_net__and_sc_application.yml
+++ b/detections/endpoint/windows_service_stop_via_net__and_sc_application.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies attempts to stop services on a system using `net.exe` or `sc.exe`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line executions. This activity is significant as adversaries often terminate security or critical services to evade detection and further their objectives. If confirmed malicious, this behavior could allow attackers to disable security defenses, facilitate ransomware encryption, or disrupt essential services, leading to potential data loss or system compromise.
+description: The following analytic identifies attempts to stop services on a system
+  using `net.exe` or `sc.exe`. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names, GUIDs, and command-line executions. This
+  activity is significant as adversaries often terminate security or critical services
+  to evade detection and further their objectives. If confirmed malicious, this behavior
+  could allow attackers to disable security defenses, facilitate ransomware encryption,
+  or disrupt essential services, leading to potential data loss or system compromise.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name = "sc.exe" OR Processes.original_file_name= "sc.exe" AND Processes.process="*stop*" by  Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_via_net__and_sc_application_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Windows OS or software may stop and restart services due to some critical update.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Endpoint.Processes where `process_net` OR  Processes.process_name
+  = "sc.exe" OR Processes.original_file_name= "sc.exe" AND Processes.process="*stop*"
+  by  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_service_stop_via_net__and_sc_application_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Windows OS or software may stop and restart services due to
+  some critical update.
 references:
 - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/prestige_ransomware/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_service_stop_win_updates.yml b/detections/endpoint/windows_service_stop_win_updates.yml
index 048c75078f..214da1b3e5 100644
--- a/detections/endpoint/windows_service_stop_win_updates.yml
+++ b/detections/endpoint/windows_service_stop_win_updates.yml
@@ -7,10 +7,25 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log System 7040
-description: The following analytic detects the disabling of Windows Update services, such as "Update Orchestrator Service for Windows Update," "WaaSMedicSvc," and "Windows Update." It leverages Windows System Event ID 7040 logs to identify changes in service start modes to 'disabled.' This activity is significant as it can indicate an adversary's attempt to evade defenses by preventing critical updates, leaving the system vulnerable to exploits. If confirmed malicious, this could allow attackers to maintain persistence and exploit unpatched vulnerabilities, compromising the integrity and security of the affected host.
-search: '`wineventlog_system` EventCode=7040 (service_name IN ("Update Orchestrator Service for Windows Update", "WaaSMedicSvc", "Windows Update") OR param1 IN ("UsoSvc", "WaaSMedicSvc", "wuauserv")) AND (param3=disabled OR start_mode = disabled) | stats count min(_time) as firstTime max(_time) as lastTime by Computer Error_Code service_name start_mode param1 param2 param3 param4 | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_service_stop_win_updates_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints (like Windows system.log Event ID 7040)
-known_false_positives: Network administrator may disable this services as part of its audit process within the network. Filter is needed.
+description: The following analytic detects the disabling of Windows Update services,
+  such as "Update Orchestrator Service for Windows Update," "WaaSMedicSvc," and "Windows
+  Update." It leverages Windows System Event ID 7040 logs to identify changes in service
+  start modes to 'disabled.' This activity is significant as it can indicate an adversary's
+  attempt to evade defenses by preventing critical updates, leaving the system vulnerable
+  to exploits. If confirmed malicious, this could allow attackers to maintain persistence
+  and exploit unpatched vulnerabilities, compromising the integrity and security of
+  the affected host.
+search: '`wineventlog_system` EventCode=7040 (service_name IN ("Update Orchestrator
+  Service for Windows Update", "WaaSMedicSvc", "Windows Update") OR param1 IN ("UsoSvc",
+  "WaaSMedicSvc", "wuauserv")) AND (param3=disabled OR start_mode = disabled) | stats
+  count min(_time) as firstTime max(_time) as lastTime by Computer Error_Code service_name
+  start_mode param1 param2 param3 param4 | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_service_stop_win_updates_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the Service name, Service File Name Service Start type, and Service Type
+  from your endpoints (like Windows system.log Event ID 7040)
+known_false_positives: Network administrator may disable this services as part of
+  its audit process within the network. Filter is needed.
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.redline_stealer
 drilldown_searches:
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -41,21 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Computer
-  - EventCode
-  - Error_Code
-  - service_name
-  - start_mode
-  - param1
-  - param2
-  - param3
-  - param4
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/win_update_services_stop/system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/redline/win_update_services_stop/system.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_sip_provider_inventory.yml b/detections/endpoint/windows_sip_provider_inventory.yml
index eb499de108..40c6ad6f84 100644
--- a/detections/endpoint/windows_sip_provider_inventory.yml
+++ b/detections/endpoint/windows_sip_provider_inventory.yml
@@ -6,10 +6,21 @@ author: Michael Haag, Splunk
 status: production
 type: Hunting
 data_source: []
-description: The following analytic identifies all SIP (Subject Interface Package) providers on a Windows system using PowerShell scripted inputs. It detects SIP providers by capturing DLL paths from relevant events. This activity is significant because malicious SIP providers can be used to bypass trust controls, potentially allowing unauthorized code execution. If confirmed malicious, this activity could enable attackers to subvert system integrity, leading to unauthorized access or persistent threats within the environment. Analysts should review for new and non-standard paths to identify potential threats.
-search: '`subjectinterfacepackage` Dll=*\\*.dll | stats count min(_time) as firstTime max(_time) as lastTime values(Dll) by Path host| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_sip_provider_inventory_filter`'
-how_to_implement: To implement this analytic, one must first perform inventory using a scripted inputs. Review the following Gist - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1
-known_false_positives: False positives are limited as this is a hunting query for inventory.
+description: The following analytic identifies all SIP (Subject Interface Package)
+  providers on a Windows system using PowerShell scripted inputs. It detects SIP providers
+  by capturing DLL paths from relevant events. This activity is significant because
+  malicious SIP providers can be used to bypass trust controls, potentially allowing
+  unauthorized code execution. If confirmed malicious, this activity could enable
+  attackers to subvert system integrity, leading to unauthorized access or persistent
+  threats within the environment. Analysts should review for new and non-standard
+  paths to identify potential threats.
+search: '`subjectinterfacepackage` Dll=*\\*.dll | stats count min(_time) as firstTime
+  max(_time) as lastTime values(Dll) by Path host| `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `windows_sip_provider_inventory_filter`'
+how_to_implement: To implement this analytic, one must first perform inventory using
+  a scripted inputs. Review the following Gist - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1
+known_false_positives: False positives are limited as this is a hunting query for
+  inventory.
 references:
 - https://gist.github.com/MHaggis/75dd5db546c143ea67703d0e86cdbbd1
 tags:
@@ -19,7 +30,8 @@ tags:
   atomic_guid: []
   confidence: 50
   impact: 50
-  message: A list of SIP providers on the system is available. Review for new and non-standard paths for SIP providers on $host$.
+  message: A list of SIP providers on the system is available. Review for new and
+    non-standard paths for SIP providers on $host$.
   mitre_attack_id:
   - T1553.003
   observable:
@@ -31,14 +43,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Path
-  - Dll
-  - host
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_inventory.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/sip_inventory.log
     source: powershell://SubjectInterfacePackage
     sourcetype: PwSh:SubjectInterfacePackage
diff --git a/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml b/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml
index 5d75a3dfa4..1ed288f58a 100644
--- a/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml
+++ b/detections/endpoint/windows_sip_winverifytrust_failed_trust_validation.yml
@@ -7,10 +7,23 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log CAPI2 81
-description: The following analytic detects failed trust validation attempts using Windows Event Log - CAPI2 (CryptoAPI 2). It specifically triggers on EventID 81, which indicates that "The digital signature of the object did not verify." This detection leverages the CAPI2 Operational log to identify instances where digital signatures fail to validate. Monitoring this activity is crucial as it can indicate attempts to execute untrusted or potentially malicious binaries. If confirmed malicious, this activity could allow attackers to bypass security controls and execute unauthorized code, leading to potential system compromise.
-search: '`capi2_operational` EventID=81 "The digital signature of the object did not verify." | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as lastTime by Computer, UserData_Xml | rename Computer as dest | `windows_sip_winverifytrust_failed_trust_validation_filter`'
-how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational log within the Windows Event Log. Note this is a debug log for many purposes, and the analytic only focuses in on EventID 81. Review the following gist for additional enabling information.
-known_false_positives: False positives may be present in some instances of legitimate binaries with invalid signatures. Filter as needed.
+description: The following analytic detects failed trust validation attempts using
+  Windows Event Log - CAPI2 (CryptoAPI 2). It specifically triggers on EventID 81,
+  which indicates that "The digital signature of the object did not verify." This
+  detection leverages the CAPI2 Operational log to identify instances where digital
+  signatures fail to validate. Monitoring this activity is crucial as it can indicate
+  attempts to execute untrusted or potentially malicious binaries. If confirmed malicious,
+  this activity could allow attackers to bypass security controls and execute unauthorized
+  code, leading to potential system compromise.
+search: '`capi2_operational` EventID=81 "The digital signature of the object did not
+  verify." | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as
+  lastTime by Computer, UserData_Xml | rename Computer as dest | `windows_sip_winverifytrust_failed_trust_validation_filter`'
+how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational
+  log within the Windows Event Log. Note this is a debug log for many purposes, and
+  the analytic only focuses in on EventID 81. Review the following gist for additional
+  enabling information.
+known_false_positives: False positives may be present in some instances of legitimate
+  binaries with invalid signatures. Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1553/003/
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/SpecterOps_Subverting_Trust_in_Windows.pdf
@@ -23,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,14 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Computer
-  - UserData_Xml
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/capi2-operational.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.003/sip/capi2-operational.log
     source: XmlWinEventLog:Microsoft-Windows-CAPI2/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml b/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml
index cc3baf7716..99b9b2d694 100644
--- a/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml
+++ b/detections/endpoint/windows_snake_malware_file_modification_crmlog.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 11
-description: The following analytic identifies the creation of a .crmlog file within the %windows%\Registration directory, typically with a format of <RANDOM_GUID>.<RANDOM_GUID>.crmlog. This detection leverages the Endpoint.Filesystem datamodel to monitor file creation events in the specified directory. This activity is significant as it is associated with the Snake malware, which uses this file for its operations. If confirmed malicious, this could indicate the presence of Snake malware, leading to potential data exfiltration, system compromise, and further malicious activities. Immediate investigation is required to mitigate the threat.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\registration\\*" AND  Filesystem.file_name="*.crmlog"  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_snake_malware_file_modification_crmlog_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives may be present as the file pattern does match legitimate files on disk. It is possible other native tools write the same file name scheme.
+description: The following analytic identifies the creation of a .crmlog file within
+  the %windows%\Registration directory, typically with a format of <RANDOM_GUID>.<RANDOM_GUID>.crmlog.
+  This detection leverages the Endpoint.Filesystem datamodel to monitor file creation
+  events in the specified directory. This activity is significant as it is associated
+  with the Snake malware, which uses this file for its operations. If confirmed malicious,
+  this could indicate the presence of Snake malware, leading to potential data exfiltration,
+  system compromise, and further malicious activities. Immediate investigation is
+  required to mitigate the threat.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\registration\\*"
+  AND  Filesystem.file_name="*.crmlog"  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name
+  Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `windows_snake_malware_file_modification_crmlog_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives may be present as the file pattern does match
+  legitimate files on disk. It is possible other native tools write the same file
+  name scheme.
 references:
 - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF
 drilldown_searches:
@@ -19,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,17 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_crmlog-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_crmlog-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml b/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml
index 58fc3c3cdf..52889e80d1 100644
--- a/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml
+++ b/detections/endpoint/windows_snake_malware_kernel_driver_comadmin.yml
@@ -7,9 +7,24 @@ status: production
 type: TTP
 data_source:
 - Sysmon EventID 11
-description: The following analytic detects the creation of the comadmin.dat file in the %windows%\system32\Com directory, which is associated with Snake Malware. This detection leverages the Endpoint.Filesystem data model to identify file creation events matching the specified path and filename. This activity is significant because the comadmin.dat file is part of Snake Malware's installation process, which includes dropping a kernel driver and a custom DLL. If confirmed malicious, this activity could allow an attacker to load a malicious driver, potentially leading to privilege escalation and persistent access to the compromised system.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\system32\\com\\*" AND Filesystem.file_name="comadmin.dat"  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_kernel_driver_comadmin_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
+description: The following analytic detects the creation of the comadmin.dat file
+  in the %windows%\system32\Com directory, which is associated with Snake Malware.
+  This detection leverages the Endpoint.Filesystem data model to identify file creation
+  events matching the specified path and filename. This activity is significant because
+  the comadmin.dat file is part of Snake Malware's installation process, which includes
+  dropping a kernel driver and a custom DLL. If confirmed malicious, this activity
+  could allow an attacker to load a malicious driver, potentially leading to privilege
+  escalation and persistent access to the compromised system.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\windows\\system32\\com\\*"
+  AND Filesystem.file_name="comadmin.dat"  by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name
+  Filesystem.file_path Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_snake_malware_kernel_driver_comadmin_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF
@@ -19,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +50,8 @@ tags:
   - e5cb5564-cc7b-4050-86e8-f2d9eec1941f
   confidence: 80
   impact: 70
-  message: A kernel driver comadmin.dat related to Snake Malware was written to disk on $dest$.
+  message: A kernel driver comadmin.dat related to Snake Malware was written to disk
+    on $dest$.
   mitre_attack_id:
   - T1547.006
   observable:
@@ -42,17 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.file_path
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/comadmin_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/comadmin_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml b/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml
index 57c693a60b..6ce8382298 100644
--- a/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml
+++ b/detections/endpoint/windows_snake_malware_registry_modification_wav_openwithprogids.yml
@@ -8,10 +8,27 @@ type: TTP
 data_source:
 - Sysmon EventID 12
 - Sysmon EventID 13
-description: The following analytic identifies modifications to the registry path .wav\\OpenWithProgIds, associated with the Snake Malware campaign. It leverages data from the Endpoint.Registry datamodel to detect changes in this specific registry location. This activity is significant because Snake's WerFault.exe uses this registry path to decrypt an encrypted blob containing critical components like the AES key, IV, and paths for its kernel driver and loader. If confirmed malicious, this could allow the attacker to load and execute Snake's kernel driver, leading to potential system compromise and persistent access.
-search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\.wav\\OpenWithProgIds\\*"  by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `windows_snake_malware_registry_modification_wav_openwithprogids_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
-known_false_positives: False positives may be present and will require tuning based on program Ids in large organizations.
+description: The following analytic identifies modifications to the registry path
+  .wav\\OpenWithProgIds, associated with the Snake Malware campaign. It leverages
+  data from the Endpoint.Registry datamodel to detect changes in this specific registry
+  location. This activity is significant because Snake's WerFault.exe uses this registry
+  path to decrypt an encrypted blob containing critical components like the AES key,
+  IV, and paths for its kernel driver and loader. If confirmed malicious, this could
+  allow the attacker to load and execute Snake's kernel driver, leading to potential
+  system compromise and persistent access.
+search: '| tstats `security_content_summariesonly` count values(Registry.registry_key_name)
+  as registry_key_name values(Registry.registry_path) as registry_path min(_time)
+  as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path="*\\.wav\\OpenWithProgIds\\*"  by
+  Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
+  | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)`
+  | `windows_snake_malware_registry_modification_wav_openwithprogids_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the process responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Registry` node. In addition,
+  confirm the latest CIM App 4.20 or higher is installed and the latest TA for the
+  endpoint product.
+known_false_positives: False positives may be present and will require tuning based
+  on program Ids in large organizations.
 references:
 - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF
 drilldown_searches:
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +53,8 @@ tags:
   - 8318ad20-0488-4a64-98f4-72525a012f6b
   confidence: 50
   impact: 50
-  message: A registry modification related to Snake Malware has been identified on $dest$.
+  message: A registry modification related to Snake Malware has been identified on
+    $dest$.
   mitre_attack_id:
   - T1112
   observable:
@@ -43,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Registry.dest
-  - Registry.user
-  - Registry.registry_path
-  - Registry.registry_key_name
-  - Registry.registry_value_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_malware_regblob-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake_malware_regblob-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_snake_malware_service_create.yml b/detections/endpoint/windows_snake_malware_service_create.yml
index 68321509d8..9003901ef8 100644
--- a/detections/endpoint/windows_snake_malware_service_create.yml
+++ b/detections/endpoint/windows_snake_malware_service_create.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log System 7045
-description: The following analytic detects the creation of a new service named WerFaultSvc with a binary path in the Windows WinSxS directory. It leverages Windows System logs, specifically EventCode 7045, to identify this activity. This behavior is significant because it indicates the presence of Snake malware, which uses this service to maintain persistence by blending in with legitimate Windows services. If confirmed malicious, this activity could allow an attacker to execute Snake malware components, leading to potential data exfiltration, system compromise, and long-term persistence within the environment.
-search: '`wineventlog_system` EventCode=7045  ImagePath="*\\windows\\winSxS\\*" ImagePath="*\Werfault.exe" | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_snake_malware_service_create_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-known_false_positives: False positives should be limited as this is a strict primary indicator used by Snake Malware.
+description: The following analytic detects the creation of a new service named WerFaultSvc
+  with a binary path in the Windows WinSxS directory. It leverages Windows System
+  logs, specifically EventCode 7045, to identify this activity. This behavior is significant
+  because it indicates the presence of Snake malware, which uses this service to maintain
+  persistence by blending in with legitimate Windows services. If confirmed malicious,
+  this activity could allow an attacker to execute Snake malware components, leading
+  to potential data exfiltration, system compromise, and long-term persistence within
+  the environment.
+search: '`wineventlog_system` EventCode=7045  ImagePath="*\\windows\\winSxS\\*" ImagePath="*\Werfault.exe"
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode
+  ImagePath ServiceName ServiceType | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_snake_malware_service_create_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows System logs with the Service name, Service File Name Service Start type,
+  and Service Type from your endpoints.
+known_false_positives: False positives should be limited as this is a strict primary
+  indicator used by Snake Malware.
 references:
 - https://media.defense.gov/2023/May/09/2003218554/-1/-1/0/JOINT_CSA_HUNTING_RU_INTEL_SNAKE_MALWARE_20230509.PDF
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - EventCode
-  - Service_File_Name
-  - Service_Type
-  - _time
-  - Service_Name
-  - Service_Start_Type
-  - Service_Account
-  - user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake-service-windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/snakemalware/snake-service-windows-system.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_soaphound_binary_execution.yml b/detections/endpoint/windows_soaphound_binary_execution.yml
index 0a12176589..4c13dde0fc 100644
--- a/detections/endpoint/windows_soaphound_binary_execution.yml
+++ b/detections/endpoint/windows_soaphound_binary_execution.yml
@@ -9,10 +9,35 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of the SOAPHound binary (`soaphound.exe`) with specific command-line arguments. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, command-line arguments, and other process-related metadata. This activity is significant because SOAPHound is a known tool used for credential dumping and other malicious activities. If confirmed malicious, this behavior could allow an attacker to extract sensitive information, escalate privileges, or persist within the environment, posing a severe threat to organizational security.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="soaphound.exe" OR Processes.original_file_name="soaphound.exe" AND Processes.process IN ("*--buildcache *", "*--bhdump *", "*--certdump *", "*--dnsdump *", "*-c *", "*--cachefilename *", "*-o *", "*--outputdirectory *") by Processes.process Processes.dest Processes.process_current_directory Processes.process_name Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_soaphound_binary_execution_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as the command-line arguments are specific to SOAPHound. Filter as needed.
+description: The following analytic detects the execution of the SOAPHound binary
+  (`soaphound.exe`) with specific command-line arguments. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names, command-line arguments,
+  and other process-related metadata. This activity is significant because SOAPHound
+  is a known tool used for credential dumping and other malicious activities. If confirmed
+  malicious, this behavior could allow an attacker to extract sensitive information,
+  escalate privileges, or persist within the environment, posing a severe threat to
+  organizational security.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="soaphound.exe"
+  OR Processes.original_file_name="soaphound.exe" AND Processes.process IN ("*--buildcache
+  *", "*--bhdump *", "*--certdump *", "*--dnsdump *", "*-c *", "*--cachefilename *",
+  "*-o *", "*--outputdirectory *") by Processes.process Processes.dest Processes.process_current_directory
+  Processes.process_name Processes.process_path Processes.process_integrity_level
+  Processes.parent_process Processes.parent_process_path Processes.parent_process_guid
+  Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_soaphound_binary_execution_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as the command-line arguments
+  are specific to SOAPHound. Filter as needed.
 references:
 - https://github.com/FalconForceTeam/SOAPHound
 drilldown_searches:
@@ -21,7 +46,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,24 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.process
-  - Processes.dest
-  - Processes.process_current_directory
-  - Processes.process_path
-  - Processes.process_integrity_level
-  - Processes.parent_process
-  - Processes.parent_process_path
-  - Processes.parent_process_guid
-  - Processes.parent_process_id
-  - Processes.process_guid
-  - Processes.process_id
-  - Processes.user
-  - Processes.original_file_nam
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/soaphound/sysmon_soaphound.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml b/detections/endpoint/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml
index a5b6ee245b..1fe779aa9f 100644
--- a/detections/endpoint/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml
+++ b/detections/endpoint/windows_spearphishing_attachment_connect_to_none_ms_office_domain.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies suspicious Office documents that connect to non-Microsoft Office domains. It leverages Sysmon EventCode 22 to detect processes like winword.exe or excel.exe making DNS queries to domains outside of *.office.com or *.office.net. This activity is significant as it may indicate a spearphishing attempt using malicious documents to download or connect to harmful content. If confirmed malicious, this could lead to unauthorized data access, malware infection, or further network compromise.
+description: The following analytic identifies suspicious Office documents that connect
+  to non-Microsoft Office domains. It leverages Sysmon EventCode 22 to detect processes
+  like winword.exe or excel.exe making DNS queries to domains outside of *.office.com
+  or *.office.net. This activity is significant as it may indicate a spearphishing
+  attempt using malicious documents to download or connect to harmful content. If
+  confirmed malicious, this could lead to unauthorized data access, malware infection,
+  or further network compromise.
 data_source:
 - Sysmon EventID 22
-search: '`sysmon` EventCode=22 Image IN ("*\\winword.exe","*\\excel.exe","*\\powerpnt.exe","*\\mspub.exe","*\\visio.exe","*\\wordpad.exe","*\\wordview.exe","*\\onenote.exe", "*\\onenotem.exe","*\\onenoteviewer.exe","*\\onenoteim.exe", "*\\msaccess.exe") AND NOT(QueryName IN ("*.office.com", "*.office.net")) | stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryResults QueryStatus Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_connect_to_none_ms_office_domain_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: Windows Office document may contain legitimate url link other than MS office Domain. filter is needed
+search: '`sysmon` EventCode=22 Image IN ("*\\winword.exe","*\\excel.exe","*\\powerpnt.exe","*\\mspub.exe","*\\visio.exe","*\\wordpad.exe","*\\wordview.exe","*\\onenote.exe",
+  "*\\onenotem.exe","*\\onenoteviewer.exe","*\\onenoteim.exe", "*\\msaccess.exe")
+  AND NOT(QueryName IN ("*.office.com", "*.office.net")) | stats count min(_time)
+  as firstTime max(_time) as lastTime by Image QueryName QueryResults QueryStatus
+  Computer | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_spearphishing_attachment_connect_to_none_ms_office_domain_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: Windows Office document may contain legitimate url link other
+  than MS office Domain. filter is needed
 references:
 - https://www.netskope.com/blog/asyncrat-using-fully-undetected-downloader
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.asyncrat
@@ -21,7 +36,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: a office document process $Image$ connect to an URL link $QueryName$ in $dest$
+  message: a office document process $Image$ connect to an URL link $QueryName$ in
+    $dest$
   mitre_attack_id:
   - T1566.001
   - T1566
@@ -34,17 +50,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - QueryName
-  - QueryResults
-  - QueryStatus
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/office_doc_abuses_rels/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/office_doc_abuses_rels/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml b/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml
index 56220df62c..848c0c12ed 100644
--- a/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml
+++ b/detections/endpoint/windows_spearphishing_attachment_onenote_spawn_mshta.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects OneNote spawning `mshta.exe`, a behavior often associated with spearphishing attacks. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where OneNote is the parent process. This activity is significant as it is commonly used by malware families like TA551, AsyncRat, Redline, and DCRAT to execute malicious scripts. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to data exfiltration, system compromise, or further malware deployment. Immediate investigation and containment are recommended.
+description: The following analytic detects OneNote spawning `mshta.exe`, a behavior
+  often associated with spearphishing attacks. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process creation events where OneNote is
+  the parent process. This activity is significant as it is commonly used by malware
+  families like TA551, AsyncRat, Redline, and DCRAT to execute malicious scripts.
+  If confirmed malicious, this could allow attackers to execute arbitrary code, potentially
+  leading to data exfiltration, system compromise, or further malware deployment.
+  Immediate investigation and containment are recommended.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("onenote.exe", "onenotem.exe") `process_mshta` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_onenote_spawn_mshta_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("onenote.exe", "onenotem.exe") `process_mshta` by Processes.dest Processes.user
+  Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_spearphishing_attachment_onenote_spawn_mshta_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: No false positives known. Filter as needed.
 references:
 - https://www.bleepingcomputer.com/news/security/hackers-now-use-microsoft-onenote-attachments-to-spread-malware/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 90
-  message: office parent process $parent_process_name$ will execute a suspicious child process $process_name$ with process id $process_id$ in host $dest$
+  message: office parent process $parent_process_name$ will execute a suspicious child
+    process $process_name$ with process id $process_id$ in host $dest$
   mitre_attack_id:
   - T1566.001
   - T1566
@@ -49,23 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/onenote_spear_phishing/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/onenote_spear_phishing/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml b/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml
index 873ff4f057..e840ea8573 100644
--- a/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml
+++ b/detections/endpoint/windows_special_privileged_logon_on_multiple_hosts.yml
@@ -7,10 +7,24 @@ type: TTP
 status: production
 data_source:
 - Windows Event Log Security 4672
-description: The following analytic detects a user authenticating with special privileges on 30 or more remote endpoints within a 5-minute window. It leverages Event ID 4672 from Windows Security logs to identify this behavior. This activity is significant as it may indicate lateral movement or remote code execution by an adversary. If confirmed malicious, the attacker could gain extensive control over the network, potentially leading to privilege escalation, data exfiltration, or further compromise of the environment. Security teams should adjust detection thresholds based on their specific environment.
-search: '`wineventlog_security` EventCode=4672 AND NOT(Caller_User_Name IN ("DWM-1","DWM-2","DWM-3","LOCAL SERVICE","NETWORK SERVICE","SYSTEM","*$")) | bucket span=5m _time | stats dc(Computer) AS unique_targets values(Computer) as dest values(PrivilegeList) as privileges by _time, Caller_User_Name | rename Caller_User_Name as user| where unique_targets > 30 | `windows_special_privileged_logon_on_multiple_hosts_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting special logon events. The Advanced Security Audit policy setting `Audit Special Logon` within `Logon/Logoff` need to be enabled.
-known_false_positives: Vulnerability scanners or system administration tools may also trigger this detection. Filter as needed.
+description: The following analytic detects a user authenticating with special privileges
+  on 30 or more remote endpoints within a 5-minute window. It leverages Event ID 4672
+  from Windows Security logs to identify this behavior. This activity is significant
+  as it may indicate lateral movement or remote code execution by an adversary. If
+  confirmed malicious, the attacker could gain extensive control over the network,
+  potentially leading to privilege escalation, data exfiltration, or further compromise
+  of the environment. Security teams should adjust detection thresholds based on their
+  specific environment.
+search: '`wineventlog_security` EventCode=4672 AND NOT(Caller_User_Name IN ("DWM-1","DWM-2","DWM-3","LOCAL
+  SERVICE","NETWORK SERVICE","SYSTEM","*$")) | bucket span=5m _time | stats dc(Computer)
+  AS unique_targets values(Computer) as dest values(PrivilegeList) as privileges by
+  _time, Caller_User_Name | rename Caller_User_Name as user| where unique_targets
+  > 30 | `windows_special_privileged_logon_on_multiple_hosts_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  special logon events. The Advanced Security Audit policy setting `Audit Special
+  Logon` within `Logon/Logoff` need to be enabled.
+known_false_positives: Vulnerability scanners or system administration tools may also
+  trigger this detection. Filter as needed.
 references:
 - https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4672
 - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319113(v=ws.11)
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: 'A user $user$ obtained special privileges on a large number of endpoints (Count: $unique_targets$) within 5 minutes.'
+  message: 'A user $user$ obtained special privileges on a large number of endpoints
+    (Count: $unique_targets$) within 5 minutes.'
   mitre_attack_id:
   - T1087
   - T1021.002
@@ -46,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Caller_User_Name
-  - Computer
-  - PrivilegeList
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/special_logon_on_mulitple_hosts/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/special_logon_on_mulitple_hosts/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_sql_spawning_certutil.yml b/detections/endpoint/windows_sql_spawning_certutil.yml
index 790a24f65c..48eff4d10c 100644
--- a/detections/endpoint/windows_sql_spawning_certutil.yml
+++ b/detections/endpoint/windows_sql_spawning_certutil.yml
@@ -9,10 +9,33 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the use of certutil to download software, specifically when spawned by SQL-related processes. This detection leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions involving certutil with parameters like *urlcache* and *split*. This activity is significant as it may indicate a compromise by threat actors, such as Flax Typhoon, who use certutil to establish persistent VPN connections. If confirmed malicious, this behavior could allow attackers to maintain access, monitor system availability, and potentially escalate to data theft or ransomware deployment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("sqlservr.exe", "sqlagent.exe", "sqlps.exe", "launchpad.exe", "sqldumper.exe") `process_certutil` (Processes.process=*urlcache* Processes.process=*split*) OR Processes.process=*urlcache* by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.original_file_name Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_sql_spawning_certutil_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: The occurrence of false positives should be minimal, given that the SQL agent does not typically download software using CertUtil.
+description: The following analytic detects the use of certutil to download software,
+  specifically when spawned by SQL-related processes. This detection leverages Endpoint
+  Detection and Response (EDR) data, focusing on command-line executions involving
+  certutil with parameters like *urlcache* and *split*. This activity is significant
+  as it may indicate a compromise by threat actors, such as Flax Typhoon, who use
+  certutil to establish persistent VPN connections. If confirmed malicious, this behavior
+  could allow attackers to maintain access, monitor system availability, and potentially
+  escalate to data theft or ransomware deployment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("sqlservr.exe", "sqlagent.exe", "sqlps.exe", "launchpad.exe", "sqldumper.exe")
+  `process_certutil` (Processes.process=*urlcache* Processes.process=*split*) OR Processes.process=*urlcache*
+  by Processes.dest Processes.user Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process Processes.process_id Processes.original_file_name
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_sql_spawning_certutil_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: The occurrence of false positives should be minimal, given
+  that the SQL agent does not typically download software using CertUtil.
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/08/24/flax-typhoon-using-legitimate-software-to-quietly-access-taiwanese-organizations/
 tags:
@@ -22,7 +45,8 @@ tags:
   atomic_guid: []
   confidence: 100
   impact: 90
-  message: $process_name$ was launched on $dest$ by $user$. This behavior is uncommon with the SQL process identified.
+  message: $process_name$ was launched on $dest$ by $user$. This behavior is uncommon
+    with the SQL process identified.
   mitre_attack_id:
   - T1105
   observable:
@@ -42,14 +66,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.original_file_name
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml b/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml
index ca6ff11238..95f36d1457 100644
--- a/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml
+++ b/detections/endpoint/windows_sqlwriter_sqldumper_dll_sideload.yml
@@ -7,10 +7,31 @@ data_source:
 - Sysmon EventID 7
 type: TTP
 status: production
-description: The following analytic detects the abuse of SqlWriter and SQLDumper executables to sideload the vcruntime140.dll library. It leverages Sysmon EventCode 7 logs, focusing on instances where SQLDumper.exe or SQLWriter.exe load vcruntime140.dll, excluding legitimate loads from the System32 directory. This activity is significant as it indicates potential DLL sideloading, a technique used by adversaries to execute malicious code within trusted processes. If confirmed malicious, this could allow attackers to execute arbitrary code, maintain persistence, and evade detection by blending with legitimate processes.
-search: '`sysmon` EventCode=7 (Image="*\\SQLDumper.exe" OR Image="*\\SQLWriter.exe") ImageLoaded="*\\vcruntime140.dll" NOT ImageLoaded="C:\\Windows\\System32\\*" | stats values(ImageLoaded) count min(_time) as firstTime max(_time) as lastTime by Image,ImageLoaded, user, Computer, EventCode | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_sqlwriter_sqldumper_dll_sideload_filter`'
-how_to_implement: The analytic is designed to be run against Sysmon event logs collected from endpoints. The analytic requires the Sysmon event logs to be ingested into Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters out the legitimate loading of vcruntime140.dll from the System32 directory to reduce false positives. The analytic can be modified to include additional known good paths for vcruntime140.dll to further reduce false positives.
-known_false_positives: False positives are possible if legitimate processes are loading vcruntime140.dll from non-standard directories. It is recommended to investigate the context of the process loading vcruntime140.dll to determine if it is malicious or not. Modify the search to include additional known good paths for vcruntime140.dll to reduce false positives.
+description: The following analytic detects the abuse of SqlWriter and SQLDumper executables
+  to sideload the vcruntime140.dll library. It leverages Sysmon EventCode 7 logs,
+  focusing on instances where SQLDumper.exe or SQLWriter.exe load vcruntime140.dll,
+  excluding legitimate loads from the System32 directory. This activity is significant
+  as it indicates potential DLL sideloading, a technique used by adversaries to execute
+  malicious code within trusted processes. If confirmed malicious, this could allow
+  attackers to execute arbitrary code, maintain persistence, and evade detection by
+  blending with legitimate processes.
+search: '`sysmon` EventCode=7 (Image="*\\SQLDumper.exe" OR Image="*\\SQLWriter.exe")
+  ImageLoaded="*\\vcruntime140.dll" NOT ImageLoaded="C:\\Windows\\System32\\*" | stats
+  values(ImageLoaded) count min(_time) as firstTime max(_time) as lastTime by Image,ImageLoaded,
+  user, Computer, EventCode | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `windows_sqlwriter_sqldumper_dll_sideload_filter`'
+how_to_implement: The analytic is designed to be run against Sysmon event logs collected
+  from endpoints. The analytic requires the Sysmon event logs to be ingested into
+  Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe
+  or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters
+  out the legitimate loading of vcruntime140.dll from the System32 directory to reduce
+  false positives. The analytic can be modified to include additional known good paths
+  for vcruntime140.dll to further reduce false positives.
+known_false_positives: False positives are possible if legitimate processes are loading
+  vcruntime140.dll from non-standard directories. It is recommended to investigate
+  the context of the process loading vcruntime140.dll to determine if it is malicious
+  or not. Modify the search to include additional known good paths for vcruntime140.dll
+  to reduce false positives.
 references:
 - https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties
 - https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader
@@ -20,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,17 +75,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Image
-  - ImageLoaded
-  - user
-  - Computer
-  - EventCode
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/wineloader/sqlwriter_sqldumper_sideload_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/wineloader/sqlwriter_sqldumper_sideload_windows-sysmon.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
diff --git a/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml b/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml
index e9a9b49978..43e50aee37 100644
--- a/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates___esc1_abuse.yml
@@ -5,13 +5,40 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a new certificate is requested or granted against Active Directory Certificate Services (AD CS) using a Subject Alternative Name (SAN). It leverages Windows Security Event Codes 4886 and 4887 to identify these actions. This activity is significant because improperly configured certificate templates can be exploited for privilege escalation and environment compromise. If confirmed malicious, an attacker could gain elevated privileges or persist within the environment, potentially leading to unauthorized access to sensitive information and further exploitation.
+description: The following analytic detects when a new certificate is requested or
+  granted against Active Directory Certificate Services (AD CS) using a Subject Alternative
+  Name (SAN). It leverages Windows Security Event Codes 4886 and 4887 to identify
+  these actions. This activity is significant because improperly configured certificate
+  templates can be exploited for privilege escalation and environment compromise.
+  If confirmed malicious, an attacker could gain elevated privileges or persist within
+  the environment, potentially leading to unauthorized access to sensitive information
+  and further exploitation.
 data_source:
 - Windows Event Log Security 4886
 - Windows Event Log Security 4887
-search: '`wineventlog_security` EventCode IN (4886,4887) Attributes="*SAN:*upn*" Attributes="*CertificateTemplate:*" | stats count min(_time) as firstTime max(_time) as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`| fillnull | rex field=Attributes "(?i)CertificateTemplate:(?<object>[^\r\n]+)" | rex field=Attributes "(?i)ccm:(?<req_src>[^\r\n]+)" | rex max_match=10 field=Attributes "(?i)(upn=(?<req_user_1>[^\r\n&]+))" | rex max_match=10 field=Attributes "(?i)(dns=(?<req_dest_1>[^\r\n&]+))" | rex field=Requester "(.+\\\\)?(?<src_user>[^\r\n]+)" | eval flavor_text = case(EventCode=="4886","A suspicious certificate was requested using request ID: ".''RequestId'',EventCode=="4887", "A suspicious certificate was issued using request ID: ".''RequestId''.". To revoke this certifacte use this request ID or the SSL fingerprint [".''ssl_hash''."]"), dest = upper(coalesce(req_dest_1,req_dest_2)), src = upper(coalesce(req_src,Computer)) | fields - req_* | rename Attributes as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, Requester as ssl_subject_common_name| `windows_steal_authentication_certificates___esc1_abuse_filter`'
-how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. Recommend throttle correlation by RequestId/ssl_serial at minimum.
-known_false_positives: False positives may be generated in environments where administrative users or processes are allowed to generate certificates with Subject Alternative Names. Sources or templates used in these processes may need to be tuned out for accurate function.
+search: "`wineventlog_security` EventCode IN (4886,4887) Attributes=\"*SAN:*upn*\"\
+  \ Attributes=\"*CertificateTemplate:*\" | stats count min(_time) as firstTime max(_time)
+  as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject
+  values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes,
+  RequestId | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`|
+  fillnull | rex field=Attributes \"(?i)CertificateTemplate:(?<object>[^\\r\\n]+)\"\
+  \ | rex field=Attributes \"(?i)ccm:(?<req_src>[^\\r\\n]+)\" | rex max_match=10 field=Attributes
+  \"(?i)(upn=(?<req_user_1>[^\\r\\n&]+))\" | rex max_match=10 field=Attributes \"\
+  (?i)(dns=(?<req_dest_1>[^\\r\\n&]+))\" | rex field=Requester \"(.+\\\\\\\\)?(?<src_user>[^\\\
+  r\\n]+)\" | eval flavor_text = case(EventCode==\"4886\",\"A suspicious certificate
+  was requested using request ID: \".'RequestId',EventCode==\"4887\", \"A suspicious
+  certificate was issued using request ID: \".'RequestId'.\". To revoke this certifacte
+  use this request ID or the SSL fingerprint [\".'ssl_hash'.\"]\"), dest = upper(coalesce(req_dest_1,req_dest_2)),
+  src = upper(coalesce(req_src,Computer)) | fields - req_* | rename Attributes as
+  object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial,
+  Requester as ssl_subject_common_name| `windows_steal_authentication_certificates___esc1_abuse_filter`"
+how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled
+  on AD CS and within Group Policy Management for CS server. See Page 115 of first
+  reference. Recommend throttle correlation by RequestId/ssl_serial at minimum.
+known_false_positives: False positives may be generated in environments where administrative
+  users or processes are allowed to generate certificates with Subject Alternative
+  Names. Sources or templates used in these processes may need to be tuned out for
+  accurate function.
 references:
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf
 - https://github.com/ly4k/Certipy#esc1
@@ -22,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,17 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Attributes
-  - Computer
-  - EventCode
-  - Requester
-  - RequestId
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml b/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml
index 33c54b0e05..5ab907710b 100644
--- a/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates___esc1_authentication.yml
@@ -5,24 +5,59 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a suspicious certificate with a Subject Alternative Name (SAN) is issued using Active Directory Certificate Services (AD CS) and then immediately used for authentication. This detection leverages Windows Security Event Logs, specifically EventCode 4887, to identify the issuance and subsequent use of the certificate. This activity is significant because improperly configured certificate templates can be exploited for privilege escalation and environment compromise. If confirmed malicious, an attacker could gain unauthorized access, escalate privileges, and potentially compromise the entire environment.
+description: The following analytic detects when a suspicious certificate with a Subject
+  Alternative Name (SAN) is issued using Active Directory Certificate Services (AD
+  CS) and then immediately used for authentication. This detection leverages Windows
+  Security Event Logs, specifically EventCode 4887, to identify the issuance and subsequent
+  use of the certificate. This activity is significant because improperly configured
+  certificate templates can be exploited for privilege escalation and environment
+  compromise. If confirmed malicious, an attacker could gain unauthorized access,
+  escalate privileges, and potentially compromise the entire environment.
 data_source:
 - Windows Event Log Security 4887
 - Windows Event Log Security 4768
-search: '`wineventlog_security` EventCode IN (4887) Attributes="*SAN:*upn*" Attributes="*CertificateTemplate:*" | stats count min(_time) as firstTime max(_time) as lastTime values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier) as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | rex field=Attributes "(?i)CertificateTemplate:(?<object>[^\r\n]+)" | rex field=Attributes "(?i)ccm:(?<req_src>[^\r\n]+)" | rex max_match=10 field=Attributes "(?i)(upn=(?<req_user_1>[^\r\n&]+))" | rex max_match=10 field=Attributes "(?i)(dns=(?<req_dest_1>[^\r\n&]+))" | rex field=Requester "(.+\\\\)?(?<src_user>[^\r\n]+)" | rename Attributes as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial, Requester as ssl_subject_common_name | eval user = lower(coalesce(req_user_1,req_user_2))  | join user [ | search `wineventlog_security` EventCode=4768 CertThumbprint=* | rename TargetUserName as user, Computer as auth_dest, IpAddress as auth_src | fields auth_src,auth_dest,user ] | eval src = upper(coalesce(auth_src,req_src)), dest = upper(coalesce(auth_dest,req_dest_1,req_dest_2)), risk_score = 90 | eval flavor_text = case(signature_id=="4887", "User account [".''user''."] authenticated after a suspicious certificate was issued for it by [".''src_user''."] using certificate request ID: ".''ssl_serial'') | fields - req_* auth_* | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates___esc1_authentication_filter`'
-how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference. Recommend throttle correlation by RequestId/ssl_serial at minimum.
-known_false_positives: False positives may be generated in environments where administrative users or processes are allowed to generate certificates with Subject Alternative Names for authentication. Sources or templates used in these processes may need to be tuned out for accurate function.
+search: "`wineventlog_security` EventCode IN (4887) Attributes=\"*SAN:*upn*\" Attributes=\"\
+  *CertificateTemplate:*\" | stats count min(_time) as firstTime max(_time) as lastTime
+  values(name) as name values(status) as status values(Subject) as ssl_subject values(SubjectKeyIdentifier)
+  as ssl_hash by Computer, EventCode, Requester, Attributes, RequestId | rex field=Attributes
+  \"(?i)CertificateTemplate:(?<object>[^\\r\\n]+)\" | rex field=Attributes \"(?i)ccm:(?<req_src>[^\\\
+  r\\n]+)\" | rex max_match=10 field=Attributes \"(?i)(upn=(?<req_user_1>[^\\r\\n&]+))\"\
+  \ | rex max_match=10 field=Attributes \"(?i)(dns=(?<req_dest_1>[^\\r\\n&]+))\" |
+  rex field=Requester \"(.+\\\\\\\\)?(?<src_user>[^\\r\\n]+)\" | rename Attributes
+  as object_attrs, EventCode as signature_id, name as signature, RequestId as ssl_serial,
+  Requester as ssl_subject_common_name | eval user = lower(coalesce(req_user_1,req_user_2))\
+  \  | join user [ | search `wineventlog_security` EventCode=4768 CertThumbprint=*
+  | rename TargetUserName as user, Computer as auth_dest, IpAddress as auth_src |
+  fields auth_src,auth_dest,user ] | eval src = upper(coalesce(auth_src,req_src)),
+  dest = upper(coalesce(auth_dest,req_dest_1,req_dest_2)), risk_score = 90 | eval
+  flavor_text = case(signature_id==\"4887\", \"User account [\".'user'.\"] authenticated
+  after a suspicious certificate was issued for it by [\".'src_user'.\"] using certificate
+  request ID: \".'ssl_serial') | fields - req_* auth_* | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates___esc1_authentication_filter`"
+how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled
+  on AD CS and within Group Policy Management for CS server. See Page 115 of first
+  reference. Recommend throttle correlation by RequestId/ssl_serial at minimum.
+known_false_positives: False positives may be generated in environments where administrative
+  users or processes are allowed to generate certificates with Subject Alternative
+  Names for authentication. Sources or templates used in these processes may need
+  to be tuned out for accurate function.
 references:
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf
 - https://github.com/ly4k/Certipy#esc1
 - https://pentestlaboratories.com/2021/11/08/threat-hunting-certificate-account-persistence/
 drilldown_searches:
 - name: View the detection results for - "$src$" and "$dest$"
-  search: '%original_detection_search% | search  src = "$src$" dest = "$dest$" src_user = "$src_user$" user = "$user$"'
+  search: '%original_detection_search% | search  src = "$src$" dest = "$dest$" src_user
+    = "$src_user$" user = "$user$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$", "$src_user$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$dest$", "$src_user$", "$user$") starthoursago=168  | stats count min(_time)
+    as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message)
+    as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -64,20 +99,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Attributes
-  - Computer
-  - EventCode
-  - Requester
-  - RequestId
-  - TargetUserName
-  - Computer
-  - IpAddress
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_winsecurity.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml b/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml
index a2c9d3dde2..d9e9b2cfa4 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_certificate_issued.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the issuance of a new certificate by Certificate Services - AD CS, detected via Event ID 4887. This event logs the requester user context, DNS hostname of the requesting machine, and the request time. Monitoring this activity is crucial as it can indicate potential misuse of authentication certificates. If confirmed malicious, an attacker could use the issued certificate to impersonate users, escalate privileges, or maintain persistence within the environment. This detection helps in identifying and correlating suspicious certificate-related activities for further investigation.
+description: The following analytic identifies the issuance of a new certificate by
+  Certificate Services - AD CS, detected via Event ID 4887. This event logs the requester
+  user context, DNS hostname of the requesting machine, and the request time. Monitoring
+  this activity is crucial as it can indicate potential misuse of authentication certificates.
+  If confirmed malicious, an attacker could use the issued certificate to impersonate
+  users, escalate privileges, or maintain persistence within the environment. This
+  detection helps in identifying and correlating suspicious certificate-related activities
+  for further investigation.
 data_source:
 - Windows Event Log Security 4887
-search: '`wineventlog_security`  EventCode=4887 | stats count min(_time) as firstTime max(_time) as lastTime by dest, name, Requester, action, Attributes, Subject | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_steal_authentication_certificates_certificate_issued_filter`'
-how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference.
-known_false_positives: False positives will be generated based on normal certificates issued. Leave enabled to generate Risk, as this is meant to be an anomaly analytic.
+search: '`wineventlog_security`  EventCode=4887 | stats count min(_time) as firstTime
+  max(_time) as lastTime by dest, name, Requester, action, Attributes, Subject | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `windows_steal_authentication_certificates_certificate_issued_filter`'
+how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled
+  on AD CS and within Group Policy Management for CS server. See Page 115 of first
+  reference.
+known_false_positives: False positives will be generated based on normal certificates
+  issued. Leave enabled to generate Risk, as this is meant to be an anomaly analytic.
 references:
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - name
-  - Requester
-  - action
-  - Attributes
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4887_windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4887_windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml b/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml
index 5553b8f224..af1c71ed8a 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_certificate_request.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects when a new certificate is requested from Certificate Services - AD CS. It leverages Event ID 4886, which indicates that a certificate request has been received. This activity is significant because unauthorized certificate requests can be part of credential theft or lateral movement tactics. If confirmed malicious, an attacker could use the certificate to impersonate users, gain unauthorized access to resources, or establish persistent access within the environment. Monitoring and correlating this event with other suspicious activities is crucial for identifying potential security incidents.
+description: The following analytic detects when a new certificate is requested from
+  Certificate Services - AD CS. It leverages Event ID 4886, which indicates that a
+  certificate request has been received. This activity is significant because unauthorized
+  certificate requests can be part of credential theft or lateral movement tactics.
+  If confirmed malicious, an attacker could use the certificate to impersonate users,
+  gain unauthorized access to resources, or establish persistent access within the
+  environment. Monitoring and correlating this event with other suspicious activities
+  is crucial for identifying potential security incidents.
 data_source:
 - Windows Event Log Security 4886
-search: '`wineventlog_security` EventCode=4886 | stats count min(_time) as firstTime max(_time) as lastTime by dest, name, Requester, action, Attributes | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certificate_request_filter`'
-how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 115 of first reference.
-known_false_positives: False positives will be generated based on normal certificate requests. Leave enabled to generate Risk, as this is meant to be an anomaly analytic.
+search: '`wineventlog_security` EventCode=4886 | stats count min(_time) as firstTime
+  max(_time) as lastTime by dest, name, Requester, action, Attributes | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certificate_request_filter`'
+how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled
+  on AD CS and within Group Policy Management for CS server. See Page 115 of first
+  reference.
+known_false_positives: False positives will be generated based on normal certificate
+  requests. Leave enabled to generate Risk, as this is meant to be an anomaly analytic.
 references:
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +57,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - name
-  - Requester
-  - action
-  - Attributes
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4886_windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4886_windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml b/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml
index fa504e3701..87a8cf3558 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_certutil_backup.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects CertUtil.exe performing a backup of the Certificate Store. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line executions involving CertUtil with backup parameters. This activity is significant because it may indicate an attempt to steal authentication certificates, which are critical for secure communications. If confirmed malicious, an attacker could use the stolen certificates to impersonate users, decrypt sensitive data, or gain unauthorized access to systems, leading to severe security breaches.
+description: The following analytic detects CertUtil.exe performing a backup of the
+  Certificate Store. It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on specific command-line executions involving CertUtil with backup
+  parameters. This activity is significant because it may indicate an attempt to steal
+  authentication certificates, which are critical for secure communications. If confirmed
+  malicious, an attacker could use the stolen certificates to impersonate users, decrypt
+  sensitive data, or gain unauthorized access to systems, leading to severe security
+  breaches.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process IN ("*-backupdb *", "*-backup *") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certutil_backup_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be generated based on normal certificate store backups. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. If CS backups are not normal, enable as TTP.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_certutil` Processes.process
+  IN ("*-backupdb *", "*-backup *") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_certutil_backup_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be generated based on normal certificate
+  store backups. Leave enabled to generate Risk, as this is meant to be an anomaly
+  analytic. If CS backups are not normal, enable as TTP.
 references:
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf
 drilldown_searches:
@@ -21,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 50
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to backup the Certificate Store.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to backup the Certificate Store.
   mitre_attack_id:
   - T1649
   observable:
@@ -54,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/backupdb_certutil_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/backupdb_certutil_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml b/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml
index 2edf8f5fd4..0c3040efe3 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_cryptoapi.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the extraction of authentication certificates using Windows Event Log - CAPI2 (CryptoAPI 2). It leverages EventID 70, which is generated when a certificate's private key is acquired. This detection is significant because it can identify potential misuse of certificates, such as those extracted by tools like Mimikatz or Cobalt Strike. If confirmed malicious, this activity could allow attackers to impersonate users, escalate privileges, or access sensitive information, posing a severe risk to the organization's security.
+description: The following analytic detects the extraction of authentication certificates
+  using Windows Event Log - CAPI2 (CryptoAPI 2). It leverages EventID 70, which is
+  generated when a certificate's private key is acquired. This detection is significant
+  because it can identify potential misuse of certificates, such as those extracted
+  by tools like Mimikatz or Cobalt Strike. If confirmed malicious, this activity could
+  allow attackers to impersonate users, escalate privileges, or access sensitive information,
+  posing a severe risk to the organization's security.
 data_source:
 - Windows Event Log CAPI2 70
-search: '`capi2_operational` EventCode=70 | xmlkv UserData_Xml | stats count min(_time) as firstTime max(_time) as lastTime by Computer, UserData_Xml | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_cryptoapi_filter`'
-how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational log within the Windows Event Log. Note this is a debug log for many purposes, and the analytic only focuses in on EventID 70. Review the following gist for additional enabling information.
-known_false_positives: False positives may be present in some instances of legitimate applications requiring to export certificates. Filter as needed.
+search: '`capi2_operational` EventCode=70 | xmlkv UserData_Xml | stats count min(_time)
+  as firstTime max(_time) as lastTime by Computer, UserData_Xml | rename Computer
+  as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `windows_steal_authentication_certificates_cryptoapi_filter`'
+how_to_implement: To implement this analytic, one will need to enable the Microsoft-Windows-CAPI2/Operational
+  log within the Windows Event Log. Note this is a debug log for many purposes, and
+  the analytic only focuses in on EventID 70. Review the following gist for additional
+  enabling information.
+known_false_positives: False positives may be present in some instances of legitimate
+  applications requiring to export certificates. Filter as needed.
 references:
 - https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-vista/cc749296(v=ws.10)
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,14 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Computer
-  - UserData_Xml
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/capi2-operational.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/capi2-operational.log
     source: XmlWinEventLog:Microsoft-Windows-CAPI2/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml b/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml
index 93cba788ac..b726b1e863 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_cs_backup.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the backup of the Active Directory Certificate Services (AD CS) store, detected via Event ID 4876. This event is logged when a backup is performed using the CertSrv.msc UI or the CertUtil.exe -BackupDB command. Monitoring this activity is crucial as unauthorized backups can indicate an attempt to steal authentication certificates, which are critical for secure communications. If confirmed malicious, this activity could allow an attacker to impersonate users, escalate privileges, or access sensitive information, severely compromising the security of the environment.
+description: The following analytic identifies the backup of the Active Directory
+  Certificate Services (AD CS) store, detected via Event ID 4876. This event is logged
+  when a backup is performed using the CertSrv.msc UI or the CertUtil.exe -BackupDB
+  command. Monitoring this activity is crucial as unauthorized backups can indicate
+  an attempt to steal authentication certificates, which are critical for secure communications.
+  If confirmed malicious, this activity could allow an attacker to impersonate users,
+  escalate privileges, or access sensitive information, severely compromising the
+  security of the environment.
 data_source:
 - Windows Event Log Security 4876
-search: '`wineventlog_security` EventCode=4876| stats count min(_time) as firstTime max(_time) as lastTime by dest, name, action, Caller_Domain ,Caller_User_Name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_cs_backup_filter`'
-how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled on AD CS and within Group Policy Management for CS server. See Page 128 of first reference.
-known_false_positives: False positives will be generated based on normal certificate store backups. Leave enabled to generate Risk, as this is meant to be an anomaly analytic. If CS backups are not normal, enable as TTP.
+search: '`wineventlog_security` EventCode=4876| stats count min(_time) as firstTime
+  max(_time) as lastTime by dest, name, action, Caller_Domain ,Caller_User_Name |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_cs_backup_filter`'
+how_to_implement: To implement this analytic, enhanced Audit Logging must be enabled
+  on AD CS and within Group Policy Management for CS server. See Page 128 of first
+  reference.
+known_false_positives: False positives will be generated based on normal certificate
+  store backups. Leave enabled to generate Risk, as this is meant to be an anomaly
+  analytic. If CS backups are not normal, enable as TTP.
 references:
 - https://specterops.io/wp-content/uploads/sites/3/2022/06/Certified_Pre-Owned.pdf
 drilldown_searches:
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -40,17 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - name
-  - action
-  - Caller_Domain
-  - Caller_User_Name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4876_windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/4876_windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml b/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml
index 5f7727fca2..369d53fe81 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_export_certificate.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the PowerShell cmdlet 'export-certificate' executed via the command line, indicating an attempt to export a certificate from the local Windows Certificate Store. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. Exporting certificates is significant as it may indicate credential theft or preparation for man-in-the-middle attacks. If confirmed malicious, this activity could allow an attacker to impersonate users, decrypt sensitive communications, or gain unauthorized access to systems and data.
+description: The following analytic detects the use of the PowerShell cmdlet 'export-certificate'
+  executed via the command line, indicating an attempt to export a certificate from
+  the local Windows Certificate Store. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process execution logs and command-line
+  arguments. Exporting certificates is significant as it may indicate credential theft
+  or preparation for man-in-the-middle attacks. If confirmed malicious, this activity
+  could allow an attacker to impersonate users, decrypt sensitive communications,
+  or gain unauthorized access to systems and data.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-certificate*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_export_certificate_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Filtering may be requried based on automated utilities and third party applications that may export certificates.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-certificate*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_export_certificate_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Filtering may be requried based on automated utilities and
+  third party applications that may export certificates.
 references:
 - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj
 - https://learn.microsoft.com/en-us/powershell/module/pki/export-certificate?view=windowsserver2022-ps
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export a certificate from the local Windows Certificate Store.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to export a certificate from the
+    local Windows Certificate Store.
   mitre_attack_id:
   - T1649
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_certificate_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_certificate_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml b/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml
index 899cca32d4..fbb52ea7d7 100644
--- a/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml
+++ b/detections/endpoint/windows_steal_authentication_certificates_export_pfxcertificate.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate` on the command line, indicating an attempt to export a certificate from the local Windows Certificate Store. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate an attempt to exfiltrate authentication certificates, which can be used to impersonate users or decrypt sensitive data. If confirmed malicious, this could lead to unauthorized access and potential data breaches.
+description: The following analytic detects the use of the PowerShell cmdlet `export-pfxcertificate`
+  on the command line, indicating an attempt to export a certificate from the local
+  Windows Certificate Store. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs and command-line arguments.
+  This activity is significant as it may indicate an attempt to exfiltrate authentication
+  certificates, which can be used to impersonate users or decrypt sensitive data.
+  If confirmed malicious, this could lead to unauthorized access and potential data
+  breaches.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-pfxcertificate*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_export_pfxcertificate_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Filtering may be requried based on automated utilities and third party applications that may export certificates.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process="*export-pfxcertificate*"
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_authentication_certificates_export_pfxcertificate_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Filtering may be requried based on automated utilities and
+  third party applications that may export certificates.
 references:
 - https://dev.to/iamthecarisma/managing-windows-pfx-certificates-through-powershell-3pj
 - https://learn.microsoft.com/en-us/powershell/module/pki/export-pfxcertificate?view=windowsserver2022-ps
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +56,9 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to export a certificate from the local Windows Certificate Store.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to export a certificate from the
+    local Windows Certificate Store.
   mitre_attack_id:
   - T1649
   observable:
@@ -55,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_pfxcertificate_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/atomic_red_team/export_pfxcertificate_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml b/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml
index 3a331809b4..35142b85fe 100644
--- a/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml
+++ b/detections/endpoint/windows_steal_or_forge_kerberos_tickets_klist.yml
@@ -5,13 +5,34 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the execution of the Windows OS tool klist.exe, often used by post-exploitation tools like winpeas. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process details. Monitoring klist.exe is significant as it can indicate attempts to list or gather cached Kerberos tickets, which are crucial for lateral movement or privilege escalation. If confirmed malicious, this activity could enable attackers to move laterally within the network or escalate privileges, posing a severe security risk.
+description: The following analytic identifies the execution of the Windows OS tool
+  klist.exe, often used by post-exploitation tools like winpeas. This detection leverages
+  data from Endpoint Detection and Response (EDR) agents, focusing on process and
+  parent process details. Monitoring klist.exe is significant as it can indicate attempts
+  to list or gather cached Kerberos tickets, which are crucial for lateral movement
+  or privilege escalation. If confirmed malicious, this activity could enable attackers
+  to move laterally within the network or escalate privileges, posing a severe security
+  risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="klist.exe" OR Processes.original_file_name = "klist.exe" Processes.parent_process_name IN ("cmd.exe", "powershell*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_or_forge_kerberos_tickets_klist_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="klist.exe"
+  OR Processes.original_file_name = "klist.exe" Processes.parent_process_name IN ("cmd.exe",
+  "powershell*") by Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process
+  Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_steal_or_forge_kerberos_tickets_klist_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS
@@ -35,25 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml b/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml
index 99e031010a..e65707567b 100644
--- a/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml
+++ b/detections/endpoint/windows_suspect_process_with_authentication_traffic.yml
@@ -5,22 +5,44 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects executables running from public or temporary locations that are communicating over Windows domain authentication ports/protocols such as LDAP (389), LDAPS (636), and Kerberos (88). It leverages network traffic data to identify processes originating from user-controlled directories. This activity is significant because legitimate applications rarely run from these locations and attempt domain authentication, making it a potential indicator of compromise. If confirmed malicious, attackers could leverage this to access domain resources, potentially leading to further exploitation and lateral movement within the network.
+description: The following analytic detects executables running from public or temporary
+  locations that are communicating over Windows domain authentication ports/protocols
+  such as LDAP (389), LDAPS (636), and Kerberos (88). It leverages network traffic
+  data to identify processes originating from user-controlled directories. This activity
+  is significant because legitimate applications rarely run from these locations and
+  attempt domain authentication, making it a potential indicator of compromise. If
+  confirmed malicious, attackers could leverage this to access domain resources, potentially
+  leading to further exploitation and lateral movement within the network.
 data_source:
 - Sysmon EventID 3
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.process_id) as process_id  from datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port IN ("88","389","636")  AND All_Traffic.app IN ("*\\users\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*")  by All_Traffic.app,All_Traffic.src,All_Traffic.src_ip,All_Traffic.user,All_Traffic.dest,All_Traffic.dest_ip,All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)`  | rex field=app ".*\\\(?<process_name>.*)$" | rename app as process | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | `windows_suspect_process_with_authentication_traffic_filter`'
-how_to_implement: To implement this analytic, Sysmon should be installed in the environment and generating network events for  userland and/or known public writable locations.
-known_false_positives: Known applications running from these locations for legitimate purposes. Targeting only kerberos (port 88) may significantly reduce noise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(All_Traffic.process_id) as process_id  from datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port IN ("88","389","636")  AND All_Traffic.app IN ("*\\users\\*",
+  "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*")  by
+  All_Traffic.app,All_Traffic.src,All_Traffic.src_ip,All_Traffic.user,All_Traffic.dest,All_Traffic.dest_ip,All_Traffic.dest_port
+  | `drop_dm_object_name(All_Traffic)`  | rex field=app ".*\\\(?<process_name>.*)$"
+  | rename app as process | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | `windows_suspect_process_with_authentication_traffic_filter`'
+how_to_implement: To implement this analytic, Sysmon should be installed in the environment
+  and generating network events for  userland and/or known public writable locations.
+known_false_positives: Known applications running from these locations for legitimate
+  purposes. Targeting only kerberos (port 88) may significantly reduce noise.
 references:
 - https://attack.mitre.org/techniques/T1069/002/
 - https://book.hacktricks.xyz/network-services-pentesting/pentesting-kerberos-88
 drilldown_searches:
 - name: View the detection results for - "$src$" and "$dest$"
-  search: '%original_detection_search% | search  src = "$src$" dest = "$dest$" user = "$user$"'
+  search: '%original_detection_search% | search  src = "$src$" dest = "$dest$" user
+    = "$user$"'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: The process $process_name$ on $src$ has been communicating with $dest$ on $dest_port$.
+  message: The process $process_name$ on $src$ has been communicating with $dest$
+    on $dest_port$.
   mitre_attack_id:
   - T1087
   - T1087.002
@@ -56,19 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.app
-  - All_Traffic.src
-  - All_Traffic.src_ip
-  - All_Traffic.user
-  - All_Traffic.dest
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1649/certify_abuse/certify_esc1_abuse_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml b/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml
index c41e22d1b1..ca0bb0f005 100644
--- a/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml
+++ b/detections/endpoint/windows_system_binary_proxy_execution_compiled_html_file_decompile.yml
@@ -5,13 +5,32 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of the decompile parameter with the HTML Help application (HH.exe). This behavior is identified through Endpoint Detection and Response (EDR) telemetry, focusing on command-line executions involving the decompile parameter. This activity is significant because it is an uncommon command and has been associated with APT41 campaigns, where it was used to unpack HTML help files for further malicious actions. If confirmed malicious, this technique could allow attackers to execute arbitrary commands, potentially leading to further compromise and persistence within the environment.
+description: The following analytic detects the use of the decompile parameter with
+  the HTML Help application (HH.exe). This behavior is identified through Endpoint
+  Detection and Response (EDR) telemetry, focusing on command-line executions involving
+  the decompile parameter. This activity is significant because it is an uncommon
+  command and has been associated with APT41 campaigns, where it was used to unpack
+  HTML help files for further malicious actions. If confirmed malicious, this technique
+  could allow attackers to execute arbitrary commands, potentially leading to further
+  compromise and persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*-decompile* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_binary_proxy_execution_compiled_html_file_decompile_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*-decompile*
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_binary_proxy_execution_compiled_html_file_decompile_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: False positives should be limited, filter as needed.
 references:
 - https://www.ptsecurity.com/ww-en/analytics/pt-esc-threat-intelligence/higaisa-or-winnti-apt-41-backdoors-old-and-new/
@@ -24,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 100
-  message: $process_name$ has been identified using decompile against a CHM on $dest$ under user $user$.
+  message: $process_name$ has been identified using decompile against a CHM on $dest$
+    under user $user$.
   mitre_attack_id:
   - T1218.001
   - T1218
@@ -55,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/hh_decom_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/hh_decom_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml b/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml
index 21cb8f78be..8d9ac50442 100644
--- a/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml
+++ b/detections/endpoint/windows_system_discovery_using_ldap_nslookup.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of nslookup.exe to query domain information using LDAP. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as nslookup.exe can be abused by malware like Qakbot to gather critical domain details, such as SRV records and server names. If confirmed malicious, this behavior could allow attackers to map the network, identify key servers, and plan further attacks, potentially leading to data exfiltration or lateral movement within the network.
+description: The following analytic detects the execution of nslookup.exe to query
+  domain information using LDAP. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process names and command-line arguments. This activity
+  is significant as nslookup.exe can be abused by malware like Qakbot to gather critical
+  domain details, such as SRV records and server names. If confirmed malicious, this
+  behavior could allow attackers to map the network, identify key servers, and plan
+  further attacks, potentially leading to data exfiltration or lateral movement within
+  the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "nslookup.exe" OR Processes.original_file_name = "nslookup.exe") AND Processes.process = "*_ldap._tcp.dc._msdcs*" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_system_discovery_using_ldap_nslookup_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: dministrator may execute this commandline tool for auditing purposes. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = "nslookup.exe"
+  OR Processes.original_file_name = "nslookup.exe") AND Processes.process = "*_ldap._tcp.dc._msdcs*"
+  by Processes.parent_process Processes.parent_process_name Processes.process_name
+  Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest
+  Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_system_discovery_using_ldap_nslookup_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: dministrator may execute this commandline tool for auditing
+  purposes. Filter as needed.
 references:
 - https://securelist.com/qakbot-technical-analysis/103931/
 - https://learn.microsoft.com/en-us/troubleshoot/windows-server/networking/verify-srv-dns-records-have-been-created
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_discovery_using_qwinsta.yml b/detections/endpoint/windows_system_discovery_using_qwinsta.yml
index b55b794d83..a3b81f53f7 100644
--- a/detections/endpoint/windows_system_discovery_using_qwinsta.yml
+++ b/detections/endpoint/windows_system_discovery_using_qwinsta.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of "qwinsta.exe" on a Windows operating system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. The "qwinsta.exe" tool is significant because it can display detailed session information on a remote desktop session host server. This behavior is noteworthy as it is commonly abused by Qakbot malware to gather system information and send it back to its Command and Control (C2) server. If confirmed malicious, this activity could lead to unauthorized data exfiltration and further compromise of the host.
+description: The following analytic detects the execution of "qwinsta.exe" on a Windows
+  operating system. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs. The "qwinsta.exe" tool is significant
+  because it can display detailed session information on a remote desktop session
+  host server. This behavior is noteworthy as it is commonly abused by Qakbot malware
+  to gather system information and send it back to its Command and Control (C2) server.
+  If confirmed malicious, this activity could lead to unauthorized data exfiltration
+  and further compromise of the host.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "qwinsta.exe" OR Processes.original_file_name = "qwinsta.exe" by Processes.parent_process Processes.parent_process_name Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `windows_system_discovery_using_qwinsta_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator may execute this commandline tool for auditing purposes. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "qwinsta.exe"
+  OR Processes.original_file_name = "qwinsta.exe" by Processes.parent_process Processes.parent_process_name
+  Processes.process_name Processes.process_id Processes.process_guid Processes.process
+  Processes.user Processes.dest Processes.parent_process_id Processes.original_file_name
+  | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
+  | `windows_system_discovery_using_qwinsta_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator may execute this commandline tool for auditing
+  purposes. Filter as needed.
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/qwinsta
 - https://securelist.com/qakbot-technical-analysis/103931/
@@ -34,23 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/qakbot_discovery_cmdline/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_file_on_disk.yml b/detections/endpoint/windows_system_file_on_disk.yml
index c5386c8fca..e378af3cc4 100644
--- a/detections/endpoint/windows_system_file_on_disk.yml
+++ b/detections/endpoint/windows_system_file_on_disk.yml
@@ -5,11 +5,26 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the creation of new .sys files on disk. It leverages the Endpoint.Filesystem data model to identify and log instances where .sys files are written to the filesystem. This activity is significant because .sys files are often used as kernel mode drivers, and their unauthorized creation can indicate malicious activity such as rootkit installation. If confirmed malicious, this could allow an attacker to gain kernel-level access, leading to full system compromise, persistent control, and the ability to bypass security mechanisms.
+description: The following analytic detects the creation of new .sys files on disk.
+  It leverages the Endpoint.Filesystem data model to identify and log instances where
+  .sys files are written to the filesystem. This activity is significant because .sys
+  files are often used as kernel mode drivers, and their unauthorized creation can
+  indicate malicious activity such as rootkit installation. If confirmed malicious,
+  this could allow an attacker to gain kernel-level access, leading to full system
+  compromise, persistent control, and the ability to bypass security mechanisms.
 data_source:
 - Sysmon EventID 11
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.sys*" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path Filesystem.file_hash | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_system_file_on_disk_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on files from your endpoints into the `Endpoint` datamodel in the `Filesystem` node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product. In addition, filtering may occur by adding NOT (Filesystem.file_path IN ("*\\Windows\\*", "*\\Program File*", "*\\systemroot\\*","%SystemRoot%*", "system32\*")). This will level out the noise generated to potentally lead to generating notables.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.sys*"
+  by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name
+  Filesystem.file_path Filesystem.file_hash | `drop_dm_object_name(Filesystem)` |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_system_file_on_disk_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on files from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+  In addition, confirm the latest CIM App 4.20 or higher is installed and the latest
+  TA for the endpoint product. In addition, filtering may occur by adding NOT (Filesystem.file_path
+  IN ("*\\Windows\\*", "*\\Program File*", "*\\systemroot\\*","%SystemRoot%*", "system32\*")).
+  This will level out the noise generated to potentally lead to generating notables.
 known_false_positives: False positives will be present. Filter as needed.
 references:
 - https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/
@@ -32,21 +47,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sysmon_sys_filemod.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/drivers/sysmon_sys_filemod.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_logoff_commandline.yml b/detections/endpoint/windows_system_logoff_commandline.yml
index 2971a2adea..c459f0aad2 100644
--- a/detections/endpoint/windows_system_logoff_commandline.yml
+++ b/detections/endpoint/windows_system_logoff_commandline.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the Windows command line to log off a host machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes involving `shutdown.exe` with specific parameters. This activity is significant as it is often associated with Advanced Persistent Threats (APTs) and Remote Access Trojans (RATs) like dcrat, which use this technique to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed malicious, this could lead to system downtime, data loss, or hindered incident response efforts.
+description: The following analytic detects the execution of the Windows command line
+  to log off a host machine. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on processes involving `shutdown.exe` with specific parameters.
+  This activity is significant as it is often associated with Advanced Persistent
+  Threats (APTs) and Remote Access Trojans (RATs) like dcrat, which use this technique
+  to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed
+  malicious, this could lead to system downtime, data loss, or hindered incident response
+  efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" Processes.process IN ("* /l*", "* -l*") Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_logoff_commandline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator may execute this commandline to trigger shutdown, logoff or restart the host machine.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe)
+  Processes.process="*shutdown*" Processes.process IN ("* /l*", "* -l*") Processes.process
+  IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_logoff_commandline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator may execute this commandline to trigger shutdown,
+  logoff or restart the host machine.
 references:
 - https://attack.mitre.org/techniques/T1529/
 - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor
@@ -22,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_network_config_discovery_display_dns.yml b/detections/endpoint/windows_system_network_config_discovery_display_dns.yml
index 38b6602bf8..19768fb7d6 100644
--- a/detections/endpoint/windows_system_network_config_discovery_display_dns.yml
+++ b/detections/endpoint/windows_system_network_config_discovery_display_dns.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the execution of the "ipconfig /displaydns" command, which retrieves DNS reply information using the built-in Windows tool IPConfig. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. Monitoring this activity is significant as threat actors and post-exploitation tools like WINPEAS often abuse this command to gather network information. If confirmed malicious, this activity could allow attackers to map the network, identify DNS servers, and potentially facilitate further network-based attacks or lateral movement.
+description: The following analytic identifies the execution of the "ipconfig /displaydns"
+  command, which retrieves DNS reply information using the built-in Windows tool IPConfig.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on process command-line executions. Monitoring this activity is significant
+  as threat actors and post-exploitation tools like WINPEAS often abuse this command
+  to gather network information. If confirmed malicious, this activity could allow
+  attackers to map the network, identify DNS servers, and potentially facilitate further
+  network-based attacks or lateral movement.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="ipconfig.exe" OR Processes.original_file_name = "ipconfig.exe" AND Processes.process = "*/displaydns*" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_network_config_discovery_display_dns_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="ipconfig.exe"
+  OR Processes.original_file_name = "ipconfig.exe" AND Processes.process = "*/displaydns*"
+  by Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_network_config_discovery_display_dns_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://superuser.com/questions/230308/explain-output-of-ipconfig-displaydns
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,25 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_network_connections_discovery_netsh.yml b/detections/endpoint/windows_system_network_connections_discovery_netsh.yml
index 55e98f7199..2277916696 100644
--- a/detections/endpoint/windows_system_network_connections_discovery_netsh.yml
+++ b/detections/endpoint/windows_system_network_connections_discovery_netsh.yml
@@ -5,13 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects the execution of the Windows built-in tool netsh.exe to display the state, configuration, and profile of the host firewall. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. Monitoring this activity is crucial as netsh.exe can be used by adversaries to bypass firewall rules or discover firewall settings. If confirmed malicious, this activity could allow attackers to manipulate firewall configurations, potentially leading to unauthorized network access or data exfiltration.
+description: The following analytic detects the execution of the Windows built-in
+  tool netsh.exe to display the state, configuration, and profile of the host firewall.
+  This detection leverages data from Endpoint Detection and Response (EDR) agents,
+  focusing on command-line executions and process metadata. Monitoring this activity
+  is crucial as netsh.exe can be used by adversaries to bypass firewall rules or discover
+  firewall settings. If confirmed malicious, this activity could allow attackers to
+  manipulate firewall configurations, potentially leading to unauthorized network
+  access or data exfiltration.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_netsh`AND Processes.process = "* show *" Processes.process IN ("*state*", "*config*", "*wlan*", "*profile*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_network_connections_discovery_netsh_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_netsh`AND Processes.process
+  = "* show *" Processes.process IN ("*state*", "*config*", "*wlan*", "*profile*")
+  by Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_network_connections_discovery_netsh_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: network administrator can use this tool for auditing process.
 references:
 - https://attack.mitre.org/techniques/T1049/
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,25 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_reboot_commandline.yml b/detections/endpoint/windows_system_reboot_commandline.yml
index a4427ce8a8..32d935d004 100644
--- a/detections/endpoint/windows_system_reboot_commandline.yml
+++ b/detections/endpoint/windows_system_reboot_commandline.yml
@@ -5,14 +5,37 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the execution of the Windows command line to reboot a host machine using "shutdown.exe" with specific parameters. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant as it is often associated with advanced persistent threats (APTs) and remote access trojans (RATs) like dcrat, which may use system reboots to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed malicious, this could lead to system downtime, data loss, or hindered incident response efforts.
+description: The following analytic identifies the execution of the Windows command
+  line to reboot a host machine using "shutdown.exe" with specific parameters. This
+  detection leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process names and command-line arguments. This activity is significant as it
+  is often associated with advanced persistent threats (APTs) and remote access trojans
+  (RATs) like dcrat, which may use system reboots to disrupt operations, aid in system
+  destruction, or inhibit recovery. If confirmed malicious, this could lead to system
+  downtime, data loss, or hindered incident response efforts.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" Processes.process IN ("* /r*", "* -r*") Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_reboot_commandline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator may execute this commandline to trigger shutdown or restart the host machine.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe)
+  Processes.process="*shutdown*" Processes.process IN ("* /r*", "* -r*") Processes.process
+  IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_reboot_commandline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator may execute this commandline to trigger shutdown
+  or restart the host machine.
 references:
 - https://attack.mitre.org/techniques/T1529/
 - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor
@@ -22,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/reboot_logoff_commandline/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml b/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml
index 0a26bc7e4a..89dd9fa06c 100644
--- a/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml
+++ b/detections/endpoint/windows_system_script_proxy_execution_syncappvpublishingserver.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of Syncappvpublishingserver.vbs via wscript.exe or cscript.exe, which may indicate an attempt to download remote files or perform privilege escalation. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. Monitoring this activity is crucial as it can signify malicious use of a native Windows script for unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized file downloads or elevated privileges, posing a significant security risk.
+description: The following analytic detects the execution of Syncappvpublishingserver.vbs
+  via wscript.exe or cscript.exe, which may indicate an attempt to download remote
+  files or perform privilege escalation. This detection leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on process names and command-line
+  executions. Monitoring this activity is crucial as it can signify malicious use
+  of a native Windows script for unauthorized actions. If confirmed malicious, this
+  behavior could lead to unauthorized file downloads or elevated privileges, posing
+  a significant security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("wscript.exe","cscript.exe") Processes.process="*syncappvpublishingserver.vbs*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_script_proxy_execution_syncappvpublishingserver_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives may be present if the vbscript syncappvpublishingserver is used for legitimate purposes. Filter as needed. Adding a n; to the command-line arguments may help reduce any noise.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("wscript.exe","cscript.exe")
+  Processes.process="*syncappvpublishingserver.vbs*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_script_proxy_execution_syncappvpublishingserver_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives may be present if the vbscript syncappvpublishingserver
+  is used for legitimate purposes. Filter as needed. Adding a n; to the command-line
+  arguments may help reduce any noise.
 references:
 - https://lolbas-project.github.io/lolbas/Scripts/Syncappvpublishingserver/
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1216/T1216.md#atomic-test-1---syncappvpublishingserver-signed-script-powershell-command-execution
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,9 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 60
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to download files or evade critical controls.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to download files or evade critical
+    controls.
   mitre_attack_id:
   - T1216
   - T1218
@@ -56,23 +85,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1216/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1216/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_shutdown_commandline.yml b/detections/endpoint/windows_system_shutdown_commandline.yml
index b0f343d423..0bfb668b9a 100644
--- a/detections/endpoint/windows_system_shutdown_commandline.yml
+++ b/detections/endpoint/windows_system_shutdown_commandline.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the execution of the Windows shutdown command via the command line interface. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because attackers may use the shutdown command to erase tracks, cause disruption, or ensure changes take effect after installing backdoors. If confirmed malicious, this activity could lead to system downtime, denial of service, or evasion of security tools, impacting the overall security posture of the network.
+description: The following analytic identifies the execution of the Windows shutdown
+  command via the command line interface. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process names and command-line arguments.
+  This activity is significant because attackers may use the shutdown command to erase
+  tracks, cause disruption, or ensure changes take effect after installing backdoors.
+  If confirmed malicious, this activity could lead to system downtime, denial of service,
+  or evasion of security tools, impacting the overall security posture of the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe) Processes.process="*shutdown*" AND Processes.process IN("* /s*", "* -s*") AND Processes.process IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_shutdown_commandline_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrator may execute this commandline to trigger shutdown or restart the host machine.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where (Processes.process_name = shutdown.exe OR Processes.original_file_name = shutdown.exe)
+  Processes.process="*shutdown*" AND Processes.process IN("* /s*", "* -s*") AND Processes.process
+  IN ("* /t*","* -t*","* /f*","* -f*") by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_shutdown_commandline_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrator may execute this commandline to trigger shutdown
+  or restart the host machine.
 references:
 - https://attack.mitre.org/techniques/T1529/
 - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,23 +74,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/shutdown_commandline/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/shutdown_commandline/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml b/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml
index 28a1d88b8b..113a2bf929 100644
--- a/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml
+++ b/detections/endpoint/windows_system_time_discovery_w32tm_delay.yml
@@ -5,13 +5,35 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies the use of the w32tm.exe utility with the /stripchart function, which is indicative of DCRat malware delaying its payload execution. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line arguments used by w32tm.exe. This activity is significant as it may indicate an attempt to evade detection by delaying malicious actions such as C2 communication and beaconing. If confirmed malicious, this behavior could allow an attacker to maintain persistence and execute further malicious activities undetected.
+description: The following analytic identifies the use of the w32tm.exe utility with
+  the /stripchart function, which is indicative of DCRat malware delaying its payload
+  execution. This detection leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on specific command-line arguments used by w32tm.exe. This activity
+  is significant as it may indicate an attempt to evade detection by delaying malicious
+  actions such as C2 communication and beaconing. If confirmed malicious, this behavior
+  could allow an attacker to maintain persistence and execute further malicious activities
+  undetected.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = w32tm.exe Processes.process= "* /stripchart *" Processes.process= "* /computer:localhost *" Processes.process= "* /period:*" Processes.process= "* /dataonly *" Processes.process= "* /samples:*" by  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_time_discovery_w32tm_delay_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where Processes.process_name = w32tm.exe Processes.process= "* /stripchart *" Processes.process=
+  "* /computer:localhost *" Processes.process= "* /period:*" Processes.process= "*
+  /dataonly *" Processes.process= "* /samples:*" by  Processes.parent_process Processes.process_name
+  Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_system_time_discovery_w32tm_delay_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://cert.gov.ua/article/405538
@@ -23,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 60
   impact: 60
-  message: Process name w32tm.exe is using suspcicious command line arguments $process$ on host $dest$.
+  message: Process name w32tm.exe is using suspcicious command line arguments $process$
+    on host $dest$.
   mitre_attack_id:
   - T1124
   observable:
@@ -44,23 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_delay_execution/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/dcrat/dcrat_delay_execution/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_user_discovery_via_quser.yml b/detections/endpoint/windows_system_user_discovery_via_quser.yml
index 421f769503..cebd5f992a 100644
--- a/detections/endpoint/windows_system_user_discovery_via_quser.yml
+++ b/detections/endpoint/windows_system_user_discovery_via_quser.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of the Windows OS tool quser.exe, commonly used to gather information about user sessions on a Remote Desktop Session Host server. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial as quser.exe is often abused by post-exploitation tools like winpeas, used in ransomware attacks to enumerate user sessions. If confirmed malicious, attackers could leverage this information to further compromise the system, maintain persistence, or escalate privileges.
+description: The following analytic detects the execution of the Windows OS tool quser.exe,
+  commonly used to gather information about user sessions on a Remote Desktop Session
+  Host server. This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process execution logs. Monitoring this activity is crucial
+  as quser.exe is often abused by post-exploitation tools like winpeas, used in ransomware
+  attacks to enumerate user sessions. If confirmed malicious, attackers could leverage
+  this information to further compromise the system, maintain persistence, or escalate
+  privileges.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="quser.exe" OR Processes.original_file_name = "quser.exe" by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_user_discovery_via_quser_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: network administrator can use this command tool to audit RDP access of user in specific network or host.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="quser.exe"
+  OR Processes.original_file_name = "quser.exe" by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_system_user_discovery_via_quser_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: network administrator can use this command tool to audit RDP
+  access of user in specific network or host.
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/quser
 - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS
@@ -36,25 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_system_user_privilege_discovery.yml b/detections/endpoint/windows_system_user_privilege_discovery.yml
index 99b9cb2a8d..853a541be8 100644
--- a/detections/endpoint/windows_system_user_privilege_discovery.yml
+++ b/detections/endpoint/windows_system_user_privilege_discovery.yml
@@ -9,10 +9,30 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of `whoami.exe` with the `/priv` parameter, which displays the privileges assigned to the current user account. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it may indicate an adversary attempting to enumerate user privileges, a common step in the reconnaissance phase of an attack. If confirmed malicious, this could lead to privilege escalation or further exploitation within the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="whoami.exe" Processes.process= "*/priv*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_user_privilege_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators or power users may use this command for troubleshooting. Filter as needed.
+description: The following analytic detects the execution of `whoami.exe` with the
+  `/priv` parameter, which displays the privileges assigned to the current user account.
+  It leverages data from Endpoint Detection and Response (EDR) agents, focusing on
+  process names and command-line executions. This activity is significant as it may
+  indicate an adversary attempting to enumerate user privileges, a common step in
+  the reconnaissance phase of an attack. If confirmed malicious, this could lead to
+  privilege escalation or further exploitation within the environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name="whoami.exe"
+  Processes.process= "*/priv*" by Processes.dest Processes.user Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_system_user_privilege_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators or power users may use this command for troubleshooting.
+  Filter as needed.
 references:
 - https://attack.mitre.org/techniques/T1033/
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-347a
@@ -22,7 +42,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 30
-  message: Activity related to system user privilege discovery detected on $dest$ using whoami.exe.
+  message: Activity related to system user privilege discovery detected on $dest$
+    using whoami.exe.
   mitre_attack_id:
   - T1033
   observable:
@@ -34,23 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/whoami_priv/whoami-priv-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1033/whoami_priv/whoami-priv-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_terminating_lsass_process.yml b/detections/endpoint/windows_terminating_lsass_process.yml
index 6af3c5ceac..3881f3ffa7 100644
--- a/detections/endpoint/windows_terminating_lsass_process.yml
+++ b/detections/endpoint/windows_terminating_lsass_process.yml
@@ -5,11 +5,27 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects a suspicious process attempting to terminate the Lsass.exe process. It leverages Sysmon EventCode 10 logs to identify processes granted PROCESS_TERMINATE access to Lsass.exe. This activity is significant because Lsass.exe is a critical process responsible for enforcing security policies and handling user credentials. If confirmed malicious, this behavior could indicate an attempt to perform credential dumping, privilege escalation, or evasion of security policies, potentially leading to unauthorized access and persistence within the environment.
+description: The following analytic detects a suspicious process attempting to terminate
+  the Lsass.exe process. It leverages Sysmon EventCode 10 logs to identify processes
+  granted PROCESS_TERMINATE access to Lsass.exe. This activity is significant because
+  Lsass.exe is a critical process responsible for enforcing security policies and
+  handling user credentials. If confirmed malicious, this behavior could indicate
+  an attempt to perform credential dumping, privilege escalation, or evasion of security
+  policies, potentially leading to unauthorized access and persistence within the
+  environment.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 TargetImage=*lsass.exe GrantedAccess = 0x1 | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage, TargetImage, TargetProcessId, SourceProcessId, GrantedAccess CallTrace, dest | rename dest as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_terminating_lsass_process_filter`'
-how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
+search: '`sysmon` EventCode=10 TargetImage=*lsass.exe GrantedAccess = 0x1 | stats
+  count min(_time) as firstTime max(_time) as lastTime by SourceImage, TargetImage,
+  TargetProcessId, SourceProcessId, GrantedAccess CallTrace, dest | rename dest as
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_terminating_lsass_process_filter`'
+how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which
+  includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`.
+  We strongly recommend that you specify your environment-specific configurations
+  (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition
+  with configurations for your Splunk environment. The search also uses a post-filter
+  macro designed to filter out known false positives.
 known_false_positives: unknown
 references:
 - https://blog.talosintelligence.com/2022/03/threat-advisory-doublezero.html
@@ -19,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetImage
-  - CallTrace
-  - dest
-  - TargetProcessId
-  - SourceImage
-  - SourceProcessId
-  - GrantedAccess
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/doublezero_wiper/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_time_based_evasion.yml b/detections/endpoint/windows_time_based_evasion.yml
index 6e056b8410..5ffc355646 100644
--- a/detections/endpoint/windows_time_based_evasion.yml
+++ b/detections/endpoint/windows_time_based_evasion.yml
@@ -9,9 +9,29 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects potentially malicious processes that initiate a ping delay using an invalid IP address. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "ping 0 -n". This behavior is significant as it is commonly used by malware like NJRAT to introduce time delays for evasion tactics, such as delaying self-deletion. If confirmed malicious, this activity could indicate an active infection attempting to evade detection, potentially leading to further compromise and persistence within the environment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "ping.exe" Processes.parent_process = "* ping 0 -n *" OR Processes.process = "* ping 0 -n *" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_time_based_evasion_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+description: The following analytic detects potentially malicious processes that initiate
+  a ping delay using an invalid IP address. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions involving "ping 0
+  -n". This behavior is significant as it is commonly used by malware like NJRAT to
+  introduce time delays for evasion tactics, such as delaying self-deletion. If confirmed
+  malicious, this activity could indicate an active infection attempting to evade
+  detection, potentially leading to further compromise and persistence within the
+  environment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "ping.exe"
+  Processes.parent_process = "* ping 0 -n *" OR Processes.process = "* ping 0 -n *"
+  by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid
+  Processes.process Processes.user Processes.dest | `drop_dm_object_name("Processes")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_time_based_evasion_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
@@ -21,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/njrat_ping_delay_before_delete/ping_0.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/njrat_ping_delay_before_delete/ping_0.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml b/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml
index 40d2f15307..4c606ff6ed 100644
--- a/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml
+++ b/detections/endpoint/windows_time_based_evasion_via_choice_exec.yml
@@ -9,10 +9,31 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the use of choice.exe in batch files as a delay tactic, a technique observed in SnakeKeylogger malware. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential time-based evasion techniques used by malware to avoid detection. If confirmed malicious, this behavior could allow attackers to execute code stealthily, delete malicious files, and persist on compromised hosts, making it crucial for SOC analysts to investigate promptly.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name =choice.exe  Processes.process = "*/T*"  Processes.process = "*/N*" by Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_time_based_evasion_via_choice_exec_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: administrator may use choice.exe to allow user to choose from and indexes of choices from a batch script.
+description: The following analytic detects the use of choice.exe in batch files as
+  a delay tactic, a technique observed in SnakeKeylogger malware. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on process names and
+  command-line executions. This activity is significant as it indicates potential
+  time-based evasion techniques used by malware to avoid detection. If confirmed malicious,
+  this behavior could allow attackers to execute code stealthily, delete malicious
+  files, and persist on compromised hosts, making it crucial for SOC analysts to investigate
+  promptly.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name =choice.exe  Processes.process
+  = "*/T*"  Processes.process = "*/N*" by Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_time_based_evasion_via_choice_exec_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: administrator may use choice.exe to allow user to choose from
+  and indexes of choices from a batch script.
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/choice
 - https://malpedia.caad.fkie.fraunhofer.de/details/win.404keylogger
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,20 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/time_delay_using_choice_exe/snakekeylogger_choice.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1497.003/time_delay_using_choice_exe/snakekeylogger_choice.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml b/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml
index e3dad19e87..a223a96237 100644
--- a/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml
+++ b/detections/endpoint/windows_uac_bypass_suspicious_child_process.yml
@@ -5,14 +5,32 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when an executable known for User Account Control (UAC) bypass exploitation spawns a child process in a user-controlled location or a command shell executable (e.g., cmd.exe, powershell.exe). This detection leverages Sysmon EventID 1 data, focusing on high or system integrity level processes with specific parent-child process relationships. This activity is significant as it may indicate an attacker has successfully used a UAC bypass exploit to escalate privileges. If confirmed malicious, this could allow the attacker to execute arbitrary commands with elevated privileges, potentially compromising the entire system.
+description: The following analytic detects when an executable known for User Account
+  Control (UAC) bypass exploitation spawns a child process in a user-controlled location
+  or a command shell executable (e.g., cmd.exe, powershell.exe). This detection leverages
+  Sysmon EventID 1 data, focusing on high or system integrity level processes with
+  specific parent-child process relationships. This activity is significant as it
+  may indicate an attacker has successfully used a UAC bypass exploit to escalate
+  privileges. If confirmed malicious, this could allow the attacker to execute arbitrary
+  commands with elevated privileges, potentially compromising the entire system.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("high","system") AND Processes.parent_process_name IN (`uacbypass_process_name`) AND (Processes.process_name IN ("cmd.exe","powershell.exe","pwsh.exe","wscript","cscript.exe","bash.exe","werfault.exe") OR Processes.process IN ("*\\\\*","*\\Users\\*","*\\ProgramData\\*","*\\Temp\\*")) by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.parent_process, Processes.parent_process_name Processes.process_name Processes.process, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | where parent_process_name != process_name | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_child_process_filter`'
-how_to_implement: Target environment must ingest sysmon data, specifically Event ID 1 with process integrity level data.
-known_false_positives: Including Werfault.exe may cause some unintended false positives related to normal application faulting, but is used in a number of UAC bypass techniques.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level
+  IN ("high","system") AND Processes.parent_process_name IN (`uacbypass_process_name`)
+  AND (Processes.process_name IN ("cmd.exe","powershell.exe","pwsh.exe","wscript","cscript.exe","bash.exe","werfault.exe")
+  OR Processes.process IN ("*\\\\*","*\\Users\\*","*\\ProgramData\\*","*\\Temp\\*"))
+  by Processes.dest, Processes.user, Processes.parent_process_guid, Processes.parent_process,
+  Processes.parent_process_name Processes.process_name Processes.process, Processes.process_path,
+  Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)`
+  | where parent_process_name != process_name | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_child_process_filter`'
+how_to_implement: Target environment must ingest sysmon data, specifically Event ID
+  1 with process integrity level data.
+known_false_positives: Including Werfault.exe may cause some unintended false positives
+  related to normal application faulting, but is used in a number of UAC bypass techniques.
 references:
 - https://attack.mitre.org/techniques/T1548/002/
 - https://atomicredteam.io/defense-evasion/T1548.002/
@@ -24,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +57,8 @@ tags:
   asset_type: Endpoint
   confidence: 75
   impact: 60
-  message: A UAC bypass parent process- $parent_process_name$ on host- $dest$ launched a suspicious child process - $process_name$.
+  message: A UAC bypass parent process- $parent_process_name$ on host- $dest$ launched
+    a suspicious child process - $process_name$.
   mitre_attack_id:
   - T1548
   - T1548.002
@@ -55,20 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_guid
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name Processes.process
-  - Processes.process_path
-  - Processes.process_integrity_level
-  - Processes.process_current_directory
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml b/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml
index 3e4a60f82c..66128281fe 100644
--- a/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml
+++ b/detections/endpoint/windows_uac_bypass_suspicious_escalation_behavior.yml
@@ -5,14 +5,44 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: TTP
-description: The following analytic detects when a process spawns an executable known for User Account Control (UAC) bypass exploitation and subsequently monitors for any child processes with a higher integrity level than the original process. This detection leverages Sysmon EventID 1 data, focusing on process integrity levels and known UAC bypass executables. This activity is significant as it may indicate an attacker has successfully used a UAC bypass exploit to escalate privileges. If confirmed malicious, the attacker could gain elevated privileges, potentially leading to further system compromise and persistent access.
+description: The following analytic detects when a process spawns an executable known
+  for User Account Control (UAC) bypass exploitation and subsequently monitors for
+  any child processes with a higher integrity level than the original process. This
+  detection leverages Sysmon EventID 1 data, focusing on process integrity levels
+  and known UAC bypass executables. This activity is significant as it may indicate
+  an attacker has successfully used a UAC bypass exploit to escalate privileges. If
+  confirmed malicious, the attacker could gain elevated privileges, potentially leading
+  to further system compromise and persistent access.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium") by Processes.dest, Processes.user, Processes.process_name, Processes.process, Processes.process_guid, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | eval original_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0) | rename process_guid as join_guid_1, process* as parent_process* | join max=0 dest join_guid_1 [| tstats `security_content_summariesonly` count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("high","system") AND Processes.process_name IN (`uacbypass_process_name`) by Processes.dest, Processes.parent_process_guid, Processes.process_name, Processes.process_guid | `drop_dm_object_name(Processes)` | rename parent_process_guid as join_guid_1, process_guid as join_guid_2, process_name as uac_process_name ] | join max=0 dest join_guid_2 [| tstats `security_content_summariesonly` count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN (`uacbypass_process_name`) AND Processes.process_integrity_level IN ("high","system") by Processes.dest, Processes.parent_process_guid, Processes.process_name, Processes.process, Processes.process_guid, Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory | `drop_dm_object_name(Processes)` | rename parent_process_guid as join_guid_2 | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)] | where elevated_integrity_level > original_integrity_level | table dest user parent_process parent_process_name parent_process_integrity_level process_integrity_level process process_name uac_process_name count firstTime lastTime | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_escalation_behavior_filter`'
-how_to_implement: Target environment must ingest sysmon data, specifically Event ID 1 with process integrity level data.
-known_false_positives: Including Werfault.exe may cause some unintended false positives related to normal application faulting, but is used in a number of UAC bypass techniques.
+search: '| tstats `security_content_summariesonly` count max(_time) as lastTime from
+  datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("low","medium")
+  by Processes.dest, Processes.user, Processes.process_name, Processes.process, Processes.process_guid,
+  Processes.process_path, Processes.process_integrity_level, Processes.process_current_directory
+  | `drop_dm_object_name(Processes)` | eval original_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)
+  | rename process_guid as join_guid_1, process* as parent_process* | join max=0 dest
+  join_guid_1 [| tstats `security_content_summariesonly` count min(_time) as firstTime
+  from datamodel=Endpoint.Processes where Processes.process_integrity_level IN ("high","system")
+  AND Processes.process_name IN (`uacbypass_process_name`) by Processes.dest, Processes.parent_process_guid,
+  Processes.process_name, Processes.process_guid | `drop_dm_object_name(Processes)`
+  | rename parent_process_guid as join_guid_1, process_guid as join_guid_2, process_name
+  as uac_process_name ] | join max=0 dest join_guid_2 [| tstats `security_content_summariesonly`
+  count min(_time) as firstTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN (`uacbypass_process_name`) AND Processes.process_integrity_level IN ("high","system")
+  by Processes.dest, Processes.parent_process_guid, Processes.process_name, Processes.process,
+  Processes.process_guid, Processes.process_path, Processes.process_integrity_level,
+  Processes.process_current_directory | `drop_dm_object_name(Processes)` | rename
+  parent_process_guid as join_guid_2 | eval elevated_integrity_level = CASE(match(process_integrity_level,"low"),1,match(process_integrity_level,"medium"),2,match(process_integrity_level,"high"),3,match(process_integrity_level,"system"),4,true(),0)]
+  | where elevated_integrity_level > original_integrity_level | table dest user parent_process
+  parent_process_name parent_process_integrity_level process_integrity_level process
+  process_name uac_process_name count firstTime lastTime | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_uac_bypass_suspicious_escalation_behavior_filter`'
+how_to_implement: Target environment must ingest sysmon data, specifically Event ID
+  1 with process integrity level data.
+known_false_positives: Including Werfault.exe may cause some unintended false positives
+  related to normal application faulting, but is used in a number of UAC bypass techniques.
 references:
 - https://attack.mitre.org/techniques/T1548/002/
 - https://atomicredteam.io/defense-evasion/T1548.002/
@@ -24,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +69,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: A UAC bypass behavior was detected by parent process name- $parent_process_name$ on host $dest$ by $user$.
+  message: A UAC bypass behavior was detected by parent process name- $parent_process_name$
+    on host $dest$ by $user$.
   mitre_attack_id:
   - T1548
   - T1548.002
@@ -63,20 +99,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_guid
-  - Processes.parent_process
-  - Processes.parent_process_name
-  - Processes.process_name Processes.process
-  - Processes.process_path
-  - Processes.process_integrity_level
-  - Processes.process_current_directory
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/uac_behavior/uac_behavior_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml b/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml
index 1836d6b9d1..ee6acc3c53 100644
--- a/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml
+++ b/detections/endpoint/windows_unsecured_outlook_credentials_access_in_registry.yml
@@ -7,9 +7,23 @@ status: production
 type: Anomaly
 data_source:
 - Windows Event Log Security 4663
-description: The following analytic detects unauthorized access to Outlook credentials stored in the Windows registry. It leverages Windows Security Event logs, specifically EventCode 4663, to identify access attempts to registry paths associated with Outlook profiles. This activity is significant as it may indicate attempts to steal sensitive email credentials, which could lead to unauthorized access to email accounts. If confirmed malicious, this could allow attackers to exfiltrate sensitive information, impersonate users, or execute further unauthorized actions within Outlook, posing a significant security risk.
-search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\Profiles\\Outlook\\9375CFF0413111d3B88A00104B2A6676*", "*\\Windows Messaging Subsystem\\Profiles\\9375CFF0413111d3B88A00104B2A6676*") AND process_name != *\\outlook.exe | stats count min(_time) as firstTime max(_time) as lastTime by object_file_name object_file_path process_name process_path  process_id EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsecured_outlook_credentials_access_in_registry_filter`'
-how_to_implement: To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."
+description: The following analytic detects unauthorized access to Outlook credentials
+  stored in the Windows registry. It leverages Windows Security Event logs, specifically
+  EventCode 4663, to identify access attempts to registry paths associated with Outlook
+  profiles. This activity is significant as it may indicate attempts to steal sensitive
+  email credentials, which could lead to unauthorized access to email accounts. If
+  confirmed malicious, this could allow attackers to exfiltrate sensitive information,
+  impersonate users, or execute further unauthorized actions within Outlook, posing
+  a significant security risk.
+search: '`wineventlog_security` EventCode=4663 object_file_path IN ("*\\Profiles\\Outlook\\9375CFF0413111d3B88A00104B2A6676*",
+  "*\\Windows Messaging Subsystem\\Profiles\\9375CFF0413111d3B88A00104B2A6676*") AND
+  process_name != *\\outlook.exe | stats count min(_time) as firstTime max(_time)
+  as lastTime by object_file_name object_file_path process_name process_path  process_id
+  EventCode dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_unsecured_outlook_credentials_access_in_registry_filter`'
+how_to_implement: To successfully implement this search, you must ingest Windows Security
+  Event logs and track event code 4663. For 4663, enable "Audit Object Access" in
+  Group Policy. Then check the two boxes listed for both "Success" and "Failure."
 known_false_positives: third party software may access this outlook registry.
 references:
 - https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/choice
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: A suspicious process $process_name$ accessing outlook credentials registry on $dest$
+  message: A suspicious process $process_name$ accessing outlook credentials registry
+    on $dest$
   mitre_attack_id:
   - T1552
   observable:
@@ -41,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - object_file_name
-  - object_file_path
-  - process_name
-  - process_path
-  - process_id
-  - EventCode
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1552/snakey_keylogger_outlook_reg_access/snakekeylogger_4663.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unsigned_dll_side_loading.yml b/detections/endpoint/windows_unsigned_dll_side_loading.yml
index 1ad7e29669..7c8f1ea14f 100644
--- a/detections/endpoint/windows_unsigned_dll_side_loading.yml
+++ b/detections/endpoint/windows_unsigned_dll_side_loading.yml
@@ -7,10 +7,24 @@ status: production
 type: Anomaly
 data_source:
 - Sysmon EventID 7
-description: The following analytic detects the creation of potentially malicious unsigned DLLs in the c:\windows\system32 or c:\windows\syswow64 folders. It leverages Sysmon EventCode 7 logs to identify unsigned DLLs with unavailable signatures loaded in these critical directories. This activity is significant as it may indicate a DLL hijacking attempt, a technique used by attackers to gain unauthorized access and execute malicious code. If confirmed malicious, this could lead to privilege escalation, allowing the attacker to gain elevated privileges and further compromise the target system.
-search: '`sysmon` EventCode=7 Signed=false OriginalFileName = "-" SignatureStatus="unavailable" ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*") | stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed SignatureStatus OriginalFileName process_name dest EventCode ProcessId Hashes IMPHASH | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: It is possible some Administrative utilities will load dismcore.dll outside of normal system paths, filter as needed.
+description: The following analytic detects the creation of potentially malicious
+  unsigned DLLs in the c:\windows\system32 or c:\windows\syswow64 folders. It leverages
+  Sysmon EventCode 7 logs to identify unsigned DLLs with unavailable signatures loaded
+  in these critical directories. This activity is significant as it may indicate a
+  DLL hijacking attempt, a technique used by attackers to gain unauthorized access
+  and execute malicious code. If confirmed malicious, this could lead to privilege
+  escalation, allowing the attacker to gain elevated privileges and further compromise
+  the target system.
+search: '`sysmon` EventCode=7 Signed=false OriginalFileName = "-" SignatureStatus="unavailable"
+  ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*") | stats
+  count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed
+  SignatureStatus OriginalFileName process_name dest EventCode ProcessId Hashes IMPHASH
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+known_false_positives: It is possible some Administrative utilities will load dismcore.dll
+  outside of normal system paths, filter as needed.
 references:
 - https://asec.ahnlab.com/en/17692/
 - https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/warzone#:~:text=Warzone%20RAT%20(AKA%20Ave%20Maria)%20is%20a%20remote%20access%20trojan,is%20as%20an%20information%20stealer.
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,23 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - Signed
-  - SignatureStatus
-  - OriginalFileName
-  - process_name
-  - dest
-  - EventCode
-  - ProcessId
-  - Hashes
-  - IMPHASH
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/unsigned_dll_loaded/loaded_unsigned_dll.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/warzone_rat/unsigned_dll_loaded/loaded_unsigned_dll.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml b/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml
index 3492cc6655..c32796b80f 100644
--- a/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml
+++ b/detections/endpoint/windows_unsigned_dll_side_loading_in_same_process_path.yml
@@ -7,9 +7,26 @@ data_source:
 - Sysmon EventID 7
 type: TTP
 status: production
-description: This detection identifies unsigned DLLs loaded through DLL side-loading with same file path with the process loaded the DLL, a technique observed in DarkGate malware. This detection monitors DLL loading, verifies signatures, and flags unsigned DLLs. Suspicious file paths and known executable associations are checked. Detecting such suspicious DLLs is crucial in preventing privilege escalation attacks and other potential security breaches. Regular security assessments, thorough monitoring, and implementing security best practices are essential in safeguarding systems from such threats.
-search: '`sysmon` EventCode=7 Signed=false SignatureStatus != Valid NOT (Image IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program Files*")) NOT (ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program Files*")) |  rex field=Image "(?<ImageFolderPath>.+\\\)" |  rex field=ImageLoaded "(?<ImageLoadedFolderPath>.+\\\)" |  where ImageFolderPath = ImageLoadedFolderPath | stats count min(_time) as firstTime max(_time) as lastTime by Image ProcessGuid ImageLoaded user Computer EventCode ImageFolderPath ImageLoadedFolderPath Company Description Product Signed SignatureStatus |  rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_in_same_process_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+description: This detection identifies unsigned DLLs loaded through DLL side-loading
+  with same file path with the process loaded the DLL, a technique observed in DarkGate
+  malware. This detection monitors DLL loading, verifies signatures, and flags unsigned
+  DLLs. Suspicious file paths and known executable associations are checked. Detecting
+  such suspicious DLLs is crucial in preventing privilege escalation attacks and other
+  potential security breaches. Regular security assessments, thorough monitoring,
+  and implementing security best practices are essential in safeguarding systems from
+  such threats.
+search: '`sysmon` EventCode=7 Signed=false SignatureStatus != Valid NOT (Image IN
+  ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program Files*"))
+  NOT (ImageLoaded IN ("*:\\windows\\system32\\*", "*:\\windows\\syswow64\\*", "c:\\Program
+  Files*")) |  rex field=Image "(?<ImageFolderPath>.+\\\)" |  rex field=ImageLoaded
+  "(?<ImageLoadedFolderPath>.+\\\)" |  where ImageFolderPath = ImageLoadedFolderPath
+  | stats count min(_time) as firstTime max(_time) as lastTime by Image ProcessGuid
+  ImageLoaded user Computer EventCode ImageFolderPath ImageLoadedFolderPath Company
+  Description Product Signed SignatureStatus |  rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_unsigned_dll_side_loading_in_same_process_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name and imageloaded executions from your endpoints. If you
+  are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: unknown
 references:
 - https://www.splunk.com/en_us/blog/security/enter-the-gates-an-analysis-of-the-darkgate-autoit-loader.html
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,23 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Image
-  - ImageLoaded
-  - Signed
-  - SignatureStatus
-  - OriginalFileName
-  - process_name
-  - dest
-  - EventCode
-  - ProcessId
-  - Hashes
-  - IMPHASH
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_loaded_same_process_path/unsigned_dll_process_path.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_loaded_same_process_path/unsigned_dll_process_path.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml b/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml
index 24178366a6..8ba32a8971 100644
--- a/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml
+++ b/detections/endpoint/windows_unsigned_ms_dll_side_loading.yml
@@ -7,10 +7,35 @@ data_source:
 - Sysmon EventID 7
 type: Anomaly
 status: production
-description: The following analytic identifies potential DLL side-loading instances involving unsigned DLLs mimicking Microsoft signatures. It detects this activity by analyzing Sysmon logs for Event Code 7, where both the `Image` and `ImageLoaded` paths do not match system directories like `system32`, `syswow64`, and `programfiles`. This behavior is significant as adversaries often exploit DLL side-loading to execute malicious code via legitimate processes. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to privilege escalation, persistence, and unauthorized access to sensitive information.
-search: '`sysmon` EventCode=7 Company="Microsoft Corporation" Signed=false SignatureStatus != Valid NOT (Image IN("C:\\Windows\\System32\\*", "C:\\Windows\\SysWow64\\*", "C:\\Program Files*")) NOT (ImageLoaded IN("C:\\Windows\\System32\\*", "C:\\Windows\\SysWow64\\*", "C:\\Program Files*")) | rex field=Image "(?<ImageFolderPath>.+\\\)" | rex field=ImageLoaded "(?<ImageLoadedFolderPath>.+\\\)" | where ImageFolderPath = ImageLoadedFolderPath | stats count min(_time) as firstTime max(_time) as lastTime by Image ProcessGuid ImageLoaded user Computer EventCode ImageFolderPath ImageLoadedFolderPath Company Description Product Signed SignatureStatus | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_unsigned_ms_dll_side_loading_filter`'
-how_to_implement: The analytic is designed to be run against Sysmon event logs collected from endpoints. The analytic requires the Sysmon event logs to be ingested into Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters out the legitimate loading of vcruntime140.dll from the System32 directory to reduce false positives. The analytic can be modified to include additional known good paths for vcruntime140.dll to further reduce false positives.
-known_false_positives: False positives are possible if legitimate processes are loading vcruntime140.dll from non-standard directories. It is recommended to investigate the context of the process loading vcruntime140.dll to determine if it is malicious or not. Modify the search to include additional known good paths for vcruntime140.dll to reduce false positives.
+description: The following analytic identifies potential DLL side-loading instances
+  involving unsigned DLLs mimicking Microsoft signatures. It detects this activity
+  by analyzing Sysmon logs for Event Code 7, where both the `Image` and `ImageLoaded`
+  paths do not match system directories like `system32`, `syswow64`, and `programfiles`.
+  This behavior is significant as adversaries often exploit DLL side-loading to execute
+  malicious code via legitimate processes. If confirmed malicious, this activity could
+  allow attackers to execute arbitrary code, potentially leading to privilege escalation,
+  persistence, and unauthorized access to sensitive information.
+search: '`sysmon` EventCode=7 Company="Microsoft Corporation" Signed=false SignatureStatus
+  != Valid NOT (Image IN("C:\\Windows\\System32\\*", "C:\\Windows\\SysWow64\\*", "C:\\Program
+  Files*")) NOT (ImageLoaded IN("C:\\Windows\\System32\\*", "C:\\Windows\\SysWow64\\*",
+  "C:\\Program Files*")) | rex field=Image "(?<ImageFolderPath>.+\\\)" | rex field=ImageLoaded
+  "(?<ImageLoadedFolderPath>.+\\\)" | where ImageFolderPath = ImageLoadedFolderPath
+  | stats count min(_time) as firstTime max(_time) as lastTime by Image ProcessGuid
+  ImageLoaded user Computer EventCode ImageFolderPath ImageLoadedFolderPath Company
+  Description Product Signed SignatureStatus | rename Computer as dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_unsigned_ms_dll_side_loading_filter`'
+how_to_implement: The analytic is designed to be run against Sysmon event logs collected
+  from endpoints. The analytic requires the Sysmon event logs to be ingested into
+  Splunk. The analytic searches for EventCode 7 where the Image is either SQLDumper.exe
+  or SQLWriter.exe and the ImageLoaded is vcruntime140.dll. The search also filters
+  out the legitimate loading of vcruntime140.dll from the System32 directory to reduce
+  false positives. The analytic can be modified to include additional known good paths
+  for vcruntime140.dll to further reduce false positives.
+known_false_positives: False positives are possible if legitimate processes are loading
+  vcruntime140.dll from non-standard directories. It is recommended to investigate
+  the context of the process loading vcruntime140.dll to determine if it is malicious
+  or not. Modify the search to include additional known good paths for vcruntime140.dll
+  to reduce false positives.
 references:
 - https://www.mandiant.com/resources/blog/apt29-wineloader-german-political-parties
 - https://www.zscaler.com/blogs/security-research/european-diplomats-targeted-spikedwine-wineloader
@@ -20,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,17 +80,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Image
-  - ImageLoaded
-  - user
-  - Computer
-  - EventCode
   security_domain: endpoint
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_load//wineloader_dll_sideload.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.002/unsigned_dll_load//wineloader_dll_sideload.log
     sourcetype: XmlWinEventLog
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
diff --git a/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml b/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml
index f9acde8a4e..a8750b1730 100644
--- a/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml
+++ b/detections/endpoint/windows_unusual_count_of_disabled_users_failed_auth_using_kerberos.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4768
 date: '2024-09-30'
-description: The following analytic identifies a source endpoint failing to authenticate with multiple disabled domain users using the Kerberos protocol. It leverages EventCode 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT) and detects failure code `0x12` (credentials revoked). This behavior is significant as it may indicate a Password Spraying attack targeting disabled accounts, potentially leading to initial access or privilege escalation. If confirmed malicious, attackers could gain unauthorized access or elevate privileges within the Active Directory environment.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a source endpoint failing to authenticate
+  with multiple disabled domain users using the Kerberos protocol. It leverages EventCode
+  4768, which is generated when the Key Distribution Center issues a Kerberos Ticket
+  Granting Ticket (TGT) and detects failure code `0x12` (credentials revoked). This
+  behavior is significant as it may indicate a Password Spraying attack targeting
+  disabled accounts, potentially leading to initial access or privilege escalation.
+  If confirmed malicious, attackers could gain unauthorized access or elevate privileges
+  within the Active Directory environment.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 id: f65aa026-b811-42ab-b4b9-d9088137648f
-known_false_positives: A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems.
+known_false_positives: A host failing to authenticate with multiple disabled domain
+  users is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners, multi-user systems missconfigured
+  systems.
 name: Windows Unusual Count Of Disabled Users Failed Auth Using Kerberos
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -15,10 +27,19 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_disabled_users_failed_auth_using_kerberos_filter`'
+search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x12 | bucket
+  span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts)
+  as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts
+  > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_disabled_users_failed_auth_using_kerberos_filter`'
 status: production
 tags:
   analytic_story:
@@ -45,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml b/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml
index 735bb3ab5e..ef1fde0a9b 100644
--- a/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml
+++ b/detections/endpoint/windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4768
 date: '2024-09-30'
-description: The following analytic identifies a source endpoint failing to authenticate with multiple invalid domain users using the Kerberos protocol. It leverages Event ID 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT) and detects failure code 0x6, indicating the user is not found in the Kerberos database. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access and potential privilege escalation within the Active Directory environment.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a source endpoint failing to authenticate
+  with multiple invalid domain users using the Kerberos protocol. It leverages Event
+  ID 4768, which is generated when the Key Distribution Center issues a Kerberos Ticket
+  Granting Ticket (TGT) and detects failure code 0x6, indicating the user is not found
+  in the Kerberos database. This behavior is significant as it may indicate a Password
+  Spraying attack, where an adversary attempts to gain initial access or elevate privileges.
+  If confirmed malicious, this activity could lead to unauthorized access and potential
+  privilege escalation within the Active Directory environment.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 id: f122cb2e-d773-4f11-8399-62a3572d8dd7
-known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems.
+known_false_positives: A host failing to authenticate with multiple invalid domain
+  users is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners, multi-user systems and missconfigured
+  systems.
 name: Windows Unusual Count Of Invalid Users Fail To Auth Using Kerberos
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -15,10 +27,19 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos_filter`'
+search: '`wineventlog_security` EventCode=4768 TargetUserName!=*$ Status=0x6 | bucket
+  span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts)
+  as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts
+  > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_invalid_users_fail_to_auth_using_kerberos_filter`'
 status: production
 tags:
   analytic_story:
@@ -45,16 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml b/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml
index f72e63dc19..b9b478a81b 100644
--- a/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml
+++ b/detections/endpoint/windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm.yml
@@ -2,10 +2,23 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4776
 date: '2024-09-30'
-description: The following analytic identifies a source endpoint failing to authenticate with multiple invalid users using the NTLM protocol. It leverages EventCode 4776 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access or privilege escalation, posing a significant threat to the Active Directory environment. This detection is focused on domain controllers.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled.
+description: The following analytic identifies a source endpoint failing to authenticate
+  with multiple invalid users using the NTLM protocol. It leverages EventCode 4776
+  and calculates the standard deviation for each host, using the 3-sigma rule to detect
+  anomalies. This behavior is significant as it may indicate a Password Spraying attack,
+  where an adversary attempts to gain initial access or elevate privileges. If confirmed
+  malicious, this activity could lead to unauthorized access or privilege escalation,
+  posing a significant threat to the Active Directory environment. This detection
+  is focused on domain controllers.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events. The Advanced Security Audit policy setting `Audit Credential
+  Validation' within `Account Logon` needs to be enabled.
 id: 15603165-147d-4a6e-9778-bd0ff39e668f
-known_false_positives: A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
+known_false_positives: A host failing to authenticate with multiple invalid domain
+  users is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners and missconfigured systems.
+  If this detection triggers on a host other than a Domain Controller, the behavior
+  could represent a password spraying attack against the host's local accounts.
 name: Windows Unusual Count Of Invalid Users Failed To Auth Using NTLM
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +30,20 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064 | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | rename Workstation as src |`windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm_filter`'
+search: '`wineventlog_security` EventCode=4776 TargetUserName!=*$ Status=0xc0000064
+  | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts)
+  as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts
+  > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | rename Workstation
+  as src |`windows_unusual_count_of_invalid_users_failed_to_auth_using_ntlm_filter`'
 status: production
 tags:
   analytic_story:
@@ -46,16 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - TargetUserName
-  - Workstation
-  - Status
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml b/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml
index 3d78bfcd11..9ae7671727 100644
--- a/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml
+++ b/detections/endpoint/windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials.yml
@@ -2,10 +2,23 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4648
 date: '2024-09-30'
-description: The following analytic identifies a source user failing to authenticate with multiple users using explicit credentials on a host. It leverages Windows Event Code 4648 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This behavior is significant as it may indicate a Password Spraying attack, where an adversary attempts to gain initial access or elevate privileges. If confirmed malicious, this activity could lead to unauthorized access, privilege escalation, or further compromise of the Active Directory environment.
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
+description: The following analytic identifies a source user failing to authenticate
+  with multiple users using explicit credentials on a host. It leverages Windows Event
+  Code 4648 and calculates the standard deviation for each host, using the 3-sigma
+  rule to detect anomalies. This behavior is significant as it may indicate a Password
+  Spraying attack, where an adversary attempts to gain initial access or elevate privileges.
+  If confirmed malicious, this activity could lead to unauthorized access, privilege
+  escalation, or further compromise of the Active Directory environment.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
 id: 14f414cf-3080-4b9b-aaf6-55a4ce947b93
-known_false_positives: A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc.
+known_false_positives: A source user failing attempting to authenticate multiple users
+  on a host is not a common behavior for regular systems. Some applications, however,
+  may exhibit this behavior in which case sets of users hosts can be added to an allow
+  list. Possible false positive scenarios include systems where several users connect
+  to like Mail servers, identity providers, remote desktop services, Citrix, etc.
 name: Windows Unusual Count Of Users Fail To Auth Wth ExplicitCredentials
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +30,20 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$ | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name) as  user by _time, Computer, Caller_User_Name | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials_filter`'
+search: '`wineventlog_security` EventCode=4648 Caller_User_Name!=*$ Target_User_Name!=*$
+  | bucket span=5m _time | stats dc(Target_User_Name) AS unique_accounts values(Target_User_Name)
+  as  user by _time, Computer, Caller_User_Name | eventstats avg(unique_accounts)
+  as comp_avg , stdev(unique_accounts) as comp_std by Computer | eval upperBound=(comp_avg+comp_std*3)
+  | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0)
+  | search isOutlier=1 | `windows_unusual_count_of_users_fail_to_auth_wth_explicitcredentials_filter`'
 status: production
 tags:
   analytic_story:
@@ -47,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Target_User_Name
-  - Caller_User_Name
-  - Computer
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml b/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml
index 55056c168e..f476ee12e3 100644
--- a/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml
+++ b/detections/endpoint/windows_unusual_count_of_users_failed_to_auth_using_kerberos.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4771
 date: '2024-09-30'
-description: The following analytic identifies a source endpoint failing to authenticate multiple valid users using the Kerberos protocol, potentially indicating a Password Spraying attack. It leverages Event 4771, which is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT) due to a wrong password (failure code 0x18). This detection uses statistical analysis, specifically the 3-sigma rule, to identify unusual authentication failures. If confirmed malicious, this activity could allow an attacker to gain initial access or elevate privileges within an Active Directory environment.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a source endpoint failing to authenticate
+  multiple valid users using the Kerberos protocol, potentially indicating a Password
+  Spraying attack. It leverages Event 4771, which is generated when the Key Distribution
+  Center fails to issue a Kerberos Ticket Granting Ticket (TGT) due to a wrong password
+  (failure code 0x18). This detection uses statistical analysis, specifically the
+  3-sigma rule, to identify unusual authentication failures. If confirmed malicious,
+  this activity could allow an attacker to gain initial access or elevate privileges
+  within an Active Directory environment.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller and Kerberos events. The Advanced Security Audit policy setting
+  `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
 id: bc9cb715-08ba-40c3-9758-6e2b26e455cb
-known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms.
+known_false_positives: A host failing to authenticate with multiple valid domain users
+  is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners, missconfigured systems and
+  multi-user systems like Citrix farms.
 name: Windows Unusual Count Of Users Failed To Auth Using Kerberos
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +29,19 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 | bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_auth_using_kerberos_filter`'
+search: '`wineventlog_security` EventCode=4771 TargetUserName!="*$" Status=0x18 |
+  bucket span=5m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, IpAddress | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts)
+  as comp_std by IpAddress | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts
+  > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_auth_using_kerberos_filter`'
 status: production
 tags:
   analytic_story:
@@ -47,16 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml
index 3aefd721cd..a23cc1b448 100644
--- a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml
+++ b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_from_process.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4625
 date: '2024-09-30'
-description: The following analytic identifies a source process failing to authenticate multiple users, potentially indicating a Password Spraying attack. It leverages Windows Event 4625, which logs failed logon attempts, and uses statistical analysis to detect anomalies. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, the attacker could compromise multiple accounts, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
+description: The following analytic identifies a source process failing to authenticate
+  multiple users, potentially indicating a Password Spraying attack. It leverages
+  Windows Event 4625, which logs failed logon attempts, and uses statistical analysis
+  to detect anomalies. This activity is significant as it may represent an adversary
+  attempting to gain initial access or elevate privileges within an Active Directory
+  environment. If confirmed malicious, the attacker could compromise multiple accounts,
+  leading to unauthorized access, data exfiltration, or further lateral movement within
+  the network.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers aas well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
 id: 25bdb6cb-2e49-4d34-a93c-d6c567c122fe
-known_false_positives: A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
+known_false_positives: A process failing to authenticate with multiple users is not
+  a common behavior for legitimate user sessions. Possible false positive scenarios
+  include but are not limited to vulnerability scanners and missconfigured systems.
 name: Windows Unusual Count Of Users Failed To Authenticate From Process
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -18,10 +30,20 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security`  EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as user by _time, ProcessName, SubjectUserName, Computer | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ProcessName, SubjectUserName, Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_from_process_filter`'
+search: '`wineventlog_security`  EventCode=4625 Logon_Type=2 ProcessName!="-" | bucket
+  span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as user by _time, ProcessName, SubjectUserName, Computer | eventstats avg(unique_accounts)
+  as comp_avg , stdev(unique_accounts) as comp_std by ProcessName, SubjectUserName,
+  Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts
+  > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_from_process_filter`'
 status: production
 tags:
   analytic_story:
@@ -48,18 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - ProcessName
-  - SubjectUserName
-  - TargetUserName
-  - Computer
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml
index a10e729c58..98f1b1ba67 100644
--- a/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml
+++ b/detections/endpoint/windows_unusual_count_of_users_failed_to_authenticate_using_ntlm.yml
@@ -2,10 +2,23 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4776
 date: '2024-09-30'
-description: The following analytic identifies a source endpoint failing to authenticate multiple valid users using the NTLM protocol, potentially indicating a Password Spraying attack. It leverages Event 4776 from Domain Controllers, calculating the standard deviation for each host and applying the 3-sigma rule to detect anomalies. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, the attacker could compromise multiple accounts, leading to unauthorized access and potential lateral movement within the network.
-how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled.
+description: The following analytic identifies a source endpoint failing to authenticate
+  multiple valid users using the NTLM protocol, potentially indicating a Password
+  Spraying attack. It leverages Event 4776 from Domain Controllers, calculating the
+  standard deviation for each host and applying the 3-sigma rule to detect anomalies.
+  This activity is significant as it may represent an adversary attempting to gain
+  initial access or elevate privileges. If confirmed malicious, the attacker could
+  compromise multiple accounts, leading to unauthorized access and potential lateral
+  movement within the network.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Domain Controller events. The Advanced Security Audit policy setting `Audit Credential
+  Validation` within `Account Logon` needs to be enabled.
 id: 6f6c8fd7-6a6b-4af9-a0e9-57cfc47a58b4
-known_false_positives: A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.
+known_false_positives: A host failing to authenticate with multiple valid domain users
+  is not a common behavior for legitimate systems. Possible false positive scenarios
+  include but are not limited to vulnerability scanners and missconfigured systems.
+  If this detection triggers on a host other than a Domain Controller, the behavior
+  could represent a password spraying attack against the host's local accounts.
 name: Windows Unusual Count Of Users Failed To Authenticate Using NTLM
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -17,10 +30,20 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Workstation$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Workstation$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security`  EventCode=4776 TargetUserName!=*$ Status=0xC000006A | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, Workstation | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_using_ntlm_filter`'
+search: '`wineventlog_security`  EventCode=4776 TargetUserName!=*$ Status=0xC000006A
+  | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, Workstation | eventstats avg(unique_accounts) as comp_avg
+  , stdev(unique_accounts) as comp_std by Workstation | eval upperBound=(comp_avg+comp_std*3)
+  | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0)
+  | search isOutlier=1 | `windows_unusual_count_of_users_failed_to_authenticate_using_ntlm_filter`'
 status: production
 tags:
   analytic_story:
@@ -42,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Status
-  - TargetUserName
-  - Workstation
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml b/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml
index a976e61e7b..0850100bac 100644
--- a/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml
+++ b/detections/endpoint/windows_unusual_count_of_users_remotely_failed_to_auth_from_host.yml
@@ -2,10 +2,22 @@ author: Mauricio Velazco, Splunk
 data_source:
 - Windows Event Log Security 4625
 date: '2024-09-30'
-description: The following analytic identifies a source host failing to authenticate against a remote host with multiple users, potentially indicating a Password Spraying attack. It leverages Windows Event 4625 (failed logon attempts) and Logon Type 3 (remote authentication) to detect this behavior. This activity is significant as it may represent an adversary attempting to gain initial access or elevate privileges within an Active Directory environment. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and further compromise of the network.
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.
+description: The following analytic identifies a source host failing to authenticate
+  against a remote host with multiple users, potentially indicating a Password Spraying
+  attack. It leverages Windows Event 4625 (failed logon attempts) and Logon Type 3
+  (remote authentication) to detect this behavior. This activity is significant as
+  it may represent an adversary attempting to gain initial access or elevate privileges
+  within an Active Directory environment. If confirmed malicious, this could lead
+  to unauthorized access, privilege escalation, and further compromise of the network.
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Event Logs from domain controllers as as well as member servers and workstations.
+  The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs
+  to be enabled.
 id: cf06a0ee-ffa9-4ed3-be77-0670ed9bab52
-known_false_positives: A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc.
+known_false_positives: A host failing to authenticate with multiple valid users against
+  a remote host is not a common behavior for legitimate systems. Possible false positive
+  scenarios include but are not limited to vulnerability scanners, remote administration
+  tools, missconfigyred systems, etc.
 name: Windows Unusual Count Of Users Remotely Failed To Auth From Host
 references:
 - https://attack.mitre.org/techniques/T1110/003/
@@ -18,10 +30,20 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$Computer$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$Computer$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
-search: '`wineventlog_security`  EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts by _time, IpAddress, Computer | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by IpAddress, Computer | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) | search isOutlier=1 | `windows_unusual_count_of_users_remotely_failed_to_auth_from_host_filter`'
+search: '`wineventlog_security`  EventCode=4625 Logon_Type=3 IpAddress!="-" | bucket
+  span=2m _time | stats dc(TargetUserName) AS unique_accounts values(TargetUserName)
+  as tried_accounts by _time, IpAddress, Computer | eventstats avg(unique_accounts)
+  as comp_avg , stdev(unique_accounts) as comp_std by IpAddress, Computer | eval upperBound=(comp_avg+comp_std*3)
+  | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0)
+  | search isOutlier=1 | `windows_unusual_count_of_users_remotely_failed_to_auth_from_host_filter`'
 status: production
 tags:
   analytic_story:
@@ -43,17 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - TargetUserName
-  - Computer
-  - IpAddress
   security_domain: endpoint
 tests:
 - attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray_xml/windows-security.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
   name: True Positive Test
diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml
index f781cfa51a..6f38553475 100644
--- a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml
+++ b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_source.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects when an unusual number NTLM authentications is attempted by the same source against multiple destinations. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a multiple domain joined Windows devices using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well.
+description: The following analytic detects when an unusual number NTLM authentications
+  is attempted by the same source against multiple destinations. This activity generally
+  results when an attacker attempts to brute force, password spray, or otherwise authenticate
+  to a multiple domain joined Windows devices using an NTLM based process/attack.
+  This same activity may also generate a large number of EventID 4776 events as well.
 data_source:
 - NTLM Operational 8004,8005,8006
-search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications ``` | `windows_unusual_ntlm_authentication_destinations_by_source_filter` | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(dest))) as unique_count by src | eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006.
-known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.
+search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src
+  = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some
+  auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment```
+  | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain
+  applications ``` | `windows_unusual_ntlm_authentication_destinations_by_source_filter`
+  | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(dest)))
+  as unique_count by src | eventstats avg(unique_count) as unique_avg , stdev(unique_count)
+  as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula
+  for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(),
+  0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+how_to_implement: The following analytic requires that NTLM Operational logs to be
+  imported from the environment Domain Controllers. This requires configuration of
+  specific auditing settings, see Microsoft references for further guidance. This
+  analytic is specific to EventID 8004~8006.
+known_false_positives: Vulnerability scanners, print servers, and applications that
+  deal with non-domain joined authentications. Recommend adjusting the upperBound_unique
+  eval for tailoring the correlation to your environment, running with a 24hr search
+  window will smooth out some statistical noise.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191
@@ -23,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 25
-  message: The device [$src$] attempted $count$ NTLM authentications against $unique_count$ destinations.
+  message: The device [$src$] attempted $count$ NTLM authentications against $unique_count$
+    destinations.
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -45,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - DomainName
-  - Security
-  - WorkstationName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
     source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml
index 97749e43ef..7faaf2681f 100644
--- a/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml
+++ b/detections/endpoint/windows_unusual_ntlm_authentication_destinations_by_user.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects when an unusual number of NTLM authentications is attempted by the same user account against multiple destinations. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to numerous domain joined Windows devices using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well.
+description: The following analytic detects when an unusual number of NTLM authentications
+  is attempted by the same user account against multiple destinations. This activity
+  generally results when an attacker attempts to brute force, password spray, or otherwise
+  authenticate to numerous domain joined Windows devices using an NTLM based process/attack.
+  This same activity may also generate a large number of EventID 4776 events as well.
 data_source:
 - NTLM Operational 8004,8005,8006
-search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications ``` | `windows_unusual_ntlm_authentication_destinations_by_user_filter` | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(dest))) as unique_count by user | eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006.
-known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.
+search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src
+  = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some
+  auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment```
+  | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain
+  applications ``` | `windows_unusual_ntlm_authentication_destinations_by_user_filter`
+  | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(dest)))
+  as unique_count by user | eventstats avg(unique_count) as unique_avg , stdev(unique_count)
+  as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula
+  for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(),
+  0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+how_to_implement: The following analytic requires that NTLM Operational logs to be
+  imported from the environment Domain Controllers. This requires configuration of
+  specific auditing settings, see Microsoft references for further guidance. This
+  analytic is specific to EventID 8004~8006.
+known_false_positives: Vulnerability scanners, print servers, and applications that
+  deal with non-domain joined authentications. Recommend adjusting the upperBound_unique
+  eval for tailoring the correlation to your environment, running with a 24hr search
+  window will smooth out some statistical noise.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191
@@ -23,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 25
-  message: The user [$user$] attempted $count$ NTLM authentications against $unique_count$ destinations.
+  message: The user [$user$] attempted $count$ NTLM authentications against $unique_count$
+    destinations.
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -45,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - DomainName
-  - Security
-  - WorkstationName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
     source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml
index 9b4f74b0f6..451ff65554 100644
--- a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml
+++ b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_destination.yml
@@ -5,12 +5,33 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device.
+description: The following analytic detects when a device is the target of numerous
+  NTLM authentications using a null domain. This activity generally results when an
+  attacker attempts to brute force, password spray, or otherwise authenticate to a
+  domain joined Windows device from a non-domain device. This activity may also generate
+  a large number of EventID 4776 events in tandem, however these events will not indicate
+  the attacker or target device.
 data_source:
 - NTLM Operational 8004,8005,8006
-search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications ``` | `windows_unusual_ntlm_authentication_users_by_destination_filter` | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user))) as unique_count by dest | eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: The following analytic detects when an unusual number of NTLM authentications is attempted against the same destination. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well.
-known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.
+search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src
+  = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some
+  auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment```
+  | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain
+  applications ``` | `windows_unusual_ntlm_authentication_users_by_destination_filter`
+  | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user)))
+  as unique_count by dest | eventstats avg(unique_count) as unique_avg , stdev(unique_count)
+  as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula
+  for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(),
+  0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+how_to_implement: The following analytic detects when an unusual number of NTLM authentications
+  is attempted against the same destination. This activity generally results when
+  an attacker attempts to brute force, password spray, or otherwise authenticate to
+  a domain joined Windows device using an NTLM based process/attack. This same activity
+  may also generate a large number of EventID 4776 events as well.
+known_false_positives: Vulnerability scanners, print servers, and applications that
+  deal with non-domain joined authentications. Recommend adjusting the upperBound_unique
+  eval for tailoring the correlation to your environment, running with a 24hr search
+  window will smooth out some statistical noise.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 25
-  message: The device [$dest$] was the target of $count$ NTLM authentications using $unique_count$ unique user accounts.
+  message: The device [$dest$] was the target of $count$ NTLM authentications using
+    $unique_count$ unique user accounts.
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -45,16 +72,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - DomainName
-  - Security
-  - WorkstationName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
     source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml
index a87788f6cd..5d95c1a16c 100644
--- a/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml
+++ b/detections/endpoint/windows_unusual_ntlm_authentication_users_by_source.yml
@@ -5,12 +5,31 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects when an unusual number of NTLM authentications is attempted by the same source. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events in as well.
+description: The following analytic detects when an unusual number of NTLM authentications
+  is attempted by the same source. This activity generally results when an attacker
+  attempts to brute force, password spray, or otherwise authenticate to a domain joined
+  Windows device using an NTLM based process/attack. This same activity may also generate
+  a large number of EventID 4776 events in as well.
 data_source:
 - NTLM Operational 8004,8005,8006
-search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment``` | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications ``` | `windows_unusual_ntlm_authentication_users_by_source_filter` | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user))) as unique_count by src | eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
-how_to_implement: The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006.
-known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.
+search: '`ntlm_audit` EventCode = 8004 SChannelName=* WorkstationName=* | eval src
+  = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some
+  auth attempts ``` | eval dest = SChannelName, user = UserName ``` CIM alignment```
+  | where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain
+  applications ``` | `windows_unusual_ntlm_authentication_users_by_source_filter`
+  | stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user)))
+  as unique_count by src | eventstats avg(unique_count) as unique_avg , stdev(unique_count)
+  as unique_std | eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula
+  for sensitivity``` | eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(),
+  0) | where isOutlier==1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+how_to_implement: The following analytic requires that NTLM Operational logs to be
+  imported from the environment Domain Controllers. This requires configuration of
+  specific auditing settings, see Microsoft references for further guidance. This
+  analytic is specific to EventID 8004~8006.
+known_false_positives: Vulnerability scanners, print servers, and applications that
+  deal with non-domain joined authentications. Recommend adjusting the upperBound_unique
+  eval for tailoring the correlation to your environment, running with a 24hr search
+  window will smooth out some statistical noise.
 references:
 - https://attack.mitre.org/techniques/T1110/003/
 - https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191
@@ -23,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 25
-  message: The device [$src$] attempted $count$ NTLM authentications using $unique_count$ user accounts.
+  message: The device [$src$] attempted $count$ NTLM authentications using $unique_count$
+    user accounts.
   mitre_attack_id:
   - T1110
   - T1110.003
@@ -45,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - DomainName
-  - Security
-  - WorkstationName
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
     source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml b/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml
index 42721122db..e95aa63d09 100644
--- a/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml
+++ b/detections/endpoint/windows_user_execution_malicious_url_shortcut_file.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of suspicious URL shortcut link files, often used by malware like CHAOS ransomware. It leverages the Endpoint.Filesystem datamodel to identify .url files created outside standard directories, such as Program Files. This activity is significant as it may indicate an attempt to execute malicious code upon system reboot. If confirmed malicious, this could allow an attacker to achieve persistence and execute harmful payloads, potentially leading to further system compromise and data loss.
+description: The following analytic detects the creation of suspicious URL shortcut
+  link files, often used by malware like CHAOS ransomware. It leverages the Endpoint.Filesystem
+  datamodel to identify .url files created outside standard directories, such as Program
+  Files. This activity is significant as it may indicate an attempt to execute malicious
+  code upon system reboot. If confirmed malicious, this could allow an attacker to
+  achieve persistence and execute harmful payloads, potentially leading to further
+  system compromise and data loss.
 data_source:
 - Sysmon EventID 11
-search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where NOT(Filesystem.file_path IN ("*\\Program Files*")) Filesystem.file_name = *.url by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user Filesystem.file_path Filesystem.process_guid Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_user_execution_malicious_url_shortcut_file_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
-known_false_positives: Administrators may allow creation of script or exe in this path.
+search: '|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Filesystem where NOT(Filesystem.file_path IN
+  ("*\\Program Files*")) Filesystem.file_name = *.url by Filesystem.file_create_time
+  Filesystem.process_id  Filesystem.file_name Filesystem.user Filesystem.file_path
+  Filesystem.process_guid Filesystem.dest | `drop_dm_object_name(Filesystem)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_user_execution_malicious_url_shortcut_file_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on process that include the name of the Filesystem responsible for the changes from
+  your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
+known_false_positives: Administrators may allow creation of script or exe in this
+  path.
 references:
 - https://attack.mitre.org/techniques/T1204/002/
 - https://www.fortinet.com/blog/threat-research/chaos-ransomware-variant-sides-with-russia
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Filesystem.file_create_time
-  - Filesystem.process_id
-  - Filesystem.file_name
-  - Filesystem.user
-  - Filesystem.file_path
-  - Filesystem.process_guid
-  - Filesystem.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/chaos_ransomware/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_valid_account_with_never_expires_password.yml b/detections/endpoint/windows_valid_account_with_never_expires_password.yml
index dddb3e4f02..4d7c29b8c9 100644
--- a/detections/endpoint/windows_valid_account_with_never_expires_password.yml
+++ b/detections/endpoint/windows_valid_account_with_never_expires_password.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the use of net.exe to update user account policies to set passwords as non-expiring. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "/maxpwage:unlimited". This activity is significant as it can indicate an attempt to maintain persistence, escalate privileges, evade defenses, or facilitate lateral movement. If confirmed malicious, this behavior could allow an attacker to maintain long-term access to compromised accounts, potentially leading to further exploitation and unauthorized access to sensitive information.
+description: The following analytic detects the use of net.exe to update user account
+  policies to set passwords as non-expiring. It leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on command-line executions involving "/maxpwage:unlimited".
+  This activity is significant as it can indicate an attempt to maintain persistence,
+  escalate privileges, evade defenses, or facilitate lateral movement. If confirmed
+  malicious, this behavior could allow an attacker to maintain long-term access to
+  compromised accounts, potentially leading to further exploitation and unauthorized
+  access to sensitive information.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND Processes.process="* accounts *" AND Processes.process="* /maxpwage:unlimited" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_valid_account_with_never_expires_password_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: This behavior is not commonly seen in production environment and not advisable, filter as needed.
+search: '| tstats `security_content_summariesonly` values(Processes.process) as process
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes
+  where `process_net` AND Processes.process="* accounts *" AND Processes.process="*
+  /maxpwage:unlimited" by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.original_file_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_valid_account_with_never_expires_password_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: This behavior is not commonly seen in production environment
+  and not advisable, filter as needed.
 references:
 - https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/
 - https://docs.microsoft.com/en-us/troubleshoot/windows-server/networking/net-commands-on-operating-systems
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ attempting to make non-expiring password on host user accounts.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ attempting to make non-expiring password on host user accounts.
   mitre_attack_id:
   - T1489
   observable:
@@ -43,23 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/azorult/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_vulnerable_3cx_software.yml b/detections/endpoint/windows_vulnerable_3cx_software.yml
index e9c4ec0dea..cff7d2b743 100644
--- a/detections/endpoint/windows_vulnerable_3cx_software.yml
+++ b/detections/endpoint/windows_vulnerable_3cx_software.yml
@@ -9,10 +9,23 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects instances of the 3CXDesktopApp.exe with a FileVersion of 18.12.x, leveraging Sysmon logs. This detection focuses on identifying vulnerable versions 18.12.407 and 18.12.416 of the 3CX desktop app. Monitoring this activity is crucial as these specific versions have known vulnerabilities that could be exploited by attackers. If confirmed malicious, exploitation of this vulnerability could lead to unauthorized access, code execution, or further compromise of the affected system, posing significant security risks.
-search: '`sysmon` (process_name=3CXDesktopApp.exe OR OriginalFileName=3CXDesktopApp.exe)  FileVersion=18.12.* | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name,process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `windows_vulnerable_3cx_software_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives: False positives may be present based on file version, modify the analytic to only look for version between 18.12.407 and 18.12.416 as needed.
+description: The following analytic detects instances of the 3CXDesktopApp.exe with
+  a FileVersion of 18.12.x, leveraging Sysmon logs. This detection focuses on identifying
+  vulnerable versions 18.12.407 and 18.12.416 of the 3CX desktop app. Monitoring this
+  activity is crucial as these specific versions have known vulnerabilities that could
+  be exploited by attackers. If confirmed malicious, exploitation of this vulnerability
+  could lead to unauthorized access, code execution, or further compromise of the
+  affected system, posing significant security risks.
+search: '`sysmon` (process_name=3CXDesktopApp.exe OR OriginalFileName=3CXDesktopApp.exe)  FileVersion=18.12.*
+  | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name,process_name,
+  OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `windows_vulnerable_3cx_software_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the process name, parent process, and command-line executions from your
+  endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the
+  Sysmon TA.
+known_false_positives: False positives may be present based on file version, modify
+  the analytic to only look for version between 18.12.407 and 18.12.416 as needed.
 references:
 - https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
 - https://www.cisa.gov/news-events/alerts/2023/03/30/supply-chain-attack-against-3cxdesktopapp
@@ -25,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +54,8 @@ tags:
   cve:
   - CVE-2023-29059
   impact: 100
-  message: A known vulnerable instance of 3CX Software $process_name$ ran on $dest$, related to a supply chain attack.
+  message: A known vulnerable instance of 3CX Software $process_name$ ran on $dest$,
+    related to a supply chain attack.
   mitre_attack_id:
   - T1195.002
   observable:
@@ -52,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - OriginalFileName
-  - process_name
-  - EventID
-  - CommandLine
-  - dest
-  - parent_process_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1195.002/3CX/3cx_windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_vulnerable_driver_installed.yml b/detections/endpoint/windows_vulnerable_driver_installed.yml
index 894458504e..64b1a6411c 100644
--- a/detections/endpoint/windows_vulnerable_driver_installed.yml
+++ b/detections/endpoint/windows_vulnerable_driver_installed.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Windows Event Log System 7045
-description: The following analytic detects the loading of known vulnerable Windows drivers, which may indicate potential persistence or privilege escalation attempts. It leverages Windows System service install EventCode 7045 to identify driver loading events and cross-references them with a list of vulnerable drivers. This activity is significant as attackers often exploit vulnerable drivers to gain elevated privileges or maintain persistence on a system. If confirmed malicious, this could allow attackers to execute arbitrary code with high privileges, leading to further system compromise and potential data exfiltration. This detection is a Windows Event Log adaptation of the Sysmon driver loaded detection written by Michael Haag.
-search: '`wineventlog_system` EventCode=7045 ServiceType="kernel mode driver" | table _time dest EventCode ImagePath ServiceName ServiceType | lookup loldrivers driver_name AS ImagePath OUTPUT is_driver driver_description | search is_driver = TRUE  | `windows_vulnerable_driver_installed_filter`'
-how_to_implement: Ensure the Splunk is collecting XmlWinEventLog:System events and the EventCode 7045 is being ingested.
-known_false_positives: False positives will be present. Drill down into the driver further by version number and cross reference by signer. Review the reference material in the lookup. In addition, modify the query to look within specific paths, which will remove a lot of "normal" drivers.
+description: The following analytic detects the loading of known vulnerable Windows
+  drivers, which may indicate potential persistence or privilege escalation attempts.
+  It leverages Windows System service install EventCode 7045 to identify driver loading
+  events and cross-references them with a list of vulnerable drivers. This activity
+  is significant as attackers often exploit vulnerable drivers to gain elevated privileges
+  or maintain persistence on a system. If confirmed malicious, this could allow attackers
+  to execute arbitrary code with high privileges, leading to further system compromise
+  and potential data exfiltration. This detection is a Windows Event Log adaptation
+  of the Sysmon driver loaded detection written by Michael Haag.
+search: '`wineventlog_system` EventCode=7045 ServiceType="kernel mode driver" | table
+  _time dest EventCode ImagePath ServiceName ServiceType | lookup loldrivers driver_name
+  AS ImagePath OUTPUT is_driver driver_description | search is_driver = TRUE  | `windows_vulnerable_driver_installed_filter`'
+how_to_implement: Ensure the Splunk is collecting XmlWinEventLog:System events and
+  the EventCode 7045 is being ingested.
+known_false_positives: False positives will be present. Drill down into the driver
+  further by version number and cross reference by signer. Review the reference material
+  in the lookup. In addition, modify the query to look within specific paths, which
+  will remove a lot of "normal" drivers.
 references:
 - https://loldrivers.io/
 - https://github.com/SpikySabra/Kernel-Cactus
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 100
-  message: Potentially vulnerable/malicious driver [$ImagePath$] has been installed on $dest$
+  message: Potentially vulnerable/malicious driver [$ImagePath$] has been installed
+    on $dest$
   mitre_attack_id:
   - T1543.003
   observable:
@@ -44,13 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - ServiceType
-  - driver_name
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-system.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-system.log
     source: XmlWinEventLog:System
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_vulnerable_driver_loaded.yml b/detections/endpoint/windows_vulnerable_driver_loaded.yml
index dbe954d600..5ad2d6dd21 100644
--- a/detections/endpoint/windows_vulnerable_driver_loaded.yml
+++ b/detections/endpoint/windows_vulnerable_driver_loaded.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects the loading of known vulnerable Windows drivers, which may indicate potential persistence or privilege escalation attempts. It leverages Sysmon EventCode 6 to identify driver loading events and cross-references them with a list of vulnerable drivers. This activity is significant as attackers often exploit vulnerable drivers to gain elevated privileges or maintain persistence on a system. If confirmed malicious, this could allow attackers to execute arbitrary code with high privileges, leading to further system compromise and potential data exfiltration.
+description: The following analytic detects the loading of known vulnerable Windows
+  drivers, which may indicate potential persistence or privilege escalation attempts.
+  It leverages Sysmon EventCode 6 to identify driver loading events and cross-references
+  them with a list of vulnerable drivers. This activity is significant as attackers
+  often exploit vulnerable drivers to gain elevated privileges or maintain persistence
+  on a system. If confirmed malicious, this could allow attackers to execute arbitrary
+  code with high privileges, leading to further system compromise and potential data
+  exfiltration.
 data_source:
 - Sysmon EventID 6
-search: '`sysmon` EventCode=6  | stats  min(_time) as firstTime max(_time) as lastTime count by dest ImageLoaded | lookup loldrivers driver_name AS ImageLoaded OUTPUT is_driver driver_description | search is_driver = TRUE | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_vulnerable_driver_loaded_filter`'
-how_to_implement: Sysmon collects driver loads via EventID 6, however you may modify the query to utilize this lookup to identify potentially persistent drivers that are known to be vulnerable.
-known_false_positives: False positives will be present. Drill down into the driver further by version number and cross reference by signer. Review the reference material in the lookup. In addition, modify the query to look within specific paths, which will remove a lot of "normal" drivers.
+search: '`sysmon` EventCode=6  | stats  min(_time) as firstTime max(_time) as lastTime
+  count by dest ImageLoaded | lookup loldrivers driver_name AS ImageLoaded OUTPUT
+  is_driver driver_description | search is_driver = TRUE | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_vulnerable_driver_loaded_filter`'
+how_to_implement: Sysmon collects driver loads via EventID 6, however you may modify
+  the query to utilize this lookup to identify potentially persistent drivers that
+  are known to be vulnerable.
+known_false_positives: False positives will be present. Drill down into the driver
+  further by version number and cross reference by signer. Review the reference material
+  in the lookup. In addition, modify the query to look within specific paths, which
+  will remove a lot of "normal" drivers.
 references:
 - https://github.com/SigmaHQ/sigma/blob/master/rules/windows/driver_load/driver_load_vuln_drivers_names.yml
 - https://github.com/eclypsium/Screwed-Drivers/blob/master/DRIVERS.md
@@ -32,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An process has loaded a possible vulnerable driver on $dest$. Review and escalate as needed.
+  message: An process has loaded a possible vulnerable driver on $dest$. Review and
+    escalate as needed.
   mitre_attack_id:
   - T1543.003
   observable:
@@ -44,14 +60,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - ImageLoaded
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1014/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_windbg_spawning_autoit3.yml b/detections/endpoint/windows_windbg_spawning_autoit3.yml
index 211d06d05c..4eed4f3610 100644
--- a/detections/endpoint/windows_windbg_spawning_autoit3.yml
+++ b/detections/endpoint/windows_windbg_spawning_autoit3.yml
@@ -9,10 +9,33 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic identifies instances of the WinDBG process spawning AutoIt3. This behavior is detected by monitoring endpoint telemetry for processes where 'windbg.exe' is the parent process and 'autoit3.exe' or similar is the child process. This activity is significant because AutoIt3 is frequently used by threat actors for scripting malicious automation, potentially indicating an ongoing attack. If confirmed malicious, this could allow attackers to automate tasks, execute arbitrary code, and further compromise the system, leading to data exfiltration or additional malware deployment.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes  where Processes.parent_process_name=windbg.exe  AND (Processes.process_name IN ("autoit3.exe", "autoit*.exe") OR Processes.original_file_name IN ("autoit3.exe", "autoit*.exe")) by Processes.dest, Processes.user, Processes.parent_process_name, Processes.process_name, Processes.original_file_name, Processes.process, Processes.process_id, Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval matches_extension=if(match(process, "\\.(au3|a3x|exe|aut|aup)$"), "Yes", "No") | search matches_extension="Yes" | `windows_windbg_spawning_autoit3_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will only be present if the WinDBG process legitimately spawns AutoIt3. Filter as needed.
+description: The following analytic identifies instances of the WinDBG process spawning
+  AutoIt3. This behavior is detected by monitoring endpoint telemetry for processes
+  where 'windbg.exe' is the parent process and 'autoit3.exe' or similar is the child
+  process. This activity is significant because AutoIt3 is frequently used by threat
+  actors for scripting malicious automation, potentially indicating an ongoing attack.
+  If confirmed malicious, this could allow attackers to automate tasks, execute arbitrary
+  code, and further compromise the system, leading to data exfiltration or additional
+  malware deployment.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes  where Processes.parent_process_name=windbg.exe  AND
+  (Processes.process_name IN ("autoit3.exe", "autoit*.exe") OR Processes.original_file_name
+  IN ("autoit3.exe", "autoit*.exe")) by Processes.dest, Processes.user, Processes.parent_process_name,
+  Processes.process_name, Processes.original_file_name, Processes.process, Processes.process_id,
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | eval matches_extension=if(match(process,
+  "\\.(au3|a3x|exe|aut|aup)$"), "Yes", "No") | search matches_extension="Yes" | `windows_windbg_spawning_autoit3_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will only be present if the WinDBG process
+  legitimately spawns AutoIt3. Filter as needed.
 references:
 - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt
 drilldown_searches:
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +59,8 @@ tags:
   atomic_guid: []
   confidence: 100
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$.
   mitre_attack_id:
   - T1059
   observable:
@@ -55,19 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.original_file_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/windbg_autoit.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/autoit/windbg_autoit.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_winlogon_with_public_network_connection.yml b/detections/endpoint/windows_winlogon_with_public_network_connection.yml
index cb0be4c657..0ccf792de8 100644
--- a/detections/endpoint/windows_winlogon_with_public_network_connection.yml
+++ b/detections/endpoint/windows_winlogon_with_public_network_connection.yml
@@ -7,10 +7,35 @@ status: experimental
 type: Hunting
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 3
-description: The following analytic detects instances of Winlogon.exe, a critical Windows process, connecting to public IP addresses. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on network connections made by Winlogon.exe. Under normal circumstances, Winlogon.exe should not connect to public IPs, and such activity may indicate a compromise, such as the BlackLotus bootkit attack. This detection is significant as it highlights potential system integrity breaches. If confirmed malicious, attackers could maintain persistence, bypass security measures, and compromise the system at a fundamental level.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN (winlogon.exe)  Processes.process!=unknown by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port != 0 NOT (All_Traffic.dest IN (127.0.0.1,10.0.0.0/8,172.16.0.0/12, 192.168.0.0/16, 0:0:0:0:0:0:0:1)) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name(All_Traffic)` | rename dest as publicIp ] | table dest parent_process_name process_name process_path process process_id dest_port publicIp | `windows_winlogon_with_public_network_connection_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives will be present and filtering will be required. Legitimate IPs will be present and need to be filtered.
+description: The following analytic detects instances of Winlogon.exe, a critical
+  Windows process, connecting to public IP addresses. This behavior is identified
+  using Endpoint Detection and Response (EDR) telemetry, focusing on network connections
+  made by Winlogon.exe. Under normal circumstances, Winlogon.exe should not connect
+  to public IPs, and such activity may indicate a compromise, such as the BlackLotus
+  bootkit attack. This detection is significant as it highlights potential system
+  integrity breaches. If confirmed malicious, attackers could maintain persistence,
+  bypass security measures, and compromise the system at a fundamental level.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN (winlogon.exe)  Processes.process!=unknown
+  by Processes.dest Processes.user Processes.parent_process_name Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | join  process_id
+  [| tstats `security_content_summariesonly` count FROM datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port != 0 NOT (All_Traffic.dest IN (127.0.0.1,10.0.0.0/8,172.16.0.0/12,
+  192.168.0.0/16, 0:0:0:0:0:0:0:1)) by All_Traffic.process_id All_Traffic.dest All_Traffic.dest_port
+  | `drop_dm_object_name(All_Traffic)` | rename dest as publicIp ] | table dest parent_process_name
+  process_name process_path process process_id dest_port publicIp | `windows_winlogon_with_public_network_connection_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives will be present and filtering will be required.
+  Legitimate IPs will be present and need to be filtered.
 references:
 - https://www.microsoft.com/en-us/security/blog/2023/04/11/guidance-for-investigating-attacks-using-cve-2022-21894-the-blacklotus-campaign/
 tags:
@@ -20,7 +45,8 @@ tags:
   atomic_guid: []
   confidence: 50
   impact: 50
-  message: Winlogon.exe has generated a network connection to a remote destination on endpoint $dest$.
+  message: Winlogon.exe has generated a network connection to a remote destination
+    on endpoint $dest$.
   mitre_attack_id:
   - T1542.003
   observable:
@@ -32,19 +58,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - dest
-  - parent_process_name
-  - process_name
-  - process_path
-  - process
-  - process_id
-  - dest_port
-  - publicIp
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1542.003/bootkits/network-winlogon-windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1542.003/bootkits/network-winlogon-windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_wmi_impersonate_token.yml b/detections/endpoint/windows_wmi_impersonate_token.yml
index 5e29294d00..4c017e4ed2 100644
--- a/detections/endpoint/windows_wmi_impersonate_token.yml
+++ b/detections/endpoint/windows_wmi_impersonate_token.yml
@@ -5,12 +5,28 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects potential WMI token impersonation activities in a process or command. It leverages Sysmon EventCode 10 to identify instances where `wmiprvse.exe` has a duplicate handle or full granted access in a target process. This behavior is significant as it is commonly used by malware like Qakbot for privilege escalation or defense evasion. If confirmed malicious, this activity could allow an attacker to gain elevated privileges, evade defenses, and maintain persistence within the environment.
+description: The following analytic detects potential WMI token impersonation activities
+  in a process or command. It leverages Sysmon EventCode 10 to identify instances
+  where `wmiprvse.exe` has a duplicate handle or full granted access in a target process.
+  This behavior is significant as it is commonly used by malware like Qakbot for privilege
+  escalation or defense evasion. If confirmed malicious, this activity could allow
+  an attacker to gain elevated privileges, evade defenses, and maintain persistence
+  within the environment.
 data_source:
 - Sysmon EventID 10
-search: '`sysmon` EventCode=10 SourceImage = "*\\wmiprvse.exe"  GrantedAccess IN ("0x1478", "0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId GrantedAccess CallTrace dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_impersonate_token_filter`'
-how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives: administrator may execute impersonate wmi object script for auditing. Filter is needed.
+search: '`sysmon` EventCode=10 SourceImage = "*\\wmiprvse.exe"  GrantedAccess IN ("0x1478",
+  "0x1fffff") | stats count min(_time) as firstTime max(_time) as lastTime by SourceImage
+  TargetImage SourceProcessGUID TargetProcessGUID SourceProcessId TargetProcessId
+  GrantedAccess CallTrace dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_wmi_impersonate_token_filter`'
+how_to_implement: This search requires Sysmon Logs and a Sysmon configuration, which
+  includes EventCode 10. This search uses an input macro named `sysmon`. We strongly
+  recommend that you specify your environment-specific configurations (index, source,
+  sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations
+  for your Splunk environment. The search also uses a post-filter macro designed to
+  filter out known false positives.
+known_false_positives: administrator may execute impersonate wmi object script for
+  auditing. Filter is needed.
 references:
 - https://github.com/trustedsec/SysmonCommunityGuide/blob/master/chapters/process-access.md
 - https://www.joesandbox.com/analysis/278341/0/html
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: wmiprvse.exe process having a duplicate or full Granted Access $GrantedAccess$ to $TargetImage$ process in $dest$
+  message: wmiprvse.exe process having a duplicate or full Granted Access $GrantedAccess$
+    to $TargetImage$ process in $dest$
   mitre_attack_id:
   - T1047
   observable:
@@ -41,21 +63,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - SourceImage
-  - TargetImage
-  - SourceProcessGUID
-  - TargetProcessGUID
-  - SourceProcessId
-  - TargetProcessId
-  - GrantedAccess
-  - CallTrace
-  - dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/wmi_impersonate/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/wmi_impersonate/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_wmi_process_and_service_list.yml b/detections/endpoint/windows_wmi_process_and_service_list.yml
index 073a18546b..3a904934dc 100644
--- a/detections/endpoint/windows_wmi_process_and_service_list.yml
+++ b/detections/endpoint/windows_wmi_process_and_service_list.yml
@@ -5,14 +5,36 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies suspicious WMI command lines querying for running processes or services. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific process and command-line events. This activity is significant as adversaries often use WMI to gather system information and identify services on compromised machines. If confirmed malicious, this behavior could allow attackers to map out the system, identify critical services, and plan further attacks, potentially leading to privilege escalation or persistence within the environment.
+description: The following analytic identifies suspicious WMI command lines querying
+  for running processes or services. It leverages data from Endpoint Detection and
+  Response (EDR) agents, focusing on specific process and command-line events. This
+  activity is significant as adversaries often use WMI to gather system information
+  and identify services on compromised machines. If confirmed malicious, this behavior
+  could allow attackers to map out the system, identify critical services, and plan
+  further attacks, potentially leading to privilege escalation or persistence within
+  the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process IN ("*process list*", "*service list*") by Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_process_and_service_list_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: netowrk administrator or IT may execute this command for auditing processes and services.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process
+  IN ("*process list*", "*service list*") by Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.process_guid Processes.parent_process_name
+  Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `windows_wmi_process_and_service_list_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: netowrk administrator or IT may execute this command for auditing
+  processes and services.
 references:
 - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS
 - https://www.microsoft.com/en-us/security/blog/2022/10/14/new-prestige-ransomware-impacts-organizations-in-ukraine-and-poland/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,25 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.parent_process_guid
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/winpeas/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/windows_wmi_process_call_create.yml b/detections/endpoint/windows_wmi_process_call_create.yml
index 5a5b2345be..6d5622bfd2 100644
--- a/detections/endpoint/windows_wmi_process_call_create.yml
+++ b/detections/endpoint/windows_wmi_process_call_create.yml
@@ -5,13 +5,35 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of WMI command lines used to create or execute processes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line events that include specific keywords like "process," "call," and "create." This activity is significant because adversaries often use WMI to execute malicious payloads on local or remote hosts, potentially bypassing traditional security controls. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe threat to organizational security.
+description: The following analytic detects the execution of WMI command lines used
+  to create or execute processes. It leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on command-line events that include specific keywords like
+  "process," "call," and "create." This activity is significant because adversaries
+  often use WMI to execute malicious payloads on local or remote hosts, potentially
+  bypassing traditional security controls. If confirmed malicious, this behavior could
+  allow attackers to execute arbitrary code, escalate privileges, or maintain persistence
+  within the environment, posing a severe threat to organizational security.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process = "* process *" Processes.process = "* call *" Processes.process = "* create *" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_path Processes.process_guid Processes.parent_process_id Processes.dest Processes.user Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_wmi_process_call_create_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process
+  = "* process *" Processes.process = "* call *" Processes.process = "* create *"
+  by Processes.parent_process_name Processes.parent_process Processes.process_name
+  Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_path
+  Processes.process_guid Processes.parent_process_id Processes.dest Processes.user
+  Processes.process_path | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_wmi_process_call_create_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators may execute this command for testing or auditing.
 references:
 - https://github.com/NVISOsecurity/sigma-public/blob/master/rules/windows/process_creation/win_susp_wmi_execution.yml
@@ -44,24 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
-  - Processes.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml b/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml
index b8663d2b8c..be035517b2 100644
--- a/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml
+++ b/detections/endpoint/winevent_scheduled_task_created_to_spawn_shell.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of scheduled tasks designed to execute commands using native Windows shells like PowerShell, Cmd, Wscript, or Cscript. It leverages Windows Security EventCode 4698 to identify when such tasks are registered. This activity is significant as it may indicate an attempt to establish persistence or execute malicious commands on a system. If confirmed malicious, this could allow an attacker to maintain access, execute arbitrary code, or escalate privileges, posing a severe threat to the environment.
+description: The following analytic detects the creation of scheduled tasks designed
+  to execute commands using native Windows shells like PowerShell, Cmd, Wscript, or
+  Cscript. It leverages Windows Security EventCode 4698 to identify when such tasks
+  are registered. This activity is significant as it may indicate an attempt to establish
+  persistence or execute malicious commands on a system. If confirmed malicious, this
+  could allow an attacker to maintain access, execute arbitrary code, or escalate
+  privileges, posing a severe threat to the environment.
 data_source:
 - Windows Event Log Security 4698
-search: '`wineventlog_security` EventCode=4698 TaskContent IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TaskName, TaskContent | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_to_spawn_shell_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
-known_false_positives: False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately.
+search: '`wineventlog_security` EventCode=4698 TaskContent IN ("*powershell.exe*",
+  "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*",
+  "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") | stats count min(_time) as firstTime
+  max(_time) as lastTime by Computer, TaskName, TaskContent | rename Computer as dest
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_to_spawn_shell_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: False positives are possible if legitimate applications are
+  allowed to register tasks that call a shell to be spawned. Filter as needed based
+  on command-line or processes that are used legitimately.
 references:
 - https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
 - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -37,7 +56,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: 'A windows scheduled task was created (task name=$TaskName$) on $dest$ by the following command: $TaskContent$'
+  message: 'A windows scheduled task was created (task name=$TaskName$) on $dest$
+    by the following command: $TaskContent$'
   mitre_attack_id:
   - T1053.005
   - T1053
@@ -50,16 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Task_Name
-  - Description
-  - Command
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml b/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml
index db187d6251..eb7f4c9430 100644
--- a/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml
+++ b/detections/endpoint/winevent_scheduled_task_created_within_public_path.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects the creation of scheduled tasks within user-writable paths using Windows Security EventCode 4698. It identifies tasks registered via schtasks.exe or TaskService that execute commands from directories like Public, ProgramData, Temp, and AppData. This behavior is significant as it may indicate an attempt to establish persistence or execute unauthorized commands. If confirmed malicious, an attacker could maintain long-term access, escalate privileges, or execute arbitrary code, posing a severe threat to system integrity and security.
+description: The following analytic detects the creation of scheduled tasks within
+  user-writable paths using Windows Security EventCode 4698. It identifies tasks registered
+  via schtasks.exe or TaskService that execute commands from directories like Public,
+  ProgramData, Temp, and AppData. This behavior is significant as it may indicate
+  an attempt to establish persistence or execute unauthorized commands. If confirmed
+  malicious, an attacker could maintain long-term access, escalate privileges, or
+  execute arbitrary code, posing a severe threat to system integrity and security.
 data_source:
 - Windows Event Log Security 4698
-search: '`wineventlog_security` EventCode=4698 TaskContent IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*") | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TaskName, TaskContent |  rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winevent_scheduled_task_created_within_public_path_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
-known_false_positives: False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately.
+search: '`wineventlog_security` EventCode=4698 TaskContent IN ("*\\users\\public\\*",
+  "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*", "*\\perflogs\\*")
+  | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TaskName,
+  TaskContent |  rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `winevent_scheduled_task_created_within_public_path_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also
+  required.
+known_false_positives: False positives are possible if legitimate applications are
+  allowed to register tasks in public paths. Filter as needed based on paths that
+  are used legitimately.
 references:
 - https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
 - https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -57,16 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - Task_Name
-  - Description
-  - Command
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_scheduled_task_created_to_spawn_shell/windows-xml.log
     source: XmlWinEventLog:Security
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml b/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml
index e18e7ee260..aaad42bc50 100644
--- a/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml
+++ b/detections/endpoint/winevent_windows_task_scheduler_event_action_started.yml
@@ -5,7 +5,14 @@ date: '2024-10-24'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects the execution of tasks registered in Windows Task Scheduler by monitoring EventID 200 (action run) and 201 (action completed) from the Task Scheduler logs. This detection leverages Task Scheduler logs to identify potentially suspicious or unauthorized task executions. Monitoring these events is significant for a SOC as it helps uncover evasive techniques used for persistence, unauthorized code execution, or other malicious activities. If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or the execution of harmful payloads, posing a significant threat to the environment.
+description: The following analytic detects the execution of tasks registered in Windows
+  Task Scheduler by monitoring EventID 200 (action run) and 201 (action completed)
+  from the Task Scheduler logs. This detection leverages Task Scheduler logs to identify
+  potentially suspicious or unauthorized task executions. Monitoring these events
+  is significant for a SOC as it helps uncover evasive techniques used for persistence,
+  unauthorized code execution, or other malicious activities. If confirmed malicious,
+  this activity could lead to unauthorized access, data exfiltration, or the execution
+  of harmful payloads, posing a significant threat to the environment.
 data_source:
 - Windows Event Log TaskScheduler 200
 - Windows Event Log TaskScheduler 201
@@ -55,17 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - TaskName
-  - ActionName
-  - EventID
-  - dvc
-  - ProcessID
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_windows_task_scheduler_event_action_started/windows-xml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/winevent_windows_task_scheduler_event_action_started/windows-xml.log
     source: XmlWinEventLog:Microsoft-Windows-TaskScheduler/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winhlp32_spawning_a_process.yml b/detections/endpoint/winhlp32_spawning_a_process.yml
index a206d497a7..81740e8160 100644
--- a/detections/endpoint/winhlp32_spawning_a_process.yml
+++ b/detections/endpoint/winhlp32_spawning_a_process.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects winhlp32.exe spawning a child process that loads a file from appdata, programdata, or temp directories. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events. This activity is significant because winhlp32.exe has known vulnerabilities and can be exploited to execute malicious code. If confirmed malicious, an attacker could use this technique to execute arbitrary scripts, escalate privileges, or maintain persistence within the environment. Analysts should review parallel processes, module loads, and file modifications for further suspicious behavior.
+description: The following analytic detects winhlp32.exe spawning a child process
+  that loads a file from appdata, programdata, or temp directories. This detection
+  leverages data from Endpoint Detection and Response (EDR) agents, focusing on process
+  creation events. This activity is significant because winhlp32.exe has known vulnerabilities
+  and can be exploited to execute malicious code. If confirmed malicious, an attacker
+  could use this technique to execute arbitrary scripts, escalate privileges, or maintain
+  persistence within the environment. Analysts should review parallel processes, module
+  loads, and file modifications for further suspicious behavior.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winhlp32.exe Processes.process IN ("*\\appdata\\*","*\\programdata\\*", "*\\temp\\*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winhlp32_spawning_a_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited as winhlp32.exe is typically not used with the latest flavors of Windows OS. However, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winhlp32.exe
+  Processes.process IN ("*\\appdata\\*","*\\programdata\\*", "*\\temp\\*") by Processes.dest
+  Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winhlp32_spawning_a_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited as winhlp32.exe is typically
+  not used with the latest flavors of Windows OS. However, filter as needed.
 references:
 - https://www.exploit-db.com/exploits/16541
 - https://tria.ge/210929-ap75vsddan
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$, and is not typical activity for this process.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$, and is not typical activity for this process.
   mitre_attack_id:
   - T1055
   observable:
@@ -56,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/remcos/remcos/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winrar_spawning_shell_application.yml b/detections/endpoint/winrar_spawning_shell_application.yml
index 8c7e914bfb..a6c3ab11c5 100644
--- a/detections/endpoint/winrar_spawning_shell_application.yml
+++ b/detections/endpoint/winrar_spawning_shell_application.yml
@@ -9,10 +9,36 @@ data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-description: The following analytic detects the execution of Windows shell processes initiated by WinRAR, such as "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe", or "bitsadmin.exe". This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant because it may indicate exploitation of the WinRAR CVE-2023-38831 vulnerability, where malicious scripts are executed from spoofed ZIP archives. If confirmed malicious, this could lead to unauthorized access, financial loss, and further malicious activities like data theft or ransomware attacks.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winrar.exe `windows_shells` OR Processes.process_name IN ("certutil.exe","mshta.exe","bitsadmin.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrar_spawning_shell_application_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Be aware of potential false positives - legitimate uses of WinRAR and the listed processes in your environment may cause benign activities to be flagged. Upon triage, review the destination, user, parent process, and process name involved in the flagged activity. Capture and inspect any relevant on-disk artifacts, and look for concurrent processes to identify the attack source. This approach helps analysts detect potential threats earlier and mitigate the risks.
+description: The following analytic detects the execution of Windows shell processes
+  initiated by WinRAR, such as "cmd.exe", "powershell.exe", "certutil.exe", "mshta.exe",
+  or "bitsadmin.exe". This detection leverages data from Endpoint Detection and Response
+  (EDR) agents, focusing on process and parent process relationships. This activity
+  is significant because it may indicate exploitation of the WinRAR CVE-2023-38831
+  vulnerability, where malicious scripts are executed from spoofed ZIP archives. If
+  confirmed malicious, this could lead to unauthorized access, financial loss, and
+  further malicious activities like data theft or ransomware attacks.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winrar.exe
+  `windows_shells` OR Processes.process_name IN ("certutil.exe","mshta.exe","bitsadmin.exe")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `winrar_spawning_shell_application_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Be aware of potential false positives - legitimate uses of
+  WinRAR and the listed processes in your environment may cause benign activities
+  to be flagged. Upon triage, review the destination, user, parent process, and process
+  name involved in the flagged activity. Capture and inspect any relevant on-disk
+  artifacts, and look for concurrent processes to identify the attack source. This
+  approach helps analysts detect potential threats earlier and mitigate the risks.
 references:
 - https://www.group-ib.com/blog/cve-2023-38831-winrar-zero-day/
 - https://github.com/BoredHackerBlog/winrar_CVE-2023-38831_lazy_poc
@@ -23,7 +49,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +66,8 @@ tags:
   atomic_guid: []
   confidence: 70
   impact: 100
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ attempting to decode a file.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ attempting to decode a file.
   mitre_attack_id:
   - T1105
   observable:
@@ -59,18 +91,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/winrar.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/winrar.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winrm_spawning_a_process.yml b/detections/endpoint/winrm_spawning_a_process.yml
index ef8210be31..7d30ac88df 100644
--- a/detections/endpoint/winrm_spawning_a_process.yml
+++ b/detections/endpoint/winrm_spawning_a_process.yml
@@ -5,14 +5,34 @@ date: '2024-10-17'
 author: Drew Church, Michael Haag, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects suspicious processes spawned by WinRM (wsmprovhost.exe). It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific child processes like cmd.exe, powershell.exe, and others. This activity is significant as it may indicate exploitation attempts of vulnerabilities like CVE-2021-31166, which could lead to system instability or compromise. If confirmed malicious, attackers could execute arbitrary commands, escalate privileges, or maintain persistence, posing a severe threat to the environment.
+description: The following analytic detects suspicious processes spawned by WinRM
+  (wsmprovhost.exe). It leverages data from Endpoint Detection and Response (EDR)
+  agents, focusing on specific child processes like cmd.exe, powershell.exe, and others.
+  This activity is significant as it may indicate exploitation attempts of vulnerabilities
+  like CVE-2021-31166, which could lead to system instability or compromise. If confirmed
+  malicious, attackers could execute arbitrary commands, escalate privileges, or maintain
+  persistence, posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrm_spawning_a_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Unknown. Add new processes or filter as needed. It is possible system management software may spawn processes from `wsmprovhost.exe`.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe
+  Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe")
+  by Processes.dest Processes.user Processes.parent_process Processes.process_name
+  Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winrm_spawning_a_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Unknown. Add new processes or filter as needed. It is possible
+  system management software may spawn processes from `wsmprovhost.exe`.
 references:
 - https://github.com/SigmaHQ/sigma/blob/9b7fb0c0f3af2e53ed483e29e0d0f88ccf1c08ca/rules/windows/process_access/win_susp_shell_spawn_from_winrm.yml
 - https://www.zerodayinitiative.com/blog/2021/5/17/cve-2021-31166-a-wormable-code-execution-bug-in-httpsys
@@ -43,13 +63,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_id
   security_domain: endpoint
diff --git a/detections/endpoint/winword_spawning_cmd.yml b/detections/endpoint/winword_spawning_cmd.yml
index 12e85f6f67..52a47c19e4 100644
--- a/detections/endpoint/winword_spawning_cmd.yml
+++ b/detections/endpoint/winword_spawning_cmd.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where Microsoft Word (winword.exe) spawns the command prompt (cmd.exe). This behavior is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is winword.exe. This activity is significant because it is uncommon and often associated with spearphishing attacks, where malicious attachments execute commands via cmd.exe. If confirmed malicious, this could allow an attacker to execute arbitrary commands, potentially leading to further system compromise, data exfiltration, or lateral movement within the network.
+description: The following analytic identifies instances where Microsoft Word (winword.exe)
+  spawns the command prompt (cmd.exe). This behavior is detected using Endpoint Detection
+  and Response (EDR) telemetry, focusing on process creation events where the parent
+  process is winword.exe. This activity is significant because it is uncommon and
+  often associated with spearphishing attacks, where malicious attachments execute
+  commands via cmd.exe. If confirmed malicious, this could allow an attacker to execute
+  arbitrary commands, potentially leading to further system compromise, data exfiltration,
+  or lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe `process_cmd` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winword_spawning_cmd_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, but if any are present, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe
+  `process_cmd` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.original_file_name Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `winword_spawning_cmd_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, but if any are present,
+  filter as needed.
 references:
 - https://app.any.run/tasks/73af0064-a785-4c0a-ab0d-cde593fe16ef/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: '$parent_process_name$ on $dest$ by $user$ launched command: $process_name$ which is very common in spearphishing attacks.'
+  message: '$parent_process_name$ on $dest$ by $user$ launched command: $process_name$
+    which is very common in spearphishing attacks.'
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -53,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winword_spawning_powershell.yml b/detections/endpoint/winword_spawning_powershell.yml
index c5c996c060..fdbe80149b 100644
--- a/detections/endpoint/winword_spawning_powershell.yml
+++ b/detections/endpoint/winword_spawning_powershell.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where Microsoft Word (winword.exe) spawns a PowerShell process. This behavior is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is winword.exe. This activity is significant because it is uncommon and often associated with spearphishing attacks, where malicious documents execute encoded PowerShell commands. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to data exfiltration, system compromise, or further lateral movement within the network.
+description: The following analytic identifies instances where Microsoft Word (winword.exe)
+  spawns a PowerShell process. This behavior is detected using Endpoint Detection
+  and Response (EDR) telemetry, focusing on process creation events where the parent
+  process is winword.exe. This activity is significant because it is uncommon and
+  often associated with spearphishing attacks, where malicious documents execute encoded
+  PowerShell commands. If confirmed malicious, this could allow an attacker to execute
+  arbitrary code, potentially leading to data exfiltration, system compromise, or
+  further lateral movement within the network.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" `process_powershell` by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `winword_spawning_powershell_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives should be limited, but if any are present, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe"
+  `process_powershell` by Processes.dest Processes.user Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.original_file_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `winword_spawning_powershell_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives should be limited, but if any are present,
+  filter as needed.
 references:
 - https://redcanary.com/threat-detection-report/techniques/powershell/
 - https://attack.mitre.org/techniques/T1566/001/
@@ -24,7 +45,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +61,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 70
-  message: '$parent_process_name$ on $dest$ by $user$ launched the following powershell process: $process_name$ which is very common in spearphishing attacks'
+  message: '$parent_process_name$ on $dest$ by $user$ launched the following powershell
+    process: $process_name$ which is very common in spearphishing attacks'
   mitre_attack_id:
   - T1566
   - T1566.001
@@ -56,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/winword_spawning_windows_script_host.yml b/detections/endpoint/winword_spawning_windows_script_host.yml
index c5988dfb9e..f7eeb262fa 100644
--- a/detections/endpoint/winword_spawning_windows_script_host.yml
+++ b/detections/endpoint/winword_spawning_windows_script_host.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies instances where Microsoft Winword.exe spawns Windows Script Host processes (cscript.exe or wscript.exe). This behavior is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process creation events where the parent process is Winword.exe. This activity is significant because it is uncommon and often associated with spearphishing attacks, where malicious scripts are executed via document macros. If confirmed malicious, this could lead to code execution, allowing attackers to gain initial access, execute further payloads, or establish persistence within the environment.
+description: The following analytic identifies instances where Microsoft Winword.exe
+  spawns Windows Script Host processes (cscript.exe or wscript.exe). This behavior
+  is detected using Endpoint Detection and Response (EDR) telemetry, focusing on process
+  creation events where the parent process is Winword.exe. This activity is significant
+  because it is uncommon and often associated with spearphishing attacks, where malicious
+  scripts are executed via document macros. If confirmed malicious, this could lead
+  to code execution, allowing attackers to gain initial access, execute further payloads,
+  or establish persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `winword_spawning_windows_script_host_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: There will be limited false positives and it will be different for every environment. Tune by child process or command-line as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe"
+  Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user
+  Processes.parent_process Processes.process_name Processes.process Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `winword_spawning_windows_script_host_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: There will be limited false positives and it will be different
+  for every environment. Tune by child process or command-line as needed.
 references:
 - https://attack.mitre.org/techniques/T1566/001/
 drilldown_searches:
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -52,18 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - process_name
-  - process_id
-  - parent_process_name
-  - dest
-  - user
-  - parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_wsh.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_wsh.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wmi_permanent_event_subscription.yml b/detections/endpoint/wmi_permanent_event_subscription.yml
index 88e8622ba8..da1f9408a0 100644
--- a/detections/endpoint/wmi_permanent_event_subscription.yml
+++ b/detections/endpoint/wmi_permanent_event_subscription.yml
@@ -5,11 +5,25 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the creation of permanent event subscriptions using Windows Management Instrumentation (WMI). It leverages Sysmon EventID 5 data to identify instances where the event consumers are not the expected "NTEventLogEventConsumer." This activity is significant because it suggests an attacker is attempting to achieve persistence by running malicious scripts or binaries in response to specific system events. If confirmed malicious, this could lead to severe impacts such as data theft, ransomware deployment, or other damaging outcomes. Investigate the associated scripts or binaries to identify the source of the attack.
+description: The following analytic detects the creation of permanent event subscriptions
+  using Windows Management Instrumentation (WMI). It leverages Sysmon EventID 5 data
+  to identify instances where the event consumers are not the expected "NTEventLogEventConsumer."
+  This activity is significant because it suggests an attacker is attempting to achieve
+  persistence by running malicious scripts or binaries in response to specific system
+  events. If confirmed malicious, this could lead to severe impacts such as data theft,
+  ransomware deployment, or other damaging outcomes. Investigate the associated scripts
+  or binaries to identify the source of the attack.
 data_source: []
-search: '`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?<consumer>[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | rename ComputerName as dest | `wmi_permanent_event_subscription_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
-known_false_positives: Although unlikely, administrators may use event subscriptions for legitimate purposes.
+search: '`wmi` EventCode=5861 Binding | rex field=Message "Consumer =\s+(?<consumer>[^;|^$]+)"
+  | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats
+  count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer,
+  Message | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | rename ComputerName as dest | `wmi_permanent_event_subscription_filter`'
+how_to_implement: To successfully implement this search, you must be ingesting the
+  Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on
+  the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
+known_false_positives: Although unlikely, administrators may use event subscriptions
+  for legitimate purposes.
 references: []
 tags:
   analytic_story:
@@ -29,10 +43,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - consumer
-  - ComputerName
   security_domain: endpoint
diff --git a/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml b/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml
index f5c1e17a75..6fd8a587e6 100644
--- a/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml
+++ b/detections/endpoint/wmi_permanent_event_subscription___sysmon.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Rico Valdez, Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies the creation of WMI permanent event subscriptions, which can be used to establish persistence or perform privilege escalation. It leverages Sysmon data, specifically EventCodes 19, 20, and 21, to detect the creation of WMI EventFilters, EventConsumers, and FilterToConsumerBindings. This activity is significant as it may indicate an attacker setting up mechanisms to execute code with elevated SYSTEM privileges when specific events occur. If confirmed malicious, this could allow the attacker to maintain persistence, escalate privileges, and execute arbitrary code, posing a severe threat to the environment.
+description: The following analytic identifies the creation of WMI permanent event
+  subscriptions, which can be used to establish persistence or perform privilege escalation.
+  It leverages Sysmon data, specifically EventCodes 19, 20, and 21, to detect the
+  creation of WMI EventFilters, EventConsumers, and FilterToConsumerBindings. This
+  activity is significant as it may indicate an attacker setting up mechanisms to
+  execute code with elevated SYSTEM privileges when specific events occur. If confirmed
+  malicious, this could allow the attacker to maintain persistence, escalate privileges,
+  and execute arbitrary code, posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 21
-search: '`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation, EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter`'
-how_to_implement: To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity (eventID= 19, 20, 21). In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.
-known_false_positives: Although unlikely, administrators may use event subscriptions for legitimate purposes.
+search: '`sysmon` EventCode=21 | rename host as dest | table _time, dest, user, Operation,
+  EventType, Query, Consumer, Filter | `wmi_permanent_event_subscription___sysmon_filter`'
+how_to_implement: To successfully implement this search, you must be collecting Sysmon
+  data using Sysmon version 6.1 or greater and have Sysmon configured to generate
+  alerts for WMI activity (eventID= 19, 20, 21). In addition, you must have at least
+  version 6.0.4 of the Sysmon TA installed to properly parse the fields.
+known_false_positives: Although unlikely, administrators may use event subscriptions
+  for legitimate purposes.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md
 - https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/
@@ -22,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,20 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - host
-  - user
-  - Operation
-  - EventType
-  - Query
-  - Consumer
-  - Filter
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wmi_recon_running_process_or_services.yml b/detections/endpoint/wmi_recon_running_process_or_services.yml
index 18db7cdfe4..2ecd6342be 100644
--- a/detections/endpoint/wmi_recon_running_process_or_services.yml
+++ b/detections/endpoint/wmi_recon_running_process_or_services.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies suspicious PowerShell script execution via EventCode 4104, where WMI performs an event query to list running processes or services. This detection leverages PowerShell Script Block Logging to capture and analyze script block text for specific WMI queries. This activity is significant as it is commonly used by malware and APT actors to map security applications or services on a compromised machine. If confirmed malicious, this could allow attackers to identify and potentially disable security defenses, facilitating further compromise and persistence within the environment.
+description: The following analytic identifies suspicious PowerShell script execution
+  via EventCode 4104, where WMI performs an event query to list running processes
+  or services. This detection leverages PowerShell Script Block Logging to capture
+  and analyze script block text for specific WMI queries. This activity is significant
+  as it is commonly used by malware and APT actors to map security applications or
+  services on a compromised machine. If confirmed malicious, this could allow attackers
+  to identify and potentially disable security defenses, facilitating further compromise
+  and persistence within the environment.
 data_source:
 - Powershell Script Block Logging 4104
-search: '`powershell` EventCode=4104 ScriptBlockText= "*SELECT*" AND (ScriptBlockText="*Win32_Process*" OR ScriptBlockText="*Win32_Service*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmi_recon_running_process_or_services_filter`'
-how_to_implement: To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
+search: '`powershell` EventCode=4104 ScriptBlockText= "*SELECT*" AND (ScriptBlockText="*Win32_Process*"
+  OR ScriptBlockText="*Win32_Service*") | stats count min(_time) as firstTime max(_time)
+  as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest
+  | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `wmi_recon_running_process_or_services_filter`'
+how_to_implement: To successfully implement this analytic, you will need to enable
+  PowerShell Script Block Logging on some or all endpoints. Additional setup here
+  https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
 known_false_positives: Network administrator may used this command for checking purposes
 references:
 - https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +51,9 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 20
-  message: Suspicious powerShell script execution by $user$ on $dest$ via EventCode 4104, where WMI is performing an event query looking for running processes or running services
+  message: Suspicious powerShell script execution by $user$ on $dest$ via EventCode
+    4104, where WMI is performing an event query looking for running processes or
+    running services
   mitre_attack_id:
   - T1592
   observable:
@@ -49,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - ScriptBlockText
-  - Opcode
-  - Computer
-  - UserID
-  - EventCode
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/win32process.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/win32process.log
     source: XmlWinEventLog:Microsoft-Windows-PowerShell/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wmi_temporary_event_subscription.yml b/detections/endpoint/wmi_temporary_event_subscription.yml
index 1e87856130..d6de3cb98c 100644
--- a/detections/endpoint/wmi_temporary_event_subscription.yml
+++ b/detections/endpoint/wmi_temporary_event_subscription.yml
@@ -5,11 +5,29 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the creation of WMI temporary event subscriptions. It leverages Windows Event Logs, specifically EventCode 5860, to identify these activities. This detection is significant because attackers often use WMI to execute commands, gather information, or maintain persistence within a compromised system. If confirmed malicious, this activity could allow an attacker to execute arbitrary code, escalate privileges, or persist in the environment. Analysts should review the specific WMI queries and assess their intent, considering potential false positives from legitimate administrative tasks.
+description: The following analytic detects the creation of WMI temporary event subscriptions.
+  It leverages Windows Event Logs, specifically EventCode 5860, to identify these
+  activities. This detection is significant because attackers often use WMI to execute
+  commands, gather information, or maintain persistence within a compromised system.
+  If confirmed malicious, this activity could allow an attacker to execute arbitrary
+  code, escalate privileges, or persist in the environment. Analysts should review
+  the specific WMI queries and assess their intent, considering potential false positives
+  from legitimate administrative tasks.
 data_source: []
-search: '`wmi` EventCode=5860 Temporary | rex field=Message "NotificationQuery =\s+(?<query>[^;|^$]+)" | search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = ''wsmprovhost.exe''" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA ''AntiVirusProduct'' OR TargetInstance ISA ''FirewallProduct'' OR TargetInstance ISA ''AntiSpywareProduct''" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter`'
-how_to_implement: To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
-known_false_positives: Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events.
+search: "`wmi` EventCode=5860 Temporary | rex field=Message \"NotificationQuery =\\\
+  s+(?<query>[^;|^$]+)\" | search query!=\"SELECT * FROM Win32_ProcessStartTrace WHERE
+  ProcessName = 'wsmprovhost.exe'\" AND query!=\"SELECT * FROM __InstanceOperationEvent
+  WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct'
+  OR TargetInstance ISA 'AntiSpywareProduct'\" | stats count min(_time) as firstTime
+  max(_time) as lastTime by ComputerName, query  | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `wmi_temporary_event_subscription_filter`"
+how_to_implement: To successfully implement this search, you must be ingesting the
+  Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on
+  the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
+known_false_positives: Some software may create WMI temporary event subscriptions
+  for various purposes. The included search contains an exception for two of these
+  that occur by default on Windows 10 systems. You may need to modify the search to
+  create exceptions for other legitimate events.
 references: []
 tags:
   analytic_story:
@@ -29,9 +47,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - EventCode
-  - Message
-  - query
   security_domain: endpoint
diff --git a/detections/endpoint/wmic_group_discovery.yml b/detections/endpoint/wmic_group_discovery.yml
index e0c1d31d97..4904eb7915 100644
--- a/detections/endpoint/wmic_group_discovery.yml
+++ b/detections/endpoint/wmic_group_discovery.yml
@@ -5,13 +5,33 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the use of `wmic.exe` to enumerate local groups on an endpoint. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs, including command-line details. Monitoring this activity is significant as it can indicate reconnaissance efforts by an attacker to understand group memberships, which could be a precursor to privilege escalation or lateral movement. If confirmed malicious, this activity could allow an attacker to map out privileged groups, aiding in further exploitation and persistence within the environment.
+description: The following analytic identifies the use of `wmic.exe` to enumerate
+  local groups on an endpoint. This detection leverages data from Endpoint Detection
+  and Response (EDR) agents, focusing on process execution logs, including command-line
+  details. Monitoring this activity is significant as it can indicate reconnaissance
+  efforts by an attacker to understand group memberships, which could be a precursor
+  to privilege escalation or lateral movement. If confirmed malicious, this activity
+  could allow an attacker to map out privileged groups, aiding in further exploitation
+  and persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe (Processes.process="*group get name*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `wmic_group_discovery_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe
+  (Processes.process="*group get name*") by Processes.dest Processes.user Processes.parent_process_name
+  Processes.process_name Processes.process Processes.original_file_name Processes.process_id
+  Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`|
+  `security_content_ctime(lastTime)` | `wmic_group_discovery_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: Administrators or power users may use this command for troubleshooting.
 references:
 - https://attack.mitre.org/techniques/T1069/001/
@@ -39,23 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1069.001/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wmic_noninteractive_app_uninstallation.yml b/detections/endpoint/wmic_noninteractive_app_uninstallation.yml
index c115a0de90..9021234651 100644
--- a/detections/endpoint/wmic_noninteractive_app_uninstallation.yml
+++ b/detections/endpoint/wmic_noninteractive_app_uninstallation.yml
@@ -5,14 +5,36 @@ date: '2024-10-17'
 author: Teoderick Contreras, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies the use of the WMIC command-line tool attempting to uninstall applications non-interactively. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns associated with WMIC. This activity is significant because it is uncommon and may indicate an attempt to evade detection by uninstalling security software, as seen in IcedID malware campaigns. If confirmed malicious, this behavior could allow an attacker to disable security defenses, facilitating further compromise and persistence within the environment.
+description: The following analytic identifies the use of the WMIC command-line tool
+  attempting to uninstall applications non-interactively. It leverages data from Endpoint
+  Detection and Response (EDR) agents, focusing on specific command-line patterns
+  associated with WMIC. This activity is significant because it is uncommon and may
+  indicate an attempt to evade detection by uninstalling security software, as seen
+  in IcedID malware campaigns. If confirmed malicious, this behavior could allow an
+  attacker to disable security defenses, facilitating further compromise and persistence
+  within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe Processes.process="* product *" Processes.process="*where name*" Processes.process="*call uninstall*" Processes.process="*/nointeractive*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmic_noninteractive_app_uninstallation_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Third party application may use this approach to uninstall applications.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe
+  Processes.process="* product *" Processes.process="*where name*" Processes.process="*call
+  uninstall*" Processes.process="*/nointeractive*" by Processes.dest Processes.user
+  Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmic_noninteractive_app_uninstallation_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Third party application may use this approach to uninstall
+  applications.
 references:
 - https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/
 tags:
@@ -22,7 +44,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: Wmic $process_name$ with command-line $process$ on $dest$ attempting to uninstall software.
+  message: Wmic $process_name$ with command-line $process$ on $dest$ attempting to
+    uninstall software.
   mitre_attack_id:
   - T1562.001
   - T1562
@@ -43,23 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/disable_av/sysmon2.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wmic_xsl_execution_via_url.yml b/detections/endpoint/wmic_xsl_execution_via_url.yml
index 75ed576e51..739334a9a9 100644
--- a/detections/endpoint/wmic_xsl_execution_via_url.yml
+++ b/detections/endpoint/wmic_xsl_execution_via_url.yml
@@ -5,14 +5,35 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects `wmic.exe` loading a remote XSL script via a URL. This detection leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions that include HTTP/HTTPS URLs and the /FORMAT switch. This activity is significant as it indicates a potential application control bypass, allowing adversaries to execute JScript or VBScript within an XSL file. If confirmed malicious, this technique can enable attackers to execute arbitrary code, escalate privileges, or maintain persistence using a trusted Windows tool, posing a severe threat to the environment.
+description: The following analytic detects `wmic.exe` loading a remote XSL script
+  via a URL. This detection leverages Endpoint Detection and Response (EDR) data,
+  focusing on command-line executions that include HTTP/HTTPS URLs and the /FORMAT
+  switch. This activity is significant as it indicates a potential application control
+  bypass, allowing adversaries to execute JScript or VBScript within an XSL file.
+  If confirmed malicious, this technique can enable attackers to execute arbitrary
+  code, escalate privileges, or maintain persistence using a trusted Windows tool,
+  posing a severe threat to the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process IN ("*http://*", "*https://*") Processes.process="*/format:*" by Processes.parent_process_name Processes.original_file_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmic_xsl_execution_via_url_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: False positives are limited as legitimate applications typically do not download files or xsl using WMIC. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process
+  IN ("*http://*", "*https://*") Processes.process="*/format:*" by Processes.parent_process_name
+  Processes.original_file_name Processes.parent_process Processes.process_name Processes.process_id
+  Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmic_xsl_execution_via_url_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: False positives are limited as legitimate applications typically
+  do not download files or xsl using WMIC. Filter as needed.
 references:
 - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1220/T1220.md
 - https://web.archive.org/web/20190814201250/https://subt0x11.blogspot.com/2018/04/wmicexe-whitelisting-bypass-hacking.html
@@ -23,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +58,8 @@ tags:
   asset_type: Endpoint
   confidence: 100
   impact: 80
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to download a remote XSL script.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ utilizing wmic to download a remote XSL script.
   mitre_attack_id:
   - T1220
   observable:
@@ -56,23 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1220/atomic_red_team/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1220/atomic_red_team/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wmiprsve_lolbas_execution_process_spawn.yml b/detections/endpoint/wmiprsve_lolbas_execution_process_spawn.yml
index 16d2e0e9dd..3b02e00405 100644
--- a/detections/endpoint/wmiprsve_lolbas_execution_process_spawn.yml
+++ b/detections/endpoint/wmiprsve_lolbas_execution_process_spawn.yml
@@ -5,14 +5,45 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic detects `wmiprvse.exe` spawning a LOLBAS execution process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where `wmiprvse.exe` is the parent process and the child process is a known LOLBAS binary. This activity is significant as it may indicate lateral movement or remote code execution by an adversary abusing Windows Management Instrumentation (WMI). If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment, posing a severe security risk.
+description: The following analytic detects `wmiprvse.exe` spawning a LOLBAS execution
+  process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing
+  on process creation events where `wmiprvse.exe` is the parent process and the child
+  process is a known LOLBAS binary. This activity is significant as it may indicate
+  lateral movement or remote code execution by an adversary abusing Windows Management
+  Instrumentation (WMI). If confirmed malicious, this behavior could allow attackers
+  to execute arbitrary code, escalate privileges, or maintain persistence within the
+  environment, posing a severe security risk.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmiprsve_lolbas_execution_process_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate applications may trigger this behavior, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wmiprvse.exe)
+  (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe",
+  "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe",
+  "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe",
+  "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe",
+  "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe",
+  "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe",
+  "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe",
+  "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe",
+  "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe",
+  "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe",
+  "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wmiprsve_lolbas_execution_process_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate applications may trigger this behavior, filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1047/
 - https://www.ired.team/offensive-security/lateral-movement/t1047-wmi-for-lateral-movement
@@ -23,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,23 +80,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement_lolbas/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/lateral_movement_lolbas/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml b/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml
index 3ef3f3d14c..ea3dda4fb1 100644
--- a/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml
+++ b/detections/endpoint/wscript_or_cscript_suspicious_child_process.yml
@@ -5,14 +5,34 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: This analytic identifies a suspicious spawned process by WScript or CScript process. This technique was a common technique used by adversaries and malware to execute different LOLBIN, other scripts like PowerShell or spawn a suspended process to inject its code as a defense evasion. This TTP may detect some normal script that uses several application tools that are in the list of the child process it detects but a good pivot and indicator that a script may execute suspicious code.
+description: This analytic identifies a suspicious spawned process by WScript or CScript
+  process. This technique was a common technique used by adversaries and malware to
+  execute different LOLBIN, other scripts like PowerShell or spawn a suspended process
+  to inject its code as a defense evasion. This TTP may detect some normal script
+  that uses several application tools that are in the list of the child process it
+  detects but a good pivot and indicator that a script may execute suspicious code.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("cscript.exe", "wscript.exe") Processes.process_name IN ("regsvr32.exe", "rundll32.exe","winhlp32.exe","certutil.exe","msbuild.exe","cmd.exe","powershell*","wmic.exe","mshta.exe") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wscript_or_cscript_suspicious_child_process_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Administrators may create vbs or js script that use several tool as part of its execution. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name
+  IN ("cscript.exe", "wscript.exe") Processes.process_name IN ("regsvr32.exe", "rundll32.exe","winhlp32.exe","certutil.exe","msbuild.exe","cmd.exe","powershell*","wmic.exe","mshta.exe")
+  by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process
+  Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+  | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `wscript_or_cscript_suspicious_child_process_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Administrators may create vbs or js script that use several
+  tool as part of its execution. Filter as needed.
 references:
 - https://www.hybrid-analysis.com/sample/8da5b75b6380a41eee3a399c43dfe0d99eeefaa1fd21027a07b1ecaa4cd96fdd?environmentId=120
 - https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/
@@ -22,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -56,23 +81,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.005/vbs_wscript/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml b/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml
index f1c96ce5f6..830d5f130d 100644
--- a/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml
+++ b/detections/endpoint/wsmprovhost_lolbas_execution_process_spawn.yml
@@ -5,14 +5,45 @@ date: '2024-09-30'
 author: Mauricio Velazco, Splunk
 status: production
 type: TTP
-description: The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS execution process. It leverages Endpoint Detection and Response (EDR) data to detect when `Wsmprovhost.exe` spawns child processes that are known LOLBAS (Living Off the Land Binaries and Scripts) executables. This activity is significant because it may indicate an adversary using Windows Remote Management (WinRM) to execute code on remote endpoints, a common technique for lateral movement. If confirmed malicious, this could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
+description: The following analytic identifies `Wsmprovhost.exe` spawning a LOLBAS
+  execution process. It leverages Endpoint Detection and Response (EDR) data to detect
+  when `Wsmprovhost.exe` spawns child processes that are known LOLBAS (Living Off
+  the Land Binaries and Scripts) executables. This activity is significant because
+  it may indicate an adversary using Windows Remote Management (WinRM) to execute
+  code on remote endpoints, a common technique for lateral movement. If confirmed
+  malicious, this could allow attackers to execute arbitrary code, escalate privileges,
+  or maintain persistence within the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wsmprovhost.exe) (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe", "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe", "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe", "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe", "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe", "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe", "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe", "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe", "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe", "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe", "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wsmprovhost_lolbas_execution_process_spawn_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
-known_false_positives: Legitimate applications may trigger this behavior, filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=wsmprovhost.exe)
+  (Processes.process_name IN ("Regsvcs.exe", "Ftp.exe", "OfflineScannerShell.exe",
+  "Rasautou.exe", "Schtasks.exe", "Xwizard.exe", "Dllhost.exe", "Pnputil.exe", "Atbroker.exe",
+  "Pcwrun.exe", "Ttdinject.exe","Mshta.exe", "Bitsadmin.exe", "Certoc.exe", "Ieexec.exe",
+  "Microsoft.Workflow.Compiler.exe", "Runscripthelper.exe", "Forfiles.exe", "Msbuild.exe",
+  "Register-cimprovider.exe", "Tttracer.exe", "Ie4uinit.exe", "Bash.exe", "Hh.exe",
+  "SettingSyncHost.exe", "Cmstp.exe", "Mmc.exe", "Stordiag.exe", "Scriptrunner.exe",
+  "Odbcconf.exe", "Extexport.exe", "Msdt.exe", "WorkFolders.exe", "Diskshadow.exe",
+  "Mavinject.exe", "Regasm.exe", "Gpscript.exe", "Rundll32.exe", "Regsvr32.exe", "Msiexec.exe",
+  "Wuauclt.exe", "Presentationhost.exe", "Wmic.exe", "Runonce.exe", "Syncappvpublishingserver.exe",
+  "Verclsid.exe", "Infdefaultinstall.exe", "Explorer.exe", "Installutil.exe", "Netsh.exe",
+  "Wab.exe", "Dnscmd.exe", "At.exe", "Pcalua.exe", "Msconfig.exe")) by Processes.dest
+  Processes.user Processes.parent_process Processes.process_name Processes.process
+  Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `wsmprovhost_lolbas_execution_process_spawn_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
+known_false_positives: Legitimate applications may trigger this behavior, filter as
+  needed.
 references:
 - https://attack.mitre.org/techniques/T1021/006/
 - https://lolbas-project.github.io/
@@ -23,7 +54,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,23 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.dest
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.original_file_name
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_id
-  - Processes.parent_process_path
-  - Processes.process_path
-  - Processes.parent_process_id
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_lolbas/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.006/lateral_movement_lolbas/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/wsreset_uac_bypass.yml b/detections/endpoint/wsreset_uac_bypass.yml
index 79eeaa02fa..202177a9d3 100644
--- a/detections/endpoint/wsreset_uac_bypass.yml
+++ b/detections/endpoint/wsreset_uac_bypass.yml
@@ -5,12 +5,39 @@ date: '2024-09-30'
 author: Steven Dick, Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects a suspicious modification of the registry aimed at bypassing User Account Control (UAC) by leveraging WSReset.exe. It identifies the creation or modification of specific registry values under the path "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*". This detection uses data from Endpoint Detection and Response (EDR) agents, focusing on process and registry events. This activity is significant because UAC bypass techniques can allow attackers to execute high-privilege actions without user consent. If confirmed malicious, this could lead to unauthorized code execution and potential system compromise.
+description: The following analytic detects a suspicious modification of the registry
+  aimed at bypassing User Account Control (UAC) by leveraging WSReset.exe. It identifies
+  the creation or modification of specific registry values under the path "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*".
+  This detection uses data from Endpoint Detection and Response (EDR) agents, focusing
+  on process and registry events. This activity is significant because UAC bypass
+  techniques can allow attackers to execute high-privilege actions without user consent.
+  If confirmed malicious, this could lead to unauthorized code execution and potential
+  system compromise.
 data_source:
 - Sysmon EventID 1 AND Sysmon EventID 12
 - Sysmon EventID 1 AND Sysmon EventID 13
-search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time) AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)` | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" AND (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name = "DelegateExecute") by _time span=1h Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime dest user parent_process_name parent_process process_name process_path process registry_key_name registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) AS firstTime max(_time)
+  AS lastTime FROM datamodel=Endpoint.Processes BY _time span=1h Processes.user Processes.process_id
+  Processes.process_name Processes.process Processes.process_path Processes.dest Processes.parent_process_name
+  Processes.parent_process Processes.process_guid | `drop_dm_object_name(Processes)`
+  | join process_guid [| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry
+  WHERE Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*"
+  AND (Registry.registry_value_name = "(Default)" OR Registry.registry_value_name
+  = "DelegateExecute") by _time span=1h Registry.dest Registry.user Registry.registry_path
+  Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data
+  Registry.process_guid | `drop_dm_object_name(Registry)`] | fields firstTime lastTime
+  dest user parent_process_name parent_process process_name process_path process registry_key_name
+  registry_path registry_value_name registry_value_data process_guid | where isnotnull(registry_value_data)
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wsreset_uac_bypass_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://github.com/hfiref0x/UACME
@@ -21,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +65,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: Suspicious modification of registry $registry_path$ with possible payload path $registry_value_name$ in $dest$
+  message: Suspicious modification of registry $registry_path$ with possible payload
+    path $registry_value_name$ in $dest$
   mitre_attack_id:
   - T1548.002
   - T1548
@@ -46,27 +79,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.dest
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.process
-  - Processes.process_path
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_guid
-  - Registry.dest
-  - Registry.registry_value_name
-  - Registry.registry_key_name
-  - Registry.registry_path
-  - Registry.registry_value_data
-  - Registry.process_guid
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/xmrig_driver_loaded.yml b/detections/endpoint/xmrig_driver_loaded.yml
index d22025707b..2f2c5735bc 100644
--- a/detections/endpoint/xmrig_driver_loaded.yml
+++ b/detections/endpoint/xmrig_driver_loaded.yml
@@ -5,11 +5,23 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the installation of the XMRIG coinminer driver on a system. It identifies the loading of the `WinRing0x64.sys` driver, commonly associated with XMRIG, by analyzing Sysmon EventCode 6 logs for specific signatures and image loads. This activity is significant because XMRIG is an open-source CPU miner frequently exploited by adversaries to mine cryptocurrency illicitly. If confirmed malicious, this activity could lead to unauthorized resource consumption, degraded system performance, and potential financial loss due to unauthorized cryptocurrency mining.
+description: The following analytic detects the installation of the XMRIG coinminer
+  driver on a system. It identifies the loading of the `WinRing0x64.sys` driver, commonly
+  associated with XMRIG, by analyzing Sysmon EventCode 6 logs for specific signatures
+  and image loads. This activity is significant because XMRIG is an open-source CPU
+  miner frequently exploited by adversaries to mine cryptocurrency illicitly. If confirmed
+  malicious, this activity could lead to unauthorized resource consumption, degraded
+  system performance, and potential financial loss due to unauthorized cryptocurrency
+  mining.
 data_source:
 - Sysmon EventID 6
-search: '`sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" |  stats  min(_time) as firstTime max(_time) as lastTime count by  dest ImageLoaded Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xmrig_driver_loaded_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+search: '`sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys"
+  |  stats  min(_time) as firstTime max(_time) as lastTime count by  dest ImageLoaded
+  Hashes IMPHASH Signature Signed | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `xmrig_driver_loaded_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the driver loaded and Signature from your endpoints. If you are using
+  Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
 known_false_positives: False positives should be limited.
 references:
 - https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,18 +59,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - dest
-  - ImageLoaded
-  - Hashes
-  - IMPHASH
-  - Signature
-  - Signed
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/endpoint/xsl_script_execution_with_wmic.yml b/detections/endpoint/xsl_script_execution_with_wmic.yml
index 397d3758c9..977dc2d2d1 100644
--- a/detections/endpoint/xsl_script_execution_with_wmic.yml
+++ b/detections/endpoint/xsl_script_execution_with_wmic.yml
@@ -5,13 +5,33 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects the execution of an XSL script using the WMIC process, which is often indicative of malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving WMIC and XSL files. This behavior is significant as it has been associated with the FIN7 group, known for using this technique to execute malicious scripts. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to system compromise and further malicious actions within the environment.
+description: The following analytic detects the execution of an XSL script using the
+  WMIC process, which is often indicative of malicious activity. It leverages data
+  from Endpoint Detection and Response (EDR) agents, focusing on command-line executions
+  involving WMIC and XSL files. This behavior is significant as it has been associated
+  with the FIN7 group, known for using this technique to execute malicious scripts.
+  If confirmed malicious, this activity could allow attackers to execute arbitrary
+  code, potentially leading to system compromise and further malicious actions within
+  the environment.
 data_source:
 - Sysmon EventID 1
 - Windows Event Log Security 4688
 - CrowdStrike ProcessRollup2
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process = "*os get*" Processes.process="*/format:*" Processes.process = "*.xsl*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `xsl_script_execution_with_wmic_filter`'
-how_to_implement: The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process
+  = "*os get*" Processes.process="*/format:*" Processes.process = "*.xsl*" by Processes.parent_process_name
+  Processes.parent_process Processes.process_name Processes.process_id Processes.process
+  Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `xsl_script_execution_with_wmic_filter`'
+how_to_implement: The detection is based on data that originates from Endpoint Detection
+  and Response (EDR) agents. These agents are designed to provide security-related
+  telemetry from the endpoints where the agent is installed. To implement this search,
+  you must ingest logs that contain the process GUID, process name, and parent process.
+  Additionally, you must ingest complete command-line executions. These logs must
+  be processed using the appropriate Splunk Technology Add-ons that are specific to
+  the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint`
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process.
 known_false_positives: unknown
 references:
 - https://www.mandiant.com/resources/fin7-pursuing-an-enigmatic-and-evasive-global-criminal-operation
@@ -24,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +59,8 @@ tags:
   asset_type: Endpoint
   confidence: 70
   impact: 70
-  message: An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to load a XSL script.
+  message: An instance of $parent_process_name$ spawning $process_name$ was identified
+    on endpoint $dest$ by user $user$ utilizing wmic to load a XSL script.
   mitre_attack_id:
   - T1220
   observable:
@@ -58,19 +84,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Processes.parent_process_name
-  - Processes.parent_process
-  - Processes.process_name
-  - Processes.process_id
-  - Processes.process
-  - Processes.dest
-  - Processes.user
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/fin7/fin7_macro_js_1/sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/network/detect_arp_poisoning.yml b/detections/network/detect_arp_poisoning.yml
index 2ac251305e..b83022724e 100644
--- a/detections/network/detect_arp_poisoning.yml
+++ b/detections/network/detect_arp_poisoning.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects ARP Poisoning attacks by monitoring for Dynamic ARP Inspection (DAI) errors on Cisco network devices. It leverages logs from Cisco devices, specifically looking for events where the ARP inspection feature has disabled an interface due to suspicious activity. This activity is significant because ARP Poisoning can allow attackers to intercept, modify, or disrupt network traffic, leading to potential data breaches or denial of service. If confirmed malicious, this could enable attackers to perform man-in-the-middle attacks, compromising the integrity and confidentiality of network communications.
+description: The following analytic detects ARP Poisoning attacks by monitoring for
+  Dynamic ARP Inspection (DAI) errors on Cisco network devices. It leverages logs
+  from Cisco devices, specifically looking for events where the ARP inspection feature
+  has disabled an interface due to suspicious activity. This activity is significant
+  because ARP Poisoning can allow attackers to intercept, modify, or disrupt network
+  traffic, leading to potential data breaches or denial of service. If confirmed malicious,
+  this could enable attackers to perform man-in-the-middle attacks, compromising the
+  integrity and confidentiality of network communications.
 data_source: []
-search: '`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_arp_poisoning_filter`'
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-known_false_positives: This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely).
+search: '`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection"
+  | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime
+  max(_time) AS lastTime count BY host src_interface | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
+  `detect_arp_poisoning_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must be configured with DHCP Snooping (see 
+  https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html)
+  and Dynamic ARP Inspection (see 
+  https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html)
+  and log with a severity level of minimum "5 - notification". The search also requires
+  that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
+  is used to parse the logs from the Cisco network devices.
+known_false_positives: This search might be prone to high false positives if DHCP
+  Snooping or ARP inspection has been incorrectly configured, or if a device normally
+  sends many ARP packets (unlikely).
 references: []
 tags:
   analytic_story:
@@ -32,13 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - facility
-  - mnemonic
-  - disable_cause
-  - src_int_prefix_long
-  - src_int_suffix
-  - host
-  - src_interface
   security_domain: network
diff --git a/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml b/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml
index a76ea13f78..d849e3a0ab 100644
--- a/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml
+++ b/detections/network/detect_dga_domains_using_pretrained_model_in_dsdl.yml
@@ -5,27 +5,39 @@ date: '2024-10-17'
 author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies Domain Generation Algorithm (DGA) generated domains using a pre-trained deep learning model. It leverages the Network Resolution data model to analyze domain names and detect unusual character sequences indicative of DGA activity. This behavior is significant as adversaries often use DGAs to generate numerous domain names for command-and-control servers, making it harder to block malicious traffic. If confirmed malicious, this activity could enable attackers to maintain persistent communication with compromised systems, evade detection, and execute further malicious actions.
+description: The following analytic identifies Domain Generation Algorithm (DGA) generated
+  domains using a pre-trained deep learning model. It leverages the Network Resolution
+  data model to analyze domain names and detect unusual character sequences indicative
+  of DGA activity. This behavior is significant as adversaries often use DGAs to generate
+  numerous domain names for command-and-control servers, making it harder to block
+  malicious traffic. If confirmed malicious, this activity could enable attackers
+  to maintain persistent communication with compromised systems, evade detection,
+  and execute further malicious actions.
 data_source: []
-search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time) as firstTime  max(_time) as lastTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name(DNS)` | rename query AS domain | fields IPs, src, domain, firstTime, lastTime | apply pretrained_dga_model_dsdl | rename pred_dga_proba AS dga_score | where dga_score>0.5 | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` | table src, domain, IPs, firstTime, lastTime, dga_score | `detect_dga_domains_using_pretrained_model_in_dsdl_filter`'
-how_to_implement: 'Steps to deploy DGA detection model into Splunk App DSDL.\ This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU. * Download the artifacts .tar.gz file from the link  `https://seal.splunkresearch.com/pretrained_dga_model_dsdl.tar.gz`
-
-  * Download the pretrained_dga_model_dsdl.ipynb Jupyter notebook from `https://github.com/splunk/security_content/notebooks`
-
-  * Login to the Jupyter Lab for pretrained_dga_model_dsdl container. This container should be listed on Containers page for DSDL app.
-
-  * Below steps need to be followed inside Jupyter lab
-
-  * Upload the pretrained_dga_model_dsdl.tar.gz file into `app/model/data` path using the upload option in the jupyter notebook.
-
-  *  Untar the artifact `pretrained_dga_model_dsdl.tar.gz` using `tar -xf app/model/data/pretrained_dga_model_dsdl.tar.gz -C app/model/data`
-
-  * Upload `pretrained_dga_model_dsdl.pynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab
-
-  * Save the notebook using the save option in jupyter notebook.
-
-  * Upload `pretrained_dga_model_dsdl.json` into `notebooks/data` folder.'
-known_false_positives: False positives may be present if domain name is similar to dga generated domains.
+search: '| tstats `security_content_summariesonly` values(DNS.answer) as IPs min(_time)
+  as firstTime  max(_time) as lastTime from datamodel=Network_Resolution by DNS.src,
+  DNS.query | `drop_dm_object_name(DNS)` | rename query AS domain | fields IPs, src,
+  domain, firstTime, lastTime | apply pretrained_dga_model_dsdl | rename pred_dga_proba
+  AS dga_score | where dga_score>0.5 | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  | table src, domain, IPs, firstTime, lastTime, dga_score | `detect_dga_domains_using_pretrained_model_in_dsdl_filter`'
+how_to_implement: "Steps to deploy DGA detection model into Splunk App DSDL.\\ This
+  detection depends on the Splunk app for Data Science and Deep Learning which can
+  be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution
+  datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The
+  detection uses a pre-trained deep learning model that needs to be deployed in DSDL
+  app. Follow the steps for deployment here - https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU.
+  * Download the artifacts .tar.gz file from the link  `https://seal.splunkresearch.com/pretrained_dga_model_dsdl.tar.gz`\n
+  * Download the pretrained_dga_model_dsdl.ipynb Jupyter notebook from `https://github.com/splunk/security_content/notebooks`\n
+  * Login to the Jupyter Lab for pretrained_dga_model_dsdl container. This container
+  should be listed on Containers page for DSDL app.\n* Below steps need to be followed
+  inside Jupyter lab\n* Upload the pretrained_dga_model_dsdl.tar.gz file into `app/model/data`
+  path using the upload option in the jupyter notebook.\n*  Untar the artifact `pretrained_dga_model_dsdl.tar.gz`
+  using `tar -xf app/model/data/pretrained_dga_model_dsdl.tar.gz -C app/model/data`\n
+  * Upload `pretrained_dga_model_dsdl.pynb` into Jupyter lab notebooks folder using
+  the upload option in Jupyter lab\n* Save the notebook using the save option in jupyter
+  notebook.\n* Upload `pretrained_dga_model_dsdl.json` into `notebooks/data` folder."
+known_false_positives: False positives may be present if domain name is similar to
+  dga generated domains.
 references:
 - https://attack.mitre.org/techniques/T1568/002/
 - https://unit42.paloaltonetworks.com/threat-brief-understanding-domain-generation-algorithms-dga/
@@ -40,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A potential connection to a DGA domain $domain$ was detected from host $src$, kindly review.
+  message: A potential connection to a DGA domain $domain$ was detected from host
+    $src$, kindly review.
   mitre_attack_id:
   - T1568.002
   observable:
@@ -56,10 +69,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - IPs
-  - src
-  - domain
-  - firstTime
-  - lastTime
   security_domain: network
diff --git a/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml b/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml
index 55007f009a..7c0555903f 100644
--- a/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml
+++ b/detections/network/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.yml
@@ -6,10 +6,43 @@ status: experimental
 author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk
 type: Anomaly
 data_source: []
-description: The following analytic identifies potential DNS data exfiltration using a pre-trained deep learning model. It leverages DNS request data from the Network Resolution datamodel and computes features from past events between the same source and domain. The model generates a probability score (pred_is_exfiltration_proba) indicating the likelihood of data exfiltration. This activity is significant as DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising the organization's security posture.
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.src _time DNS.query | `drop_dm_object_name("DNS")` | sort - _time,src, query | streamstats count as rank by src query | where rank < 10 | table src,query,rank,_time | apply detect_dns_data_exfiltration_using_pretrained_model_in_dsdl | table src,_time,query,rank,pred_is_dns_data_exfiltration_proba,pred_is_dns_data_exfiltration | where rank == 1 | rename pred_is_dns_data_exfiltration_proba as is_exfiltration_score | rename pred_is_dns_data_exfiltration as is_exfiltration | where is_exfiltration_score > 0.5 | `security_content_ctime(_time)` | table src, _time,query,is_exfiltration_score,is_exfiltration | `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter`'
-how_to_implement: "Steps to deploy detect DNS data exfiltration model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n * Download the `artifacts .tar.gz` file from the link - https://seal.splunkresearch.com/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz Download the `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.ipynb` Jupyter notebook from https://github.com/splunk/security_content/notebooks\n* Login to the Jupyter Lab assigned for detect_dns_data_exfiltration_using_pretrained_model_in_dsdl container. This container should be listed on Containers page for DSDL app.\n* Below steps need to be followed inside Jupyter lab\n* Upload the detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz file into `app/model/data` path using the upload option in the jupyter notebook.\n * Untar the artifact detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data`\n* Upload detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.pynb into Jupyter lab notebooks folder using the upload option in Jupyter lab\n* Save the notebook using the save option in jupyter notebook.\n* Upload `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.json` into `notebooks/data` folder."
-known_false_positives: False positives may be present if DNS data exfiltration request look very similar to benign DNS requests.
+description: The following analytic identifies potential DNS data exfiltration using
+  a pre-trained deep learning model. It leverages DNS request data from the Network
+  Resolution datamodel and computes features from past events between the same source
+  and domain. The model generates a probability score (pred_is_exfiltration_proba)
+  indicating the likelihood of data exfiltration. This activity is significant as
+  DNS tunneling can be used by attackers to covertly exfiltrate sensitive data. If
+  confirmed malicious, this could lead to unauthorized data access and potential data
+  breaches, compromising the organization's security posture.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  by DNS.src _time DNS.query | `drop_dm_object_name("DNS")` | sort - _time,src, query
+  | streamstats count as rank by src query | where rank < 10 | table src,query,rank,_time
+  | apply detect_dns_data_exfiltration_using_pretrained_model_in_dsdl | table src,_time,query,rank,pred_is_dns_data_exfiltration_proba,pred_is_dns_data_exfiltration
+  | where rank == 1 | rename pred_is_dns_data_exfiltration_proba as is_exfiltration_score
+  | rename pred_is_dns_data_exfiltration as is_exfiltration | where is_exfiltration_score
+  > 0.5 | `security_content_ctime(_time)` | table src, _time,query,is_exfiltration_score,is_exfiltration
+  | `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl_filter`'
+how_to_implement: "Steps to deploy detect DNS data exfiltration model into Splunk
+  App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning
+  which can be found here - https://splunkbase.splunk.com/app/4607/ and the Network
+  Resolution datamodel which can be found here - https://splunkbase.splunk.com/app/1621/.
+  The detection uses a pre-trained deep learning model that needs to be deployed in
+  DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n
+  * Download the `artifacts .tar.gz` file from the link - https://seal.splunkresearch.com/detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz
+  Download the `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.ipynb`
+  Jupyter notebook from https://github.com/splunk/security_content/notebooks\n* Login
+  to the Jupyter Lab assigned for detect_dns_data_exfiltration_using_pretrained_model_in_dsdl
+  container. This container should be listed on Containers page for DSDL app.\n* Below
+  steps need to be followed inside Jupyter lab\n* Upload the detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz
+  file into `app/model/data` path using the upload option in the jupyter notebook.\n
+  * Untar the artifact detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.tar.gz
+  using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz
+  -C app/model/data`\n* Upload detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.pynb
+  into Jupyter lab notebooks folder using the upload option in Jupyter lab\n* Save
+  the notebook using the save option in jupyter notebook.\n* Upload `detect_dns_data_exfiltration_using_pretrained_model_in_dsdl.json`
+  into `notebooks/data` folder."
+known_false_positives: False positives may be present if DNS data exfiltration request
+  look very similar to benign DNS requests.
 references:
 - https://attack.mitre.org/techniques/T1048/003/
 - https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/
@@ -38,11 +71,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.message_type
-  - DNS.record_type
-  - DNS.src
-  - DNS.dest
-  - DNS.answer
   security_domain: network
diff --git a/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml b/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml
index d9c7108ec2..fc22d2e17f 100644
--- a/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml
+++ b/detections/network/detect_hosts_connecting_to_dynamic_domain_providers.yml
@@ -5,22 +5,37 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies DNS queries from internal hosts to dynamic domain providers. It leverages DNS query logs from the `Network_Resolution` data model and cross-references them with a lookup file containing known dynamic DNS providers. This activity is significant because attackers often use dynamic DNS services to host malicious payloads or command-and-control servers, making it crucial for security teams to monitor. If confirmed malicious, this activity could allow attackers to bypass firewall blocks, evade detection, and maintain persistent access to the network.
+description: The following analytic identifies DNS queries from internal hosts to
+  dynamic domain providers. It leverages DNS query logs from the `Network_Resolution`
+  data model and cross-references them with a lookup file containing known dynamic
+  DNS providers. This activity is significant because attackers often use dynamic
+  DNS services to host malicious payloads or command-and-control servers, making it
+  crucial for security teams to monitor. If confirmed malicious, this activity could
+  allow attackers to bypass firewall blocks, evade detection, and maintain persistent
+  access to the network.
 data_source:
 - Sysmon EventID 22
-search: '| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter`'
-how_to_implement: 'First, you''ll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.
-
-  This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):
-
-  * **Label:** DNS Query, **Field:** query
-
-  * **Label:** DNS Answer, **Field:** answer
-
-  * **Label:** IsDynamicDNS, **Field:** isDynDNS
-
-  Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-known_false_positives: Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.
+search: '| tstats `security_content_summariesonly` count values(DNS.answer) as answer
+  min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host | `drop_dm_object_name("DNS")`
+  | `security_content_ctime(firstTime)` | `dynamic_dns_providers` | `detect_hosts_connecting_to_dynamic_domain_providers_filter`'
+how_to_implement: "First, you'll need to ingest data from your DNS operations. This
+  can be done by ingesting logs from your server or data, collected passively by Splunk
+  Stream or a similar solution. Specifically, data that contains the domain that is
+  being queried and the IP of the host originating the request must be populating
+  the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`,
+  which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating
+  the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\n
+  This search produces fields (query, answer, isDynDNS) that are not yet supported
+  by ES Incident Review and therefore cannot be viewed when a notable event is raised.
+  These fields contribute additional context to the notable event. To see the additional
+  metadata, add the following fields, if not already present, to Incident Review.
+  Event Attributes (Configure > Incident Management > Incident Review Settings > Add
+  New Entry):\n* **Label:** DNS Query, **Field:** query\n* **Label:** DNS Answer,
+  **Field:** answer\n* **Label:** IsDynamicDNS, **Field:** isDynDNS\nDetailed documentation
+  on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
+known_false_positives: Some users and applications may leverage Dynamic DNS to reach
+  out to some domains on the Internet since dynamic DNS by itself is not malicious,
+  however this activity must be verified.
 references: []
 drilldown_searches:
 - name: View the detection results for - "$host$"
@@ -28,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -42,7 +62,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A dns query $query$ from your infra connecting to suspicious domain in host  $host$
+  message: A dns query $query$ from your infra connecting to suspicious domain in
+    host  $host$
   mitre_attack_id:
   - T1189
   observable:
@@ -54,15 +75,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.answer
-  - DNS.query
-  - host
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/network/detect_ipv6_network_infrastructure_threats.yml b/detections/network/detect_ipv6_network_infrastructure_threats.yml
index 3a68602aa0..3a90703740 100644
--- a/detections/network/detect_ipv6_network_infrastructure_threats.yml
+++ b/detections/network/detect_ipv6_network_infrastructure_threats.yml
@@ -5,10 +5,29 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects IPv6 network infrastructure threats by identifying suspicious activities such as IP and MAC address theft or packet drops. It leverages logs from Cisco network devices configured with First Hop Security measures like RA Guard and DHCP Guard. This activity is significant as it can indicate attempts to compromise network integrity and security. If confirmed malicious, attackers could manipulate network traffic, leading to potential data interception, unauthorized access, or network disruption.
+description: The following analytic detects IPv6 network infrastructure threats by
+  identifying suspicious activities such as IP and MAC address theft or packet drops.
+  It leverages logs from Cisco network devices configured with First Hop Security
+  measures like RA Guard and DHCP Guard. This activity is significant as it can indicate
+  attempts to compromise network integrity and security. If confirmed malicious, attackers
+  could manipulate network traffic, leading to potential data interception, unauthorized
+  access, or network disruption.
 data_source: []
-search: '`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface | table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)` | `detect_ipv6_network_infrastructure_threats_filter`'
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
+search: '`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP")
+  | eval src_interface=src_int_prefix_long+src_int_suffix | eval dest_interface=dest_int_prefix_long+dest_int_suffix
+  | stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac
+  values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation)
+  AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface)
+  AS dest_interface values(action) AS action count BY host src_interface | table host
+  src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation
+  action count | `security_content_ctime(firstTime)` |`security_content_ctime(lastTime)`
+  | `detect_ipv6_network_infrastructure_threats_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must be configured with one or more First Hop Security
+  measures such as RA Guard, DHCP Guard and/or device tracking. See References for
+  more information. The search also requires that the Cisco Networks Add-on for Splunk
+  (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco
+  network devices.
 known_false_positives: None currently known
 references:
 - https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2020/pdf/BRKSEC-3200.pdf
@@ -40,16 +59,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - facility
-  - mnemonic
-  - src_int_prefix_long
-  - src_int_suffix
-  - dest_int_prefix_long
-  - dest_int_suffix
-  - src_mac
-  - src_vlan
-  - vendor_explanation
-  - action
   security_domain: network
diff --git a/detections/network/detect_large_outbound_icmp_packets.yml b/detections/network/detect_large_outbound_icmp_packets.yml
index 370902c446..fa988c3eea 100644
--- a/detections/network/detect_large_outbound_icmp_packets.yml
+++ b/detections/network/detect_large_outbound_icmp_packets.yml
@@ -5,27 +5,35 @@ date: '2024-11-06'
 author: Rico Valdez, Dean Luxton, Splunk
 status: production
 type: TTP
-description: The following analytic identifies outbound ICMP packets with a size larger than 1,000 bytes. It leverages the Network_Traffic data model to detect unusually large ICMP packets that are not blocked and are destined for external IP addresses. This activity is significant because threat actors often use ICMP for command and control communication, and large ICMP packets can indicate data exfiltration or other malicious activities. If confirmed malicious, this could allow attackers to maintain covert communication channels, exfiltrate sensitive data, or further compromise the network.
+description: The following analytic identifies outbound ICMP packets with a size larger
+  than 1,000 bytes. It leverages the Network_Traffic data model to detect unusually
+  large ICMP packets that are not blocked and are destined for external IP addresses.
+  This activity is significant because threat actors often use ICMP for command and
+  control communication, and large ICMP packets can indicate data exfiltration or
+  other malicious activities. If confirmed malicious, this could allow attackers to
+  maintain covert communication channels, exfiltrate sensitive data, or further compromise
+  the network.
 data_source:
 - Palo Alto Network Traffic
-search: >- 
+search: >-
   | tstats `security_content_summariesonly` count earliest(_time) as firstTime
-    latest(_time) as lastTime values(All_Traffic.action) as action values(All_Traffic.bytes) as bytes from
-    datamodel=Network_Traffic where All_Traffic.action !=blocked  (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes
-    > 1000 AND NOT All_Traffic.dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") by All_Traffic.src_ip  All_Traffic.dest_ip All_Traffic.protocol
-  | `drop_dm_object_name("All_Traffic")` 
-  | iplocation dest_ip 
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter`
-how_to_implement: 'In order to run this search effectively, we highly recommend that
+    latest(_time) as lastTime values(All_Traffic.action) as action values(All_Traffic.bytes)
+  as bytes from
+    datamodel=Network_Traffic where All_Traffic.action !=blocked  (All_Traffic.protocol=icmp
+  OR All_Traffic.transport=icmp) All_Traffic.bytes
+    > 1000 AND NOT All_Traffic.dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16")
+  by All_Traffic.src_ip  All_Traffic.dest_ip All_Traffic.protocol
+  | `drop_dm_object_name("All_Traffic")`  | iplocation dest_ip  | `security_content_ctime(firstTime)`  |
+  `security_content_ctime(lastTime)` | `detect_large_outbound_icmp_packets_filter`
+how_to_implement: "In order to run this search effectively, we highly recommend that
   you leverage the Assets and Identity framework. It is important that you have a
   good understanding of how your network segments are designed and that you are able
   to distinguish internal from external address space. Add a category named `internal`
-  to the CIDRs that host the company''s assets in the `assets_by_cidr.csv` lookup
-  file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`.
-  More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata.
+  to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file,
+  which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More
+  information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata.
   This search also requires you to be ingesting your network traffic and populating
-  the Network_Traffic data model'
+  the Network_Traffic data model"
 known_false_positives: ICMP packets are used in a variety of ways to help troubleshoot
   networking issues and ensure the proper flow of traffic. As such, it is possible
   that a large ICMP packet could be perfectly legitimate. If large ICMP packets are
@@ -40,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$" and "$dest_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$", "$dest_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$",
+    "$dest_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -65,19 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.action
-  - All_Traffic.bytes
-  - All_Traffic.dest_category
-  - All_Traffic.protocol
-  - All_Traffic.transport
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1095/palologs/large_icmp.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1095/palologs/large_icmp.log
     sourcetype: pan:traffic
     source: pan:traffic
diff --git a/detections/network/detect_outbound_ldap_traffic.yml b/detections/network/detect_outbound_ldap_traffic.yml
index 558d5c82f4..6241e3cb29 100644
--- a/detections/network/detect_outbound_ldap_traffic.yml
+++ b/detections/network/detect_outbound_ldap_traffic.yml
@@ -5,12 +5,29 @@ date: '2024-10-17'
 author: Bhavin Patel, Johan Bjerke, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies outbound LDAP traffic to external IP addresses. It leverages the Network_Traffic data model to detect connections on ports 389 or 636 that are not directed to private IP ranges (RFC1918). This activity is significant because outbound LDAP traffic can indicate potential data exfiltration or unauthorized access attempts. If confirmed malicious, attackers could exploit this to access sensitive directory information, leading to data breaches or further network compromise.
+description: The following analytic identifies outbound LDAP traffic to external IP
+  addresses. It leverages the Network_Traffic data model to detect connections on
+  ports 389 or 636 that are not directed to private IP ranges (RFC1918). This activity
+  is significant because outbound LDAP traffic can indicate potential data exfiltration
+  or unauthorized access attempts. If confirmed malicious, attackers could exploit
+  this to access sensitive directory information, leading to data breaches or further
+  network compromise.
 data_source:
 - Bro
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.dest_ip) as dest_ip from datamodel=Network_Traffic.All_Traffic where All_Traffic.dest_port = 389 OR All_Traffic.dest_port = 636 AND NOT (All_Traffic.dest_ip = 10.0.0.0/8 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip = 172.16.0.0/12) by All_Traffic.src_ip All_Traffic.dest_ip |`drop_dm_object_name("All_Traffic")` | where src_ip != dest_ip | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)` |`detect_outbound_ldap_traffic_filter`'
-how_to_implement: In order to properly run this search, Splunk needs to ingest data from Next Generation Firewalls like Palo Alto Networks Firewalls or other network control devices that mediate the traffic allowed into an environment. The search requires the Network_Traffic data model to be populated.
-known_false_positives: Unknown at this moment. Outbound LDAP traffic should not be allowed outbound through your perimeter firewall. Please check those servers to verify if the activity is legitimate.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(All_Traffic.dest_ip) as dest_ip from datamodel=Network_Traffic.All_Traffic
+  where All_Traffic.dest_port = 389 OR All_Traffic.dest_port = 636 AND NOT (All_Traffic.dest_ip
+  = 10.0.0.0/8 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip = 172.16.0.0/12)
+  by All_Traffic.src_ip All_Traffic.dest_ip |`drop_dm_object_name("All_Traffic")`
+  | where src_ip != dest_ip | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`
+  |`detect_outbound_ldap_traffic_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from Next Generation Firewalls like Palo Alto Networks Firewalls or other network
+  control devices that mediate the traffic allowed into an environment. The search
+  requires the Network_Traffic data model to be populated.
+known_false_positives: Unknown at this moment. Outbound LDAP traffic should not be
+  allowed outbound through your perimeter firewall. Please check those servers to
+  verify if the activity is legitimate.
 references:
 - https://www.govcert.ch/blog/zero-day-exploit-targeting-popular-java-library-log4j/
 tags:
@@ -21,7 +38,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 70
-  message: An outbound LDAP connection from $src_ip$ in your infrastructure connecting to dest ip $dest_ip$
+  message: An outbound LDAP connection from $src_ip$ in your infrastructure connecting
+    to dest ip $dest_ip$
   mitre_attack_id:
   - T1190
   - T1059
@@ -38,15 +56,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
-  - All_Traffic.src_ip
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/log4shell_ldap_traffic/pantraffic.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/log4shell_ldap_traffic/pantraffic.log
     sourcetype: pan:traffic
     source: pan:traffic
diff --git a/detections/network/detect_outbound_smb_traffic.yml b/detections/network/detect_outbound_smb_traffic.yml
index f00132715b..1869a359e6 100644
--- a/detections/network/detect_outbound_smb_traffic.yml
+++ b/detections/network/detect_outbound_smb_traffic.yml
@@ -5,17 +5,28 @@ date: '2024-10-16'
 author: Bhavin Patel, Stuart Hopkins, Patrick Bareiss
 status: experimental
 type: TTP
-description: The following analytic detects outbound SMB (Server Message Block) connections from internal hosts to external servers. It identifies this activity by monitoring network traffic for SMB requests directed towards the Internet, which are unusual for standard operations. This detection is significant for a SOC as it can indicate an attacker's attempt to retrieve credential hashes through compromised servers, a key step in lateral movement and privilege escalation. If confirmed malicious, this activity could lead to unauthorized access to sensitive data and potential full system compromise.
+description: The following analytic detects outbound SMB (Server Message Block) connections
+  from internal hosts to external servers. It identifies this activity by monitoring
+  network traffic for SMB requests directed towards the Internet, which are unusual
+  for standard operations. This detection is significant for a SOC as it can indicate
+  an attacker's attempt to retrieve credential hashes through compromised servers,
+  a key step in lateral movement and privilege escalation. If confirmed malicious,
+  this activity could lead to unauthorized access to sensitive data and potential
+  full system compromise.
 data_source: []
 search: >-
-  | tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(sourcetype) as sourcetype count from datamodel=Network_Traffic where (All_Traffic.action=allowed All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") AND NOT All_Traffic.dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","100.64.0.0/10") by All_Traffic.src_ip  All_Traffic.dest_ip All_Traffic.dest_port
-  | `drop_dm_object_name("All_Traffic")` 
-  | `security_content_ctime(start_time)` 
-  | `security_content_ctime(end_time)` 
-  | iplocation dest_ip
+  | tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time)
+  as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app
+  values(sourcetype) as sourcetype count from datamodel=Network_Traffic where (All_Traffic.action=allowed
+  All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb")
+  AND All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") AND NOT
+  All_Traffic.dest_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16","100.64.0.0/10")
+  by All_Traffic.src_ip  All_Traffic.dest_ip All_Traffic.dest_port
+  | `drop_dm_object_name("All_Traffic")`  | `security_content_ctime(start_time)`  |
+  `security_content_ctime(end_time)`  | iplocation dest_ip
   | `detect_outbound_smb_traffic_filter`
-how_to_implement: 'This search also requires you to be ingesting your network traffic
-  and populating the Network_Traffic data model'
+how_to_implement: This search also requires you to be ingesting your network traffic
+  and populating the Network_Traffic data model
 known_false_positives: It is likely that the outbound Server Message Block (SMB) traffic
   is legitimate, if the company's internal networks are not well-defined in the Assets
   and Identity Framework. Categorize the internal CIDR blocks as `internal` in the
@@ -32,7 +43,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: An outbound SMB connection from $src_ip$ in your infrastructure connecting to dest ip $dest_ip$
+  message: An outbound SMB connection from $src_ip$ in your infrastructure connecting
+    to dest ip $dest_ip$
   mitre_attack_id:
   - T1071.002
   - T1071
@@ -49,19 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.action
-  - All_Traffic.app
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
-  - sourcetype
-  - All_Traffic.src_ip
-  - All_Traffic.direction
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.002/outbound_smb_traffic/zeek_conn.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.002/outbound_smb_traffic/zeek_conn.log
     sourcetype: bro:conn:json
     source: conn.log
diff --git a/detections/network/detect_port_security_violation.yml b/detections/network/detect_port_security_violation.yml
index a9b044301a..fa050f67a9 100644
--- a/detections/network/detect_port_security_violation.yml
+++ b/detections/network/detect_port_security_violation.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects port security violations on Cisco switches. It leverages logs from Cisco network devices, specifically looking for events with mnemonics indicating port security violations. This activity is significant because it indicates an unauthorized device attempting to connect to a secured port, potentially bypassing network access controls. If confirmed malicious, this could allow an attacker to gain unauthorized access to the network, leading to data exfiltration, network disruption, or further lateral movement within the environment.
+description: The following analytic detects port security violations on Cisco switches.
+  It leverages logs from Cisco network devices, specifically looking for events with
+  mnemonics indicating port security violations. This activity is significant because
+  it indicates an unauthorized device attempting to connect to a secured port, potentially
+  bypassing network access controls. If confirmed malicious, this could allow an attacker
+  to gain unauthorized access to the network, leading to data exfiltration, network
+  disruption, or further lateral movement within the environment.
 data_source: []
-search: '`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter`'
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-known_false_positives: This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network.
+search: '`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation")
+  OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN")
+  | eval src_interface=src_int_prefix_long+src_int_suffix | stats min(_time) AS firstTime
+  max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS
+  src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_port_security_violation_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must be configured with Port Security and Error Disable
+  for this to work (see 
+  https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html)
+  and log with a severity level of minimum "5 - notification". The search also requires
+  that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
+  is used to parse the logs from the Cisco network devices.
+known_false_positives: This search might be prone to high false positives if you have
+  malfunctioning devices connected to your ethernet ports or if end users periodically
+  connect physical devices to the network.
 references: []
 tags:
   analytic_story:
@@ -32,16 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - facility
-  - mnemonic
-  - disable_cause
-  - src_int_prefix_long
-  - src_int_suffix
-  - src_mac
-  - src_vlan
-  - action
-  - host
-  - src_interface
   security_domain: network
diff --git a/detections/network/detect_remote_access_software_usage_dns.yml b/detections/network/detect_remote_access_software_usage_dns.yml
index 1e59812ab2..713064de2a 100644
--- a/detections/network/detect_remote_access_software_usage_dns.yml
+++ b/detections/network/detect_remote_access_software_usage_dns.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects DNS queries to domains associated with known remote access software such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This detection is crucial as adversaries often use these tools to maintain access and control over compromised environments. Identifying such behavior is vital for a Security Operations Center (SOC) because unauthorized remote access can lead to data breaches, ransomware attacks, and other severe impacts if these threats are not mitigated promptly.
+description: The following analytic detects DNS queries to domains associated with
+  known remote access software such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer.
+  This detection is crucial as adversaries often use these tools to maintain access
+  and control over compromised environments. Identifying such behavior is vital for
+  a Security Operations Center (SOC) because unauthorized remote access can lead to
+  data breaches, ransomware attacks, and other severe impacts if these threats are
+  not mitigated promptly.
 data_source:
 - Sysmon EventID 22
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution by DNS.src DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | lookup remote_access_software remote_domain AS query OUTPUT isutility, description as signature, comment_reference as desc, category | eval dest = query | search isutility = True | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_dns_filter`'
-how_to_implement: To implement this search, you must ingest logs that contain the DNS query and the source of the query. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the DNS logs. The logs must also be mapped to the `Network_Resolution` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device-based exceptions for this set of detections.
-known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(DNS.answer) as answer from datamodel=Network_Resolution by DNS.src
+  DNS.query | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` |
+  `security_content_ctime(lastTime)` | lookup remote_access_software remote_domain
+  AS query OUTPUT isutility, description as signature, comment_reference as desc,
+  category | eval dest = query | search isutility = True | `remote_access_software_usage_exceptions`
+  | `detect_remote_access_software_usage_dns_filter`'
+how_to_implement: To implement this search, you must ingest logs that contain the
+  DNS query and the source of the query. These logs must be processed using the appropriate
+  Splunk Technology Add-ons that are specific to the DNS logs. The logs must also
+  be mapped to the `Network_Resolution` data model. Use the Splunk Common Information
+  Model (CIM) to normalize the field names and speed up the data modeling process.
+  The "exceptions" macro leverages both an Assets and Identities lookup, as well as
+  a KVStore collection called "remote_software_exceptions" that lets you track and
+  maintain device-based exceptions for this set of detections.
+known_false_positives: It is possible that legitimate remote access software is used
+  within the environment. Ensure that the lookup is reviewed and updated with any
+  additional remote access software that is used within the environment. Known false
+  positives can be added to the remote_access_software_usage_exception.csv lookup
+  to globally suppress these situations across all remote access content
 references:
 - https://attack.mitre.org/techniques/T1219/
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,16 +77,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.src
-  - DNS.query
-  - DNS.answer
   security_domain: endpoint
   manual_test: This detection uses A&I lookups from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/network/detect_remote_access_software_usage_traffic.yml b/detections/network/detect_remote_access_software_usage_traffic.yml
index 6bc0d2a403..312fc3d1b4 100644
--- a/detections/network/detect_remote_access_software_usage_traffic.yml
+++ b/detections/network/detect_remote_access_software_usage_traffic.yml
@@ -5,12 +5,34 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects network traffic associated with known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in Splunk. This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.
+description: The following analytic detects network traffic associated with known
+  remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer.
+  It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in
+  Splunk. This activity is significant because adversaries often use remote access
+  tools to maintain unauthorized access to compromised environments. If confirmed
+  malicious, this activity could allow attackers to control systems remotely, exfiltrate
+  data, or deploy additional malware, posing a severe threat to the organization's
+  security.
 data_source:
 - Palo Alto Network Traffic
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.dest_port) as dest_port latest(user) as user from datamodel=Network_Traffic by All_Traffic.src All_Traffic.dest, All_Traffic.app | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | lookup remote_access_software remote_appid AS app OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = True | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_traffic_filter`'
-how_to_implement: The following analytic was developed with Palo Alto traffic logs. Ensure that the logs are being ingested into Splunk and mapped to the Network_Traffic data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections.
-known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime values(All_Traffic.dest_port) as dest_port latest(user) as user from
+  datamodel=Network_Traffic by All_Traffic.src All_Traffic.dest, All_Traffic.app |
+  `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | lookup remote_access_software remote_appid AS app OUTPUT isutility, description
+  as signature, comment_reference as desc, category | search isutility = True | `remote_access_software_usage_exceptions`
+  | `detect_remote_access_software_usage_traffic_filter`'
+how_to_implement: The following analytic was developed with Palo Alto traffic logs.
+  Ensure that the logs are being ingested into Splunk and mapped to the Network_Traffic
+  data model. Use the Splunk Common Information Model (CIM) to normalize the field
+  names and speed up the data modeling process. The "exceptions" macro leverages both
+  an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions"
+  that lets you track and maintain device- based exceptions for this set of detections.
+known_false_positives: It is possible that legitimate remote access software is used
+  within the environment. Ensure that the lookup is reviewed and updated with any
+  additional remote access software that is used within the environment. Known false
+  positives can be added to the remote_access_software_usage_exception.csv lookup
+  to globally suppress these situations across all remote access content
 references:
 - https://attack.mitre.org/techniques/T1219/
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
@@ -22,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +60,8 @@ tags:
   asset_type: Network
   confidence: 50
   impact: 50
-  message: Application traffic for a known remote access software [$signature$] was detected from $src$.
+  message: Application traffic for a known remote access software [$signature$] was
+    detected from $src$.
   mitre_attack_id:
   - T1219
   observable:
@@ -45,18 +73,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.src
-  - All_Traffic.dest
-  - All_Traffic.app
-  - All_Traffic.dest_port
-  - user
   security_domain: network
   manual_test: This detection uses A&I lookups from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo_traffic.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo_traffic.log
     source: screenconnect_palo_traffic
     sourcetype: pan:traffic
diff --git a/detections/network/detect_rogue_dhcp_server.yml b/detections/network/detect_rogue_dhcp_server.yml
index bf38de070c..535819c7c0 100644
--- a/detections/network/detect_rogue_dhcp_server.yml
+++ b/detections/network/detect_rogue_dhcp_server.yml
@@ -5,11 +5,27 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the presence of unauthorized DHCP servers on the network. It leverages logs from Cisco network devices with DHCP Snooping enabled, specifically looking for events where DHCP leases are issued from untrusted ports. This activity is significant because rogue DHCP servers can facilitate Man-in-the-Middle attacks, leading to potential data interception and network disruption. If confirmed malicious, this could allow attackers to redirect network traffic, capture sensitive information, and compromise the integrity of the network.
+description: The following analytic identifies the presence of unauthorized DHCP servers
+  on the network. It leverages logs from Cisco network devices with DHCP Snooping
+  enabled, specifically looking for events where DHCP leases are issued from untrusted
+  ports. This activity is significant because rogue DHCP servers can facilitate Man-in-the-Middle
+  attacks, leading to potential data interception and network disruption. If confirmed
+  malicious, this could allow attackers to redirect network traffic, capture sensitive
+  information, and compromise the integrity of the network.
 data_source: []
-search: '`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`| `detect_rogue_dhcp_server_filter`'
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-known_false_positives: This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface.
+search: '`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT"
+  | stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type)
+  AS message_type values(src_mac) AS src_mac BY host | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`|
+  `detect_rogue_dhcp_server_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must be configured with DHCP Snooping enabled (see
+  https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html)
+  and log with a severity level of minimum "5 - notification". The search also requires
+  that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
+  is used to parse the logs from the Cisco network devices.
+known_false_positives: This search might be prone to high false positives if DHCP
+  Snooping has been incorrectly configured or in the unlikely event that the DHCP
+  server has been moved to another network interface.
 references: []
 tags:
   analytic_story:
@@ -31,11 +47,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - facility
-  - mnemonic
-  - message_type
-  - src_mac
-  - host
   security_domain: network
diff --git a/detections/network/detect_snicat_sni_exfiltration.yml b/detections/network/detect_snicat_sni_exfiltration.yml
index 81040b8b2e..fcdc73fa38 100644
--- a/detections/network/detect_snicat_sni_exfiltration.yml
+++ b/detections/network/detect_snicat_sni_exfiltration.yml
@@ -5,10 +5,23 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the use of SNICat tool commands within the TLS SNI field, indicating potential data exfiltration attempts. It leverages Zeek SSL data to detect specific SNICat commands such as LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito in the server_name field. This activity is significant as SNICat is a known tool for covert data exfiltration using TLS. If confirmed malicious, this could allow attackers to exfiltrate sensitive data undetected, posing a severe threat to data confidentiality and integrity.
+description: The following analytic identifies the use of SNICat tool commands within
+  the TLS SNI field, indicating potential data exfiltration attempts. It leverages
+  Zeek SSL data to detect specific SNICat commands such as LIST, LS, SIZE, LD, CB,
+  EX, ALIVE, EXIT, WHERE, and finito in the server_name field. This activity is significant
+  as SNICat is a known tool for covert data exfiltration using TLS. If confirmed malicious,
+  this could allow attackers to exfiltrate sensitive data undetected, posing a severe
+  threat to data confidentiality and integrity.
 data_source: []
-search: '`zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)" | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`'
-how_to_implement: You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito.  You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place.
+search: '`zeek_ssl` | rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)"
+  | stats count by src_ip dest_ip server_name snicat | where count>0 | table src_ip
+  dest_ip server_name snicat | `detect_snicat_sni_exfiltration_filter`'
+how_to_implement: You must be ingesting Zeek SSL data into Splunk. Zeek data should
+  also be getting ingested in JSON format.  We are detecting when any of the predefined
+  SNICat commands are found within the server_name (SNI) field. These commands are
+  LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito.  You can go further
+  once this has been detected, and run other searches to decode the SNI data to prove
+  or disprove if any data exfiltration has taken place.
 known_false_positives: Unknown
 references:
 - https://www.mnemonic.io/resources/blog/introducing-snicat/
@@ -32,9 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - server_name
-  - src_ip
-  - dest_ip
   security_domain: network
diff --git a/detections/network/detect_software_download_to_network_device.yml b/detections/network/detect_software_download_to_network_device.yml
index 0a6bdd40a2..95c9bb8847 100644
--- a/detections/network/detect_software_download_to_network_device.yml
+++ b/detections/network/detect_software_download_to_network_device.yml
@@ -5,11 +5,31 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies unauthorized software downloads to network devices via TFTP, FTP, or SSH/SCP. It detects this activity by analyzing network traffic events on specific ports (69, 21, 22) from devices categorized as network, router, or switch. This activity is significant because adversaries may exploit netbooting to load unauthorized operating systems, potentially compromising network integrity. If confirmed malicious, this could lead to unauthorized control over network devices, enabling further attacks, data exfiltration, or persistent access within the network.
+description: The following analytic identifies unauthorized software downloads to
+  network devices via TFTP, FTP, or SSH/SCP. It detects this activity by analyzing
+  network traffic events on specific ports (69, 21, 22) from devices categorized as
+  network, router, or switch. This activity is significant because adversaries may
+  exploit netbooting to load unauthorized operating systems, potentially compromising
+  network integrity. If confirmed malicious, this could lead to unauthorized control
+  over network devices, enabling further attacks, data exfiltration, or persistent
+  access within the network.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter`'
-how_to_implement: This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory.
-known_false_positives: This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND
+  All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21)
+  OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination
+  AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch
+  by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")`
+  | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `detect_software_download_to_network_device_filter`'
+how_to_implement: This search looks for Network Traffic events to TFTP, FTP or SSH/SCP
+  ports from network devices. Make sure to tag any network devices as network, router
+  or switch in order for this detection to work. If the TFTP traffic doesn't traverse
+  a firewall nor packet inspection, these events will not be logged. This is typically
+  an issue if the TFTP server is on the same subnet as the network device. There is
+  also a chance of the network device loading software using a DHCP assigned IP address
+  (netboot) which is not in the Asset inventory.
+known_false_positives: This search will also report any legitimate attempts of software
+  downloads to network devices as well as outbound SSH sessions from network devices.
 references: []
 tags:
   analytic_story:
@@ -30,12 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.transport
-  - All_Traffic.dest_port
-  - All_Traffic.dest_category
-  - All_Traffic.src_category
-  - All_Traffic.src
-  - All_Traffic.dest
   security_domain: network
diff --git a/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml b/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml
index 4db3c98ee7..b92b40e592 100644
--- a/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml
+++ b/detections/network/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.yml
@@ -5,29 +5,43 @@ date: '2024-10-17'
 author: Abhinav Mishra, Kumar Sharad and Namratha Sreekanta, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies suspicious DNS TXT records using a pre-trained deep learning model. It leverages DNS response data from the Network Resolution data model, categorizing TXT records into known types via regular expressions. Records that do not match known patterns are flagged as suspicious. This activity is significant as DNS TXT records can be used for data exfiltration or command-and-control communication. If confirmed malicious, attackers could use these records to covertly transfer data or receive instructions, posing a severe threat to network security.
+description: The following analytic identifies suspicious DNS TXT records using a
+  pre-trained deep learning model. It leverages DNS response data from the Network
+  Resolution data model, categorizing TXT records into known types via regular expressions.
+  Records that do not match known patterns are flagged as suspicious. This activity
+  is significant as DNS TXT records can be used for data exfiltration or command-and-control
+  communication. If confirmed malicious, attackers could use these records to covertly
+  transfer data or receive instructions, posing a severe threat to network security.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | rename answer as text | fields firstTime, lastTime, message_type,record_type,src,dest, text | apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl | rename predicted_is_unknown as is_suspicious_score | where is_suspicious_score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score | `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter`'
-how_to_implement: 'Steps to deploy detect suspicious DNS TXT records model into Splunk App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning which can be found here - `https://splunkbase.splunk.com/app/4607/` and the Network Resolution datamodel which can be found here - `https://splunkbase.splunk.com/app/1621/`. The detection uses a pre-trained deep learning model that needs to be deployed in DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.
-
-  * Download the `artifacts .tar.gz` file from the link - `https://seal.splunkresearch.com/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`.
-
-  * Download the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` Jupyter notebook from `https://github.com/splunk/security_content/notebooks`.
-
-  * Login to the Jupyter Lab assigned for  `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl` container. This container should be listed on Containers page for DSDL app.
-
-  * Below steps need to be followed inside Jupyter lab.
-
-  * Upload the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` file into `app/model/data` path using the upload option in the jupyter notebook.
-
-  * Untar the artifact `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz` using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz -C app/model/data`.
-
-  * Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb` into Jupyter lab notebooks folder using the upload option in Jupyter lab.
-
-  * Save the notebook using the save option in Jupyter notebook.
-
-  * Upload `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json` into `notebooks/data` folder.'
-known_false_positives: False positives may be present if DNS TXT record contents are similar to benign DNS TXT record contents.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND
+  DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")`
+  | rename answer as text | fields firstTime, lastTime, message_type,record_type,src,dest,
+  text | apply detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl |
+  rename predicted_is_unknown as is_suspicious_score | where is_suspicious_score >
+  0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` |
+  table src,dest,text,record_type, firstTime, lastTime,is_suspicious_score | `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl_filter`'
+how_to_implement: "Steps to deploy detect suspicious DNS TXT records model into Splunk
+  App DSDL. This detection depends on the Splunk app for Data Science and Deep Learning
+  which can be found here - `https://splunkbase.splunk.com/app/4607/` and the Network
+  Resolution datamodel which can be found here - `https://splunkbase.splunk.com/app/1621/`.
+  The detection uses a pre-trained deep learning model that needs to be deployed in
+  DSDL app. Follow the steps for deployment here - `https://github.com/splunk/security_content/wiki/How-to-deploy-pre-trained-Deep-Learning-models-for-ESCU`.\n
+  * Download the `artifacts .tar.gz` file from the link - `https://seal.splunkresearch.com/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`.\n
+  * Download the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb`
+  Jupyter notebook from `https://github.com/splunk/security_content/notebooks`.\n
+  * Login to the Jupyter Lab assigned for  `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl`
+  container. This container should be listed on Containers page for DSDL app.\n* Below
+  steps need to be followed inside Jupyter lab.\n* Upload the `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`
+  file into `app/model/data` path using the upload option in the jupyter notebook.\n
+  * Untar the artifact `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz`
+  using `tar -xf app/model/data/detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.tar.gz
+  -C app/model/data`.\n* Upload detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.ipynb`
+  into Jupyter lab notebooks folder using the upload option in Jupyter lab.\n* Save
+  the notebook using the save option in Jupyter notebook.\n* Upload `detect_suspicious_dns_txt_records_using_pretrained_model_in_dsdl.json`
+  into `notebooks/data` folder."
+known_false_positives: False positives may be present if DNS TXT record contents are
+  similar to benign DNS TXT record contents.
 references:
 - https://attack.mitre.org/techniques/T1071/004/
 - https://unit42.paloaltonetworks.com/dns-tunneling-how-dns-can-be-abused-by-malicious-actors/
@@ -56,11 +70,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.message_type
-  - DNS.record_type
-  - DNS.src
-  - DNS.dest
-  - DNS.answer
   security_domain: network
diff --git a/detections/network/detect_traffic_mirroring.yml b/detections/network/detect_traffic_mirroring.yml
index b7a49244e7..3a1acaad93 100644
--- a/detections/network/detect_traffic_mirroring.yml
+++ b/detections/network/detect_traffic_mirroring.yml
@@ -5,11 +5,29 @@ date: '2024-10-17'
 author: Mikael Bjerkeland, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the initiation of traffic mirroring sessions on Cisco network devices. It leverages logs with specific mnemonics and facilities related to traffic mirroring, such as "ETH_SPAN_SESSION_UP" and "PKTCAP_START." This activity is significant because adversaries may use traffic mirroring to exfiltrate data by duplicating and forwarding network traffic to an external destination. If confirmed malicious, this could allow attackers to capture sensitive information, monitor network communications, and potentially compromise the integrity and confidentiality of the network.
+description: The following analytic detects the initiation of traffic mirroring sessions
+  on Cisco network devices. It leverages logs with specific mnemonics and facilities
+  related to traffic mirroring, such as "ETH_SPAN_SESSION_UP" and "PKTCAP_START."
+  This activity is significant because adversaries may use traffic mirroring to exfiltrate
+  data by duplicating and forwarding network traffic to an external destination. If
+  confirmed malicious, this could allow attackers to capture sensitive information,
+  monitor network communications, and potentially compromise the integrity and confidentiality
+  of the network.
 data_source: []
-search: '`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)` | `detect_traffic_mirroring_filter`'
-how_to_implement: This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring.
-known_false_positives: This search will return false positives for any legitimate traffic captures by network administrators.
+search: '`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN"
+  mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD"
+  command="monitor session*") | stats min(_time) AS firstTime max(_time) AS lastTime
+  count BY host facility mnemonic | `security_content_ctime(firstTime)`|`security_content_ctime(lastTime)`
+  | `detect_traffic_mirroring_filter`'
+how_to_implement: This search uses a standard SPL query on logs from Cisco Network
+  devices. The network devices must log with a severity level of minimum "5 - notification".
+  The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467)
+  is used to parse the logs from the Cisco network devices and that the devices have
+  been configured according to the documentation of the Cisco Networks Add-on. Also
+  note that an attacker may disable logging from the device prior to enabling traffic
+  mirroring.
+known_false_positives: This search will return false positives for any legitimate
+  traffic captures by network administrators.
 references: []
 tags:
   analytic_story:
@@ -32,9 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - facility
-  - mnemonic
-  - host
   security_domain: network
diff --git a/detections/network/detect_unauthorized_assets_by_mac_address.yml b/detections/network/detect_unauthorized_assets_by_mac_address.yml
index 0dc6c450cc..2fd12c1516 100644
--- a/detections/network/detect_unauthorized_assets_by_mac_address.yml
+++ b/detections/network/detect_unauthorized_assets_by_mac_address.yml
@@ -5,11 +5,30 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies unauthorized devices attempting to connect to the organization's network by inspecting DHCP request packets. It detects this activity by comparing the MAC addresses in DHCP requests against a list of known authorized devices stored in the assets_by_str.csv file. This activity is significant for a SOC because unauthorized devices can pose security risks, including potential data breaches or network disruptions. If confirmed malicious, this activity could allow an attacker to gain unauthorized network access, potentially leading to further exploitation or data exfiltration.
+description: The following analytic identifies unauthorized devices attempting to
+  connect to the organization's network by inspecting DHCP request packets. It detects
+  this activity by comparing the MAC addresses in DHCP requests against a list of
+  known authorized devices stored in the assets_by_str.csv file. This activity is
+  significant for a SOC because unauthorized devices can pose security risks, including
+  potential data breaches or network disruptions. If confirmed malicious, this activity
+  could allow an attacker to gain unauthorized network access, potentially leading
+  to further exploitation or data exfiltration.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.tag=dhcp by All_Sessions.dest_ip All_Sessions.dest_mac | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")` | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter`'
-how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated.
-known_false_positives: This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Sessions
+  where nodename=All_Sessions.DHCP All_Sessions.tag=dhcp by All_Sessions.dest_ip All_Sessions.dest_mac
+  | dedup All_Sessions.dest_mac| `drop_dm_object_name("Network_Sessions")`|`drop_dm_object_name("All_Sessions")`
+  | search NOT [| inputlookup asset_lookup_by_str |rename mac as dest_mac | fields
+  + dest_mac] | `detect_unauthorized_assets_by_mac_address_filter`'
+how_to_implement: This search uses the Network_Sessions data model shipped with Enterprise
+  Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv
+  file located in SA-IdentityManagement, which will contain a list of known authorized
+  organizational assets including their MAC addresses. Ensure that all inventoried
+  systems have their MAC address populated.
+known_false_positives: This search might be prone to high false positives. Please
+  consider this when conducting analysis or investigations. Authorized devices may
+  be detected as unauthorized. If this is the case, verify the MAC address of the
+  system responsible for the false positive and add it to the Assets and Identity
+  framework with the proper information.
 references: []
 tags:
   analytic_story:
@@ -27,9 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Sessions.signature
-  - All_Sessions.src_ip
-  - All_Sessions.dest_mac
   security_domain: network
diff --git a/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml b/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml
index 9579a46b02..da8f51fec0 100644
--- a/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml
+++ b/detections/network/detect_windows_dns_sigred_via_splunk_stream.yml
@@ -5,10 +5,23 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects attempts to exploit the SIGRed vulnerability (CVE-2020-1350) in Windows DNS servers. It leverages Splunk Stream DNS and TCP data to identify DNS SIG and KEY records, as well as TCP payloads exceeding 65KB. This activity is significant because SIGRed is a critical wormable vulnerability that allows remote code execution. If confirmed malicious, an attacker could gain unauthorized access, execute arbitrary code, and potentially disrupt services, leading to severe data breaches and infrastructure compromise. Immediate investigation and remediation are crucial to mitigate these risks.
+description: The following analytic detects attempts to exploit the SIGRed vulnerability
+  (CVE-2020-1350) in Windows DNS servers. It leverages Splunk Stream DNS and TCP data
+  to identify DNS SIG and KEY records, as well as TCP payloads exceeding 65KB. This
+  activity is significant because SIGRed is a critical wormable vulnerability that
+  allows remote code execution. If confirmed malicious, an attacker could gain unauthorized
+  access, execute arbitrary code, and potentially disrupt services, leading to severe
+  data breaches and infrastructure compromise. Immediate investigation and remediation
+  are crucial to mitigate these risks.
 data_source: []
-search: '`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY) | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp` bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats count by flow_id | where count>1 | fields - count'
-how_to_implement: You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp.  Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment.
+search: '`stream_dns` | spath "query_type{}" | search "query_type{}" IN (SIG,KEY)
+  | spath protocol_stack | search protocol_stack="ip:tcp:dns" | append [search `stream_tcp`
+  bytes_out>65000] | `detect_windows_dns_sigred_via_splunk_stream_filter` | stats
+  count by flow_id | where count>1 | fields - count'
+how_to_implement: You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We
+  are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size
+  via stream:tcp.  Replace the macro definitions ('stream:dns' and 'stream:tcp') with
+  configurations for your Splunk environment.
 known_false_positives: unknown
 references: []
 tags:
@@ -31,6 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: network
diff --git a/detections/network/detect_windows_dns_sigred_via_zeek.yml b/detections/network/detect_windows_dns_sigred_via_zeek.yml
index 6de3225e38..694092523c 100644
--- a/detections/network/detect_windows_dns_sigred_via_zeek.yml
+++ b/detections/network/detect_windows_dns_sigred_via_zeek.yml
@@ -5,10 +5,25 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the presence of SIGRed, a critical DNS vulnerability, using Zeek DNS and Zeek Conn data. It identifies specific DNS query types (SIG and KEY) and checks for high data transfer within a flow. This detection is significant because SIGRed allows attackers to execute remote code on Windows DNS servers, potentially leading to unauthorized access and control. If confirmed malicious, this activity could result in data exfiltration, service disruption, or further network compromise. Immediate investigation and mitigation, such as patching or isolating the affected server, are crucial.
+description: The following analytic detects the presence of SIGRed, a critical DNS
+  vulnerability, using Zeek DNS and Zeek Conn data. It identifies specific DNS query
+  types (SIG and KEY) and checks for high data transfer within a flow. This detection
+  is significant because SIGRed allows attackers to execute remote code on Windows
+  DNS servers, potentially leading to unauthorized access and control. If confirmed
+  malicious, this activity could result in data exfiltration, service disruption,
+  or further network compromise. Immediate investigation and mitigation, such as patching
+  or isolating the affected server, are crucial.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id | append [| tstats  `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id | where count>1 | fields - count'
-how_to_implement: You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The Network Resolution and Network Traffic datamodels are in use for this search.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  where DNS.query_type IN (SIG,KEY) by DNS.flow_id | rename DNS.flow_id as flow_id
+  | append [| tstats  `security_content_summariesonly` count from datamodel=Network_Traffic
+  where All_Traffic.bytes_in>65000 by All_Traffic.flow_id | rename All_Traffic.flow_id
+  as flow_id] | `detect_windows_dns_sigred_via_zeek_filter` | stats count by flow_id
+  | where count>1 | fields - count'
+how_to_implement: You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek
+  data should also be getting ingested in JSON format.  We are detecting SIG and KEY
+  records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The
+  Network Resolution and Network Traffic datamodels are in use for this search.
 known_false_positives: unknown
 references: []
 tags:
@@ -31,10 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.query_type
-  - DNS.flow_id
-  - All_Traffic.bytes_in
-  - All_Traffic.flow_id
   security_domain: endpoint
diff --git a/detections/network/detect_zerologon_via_zeek.yml b/detections/network/detect_zerologon_via_zeek.yml
index 28915436cd..de4b9c3326 100644
--- a/detections/network/detect_zerologon_via_zeek.yml
+++ b/detections/network/detect_zerologon_via_zeek.yml
@@ -5,10 +5,24 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: 'The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability via Zeek RPC. It leverages Zeek DCE-RPC data to identify specific operations: NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3. This activity is significant because it indicates an attempt to gain unauthorized access to a domain controller, potentially leading to a complete takeover of an organization''s IT infrastructure. If confirmed malicious, the impact could be severe, including data theft, ransomware deployment, or other devastating outcomes. Immediate investigation of the identified IP addresses and RPC operations is crucial.'
+description: "The following analytic detects attempts to exploit the Zerologon CVE-2020-1472
+  vulnerability via Zeek RPC. It leverages Zeek DCE-RPC data to identify specific
+  operations: NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3.
+  This activity is significant because it indicates an attempt to gain unauthorized
+  access to a domain controller, potentially leading to a complete takeover of an
+  organization's IT infrastructure. If confirmed malicious, the impact could be severe,
+  including data theft, ransomware deployment, or other devastating outcomes. Immediate
+  investigation of the identified IP addresses and RPC operations is crucial."
 data_source: []
-search: '`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4 passcount>0 | search `detect_zerologon_via_zeek_filter`'
-how_to_implement: You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.
+search: '`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3)
+  | bin span=5m _time | stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge"))
+  as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2"))
+  as passcount count as totalcount by _time,src_ip,dest_ip | search opscount=3 authcount>4
+  passcount>0 | search `detect_zerologon_via_zeek_filter`'
+how_to_implement: You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should
+  also be getting ingested in JSON format.  We are detecting when all three RPC operations
+  (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app
+  via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.
 known_false_positives: unknown
 references:
 - https://www.secura.com/blog/zero-logon
@@ -36,7 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - operation
   security_domain: network
diff --git a/detections/network/dns_query_length_outliers___mltk.yml b/detections/network/dns_query_length_outliers___mltk.yml
index de6fe0efed..7425303c26 100644
--- a/detections/network/dns_query_length_outliers___mltk.yml
+++ b/detections/network/dns_query_length_outliers___mltk.yml
@@ -5,11 +5,42 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies DNS requests with unusually large query lengths for the record type being requested. It leverages the Network_Resolution data model and applies a machine learning model to detect outliers in DNS query lengths. This activity is significant because unusually large DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to exfiltrate sensitive data or maintain persistent communication channels with compromised systems.
+description: The following analytic identifies DNS requests with unusually large query
+  lengths for the record type being requested. It leverages the Network_Resolution
+  data model and applies a machine learning model to detect outliers in DNS query
+  lengths. This activity is significant because unusually large DNS queries can indicate
+  data exfiltration or command-and-control communication attempts. If confirmed malicious,
+  this activity could allow attackers to exfiltrate sensitive data or maintain persistent
+  communication channels with compromised systems.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* |  `drop_dm_object_name(DNS)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)" as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter`'
-how_to_implement: "To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search \"Baseline of DNS Query Length - MLTK\" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\nThis search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\n * **Label:** DNS Query, **Field:** query\n* **Label:** DNS Query Length, **Field:** query_length\n* **Label:** Number of events, **Field:** count\nDetailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
-known_false_positives: If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.
+search: '| tstats `security_content_summariesonly` count min(_time) as start_time
+  max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution
+  by DNS.query DNS.record_type | search DNS.record_type=* |  `drop_dm_object_name(DNS)`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | eval
+  query_length = len(query) | apply dns_query_pdfmodel threshold=0.01 | rename "IsOutlier(query_length)"
+  as isOutlier | search isOutlier > 0 | sort -query_length | table start_time end_time
+  query record_type count src dest query_length | `dns_query_length_outliers___mltk_filter`'
+how_to_implement: "To successfully implement this search, you will need to ensure
+  that DNS data is populating the Network_Resolution data model. In addition, the
+  Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your
+  search heads, along with any required dependencies. Finally, the support search
+  \"Baseline of DNS Query Length - MLTK\" must be executed before this detection search,
+  because it builds a machine-learning (ML) model over the historical data used by
+  this search. It is important that this search is run in the same app context as
+  the associated support search, so that the model created by the support search is
+  available for use. You should periodically re-run the support search to rebuild
+  the model with the latest data available in your environment.\nThis search produces
+  fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident
+  Review and therefore cannot be viewed when a notable event is raised. These fields
+  contribute additional context to the notable. To see the additional metadata, add
+  the following fields, if not already present, to Incident Review - Event Attributes
+  (Configure > Incident Management > Incident Review Settings > Add New Entry):\n
+  * **Label:** DNS Query, **Field:** query\n* **Label:** DNS Query Length, **Field:**
+  query_length\n* **Label:** Number of events, **Field:** count\nDetailed documentation
+  on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
+known_false_positives: If you are seeing more results than desired, you may consider
+  reducing the value for threshold in the search. You should also periodically re-run
+  the support search to re-build the ML model on the latest data.
 references: []
 tags:
   analytic_story:
@@ -32,10 +63,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.src
-  - DNS.dest
-  - DNS.query
-  - DNS.record_type
   security_domain: network
diff --git a/detections/network/dns_query_length_with_high_standard_deviation.yml b/detections/network/dns_query_length_with_high_standard_deviation.yml
index b3243a4d7e..287454ee50 100644
--- a/detections/network/dns_query_length_with_high_standard_deviation.yml
+++ b/detections/network/dns_query_length_with_high_standard_deviation.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Bhavin Patel, Splunk
 status: production
 type: Anomaly
-description: The following analytic identifies DNS queries with unusually large lengths by computing the standard deviation of query lengths and filtering those exceeding twice the standard deviation. It leverages DNS query data from the Network_Resolution data model, focusing on the length of the domain names being resolved. This activity is significant as unusually long DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to stealthily transfer data or maintain persistent communication channels within the network.
+description: The following analytic identifies DNS queries with unusually large lengths
+  by computing the standard deviation of query lengths and filtering those exceeding
+  twice the standard deviation. It leverages DNS query data from the Network_Resolution
+  data model, focusing on the length of the domain names being resolved. This activity
+  is significant as unusually long DNS queries can indicate data exfiltration or command-and-control
+  communication attempts. If confirmed malicious, this activity could allow attackers
+  to stealthily transfer data or maintain persistent communication channels within
+  the network.
 data_source:
 - Sysmon EventID 22
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where NOT DNS.record_type IN("Pointer","PTR") by DNS.query host| `drop_dm_object_name("DNS")` | eval tlds=split(query,".") | eval tld=mvindex(tlds,-1) | eval tld_len=len(tld) | search tld_len<=24 | eval query_length = len(query) | table host query query_length record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev | `dns_query_length_with_high_standard_deviation_filter`'
-how_to_implement: To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  where NOT DNS.record_type IN("Pointer","PTR") by DNS.query host| `drop_dm_object_name("DNS")`
+  | eval tlds=split(query,".") | eval tld=mvindex(tlds,-1) | eval tld_len=len(tld)
+  | search tld_len<=24 | eval query_length = len(query) | table host query query_length
+  record_type count | eventstats stdev(query_length) AS stdev avg(query_length) AS
+  avg p50(query_length) AS p50| where query_length>(avg+stdev*2) | eval z_score=(query_length-avg)/stdev
+  | `dns_query_length_with_high_standard_deviation_filter`'
+how_to_implement: To successfully implement this search, you will need to ensure that
+  DNS data is populating the Network_Resolution data model.
 known_false_positives: It's possible there can be long domain names that are legitimate.
 references: []
 drilldown_searches:
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$host$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$host$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 70
-  message: A dns query $query$ with 2 time standard deviation of name len of the dns query in host  $host$
+  message: A dns query $query$ with 2 time standard deviation of name len of the dns
+    query in host  $host$
   mitre_attack_id:
   - T1048.003
   - T1048
@@ -42,13 +62,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.query
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/network/excessive_dns_failures.yml b/detections/network/excessive_dns_failures.yml
index 2231c1d667..29bf0ea27c 100644
--- a/detections/network/excessive_dns_failures.yml
+++ b/detections/network/excessive_dns_failures.yml
@@ -5,11 +5,29 @@ date: '2024-10-17'
 author: bowesmana, Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies excessive DNS query failures by counting DNS responses that do not indicate success, triggering when there are more than 50 occurrences. It leverages the Network_Resolution data model, focusing on DNS reply codes that signify errors. This activity is significant because a high number of DNS failures can indicate potential network misconfigurations, DNS poisoning attempts, or malware communication issues. If confirmed malicious, this activity could lead to disrupted network services, hindered communication, or data exfiltration attempts by attackers.
+description: The following analytic identifies excessive DNS query failures by counting
+  DNS responses that do not indicate success, triggering when there are more than
+  50 occurrences. It leverages the Network_Resolution data model, focusing on DNS
+  reply codes that signify errors. This activity is significant because a high number
+  of DNS failures can indicate potential network misconfigurations, DNS poisoning
+  attempts, or malware communication issues. If confirmed malicious, this activity
+  could lead to disrupted network services, hindered communication, or data exfiltration
+  attempts by attackers.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src" "DNS.query" "DNS.reply_code" | `drop_dm_object_name("DNS")` | lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain | where isnull(domain) | lookup update=true alexa_lookup_by_str domain as query OUTPUT rank | where isnull(rank) | eventstats max(count) as mc by src reply_code | eval mode_query=if(count=mc, query, null()) | stats sum(count) as count values(mode_query) as query values(mc) as max_query_count by src reply_code | where count>50 | `get_asset(src)` | `excessive_dns_failures_filter`'
-how_to_implement: To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
-known_false_positives: It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown"
+  NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src" "DNS.query" "DNS.reply_code"
+  | `drop_dm_object_name("DNS")` | lookup cim_corporate_web_domain_lookup domain as
+  query OUTPUT domain | where isnull(domain) | lookup update=true alexa_lookup_by_str
+  domain as query OUTPUT rank | where isnull(rank) | eventstats max(count) as mc by
+  src reply_code | eval mode_query=if(count=mc, query, null()) | stats sum(count)
+  as count values(mode_query) as query values(mc) as max_query_count by src reply_code
+  | where count>50 | `get_asset(src)` | `excessive_dns_failures_filter`'
+how_to_implement: To successfully implement this search you must ensure that DNS data
+  is populating the Network_Resolution data model.
+known_false_positives: It is possible legitimate traffic can trigger this rule. Please
+  investigate as appropriate. The threshold for generating an event can also be customized
+  to better suit your environment.
 references: []
 tags:
   analytic_story:
@@ -31,9 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.query
-  - DNS.reply_code
-  - DNS.src
   security_domain: network
diff --git a/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml b/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml
index b11cf0c6a0..a5944e3501 100644
--- a/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml
+++ b/detections/network/f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to exploit the F5 BIG-IP iControl REST API vulnerability (CVE-2022-1388) for unauthenticated remote code execution. It identifies suspicious URI paths and POST HTTP methods, along with specific request headers containing potential commands in the `utilcmdargs` field and a random base64 encoded value in the `X-F5-Auth-Token` field. This activity is significant as it targets a critical vulnerability that can allow attackers to execute arbitrary commands on the affected system. If confirmed malicious, this could lead to full system compromise and unauthorized access to sensitive data.
+description: The following analytic detects attempts to exploit the F5 BIG-IP iControl
+  REST API vulnerability (CVE-2022-1388) for unauthenticated remote code execution.
+  It identifies suspicious URI paths and POST HTTP methods, along with specific request
+  headers containing potential commands in the `utilcmdargs` field and a random base64
+  encoded value in the `X-F5-Auth-Token` field. This activity is significant as it
+  targets a critical vulnerability that can allow attackers to execute arbitrary commands
+  on the affected system. If confirmed malicious, this could lead to full system compromise
+  and unauthorized access to sensitive data.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count from datamodel=Web where Web.url="*/mgmt/tm/util/bash*" Web.http_method="POST" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
-known_false_positives: False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed.
+search: '| tstats count from datamodel=Web where Web.url="*/mgmt/tm/util/bash*" Web.http_method="POST"
+  by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `f5_big_ip_icontrol_rest_vulnerability_cve_2022_1388_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  web or proxy logs, or ensure it is being filled by a proxy like device, into the
+  Web Datamodel. For additional filtering, allow list private IP space or restrict
+  by known good.
+known_false_positives: False positives may be present if the activity is blocked or
+  was not successful. Filter known vulnerablity scanners. Filter as needed.
 references:
 - https://github.com/dk4trin/templates-nuclei/blob/main/CVE-2022-1388.yaml
 - https://www.randori.com/blog/vulnerability-analysis-cve-2022-1388/
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +54,8 @@ tags:
   cve:
   - CVE-2022-1388
   impact: 100
-  message: An attempt to exploit CVE-2022-1388 against an F5 appliance $dest$ has occurred.
+  message: An attempt to exploit CVE-2022-1388 against an F5 appliance $dest$ has
+    occurred.
   mitre_attack_id:
   - T1190
   - T1133
@@ -48,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml b/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml
index 4ea1599681..16e781976c 100644
--- a/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml
+++ b/detections/network/hosts_receiving_high_volume_of_network_traffic_from_email_server.yml
@@ -5,11 +5,38 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies hosts receiving an unusually high volume of network traffic from an email server. It leverages the Network_Traffic data model to sum incoming bytes to clients from email servers, comparing current traffic against historical averages and standard deviations. This activity is significant as it may indicate data exfiltration by a malicious actor using the email server. If confirmed malicious, this could lead to unauthorized data access and potential data breaches, compromising sensitive information and impacting organizational security.
+description: The following analytic identifies hosts receiving an unusually high volume
+  of network traffic from an email server. It leverages the Network_Traffic data model
+  to sum incoming bytes to clients from email servers, comparing current traffic against
+  historical averages and standard deviations. This activity is significant as it
+  may indicate data exfiltration by a malicious actor using the email server. If confirmed
+  malicious, this could lead to unauthorized data access and potential data breaches,
+  compromising sensitive information and impacting organizational security.
 data_source: []
-search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`'
-how_to_implement: This search requires you to be ingesting your network traffic and populating the Network_Traffic data model.  Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.
-known_false_positives: The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.
+search: '| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in
+  from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip
+  _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_in)
+  as avg_bytes_in stdev(bytes_in) as stdev_bytes_in | eventstats count as num_data_samples
+  avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in
+  stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in
+  by src_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples
+  >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in))
+  AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in))
+  AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average
+  = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average
+  = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2)
+  | table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average,
+  num_standard_deviations_away_from_client_average | `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic and
+  populating the Network_Traffic data model.  Your email servers must be categorized
+  as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold
+  and minimum_data_samples values based on the network traffic in your environment.
+  The "deviation_threshold" field is a multiplying factor to control how much variation
+  you're willing to tolerate. The "minimum_data_samples" field is the minimum number
+  of connections of data samples required for the statistic to be valid.
+known_false_positives: The false-positive rate will vary based on how you set the
+  deviation_threshold and data_samples values. Our recommendation is to adjust these
+  values based on your network traffic to and from your email servers.
 references: []
 tags:
   analytic_story:
@@ -30,9 +57,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.bytes_in
-  - All_Traffic.dest_category
-  - All_Traffic.src_ip
   security_domain: network
diff --git a/detections/network/internal_horizontal_port_scan.yml b/detections/network/internal_horizontal_port_scan.yml
index 75e3930b90..1ac4ea33cd 100644
--- a/detections/network/internal_horizontal_port_scan.yml
+++ b/detections/network/internal_horizontal_port_scan.yml
@@ -7,9 +7,28 @@ status: production
 type: TTP
 data_source:
 - AWS CloudWatchLogs VPCflow
-description: This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using the same port and protocol. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats.
-search: '| tstats `security_content_summariesonly` values(All_Traffic.action) as action values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as dest_zone values(All_Traffic.src_zone) as src_zone count from datamodel=Network_Traffic where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") by All_Traffic.src_ip All_Traffic.dest_port All_Traffic.dest_ip span=1s _time All_Traffic.transport | `drop_dm_object_name("All_Traffic")` | eval gtime=_time | bin span=1h gtime | stats min(_time) as _time values(action) as action dc(dest_ip) as totalDestIPCount values(src_category) as src_category values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip dest_port gtime transport | where totalDestIPCount>=250 | eval dest_port=transport + "/" + dest_port | stats min(_time) as _time values(action) as action sum(totalDestIPCount) as totalDestIPCount values(src_category) as src_category values(dest_port) as dest_ports values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip gtime | fields - gtime | `internal_horizontal_port_scan_filter`'
-how_to_implement: To properly run this search, Splunk needs to ingest data from networking telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure that the Network_Traffic data model is populated to enable this search effectively.
+description: This analytic identifies instances where an internal host has attempted
+  to communicate with 250 or more destination IP addresses using the same port and
+  protocol. Horizontal port scans from internal hosts can indicate reconnaissance
+  or scanning activities, potentially signaling malicious intent or misconfiguration.
+  By monitoring network traffic logs, this detection helps detect and respond to such
+  behavior promptly, enhancing network security and preventing potential threats.
+search: '| tstats `security_content_summariesonly` values(All_Traffic.action) as action
+  values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as
+  dest_zone values(All_Traffic.src_zone) as src_zone count from datamodel=Network_Traffic
+  where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") by All_Traffic.src_ip
+  All_Traffic.dest_port All_Traffic.dest_ip span=1s _time All_Traffic.transport |
+  `drop_dm_object_name("All_Traffic")` | eval gtime=_time | bin span=1h gtime | stats
+  min(_time) as _time values(action) as action dc(dest_ip) as totalDestIPCount values(src_category)
+  as src_category values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip
+  dest_port gtime transport | where totalDestIPCount>=250 | eval dest_port=transport
+  + "/" + dest_port | stats min(_time) as _time values(action) as action sum(totalDestIPCount)
+  as totalDestIPCount values(src_category) as src_category values(dest_port) as dest_ports
+  values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip gtime | fields
+  - gtime | `internal_horizontal_port_scan_filter`'
+how_to_implement: To properly run this search, Splunk needs to ingest data from networking
+  telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure
+  that the Network_Traffic data model is populated to enable this search effectively.
 known_false_positives: Unknown
 references: []
 drilldown_searches:
@@ -18,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 80
-  message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination IPs
+  message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination
+    IPs
   mitre_attack_id:
   - T1046
   observable:
@@ -39,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.action
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log
     source: aws:cloudwatchlogs:vpcflow
     sourcetype: aws:cloudwatchlogs:vpcflow
diff --git a/detections/network/internal_horizontal_port_scan_nmap_top_20.yml b/detections/network/internal_horizontal_port_scan_nmap_top_20.yml
index 9b7029776b..39c023250e 100644
--- a/detections/network/internal_horizontal_port_scan_nmap_top_20.yml
+++ b/detections/network/internal_horizontal_port_scan_nmap_top_20.yml
@@ -7,26 +7,29 @@ status: production
 type: TTP
 data_source:
 - AWS CloudWatchLogs VPCflow
-description: This analytic identifies instances where an internal host has attempted to communicate
-  with 250 or more destination IP addresses using on of the NMAP top 20 ports. Horizontal
-  port scans from internal hosts can indicate reconnaissance or scanning activities,
-  potentially signaling malicious intent or misconfiguration. By monitoring network
-  traffic logs, this detection helps detect and respond to such behavior promptly,
-  enhancing network security and preventing potential threats.
+description: This analytic identifies instances where an internal host has attempted
+  to communicate with 250 or more destination IP addresses using on of the NMAP top
+  20 ports. Horizontal port scans from internal hosts can indicate reconnaissance
+  or scanning activities, potentially signaling malicious intent or misconfiguration.
+  By monitoring network traffic logs, this detection helps detect and respond to such
+  behavior promptly, enhancing network security and preventing potential threats.
 search: >-
-  | tstats `security_content_summariesonly` values(All_Traffic.action) as action values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as dest_zone values(All_Traffic.src_zone) as src_zone count from datamodel=Network_Traffic where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") AND All_Traffic.dest_port IN (21, 22, 23, 25, 53, 80, 110, 111, 135, 139, 143, 443, 445, 993, 995, 1723, 3306, 3389, 5900, 8080) by All_Traffic.src_ip All_Traffic.dest_port All_Traffic.dest_ip span=1s _time All_Traffic.transport 
-  | `drop_dm_object_name("All_Traffic")` 
-  | eval gtime=_time 
-  | bin span=1h gtime 
-  | stats min(_time) as _time values(action) as action dc(dest_ip) as totalDestIPCount values(src_category) as src_category values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip dest_port gtime transport 
-  | where totalDestIPCount>=250 
-  | eval dest_port=transport + "/" + dest_port 
-  | stats min(_time) as _time values(action) as action sum(totalDestIPCount) as totalDestIPCount values(src_category) as src_category values(dest_port) as dest_ports values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip gtime 
-  | fields - gtime 
-  | `internal_horizontal_port_scan_nmap_top_20_filter`
-how_to_implement: To properly run this search, Splunk needs to ingest data from networking telemetry sources such as 
-  firewalls, NetFlow, or host-based networking events. Ensure that the Network_Traffic data model is populated to 
-  enable this search effectively.
+  | tstats `security_content_summariesonly` values(All_Traffic.action) as action values(All_Traffic.src_category)
+  as src_category values(All_Traffic.dest_zone) as dest_zone values(All_Traffic.src_zone)
+  as src_zone count from datamodel=Network_Traffic where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16")
+  AND All_Traffic.dest_port IN (21, 22, 23, 25, 53, 80, 110, 111, 135, 139, 143, 443,
+  445, 993, 995, 1723, 3306, 3389, 5900, 8080) by All_Traffic.src_ip All_Traffic.dest_port
+  All_Traffic.dest_ip span=1s _time All_Traffic.transport  | `drop_dm_object_name("All_Traffic")`  |
+  eval gtime=_time  | bin span=1h gtime  | stats min(_time) as _time values(action)
+  as action dc(dest_ip) as totalDestIPCount values(src_category) as src_category values(dest_zone)
+  as dest_zone values(src_zone) as src_zone by src_ip dest_port gtime transport  |
+  where totalDestIPCount>=250  | eval dest_port=transport + "/" + dest_port  | stats
+  min(_time) as _time values(action) as action sum(totalDestIPCount) as totalDestIPCount
+  values(src_category) as src_category values(dest_port) as dest_ports values(dest_zone)
+  as dest_zone values(src_zone) as src_zone by src_ip gtime  | fields - gtime  | `internal_horizontal_port_scan_nmap_top_20_filter`
+how_to_implement: To properly run this search, Splunk needs to ingest data from networking
+  telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure
+  that the Network_Traffic data model is populated to enable this search effectively.
 known_false_positives: Unknown
 references: []
 drilldown_searches:
@@ -35,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for $src_ip$
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($src_ip$) starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ($src_ip$)
+    starthoursago=168 endhoursago=1 | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 90
-  message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination IPs
+  message: $src_ip$ has scanned for ports $dest_ports$ across $totalDestIPCount$ destination
+    IPs
   mitre_attack_id:
   - T1046
   observable:
@@ -56,16 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.action
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/horizontal.log
     source: aws:cloudwatchlogs:vpcflow
-    sourcetype: aws:cloudwatchlogs:vpcflow
\ No newline at end of file
+    sourcetype: aws:cloudwatchlogs:vpcflow
diff --git a/detections/network/internal_vertical_port_scan.yml b/detections/network/internal_vertical_port_scan.yml
index cc5dff3665..aedc0a3f42 100644
--- a/detections/network/internal_vertical_port_scan.yml
+++ b/detections/network/internal_vertical_port_scan.yml
@@ -7,9 +7,29 @@ status: production
 type: TTP
 data_source:
 - AWS CloudWatchLogs VPCflow
-description: This analytic detects instances where an internal host attempts to communicate with over 500 ports on a single destination IP address. It includes filtering criteria to exclude applications performing scans over ephemeral port ranges, focusing on potential reconnaissance or scanning activities. Monitoring network traffic logs allows for timely detection and response to such behavior, enhancing network security by identifying and mitigating potential threats promptly.
-search: '| tstats `security_content_summariesonly` values(All_Traffic.action) as action values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as dest_zone values(All_Traffic.src_zone) as src_zone count from datamodel=Network_Traffic where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") by All_Traffic.src_ip All_Traffic.dest_port All_Traffic.dest_ip All_Traffic.transport span=1s _time | `drop_dm_object_name("All_Traffic")` | eval gtime=_time | bin span=1h gtime | stats min(_time) as _time values(action) as action dc(eval(if(dest_port<1024 AND transport="tcp",dest_port,null))) as privilegedDestTcpPortCount dc(eval(if(transport="tcp",dest_port,null))) as totalDestTcpPortCount dc(eval(if(dest_port<1024 AND transport="udp",dest_port,null))) as privilegedDestUdpPortCount dc(eval(if(transport="udp",dest_port,null))) as totalDestUdpPortCount values(src_category) as src_category values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip dest_ip transport gtime | eval totalDestPortCount=totalDestUdpPortCount+totalDestTcpPortCount, privilegedDestPortCount=privilegedDestTcpPortCount+privilegedDestUdpPortCount| where (totalDestPortCount>=500 AND privilegedDestPortCount>=20) | fields - gtime | `internal_vertical_port_scan_filter`'
-how_to_implement: To properly run this search, Splunk needs to ingest data from networking telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure that the Network_Traffic data model is populated to enable this search effectively.
+description: This analytic detects instances where an internal host attempts to communicate
+  with over 500 ports on a single destination IP address. It includes filtering criteria
+  to exclude applications performing scans over ephemeral port ranges, focusing on
+  potential reconnaissance or scanning activities. Monitoring network traffic logs
+  allows for timely detection and response to such behavior, enhancing network security
+  by identifying and mitigating potential threats promptly.
+search: '| tstats `security_content_summariesonly` values(All_Traffic.action) as action
+  values(All_Traffic.src_category) as src_category values(All_Traffic.dest_zone) as
+  dest_zone values(All_Traffic.src_zone) as src_zone count from datamodel=Network_Traffic
+  where All_Traffic.src_ip IN ("10.0.0.0/8","172.16.0.0/12","192.168.0.0/16") by All_Traffic.src_ip
+  All_Traffic.dest_port All_Traffic.dest_ip All_Traffic.transport span=1s _time |
+  `drop_dm_object_name("All_Traffic")` | eval gtime=_time | bin span=1h gtime | stats
+  min(_time) as _time values(action) as action dc(eval(if(dest_port<1024 AND transport="tcp",dest_port,null)))
+  as privilegedDestTcpPortCount dc(eval(if(transport="tcp",dest_port,null))) as totalDestTcpPortCount
+  dc(eval(if(dest_port<1024 AND transport="udp",dest_port,null))) as privilegedDestUdpPortCount
+  dc(eval(if(transport="udp",dest_port,null))) as totalDestUdpPortCount values(src_category)
+  as src_category values(dest_zone) as dest_zone values(src_zone) as src_zone by src_ip
+  dest_ip transport gtime | eval totalDestPortCount=totalDestUdpPortCount+totalDestTcpPortCount,
+  privilegedDestPortCount=privilegedDestTcpPortCount+privilegedDestUdpPortCount| where
+  (totalDestPortCount>=500 AND privilegedDestPortCount>=20) | fields - gtime | `internal_vertical_port_scan_filter`'
+how_to_implement: To properly run this search, Splunk needs to ingest data from networking
+  telemetry sources such as firewalls, NetFlow, or host-based networking events. Ensure
+  that the Network_Traffic data model is populated to enable this search effectively.
 known_false_positives: Unknown
 references: []
 drilldown_searches:
@@ -18,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,16 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.action
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/vertical.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1046/nmap/vertical.log
     source: aws:cloudwatchlogs:vpcflow
     sourcetype: aws:cloudwatchlogs:vpcflow
diff --git a/detections/network/internal_vulnerability_scan.yml b/detections/network/internal_vulnerability_scan.yml
index 0d1b22e11f..9ae0f5d704 100644
--- a/detections/network/internal_vulnerability_scan.yml
+++ b/detections/network/internal_vulnerability_scan.yml
@@ -6,9 +6,30 @@ author: Dean Luxton
 status: experimental
 type: TTP
 data_source: []
-description: This analytic detects internal hosts triggering multiple IDS signatures, which may include either more than 25 signatures against a single host or a single signature across over 25 destination IP addresses. Such patterns can indicate active vulnerability scanning activities within the network. By monitoring IDS logs, this detection helps identify and respond to potential vulnerability scanning attempts, enhancing the network's security posture and preventing potential exploits.
-search: '| tstats `security_content_summariesonly` values(IDS_Attacks.action) as action values(IDS_Attacks.src_category) as src_category values(IDS_Attacks.dest_category) as dest_category count from datamodel=Intrusion_Detection.IDS_Attacks where IDS_Attacks.src IN (10.0.0.0/8,192.168.0.0/16,172.16.0.0/12) IDS_Attacks.severity IN (critical, high, medium) by IDS_Attacks.src IDS_Attacks.severity IDS_Attacks.signature IDS_Attacks.dest IDS_Attacks.dest_port IDS_Attacks.transport span=1s _time | `drop_dm_object_name("IDS_Attacks")` | eval gtime=_time | bin span=1h gtime | eventstats count as sevCount by severity src | eventstats count as sigCount by signature src | eval severity=severity +"("+sevCount+")" | eval signature=signature +"("+sigCount+")" | eval dest_port=transport + "/" + dest_port | stats min(_time) as _time values(action) as action dc(dest) as destCount dc(signature) as sigCount values(signature) values(src_category) as src_category values(dest_category) as dest_category values(severity) as severity values(dest_port) as dest_ports by src gtime | fields - gtime | where destCount>25 OR sigCount>25 | `internal_vulnerability_scan_filter`'
-how_to_implement: For this detection to function effectively, it is essential to ingest IDS/IPS logs that are mapped to the Common Information Model (CIM). These logs provide the necessary security-related telemetry and contextual information needed to accurately identify and analyze potential threats.
+description: This analytic detects internal hosts triggering multiple IDS signatures,
+  which may include either more than 25 signatures against a single host or a single
+  signature across over 25 destination IP addresses. Such patterns can indicate active
+  vulnerability scanning activities within the network. By monitoring IDS logs, this
+  detection helps identify and respond to potential vulnerability scanning attempts,
+  enhancing the network's security posture and preventing potential exploits.
+search: '| tstats `security_content_summariesonly` values(IDS_Attacks.action) as action
+  values(IDS_Attacks.src_category) as src_category values(IDS_Attacks.dest_category)
+  as dest_category count from datamodel=Intrusion_Detection.IDS_Attacks where IDS_Attacks.src
+  IN (10.0.0.0/8,192.168.0.0/16,172.16.0.0/12) IDS_Attacks.severity IN (critical,
+  high, medium) by IDS_Attacks.src IDS_Attacks.severity IDS_Attacks.signature IDS_Attacks.dest
+  IDS_Attacks.dest_port IDS_Attacks.transport span=1s _time | `drop_dm_object_name("IDS_Attacks")`
+  | eval gtime=_time | bin span=1h gtime | eventstats count as sevCount by severity
+  src | eventstats count as sigCount by signature src | eval severity=severity +"("+sevCount+")"
+  | eval signature=signature +"("+sigCount+")" | eval dest_port=transport + "/" +
+  dest_port | stats min(_time) as _time values(action) as action dc(dest) as destCount
+  dc(signature) as sigCount values(signature) values(src_category) as src_category
+  values(dest_category) as dest_category values(severity) as severity values(dest_port)
+  as dest_ports by src gtime | fields - gtime | where destCount>25 OR sigCount>25
+  | `internal_vulnerability_scan_filter`'
+how_to_implement: For this detection to function effectively, it is essential to ingest
+  IDS/IPS logs that are mapped to the Common Information Model (CIM). These logs provide
+  the necessary security-related telemetry and contextual information needed to accurately
+  identify and analyze potential threats.
 known_false_positives: Internal vulnerability scanners will trigger this detection.
 references: []
 tags:
@@ -30,13 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - IDS_Attacks.action
-  - IDS_Attacks.src
-  - IDS_Attacks.dest
-  - IDS_Attacks.dest_port
-  - IDS_Attacks.severity
-  - IDS_Attacks.signature
-  - IDS_Attacks.transport
   security_domain: network
diff --git a/detections/network/large_volume_of_dns_any_queries.yml b/detections/network/large_volume_of_dns_any_queries.yml
index 0987145b7e..ca980f9edb 100644
--- a/detections/network/large_volume_of_dns_any_queries.yml
+++ b/detections/network/large_volume_of_dns_any_queries.yml
@@ -5,11 +5,23 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies a large volume of DNS ANY queries, which may indicate a DNS amplification attack. It leverages the Network_Resolution data model to count DNS queries of type "ANY" directed to specific destinations. This activity is significant because DNS amplification attacks can overwhelm network resources, leading to Denial of Service (DoS) conditions. If confirmed malicious, this activity could disrupt services, degrade network performance, and potentially be part of a larger Distributed Denial of Service (DDoS) attack, impacting the availability of critical infrastructure.
+description: The following analytic identifies a large volume of DNS ANY queries,
+  which may indicate a DNS amplification attack. It leverages the Network_Resolution
+  data model to count DNS queries of type "ANY" directed to specific destinations.
+  This activity is significant because DNS amplification attacks can overwhelm network
+  resources, leading to Denial of Service (DoS) conditions. If confirmed malicious,
+  this activity could disrupt services, degrade network performance, and potentially
+  be part of a larger Distributed Denial of Service (DDoS) attack, impacting the availability
+  of critical infrastructure.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter`'
-how_to_implement: To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
-known_false_positives: Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Resolution
+  where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest"
+  | `drop_dm_object_name("DNS")` | where count>200 | `large_volume_of_dns_any_queries_filter`'
+how_to_implement: To successfully implement this search you must ensure that DNS data
+  is populating the Network_Resolution data model.
+known_false_positives: Legitimate ANY requests may trigger this search, however it
+  is unusual to see a large volume of them under typical circumstances. You may modify
+  the threshold in the search to better suit your environment.
 references: []
 tags:
   analytic_story:
@@ -30,9 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - DNS.message_type
-  - DNS.record_type
-  - DNS.dest
   security_domain: network
diff --git a/detections/network/ngrok_reverse_proxy_on_network.yml b/detections/network/ngrok_reverse_proxy_on_network.yml
index af05692d20..a70e096eac 100644
--- a/detections/network/ngrok_reverse_proxy_on_network.yml
+++ b/detections/network/ngrok_reverse_proxy_on_network.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects DNS queries to common Ngrok domains, indicating potential use of the Ngrok reverse proxy tool. It leverages the Network Resolution datamodel to identify queries to domains such as "*.ngrok.com" and "*.ngrok.io". While Ngrok usage is not inherently malicious, it has been increasingly adopted by adversaries for covert communication and data exfiltration. If confirmed malicious, this activity could allow attackers to bypass network defenses, establish persistent connections, and exfiltrate sensitive data, posing a significant threat to the network's security.
+description: The following analytic detects DNS queries to common Ngrok domains, indicating
+  potential use of the Ngrok reverse proxy tool. It leverages the Network Resolution
+  datamodel to identify queries to domains such as "*.ngrok.com" and "*.ngrok.io".
+  While Ngrok usage is not inherently malicious, it has been increasingly adopted
+  by adversaries for covert communication and data exfiltration. If confirmed malicious,
+  this activity could allow attackers to bypass network defenses, establish persistent
+  connections, and exfiltrate sensitive data, posing a significant threat to the network's
+  security.
 data_source:
 - Sysmon EventID 22
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime  from datamodel=Network_Resolution where DNS.query IN ("*.ngrok.com","*.ngrok.io", "ngrok.*.tunnel.com", "korgn.*.lennut.com") by DNS.src DNS.query DNS.answer |  `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ngrok_reverse_proxy_on_network_filter`'
-how_to_implement: The Network Resolution Datamodel will need to have data mapped to it regarding DNS queries. Modify query as needed to use another source.
-known_false_positives: False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime  from datamodel=Network_Resolution where DNS.query IN ("*.ngrok.com","*.ngrok.io",
+  "ngrok.*.tunnel.com", "korgn.*.lennut.com") by DNS.src DNS.query DNS.answer |  `drop_dm_object_name("DNS")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ngrok_reverse_proxy_on_network_filter`'
+how_to_implement: The Network Resolution Datamodel will need to have data mapped to
+  it regarding DNS queries. Modify query as needed to use another source.
+known_false_positives: False positives will be present based on organizations that
+  allow the use of Ngrok. Filter or monitor as needed.
 references:
 - https://www.cisa.gov/uscert/sites/default/files/publications/aa22-320a_joint_csa_iranian_government-sponsored_apt_actors_compromise_federal%20network_deploy_crypto%20miner_credential_harvester.pdf
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,14 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - DNS.src
-  - DNS.query
-  - DNS.answer
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1572/ngrok/windows-sysmon.log
     source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
     sourcetype: XmlWinEventLog
diff --git a/detections/network/prohibited_network_traffic_allowed.yml b/detections/network/prohibited_network_traffic_allowed.yml
index 33b0f6c923..a769a401b4 100644
--- a/detections/network/prohibited_network_traffic_allowed.yml
+++ b/detections/network/prohibited_network_traffic_allowed.yml
@@ -5,10 +5,26 @@ date: '2024-09-30'
 author: Rico Valdez, Splunk
 status: production
 type: TTP
-description: The following analytic detects instances where network traffic, identified by port and transport layer protocol as prohibited in the "lookup_interesting_ports" table, is allowed. It uses the Network_Traffic data model to cross-reference traffic data against predefined security policies. This activity is significant for a SOC as it highlights potential misconfigurations or policy violations that could lead to unauthorized access or data exfiltration. If confirmed malicious, this could allow attackers to bypass network defenses, leading to potential data breaches and compromising the organization's security posture.
+description: The following analytic detects instances where network traffic, identified
+  by port and transport layer protocol as prohibited in the "lookup_interesting_ports"
+  table, is allowed. It uses the Network_Traffic data model to cross-reference traffic
+  data against predefined security policies. This activity is significant for a SOC
+  as it highlights potential misconfigurations or policy violations that could lead
+  to unauthorized access or data exfiltration. If confirmed malicious, this could
+  allow attackers to bypass network defenses, leading to potential data breaches and
+  compromising the organization's security posture.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter`'
-how_to_implement: In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by
+  All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action
+  | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port
+  OUTPUT app is_prohibited note transport | search is_prohibited=true | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `prohibited_network_traffic_allowed_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from firewalls or other network control devices that mediate the traffic allowed
+  into an environment. This is necessary so that the search can identify an 'action'
+  taken on the traffic of interest. The search requires the Network_Traffic data model
+  be populated.
 known_false_positives: None identified
 references: []
 drilldown_searches:
@@ -17,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,17 +65,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.action
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
   security_domain: network
   manual_test: This detection uses builtin lookup from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048/ftp_connection/zeek_conn.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048/ftp_connection/zeek_conn.log
     sourcetype: bro:conn:json
     source: conn.log
diff --git a/detections/network/protocol_or_port_mismatch.yml b/detections/network/protocol_or_port_mismatch.yml
index 32e6f8f864..58c801649c 100644
--- a/detections/network/protocol_or_port_mismatch.yml
+++ b/detections/network/protocol_or_port_mismatch.yml
@@ -5,10 +5,27 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies network traffic where the higher layer protocol does not match the expected port, such as non-HTTP traffic on TCP port 80. It leverages data from network traffic inspection technologies like Bro or Palo Alto Networks firewalls. This activity is significant because it may indicate attempts to bypass firewall restrictions or conceal malicious communications. If confirmed malicious, this behavior could allow attackers to evade detection, maintain persistence, or exfiltrate data through commonly allowed ports, posing a significant threat to network security.
+description: The following analytic identifies network traffic where the higher layer
+  protocol does not match the expected port, such as non-HTTP traffic on TCP port
+  80. It leverages data from network traffic inspection technologies like Bro or Palo
+  Alto Networks firewalls. This activity is significant because it may indicate attempts
+  to bypass firewall restrictions or conceal malicious communications. If confirmed
+  malicious, this behavior could allow attackers to evade detection, maintain persistence,
+  or exfiltrate data through commonly allowed ports, posing a significant threat to
+  network security.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter`'
-how_to_implement: Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53)
+  OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80
+  OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl
+  NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp
+  NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app,
+  All_Traffic.dest_port |`security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `drop_dm_object_name("All_Traffic")` | `protocol_or_port_mismatch_filter`'
+how_to_implement: Running this search properly requires a technology that can inspect
+  network traffic and identify common protocols. Technologies such as Bro and Palo
+  Alto Networks firewalls are two examples that will identify protocols via inspection,
+  and not just assume a specific protocol based on the transport protocol and ports.
 known_false_positives: None identified
 references: []
 tags:
@@ -31,10 +48,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.app
-  - All_Traffic.dest_port
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
   security_domain: network
diff --git a/detections/network/protocols_passing_authentication_in_cleartext.yml b/detections/network/protocols_passing_authentication_in_cleartext.yml
index bd53c550d9..e730d4b60d 100644
--- a/detections/network/protocols_passing_authentication_in_cleartext.yml
+++ b/detections/network/protocols_passing_authentication_in_cleartext.yml
@@ -5,11 +5,28 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies the use of cleartext protocols that risk leaking sensitive information. It detects network traffic on legacy protocols such as Telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21). The detection leverages the Network_Traffic data model to identify TCP traffic on these ports. Monitoring this activity is crucial as it can expose credentials and other sensitive data to interception. If confirmed malicious, attackers could capture authentication details, leading to unauthorized access and potential data breaches.
+description: The following analytic identifies the use of cleartext protocols that
+  risk leaking sensitive information. It detects network traffic on legacy protocols
+  such as Telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP
+  (port 21). The detection leverages the Network_Traffic data model to identify TCP
+  traffic on these ports. Monitoring this activity is crucial as it can expose credentials
+  and other sensitive data to interception. If confirmed malicious, attackers could
+  capture authentication details, leading to unauthorized access and potential data
+  breaches.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action!=blocked AND All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `protocols_passing_authentication_in_cleartext_filter`'
-how_to_implement: This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. For more accurate result it's better to limit destination to organization private and public IP range, like All_Traffic.dest IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22)
-known_false_positives: Some networks may use kerberized FTP or telnet servers, however, this is rare.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where All_Traffic.action!=blocked AND
+  All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143"
+  OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user
+  != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`
+  | `protocols_passing_authentication_in_cleartext_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic, and
+  populating the Network_Traffic data model. For more accurate result it's better
+  to limit destination to organization private and public IP range, like All_Traffic.dest
+  IN(192.168.0.0/16,172.16.0.0/12,10.0.0.0/8, x.x.x.x/22)
+known_false_positives: Some networks may use kerberized FTP or telnet servers, however,
+  this is rare.
 references:
 - https://www.rackaid.com/blog/secure-your-email-and-file-transfers/
 - https://www.infosecmatter.com/capture-passwords-using-wireshark/
@@ -33,12 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.transport
-  - All_Traffic.dest_port
-  - All_Traffic.user
-  - All_Traffic.src
-  - All_Traffic.dest
-  - All_Traffic.action
   security_domain: network
diff --git a/detections/network/remote_desktop_network_bruteforce.yml b/detections/network/remote_desktop_network_bruteforce.yml
index 5ccb854f1c..b167247a2f 100644
--- a/detections/network/remote_desktop_network_bruteforce.yml
+++ b/detections/network/remote_desktop_network_bruteforce.yml
@@ -5,15 +5,22 @@ date: '2024-10-16'
 author: Jose Hernandez, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies potential Remote Desktop Protocol (RDP) brute force attacks by monitoring network traffic for RDP application activity. It detects anomalies by filtering source and destination pairs that generate traffic exceeding twice the standard deviation of the average traffic. This method leverages the Network_Traffic data model to identify unusual patterns indicative of brute force attempts. This activity is significant as it may indicate an attacker attempting to gain unauthorized access to systems via RDP. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further network compromise.
+description: The following analytic identifies potential Remote Desktop Protocol (RDP)
+  brute force attacks by monitoring network traffic for RDP application activity.
+  It detects anomalies by filtering source and destination pairs that generate traffic
+  exceeding twice the standard deviation of the average traffic. This method leverages
+  the Network_Traffic data model to identify unusual patterns indicative of brute
+  force attempts. This activity is significant as it may indicate an attacker attempting
+  to gain unauthorized access to systems via RDP. If confirmed malicious, this could
+  lead to unauthorized access, data exfiltration, or further network compromise.
 data_source: []
 search: >-
-  | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=rdp OR All_Traffic.dest_port=3389) AND All_Traffic.action=allowed by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
-  | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 
-  | where count>(avg + stdev*2) 
-  | rename All_Traffic.src AS src All_Traffic.dest AS dest 
-  | table firstTime lastTime src dest count avg p50 stdev 
-  | `remote_desktop_network_bruteforce_filter`
+  | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where (All_Traffic.app=rdp OR All_Traffic.dest_port=3389)
+  AND All_Traffic.action=allowed by All_Traffic.src All_Traffic.dest All_Traffic.dest_port  |
+  eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50  | where count>(avg
+  + stdev*2)  | rename All_Traffic.src AS src All_Traffic.dest AS dest  | table firstTime
+  lastTime src dest count avg p50 stdev  | `remote_desktop_network_bruteforce_filter`
 how_to_implement: You must ensure that your network traffic data is populating the
   Network_Traffic data model.
 known_false_positives: RDP gateways may have unusually high amounts of traffic from
@@ -43,10 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.app
-  - All_Traffic.src
-  - All_Traffic.dest
-  - All_Traffic.dest_port
   security_domain: network
diff --git a/detections/network/remote_desktop_network_traffic.yml b/detections/network/remote_desktop_network_traffic.yml
index 6ebbd6ddde..b6d4e41c29 100644
--- a/detections/network/remote_desktop_network_traffic.yml
+++ b/detections/network/remote_desktop_network_traffic.yml
@@ -5,10 +5,30 @@ date: '2024-10-16'
 author: David Dorsey, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects unusual Remote Desktop Protocol (RDP) traffic on TCP/3389 by filtering out known RDP sources and destinations, focusing on atypical connections within the network. This detection leverages network traffic data to identify potentially unauthorized RDP access. Monitoring this activity is crucial for a SOC as unauthorized RDP access can indicate an attacker's attempt to control networked systems, leading to data theft, ransomware deployment, or further network compromise. If confirmed malicious, this activity could result in significant data breaches or complete system and network control loss.
+description: The following analytic detects unusual Remote Desktop Protocol (RDP)
+  traffic on TCP/3389 by filtering out known RDP sources and destinations, focusing
+  on atypical connections within the network. This detection leverages network traffic
+  data to identify potentially unauthorized RDP access. Monitoring this activity is
+  crucial for a SOC as unauthorized RDP access can indicate an attacker's attempt
+  to control networked systems, leading to data theft, ransomware deployment, or further
+  network compromise. If confirmed malicious, this activity could result in significant
+  data breaches or complete system and network control loss.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source AND All_Traffic.action="allowed" by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `remote_desktop_network_traffic_filter`'
-how_to_implement: To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework.  This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND
+  All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source
+  AND All_Traffic.action="allowed" by All_Traffic.src All_Traffic.dest All_Traffic.dest_port
+  | `drop_dm_object_name("All_Traffic")` | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)`
+  | `remote_desktop_network_traffic_filter`'
+how_to_implement: To successfully implement this search you need to identify systems
+  that commonly originate remote desktop traffic and that commonly receive remote
+  desktop traffic. You can use the included support search "Identify Systems Creating
+  Remote Desktop Traffic" to identify systems that originate the traffic and the search
+  "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive
+  a lot of remote desktop traffic. After identifying these systems, you will need
+  to add the "common_rdp_source" or "common_rdp_destination" category to that system
+  depending on the usage, using the Enterprise Security Assets and Identities framework.  This
+  can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.
 known_false_positives: Remote Desktop may be used legitimately by users on the network.
 references: []
 drilldown_searches:
@@ -17,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +71,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_port
-  - All_Traffic.dest_category
-  - All_Traffic.src_category
-  - All_Traffic.src
-  - All_Traffic.dest
-  - All_Traffic.dest_port
   security_domain: network
   manual_test: This detection uses builtin lookup from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/remote_desktop_connection/zeek_conn.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.001/remote_desktop_connection/zeek_conn.log
     sourcetype: bro:conn:json
     source: conn.log
diff --git a/detections/network/smb_traffic_spike.yml b/detections/network/smb_traffic_spike.yml
index 011d17e6e7..a2b5dd73f3 100644
--- a/detections/network/smb_traffic_spike.yml
+++ b/detections/network/smb_traffic_spike.yml
@@ -5,11 +5,27 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic detects spikes in Server Message Block (SMB) traffic connections, which are used for sharing files and resources between computers. It leverages network traffic logs to monitor connections on ports 139 and 445, and SMB application usage. By calculating the average and standard deviation of SMB connections over the past 70 minutes, it identifies sources exceeding two standard deviations from the average. This activity is significant as it may indicate potential SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers could exfiltrate data or spread malware within the network.
+description: The following analytic detects spikes in Server Message Block (SMB) traffic
+  connections, which are used for sharing files and resources between computers. It
+  leverages network traffic logs to monitor connections on ports 139 and 445, and
+  SMB application usage. By calculating the average and standard deviation of SMB
+  connections over the past 70 minutes, it identifies sources exceeding two standard
+  deviations from the average. This activity is significant as it may indicate potential
+  SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers
+  could exfiltrate data or spread malware within the network.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter`'
-how_to_implement: This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model.
-known_false_positives: A file server may experience high-demand loads that could cause this analytic to trigger.
+search: '| tstats `security_content_summariesonly` count from datamodel=Network_Traffic
+  where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb
+  by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats
+  max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime,
+  "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"),
+  count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count,
+  null))) as stdev by src | eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound
+  AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count | `smb_traffic_spike_filter`'
+how_to_implement: This search requires you to be ingesting your network traffic logs
+  and populating the `Network_Traffic` data model.
+known_false_positives: A file server may experience high-demand loads that could cause
+  this analytic to trigger.
 references: []
 tags:
   analytic_story:
@@ -33,9 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_port
-  - All_Traffic.app
-  - All_Traffic.src
   security_domain: network
diff --git a/detections/network/smb_traffic_spike___mltk.yml b/detections/network/smb_traffic_spike___mltk.yml
index 62b5045a6f..515c2395e9 100644
--- a/detections/network/smb_traffic_spike___mltk.yml
+++ b/detections/network/smb_traffic_spike___mltk.yml
@@ -5,17 +5,42 @@ date: '2024-10-17'
 author: Rico Valdez, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies spikes in the number of Server Message Block (SMB) connections using the Machine Learning Toolkit (MLTK). It leverages the Network_Traffic data model to monitor SMB traffic on ports 139 and 445, applying a machine learning model to detect anomalies. This activity is significant because sudden increases in SMB traffic can indicate lateral movement or data exfiltration attempts by attackers. If confirmed malicious, this behavior could lead to unauthorized access, data theft, or further compromise of the network.
+description: The following analytic identifies spikes in the number of Server Message
+  Block (SMB) connections using the Machine Learning Toolkit (MLTK). It leverages
+  the Network_Traffic data model to monitor SMB traffic on ports 139 and 445, applying
+  a machine learning model to detect anomalies. This activity is significant because
+  sudden increases in SMB traffic can indicate lateral movement or data exfiltration
+  attempts by attackers. If confirmed malicious, this behavior could lead to unauthorized
+  access, data theft, or further compromise of the network.
 data_source: []
-search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001 | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count | table _time src dest port count | `smb_traffic_spike___mltk_filter`'
-how_to_implement: 'To successfully implement this search, you will need to ensure that DNS data is populating the Network_Traffic data model. In addition, the latest version of Machine Learning Toolkit (MLTK) must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.
-
-  This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):
-
-  * **Label:** Number of events, **Field:** count
-
-  Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`'
-known_false_positives: If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results
+search: '| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip)
+  as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where
+  All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by
+  _time span=1h, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time,
+  "%A") | `drop_dm_object_name(All_Traffic)` | apply smb_pdfmodel threshold=0.001
+  | rename "IsOutlier(count)" as isOutlier | search isOutlier > 0 | sort -count |
+  table _time src dest port count | `smb_traffic_spike___mltk_filter`'
+how_to_implement: "To successfully implement this search, you will need to ensure
+  that DNS data is populating the Network_Traffic data model. In addition, the latest
+  version of Machine Learning Toolkit (MLTK) must be installed on your search heads,
+  along with any required dependencies. Finally, the support search \"Baseline of
+  SMB Traffic - MLTK\" must be executed before this detection search, because it builds
+  a machine-learning (ML) model over the historical data used by this search. It is
+  important that this search is run in the same app context as the associated support
+  search, so that the model created by the support search is available for use. You
+  should periodically re-run the support search to rebuild the model with the latest
+  data available in your environment.\nThis search produces a field (Number of events,count)
+  that are not yet supported by ES Incident Review and therefore cannot be viewed
+  when a notable event is raised. This field contributes additional context to the
+  notable. To see the additional metadata, add the following field, if not already
+  present, to Incident Review - Event Attributes (Configure > Incident Management
+  > Incident Review Settings > Add New Entry):\n* **Label:** Number of events, **Field:**
+  count\nDetailed documentation on how to create a new field within Incident Review
+  is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`"
+known_false_positives: If you are seeing more results than desired, you may consider
+  reducing the value of the threshold in the search. You should also periodically
+  re-run the support search to re-build the ML model on the latest data. Please update
+  the `smb_traffic_spike_mltk_filter` macro to filter out false positive results
 references: []
 tags:
   analytic_story:
@@ -39,10 +64,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
-  - All_Traffic.app
-  - All_Traffic.src
   security_domain: network
diff --git a/detections/network/ssl_certificates_with_punycode.yml b/detections/network/ssl_certificates_with_punycode.yml
index dd186d98f1..0bcbd9a117 100644
--- a/detections/network/ssl_certificates_with_punycode.yml
+++ b/detections/network/ssl_certificates_with_punycode.yml
@@ -5,11 +5,27 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects SSL certificates with Punycode domains in the SSL issuer email domain, identified by the prefix "xn--". It leverages the Certificates Datamodel to flag these domains and uses CyberChef for decoding. This activity is significant as Punycode can be used for domain spoofing and phishing attacks. If confirmed malicious, attackers could deceive users and systems, potentially leading to unauthorized access and data breaches.
+description: The following analytic detects SSL certificates with Punycode domains
+  in the SSL issuer email domain, identified by the prefix "xn--". It leverages the
+  Certificates Datamodel to flag these domains and uses CyberChef for decoding. This
+  activity is significant as Punycode can be used for domain spoofing and phishing
+  attacks. If confirmed malicious, attackers could deceive users and systems, potentially
+  leading to unauthorized access and data breaches.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Certificates.All_Certificates by All_Certificates.SSL.ssl_issuer_email_domain All_Certificates.SSL.ssl_issuer All_Certificates.SSL.ssl_subject_email All_Certificates.SSL.dest All_Certificates.SSL.src All_Certificates.SSL.sourcetype All_Certificates.SSL.ssl_subject_email_domain | `drop_dm_object_name("All_Certificates.SSL")` | eval punycode=if(like(ssl_issuer_email_domain,"%xn--%"),1,0) | where punycode=1 | cyberchef infield="ssl_issuer_email_domain" outfield="convertedPuny" jsonrecipe="[{"op":"From Punycode","args":[true]}]" | table ssl_issuer_email_domain convertedPuny ssl_issuer ssl_subject_email dest src sourcetype ssl_subject_email_domain | `ssl_certificates_with_punycode_filter`'
-how_to_implement: Ensure data is properly being ingested into the Certificates datamodel. If decoding the of interest, the CyberChef app is needed https://splunkbase.splunk.com/app/5348. If decoding is not needed, remove the cyberchef lines.
-known_false_positives: False positives may be present if the organization works with international businesses. Filter as needed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Certificates.All_Certificates by All_Certificates.SSL.ssl_issuer_email_domain
+  All_Certificates.SSL.ssl_issuer All_Certificates.SSL.ssl_subject_email All_Certificates.SSL.dest
+  All_Certificates.SSL.src All_Certificates.SSL.sourcetype All_Certificates.SSL.ssl_subject_email_domain
+  | `drop_dm_object_name("All_Certificates.SSL")` | eval punycode=if(like(ssl_issuer_email_domain,"%xn--%"),1,0)
+  | where punycode=1 | cyberchef infield="ssl_issuer_email_domain" outfield="convertedPuny"
+  jsonrecipe="[{"op":"From Punycode","args":[true]}]" | table ssl_issuer_email_domain
+  convertedPuny ssl_issuer ssl_subject_email dest src sourcetype ssl_subject_email_domain
+  | `ssl_certificates_with_punycode_filter`'
+how_to_implement: Ensure data is properly being ingested into the Certificates datamodel.
+  If decoding the of interest, the CyberChef app is needed https://splunkbase.splunk.com/app/5348.
+  If decoding is not needed, remove the cyberchef lines.
+known_false_positives: False positives may be present if the organization works with
+  international businesses. Filter as needed.
 references:
 - https://www.splunk.com/en_us/blog/security/nothing-puny-about-cve-2022-3602.html
 - https://www.openssl.org/blog/blog/2022/11/01/email-address-overflows/
@@ -21,7 +37,8 @@ tags:
   asset_type: Network
   confidence: 30
   impact: 50
-  message: A x509 certificate has been identified to have punycode in the SSL issuer email domain on $dest$.
+  message: A x509 certificate has been identified to have punycode in the SSL issuer
+    email domain on $dest$.
   mitre_attack_id:
   - T1573
   observable:
@@ -33,12 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Certificates.SSL.ssl_issuer_email_domain
-  - All_Certificates.SSL.ssl_issuer
-  - All_Certificates.SSL.ssl_subject_email
-  - All_Certificates.SSL.dest
-  - All_Certificates.SSL.src
-  - All_Certificates.SSL.sourcetype
-  - All_Certificates.SSL.ssl_subject_email_domain
   security_domain: network
diff --git a/detections/network/tor_traffic.yml b/detections/network/tor_traffic.yml
index 0801b937a8..d3c224b202 100644
--- a/detections/network/tor_traffic.yml
+++ b/detections/network/tor_traffic.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: David Dorsey, Bhavin Patel, Splunk
 status: production
 type: TTP
-description: The following analytic identifies allowed network traffic to The Onion Router (TOR), an anonymity network often exploited for malicious activities. It leverages data from Next Generation Firewalls, using the Network_Traffic data model to detect traffic where the application is TOR and the action is allowed. This activity is significant as TOR can be used to bypass conventional monitoring, facilitating hacking, data breaches, and illicit content dissemination. If confirmed malicious, this could lead to unauthorized access, data exfiltration, and severe compliance violations, compromising the integrity and security of the network.
+description: The following analytic identifies allowed network traffic to The Onion
+  Router (TOR), an anonymity network often exploited for malicious activities. It
+  leverages data from Next Generation Firewalls, using the Network_Traffic data model
+  to detect traffic where the application is TOR and the action is allowed. This activity
+  is significant as TOR can be used to bypass conventional monitoring, facilitating
+  hacking, data breaches, and illicit content dissemination. If confirmed malicious,
+  this could lead to unauthorized access, data exfiltration, and severe compliance
+  violations, compromising the integrity and security of the network.
 data_source:
 - Palo Alto Network Traffic
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `tor_traffic_filter`'
-how_to_implement: In order to properly run this search, Splunk needs to ingest data from Next Generation Firewalls like Palo Alto Networks Firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model to be populated.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed
+  by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")`
+  | `tor_traffic_filter`'
+how_to_implement: In order to properly run this search, Splunk needs to ingest data
+  from Next Generation Firewalls like Palo Alto Networks Firewalls or other network
+  control devices that mediate the traffic allowed into an environment. This is necessary
+  so that the search can identify an 'action' taken on the traffic of interest. The
+  search requires the Network_Traffic data model to be populated.
 known_false_positives: None at this time
 references:
 - https://knowledgebase.paloaltonetworks.com/KCSArticleDetail?id=kA10g000000ClRtCAK
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +52,8 @@ tags:
   asset_type: Endpoint
   confidence: 80
   impact: 100
-  message: Suspicious network traffic allowed using TOR has been detected from $src_ip$ to $dest_ip$
+  message: Suspicious network traffic allowed using TOR has been detected from $src_ip$
+    to $dest_ip$
   mitre_attack_id:
   - T1090
   - T1090.003
@@ -45,17 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - All_Traffic.app
-  - All_Traffic.action
-  - All_Traffic.src_ip
-  - All_Traffic.dest_ip
-  - All_Traffic.dest_port
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.003/pan_tor_allowed/pan_tor_allowed.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1090.003/pan_tor_allowed/pan_tor_allowed.log
     source: pan_tor_allowed
     sourcetype: pan:traffic
diff --git a/detections/network/windows_ad_replication_service_traffic.yml b/detections/network/windows_ad_replication_service_traffic.yml
index 8956ea9fd3..b6ee825c19 100644
--- a/detections/network/windows_ad_replication_service_traffic.yml
+++ b/detections/network/windows_ad_replication_service_traffic.yml
@@ -6,9 +6,23 @@ author: Steven Dick
 type: TTP
 status: experimental
 data_source: []
-description: The following analytic identifies unexpected Active Directory replication traffic from non-domain controller sources. It leverages data from the Network Traffic datamodel, specifically looking for applications related to AD replication. This activity is significant because AD replication traffic should typically only occur between domain controllers. Detection of such traffic from other sources may indicate malicious activities like DCSync or DCShadow, which are used for credential dumping. If confirmed malicious, this could allow attackers to exfiltrate sensitive credentials, leading to unauthorized access and potential domain-wide compromise.
-search: '| tstats `security_content_summariesonly` count values(All_Traffic.transport) as transport values(All_Traffic.user) as user values(All_Traffic.src_category) as src_category values(All_Traffic.dest_category) as dest_category min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app IN ("ms-dc-replication","*drsr*","ad drs") by All_Traffic.src All_Traffic.dest All_Traffic.app | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `windows_ad_replication_service_traffic_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting application aware firewall or proxy logs into the Network Datamodel. Categorize all known domain controller Assets servers with an appropriate category for filtering.
+description: The following analytic identifies unexpected Active Directory replication
+  traffic from non-domain controller sources. It leverages data from the Network Traffic
+  datamodel, specifically looking for applications related to AD replication. This
+  activity is significant because AD replication traffic should typically only occur
+  between domain controllers. Detection of such traffic from other sources may indicate
+  malicious activities like DCSync or DCShadow, which are used for credential dumping.
+  If confirmed malicious, this could allow attackers to exfiltrate sensitive credentials,
+  leading to unauthorized access and potential domain-wide compromise.
+search: '| tstats `security_content_summariesonly` count values(All_Traffic.transport)
+  as transport values(All_Traffic.user) as user values(All_Traffic.src_category) as
+  src_category values(All_Traffic.dest_category) as dest_category min(_time) as firstTime
+  max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app IN ("ms-dc-replication","*drsr*","ad
+  drs") by All_Traffic.src All_Traffic.dest All_Traffic.app | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` | `windows_ad_replication_service_traffic_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  application aware firewall or proxy logs into the Network Datamodel. Categorize
+  all known domain controller Assets servers with an appropriate category for filtering.
 known_false_positives: New domain controllers or certian scripts run by administrators.
 references:
 - https://adsecurity.org/?p=1729
@@ -38,8 +52,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Traffic.src
-  - All_Traffic.dest
-  - All_Traffic.app
   security_domain: network
diff --git a/detections/network/windows_ad_rogue_domain_controller_network_activity.yml b/detections/network/windows_ad_rogue_domain_controller_network_activity.yml
index fc5ca3a15a..7e067129c6 100644
--- a/detections/network/windows_ad_rogue_domain_controller_network_activity.yml
+++ b/detections/network/windows_ad_rogue_domain_controller_network_activity.yml
@@ -6,9 +6,20 @@ author: Dean Luxton
 type: TTP
 status: experimental
 data_source: []
-description: The following analytic identifies unauthorized replication RPC calls from non-domain controller devices. It leverages Zeek wire data to detect specific RPC operations like DrsReplicaAdd and DRSGetNCChanges, filtering out legitimate domain controllers. This activity is significant as it may indicate an attempt to introduce a rogue domain controller, which can compromise the integrity of the Active Directory environment. If confirmed malicious, this could allow attackers to manipulate directory data, escalate privileges, and persist within the network, posing a severe security risk.
-search: '`zeek_rpc` DrsReplicaAdd OR DRSGetNCChanges | where NOT (dest_category="Domain Controller") OR NOT (src_category="Domain Controller") | fillnull value="Unknown" src_category, dest_category | table _time endpoint operation src src_category dest dest_category | `windows_ad_rogue_domain_controller_network_activity_filter`'
-how_to_implement: Run zeek on domain controllers to capture the DCE RPC calls, ensure the domain controller categories are defined in Assets and Identities.
+description: The following analytic identifies unauthorized replication RPC calls
+  from non-domain controller devices. It leverages Zeek wire data to detect specific
+  RPC operations like DrsReplicaAdd and DRSGetNCChanges, filtering out legitimate
+  domain controllers. This activity is significant as it may indicate an attempt to
+  introduce a rogue domain controller, which can compromise the integrity of the Active
+  Directory environment. If confirmed malicious, this could allow attackers to manipulate
+  directory data, escalate privileges, and persist within the network, posing a severe
+  security risk.
+search: '`zeek_rpc` DrsReplicaAdd OR DRSGetNCChanges | where NOT (dest_category="Domain
+  Controller") OR NOT (src_category="Domain Controller") | fillnull value="Unknown"
+  src_category, dest_category | table _time endpoint operation src src_category dest
+  dest_category | `windows_ad_rogue_domain_controller_network_activity_filter`'
+how_to_implement: Run zeek on domain controllers to capture the DCE RPC calls, ensure
+  the domain controller categories are defined in Assets and Identities.
 known_false_positives: None.
 references:
 - https://adsecurity.org/?p=1729
@@ -34,8 +45,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - src
-  - dest
   security_domain: network
diff --git a/detections/network/zeek_x509_certificate_with_punycode.yml b/detections/network/zeek_x509_certificate_with_punycode.yml
index ae96e370b2..6f6cae9e0d 100644
--- a/detections/network/zeek_x509_certificate_with_punycode.yml
+++ b/detections/network/zeek_x509_certificate_with_punycode.yml
@@ -5,11 +5,25 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: experimental
 type: Hunting
-description: The following analytic detects the presence of punycode within x509 certificates using Zeek x509 logs. It identifies punycode in the subject alternative name email and other fields by searching for the "xn--" prefix. This activity is significant as punycode can be used in phishing attacks or to bypass domain filters, posing a security risk. If confirmed malicious, attackers could use these certificates to impersonate legitimate domains, potentially leading to unauthorized access or data breaches.
+description: The following analytic detects the presence of punycode within x509 certificates
+  using Zeek x509 logs. It identifies punycode in the subject alternative name email
+  and other fields by searching for the "xn--" prefix. This activity is significant
+  as punycode can be used in phishing attacks or to bypass domain filters, posing
+  a security risk. If confirmed malicious, attackers could use these certificates
+  to impersonate legitimate domains, potentially leading to unauthorized access or
+  data breaches.
 data_source: []
-search: '`zeek_x509` | rex field=san.email{} "\@(?<domain_detected>xn--.*)" | rex field=san.other_fields{} "\@(?<domain_detected>xn--.*)" | stats values(domain_detected) by  basic_constraints.ca source host | `zeek_x509_certificate_with_punycode_filter`'
-how_to_implement: The following analytic requires x509 certificate data to be logged entirely. In particular, for CVE-2022-3602, the punycode will be within the leaf certificate. The analytic may be modified to look for all xn--, or utilize a network IDS/monitoring tool like Zeek or Suricata to drill down into cert captured. Note for Suricata, the certificate is base64 encoded and will need to be decoded to capture the punycode (punycode will need to be decoded after).
-known_false_positives: False positives may be present if the organization works with international businesses. Filter as needed.
+search: '`zeek_x509` | rex field=san.email{} "\@(?<domain_detected>xn--.*)" | rex
+  field=san.other_fields{} "\@(?<domain_detected>xn--.*)" | stats values(domain_detected)
+  by  basic_constraints.ca source host | `zeek_x509_certificate_with_punycode_filter`'
+how_to_implement: The following analytic requires x509 certificate data to be logged
+  entirely. In particular, for CVE-2022-3602, the punycode will be within the leaf
+  certificate. The analytic may be modified to look for all xn--, or utilize a network
+  IDS/monitoring tool like Zeek or Suricata to drill down into cert captured. Note
+  for Suricata, the certificate is base64 encoded and will need to be decoded to capture
+  the punycode (punycode will need to be decoded after).
+known_false_positives: False positives may be present if the organization works with
+  international businesses. Filter as needed.
 references:
 - https://community.emergingthreats.net/t/out-of-band-ruleset-update-summary-2022-11-01/117
 - https://github.com/corelight/CVE-2022-3602/tree/master/scripts
@@ -23,7 +37,8 @@ tags:
   asset_type: Network
   confidence: 30
   impact: 50
-  message: A x509 certificate has been identified to have punycode in the subject alternative name on $dest$.
+  message: A x509 certificate has been identified to have punycode in the subject
+    alternative name on $dest$.
   mitre_attack_id:
   - T1573
   observable:
@@ -35,9 +50,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - domain_detected
-  - basic_constraints.ca
-  - source
-  - host
   security_domain: network
diff --git a/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml b/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml
index 4140d25c54..935fd214f5 100644
--- a/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml
+++ b/detections/web/access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies access to the /api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark endpoint, which is associated with CVE-2023-46805 and CVE-2024-21887 vulnerabilities. It detects this activity by monitoring for GET requests that receive a 403 Forbidden response with an empty body. This behavior is significant as it indicates potential exploitation attempts against Ivanti Connect Secure systems. If confirmed malicious, attackers could exploit these vulnerabilities to gain unauthorized access or control over the affected systems, leading to potential data breaches or system compromise.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="*/api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark*" Web.http_method=GET Web.status=403 by Web.src, Web.dest, Web.http_user_agent, Web.status, Web.url source | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: This analytic is limited to HTTP Status 403; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
+description: The following analytic identifies access to the 
+  /api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark
+  endpoint, which is associated with CVE-2023-46805 and CVE-2024-21887 vulnerabilities.
+  It detects this activity by monitoring for GET requests that receive a 403 Forbidden
+  response with an empty body. This behavior is significant as it indicates potential
+  exploitation attempts against Ivanti Connect Secure systems. If confirmed malicious,
+  attackers could exploit these vulnerabilities to gain unauthorized access or control
+  over the affected systems, leading to potential data breaches or system compromise.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url="*/api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark*"
+  Web.http_method=GET Web.status=403 by Web.src, Web.dest, Web.http_user_agent, Web.status,
+  Web.url source | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `access_to_vulnerable_ivanti_connect_secure_bookmark_endpoint_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx,
+  or Splunk for Palo Alto.
+known_false_positives: This analytic is limited to HTTP Status 403; adjust as necessary.
+  False positives may occur if the URI path is IP-restricted or externally blocked.
+  It's recommended to review the context of the alerts and adjust the analytic parameters
+  to better fit the specific environment.
 references:
 - https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse
 - https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,16 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.status
-  - Web.url
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_bookmark_web_access.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_bookmark_web_access.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/adobe_coldfusion_access_control_bypass.yml b/detections/web/adobe_coldfusion_access_control_bypass.yml
index b05561955f..38d1160372 100644
--- a/detections/web/adobe_coldfusion_access_control_bypass.yml
+++ b/detections/web/adobe_coldfusion_access_control_bypass.yml
@@ -7,10 +7,29 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects potential exploitation attempts against Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. It monitors requests to specific ColdFusion Administrator endpoints, especially those with an unexpected additional forward slash, using the Web datamodel. This activity is significant for a SOC as it indicates attempts to bypass access controls, which can lead to unauthorized access to ColdFusion administration endpoints. If confirmed malicious, this could result in data theft, brute force attacks, or further exploitation of other vulnerabilities, posing a serious security risk to the environment.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("//restplay*", "//CFIDE/restplay*", "//CFIDE/administrator*", "//CFIDE/adminapi*", "//CFIDE/main*", "//CFIDE/componentutils*", "//CFIDE/wizards*", "//CFIDE/servermanager*","/restplay*", "/CFIDE/restplay*", "/CFIDE/administrator*", "/CFIDE/adminapi*", "/CFIDE/main*", "/CFIDE/componentutils*", "/CFIDE/wizards*", "/CFIDE/servermanager*") Web.status=200 by Web.http_user_agent, Web.status, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adobe_coldfusion_access_control_bypass_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
+description: The following analytic detects potential exploitation attempts against
+  Adobe ColdFusion vulnerabilities CVE-2023-29298 and CVE-2023-26360. It monitors
+  requests to specific ColdFusion Administrator endpoints, especially those with an
+  unexpected additional forward slash, using the Web datamodel. This activity is significant
+  for a SOC as it indicates attempts to bypass access controls, which can lead to
+  unauthorized access to ColdFusion administration endpoints. If confirmed malicious,
+  this could result in data theft, brute force attacks, or further exploitation of
+  other vulnerabilities, posing a serious security risk to the environment.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("//restplay*", "//CFIDE/restplay*", "//CFIDE/administrator*",
+  "//CFIDE/adminapi*", "//CFIDE/main*", "//CFIDE/componentutils*", "//CFIDE/wizards*",
+  "//CFIDE/servermanager*","/restplay*", "/CFIDE/restplay*", "/CFIDE/administrator*",
+  "/CFIDE/adminapi*", "/CFIDE/main*", "/CFIDE/componentutils*", "/CFIDE/wizards*",
+  "/CFIDE/servermanager*") Web.status=200 by Web.http_user_agent, Web.status, Web.http_method,
+  Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adobe_coldfusion_access_control_bypass_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk
+  for Palo Alto.
+known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary.
+  False positives may occur if the URI path is IP-restricted or externally blocked.
+  It's recommended to review the context of the alerts and adjust the analytic parameters
+  to better fit the specific environment.
 references:
 - https://www.rapid7.com/blog/post/2023/07/11/cve-2023-29298-adobe-coldfusion-access-control-bypass/
 drilldown_searches:
@@ -19,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.status
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/coldfusion_cve_2023_29298.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/coldfusion_cve_2023_29298.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml b/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml
index 8db1dfed1e..84a08d6142 100644
--- a/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml
+++ b/detections/web/adobe_coldfusion_unauthenticated_arbitrary_file_read.yml
@@ -7,10 +7,28 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects potential exploitation of the Adobe ColdFusion vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read. It monitors web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path using the Web datamodel, focusing on specific ColdFusion paths to differentiate malicious activity from normal traffic. This activity is significant due to the vulnerability's high CVSS score of 9.8, indicating severe risk. If confirmed malicious, it could lead to unauthorized data access, further attacks, or severe operational disruptions, necessitating immediate investigation.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/cf_scripts/scripts/ajax/ckeditor/*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `adobe_coldfusion_unauthenticated_arbitrary_file_read_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: 'In the wild, we have observed three different types of attempts that could potentially trigger false positives if the HTTP status code is not in the query. Please check this github gist for the specific URIs : https://gist.github.com/patel-bhavin/d10830f3f375a2397233f6a4fe38d5c9 . These could be legitimate requests depending on the context of your organization. Therefore, it is recommended to modify the analytic as needed to suit your specific environment.'
+description: The following analytic detects potential exploitation of the Adobe ColdFusion
+  vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read.
+  It monitors web requests to the "/cf_scripts/scripts/ajax/ckeditor/*" path using
+  the Web datamodel, focusing on specific ColdFusion paths to differentiate malicious
+  activity from normal traffic. This activity is significant due to the vulnerability's
+  high CVSS score of 9.8, indicating severe risk. If confirmed malicious, it could
+  lead to unauthorized data access, further attacks, or severe operational disruptions,
+  necessitating immediate investigation.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/cf_scripts/scripts/ajax/ckeditor/*") Web.status=200 by Web.http_user_agent,
+  Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `adobe_coldfusion_unauthenticated_arbitrary_file_read_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk
+  for Palo Alto.
+known_false_positives: 'In the wild, we have observed three different types of attempts
+  that could potentially trigger false positives if the HTTP status code is not in
+  the query. Please check this github gist for the specific URIs : https://gist.github.com/patel-bhavin/d10830f3f375a2397233f6a4fe38d5c9
+  . These could be legitimate requests depending on the context of your organization.
+  Therefore, it is recommended to modify the analytic as needed to suit your specific
+  environment.'
 references:
 - https://www.rapid7.com/db/modules/auxiliary/gather/adobe_coldfusion_fileread_cve_2023_26360/
 - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2023/CVE-2023-26360.yaml
@@ -20,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,20 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.status
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/cve_2023_29360_coldfusion.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/adobe/cve_2023_29360_coldfusion.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/cisco_ios_xe_implant_access.yml b/detections/web/cisco_ios_xe_implant_access.yml
index 0b5d1d4189..9baefa7645 100644
--- a/detections/web/cisco_ios_xe_implant_access.yml
+++ b/detections/web/cisco_ios_xe_implant_access.yml
@@ -7,10 +7,25 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies the potential exploitation of a vulnerability (CVE-2023-20198) in the Web User Interface of Cisco IOS XE software. It detects suspicious account creation and subsequent actions, including the deployment of a non-persistent implant configuration file. The detection leverages the Web datamodel, focusing on specific URL patterns and HTTP methods. This activity is significant as it indicates unauthorized administrative access, which can lead to full control of the device. If confirmed malicious, attackers could maintain privileged access, compromising the device's integrity and security.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/webui/logoutconfirm.html?logon_hash=*") Web.http_method=POST Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `cisco_ios_xe_implant_access_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: False positives may be present, restrict to Cisco IOS XE devices or perimeter appliances. Modify the analytic as needed based on hunting for successful exploitation of CVE-2023-20198.
+description: The following analytic identifies the potential exploitation of a vulnerability
+  (CVE-2023-20198) in the Web User Interface of Cisco IOS XE software. It detects
+  suspicious account creation and subsequent actions, including the deployment of
+  a non-persistent implant configuration file. The detection leverages the Web datamodel,
+  focusing on specific URL patterns and HTTP methods. This activity is significant
+  as it indicates unauthorized administrative access, which can lead to full control
+  of the device. If confirmed malicious, attackers could maintain privileged access,
+  compromising the device's integrity and security.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/webui/logoutconfirm.html?logon_hash=*") Web.http_method=POST
+  Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length,
+  Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `cisco_ios_xe_implant_access_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk
+  for Palo Alto.
+known_false_positives: False positives may be present, restrict to Cisco IOS XE devices
+  or perimeter appliances. Modify the analytic as needed based on hunting for successful
+  exploitation of CVE-2023-20198.
 references:
 - https://blog.talosintelligence.com/active-exploitation-of-cisco-ios-xe-software/
 - https://github.com/vulncheck-oss/cisco-ios-xe-implant-scanner
@@ -20,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/iosxe/ciscocve202320198.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/cisco/iosxe/ciscocve202320198.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml b/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml
index fa23116008..df436f8c10 100644
--- a/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml
+++ b/detections/web/citrix_adc_and_gateway_unauthorized_data_disclosure.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects attempts to exploit the Citrix Bleed vulnerability (CVE-2023-4966), which can lead to the leaking of session tokens. It identifies HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration URL endpoint. By parsing web traffic and filtering based on user agent details, HTTP method, source and destination IPs, and sourcetype, it aims to identify potentially malicious requests. This activity is significant for a SOC because successful exploitation can allow attackers to impersonate legitimate users, bypass authentication, and access sensitive data. If confirmed malicious, it could lead to unauthorized data access, network propagation, and critical information exfiltration.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/oauth/idp/.well-known/openid-configuration*")  Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `citrix_adc_and_gateway_unauthorized_data_disclosure_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. We recommend hunting in the environment first to understand the scope of the issue and then deploying this detection to monitor for future exploitation attempts. Limit or restrict to Citrix devices only if possible.
-known_false_positives: False positives may be present based on organization use of Citrix ADC and Gateway. Filter, or restrict the analytic to Citrix devices only.
+description: The following analytic detects attempts to exploit the Citrix Bleed vulnerability
+  (CVE-2023-4966), which can lead to the leaking of session tokens. It identifies
+  HTTP requests with a 200 status code targeting the /oauth/idp/.well-known/openid-configuration
+  URL endpoint. By parsing web traffic and filtering based on user agent details,
+  HTTP method, source and destination IPs, and sourcetype, it aims to identify potentially
+  malicious requests. This activity is significant for a SOC because successful exploitation
+  can allow attackers to impersonate legitimate users, bypass authentication, and
+  access sensitive data. If confirmed malicious, it could lead to unauthorized data
+  access, network propagation, and critical information exfiltration.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/oauth/idp/.well-known/openid-configuration*")  Web.status=200
+  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src,
+  Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `citrix_adc_and_gateway_unauthorized_data_disclosure_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk
+  for Palo Alto. We recommend hunting in the environment first to understand the scope
+  of the issue and then deploying this detection to monitor for future exploitation
+  attempts. Limit or restrict to Citrix devices only if possible.
+known_false_positives: False positives may be present based on organization use of
+  Citrix ADC and Gateway. Filter, or restrict the analytic to Citrix devices only.
 references:
 - https://www.assetnote.io/resources/research/citrix-bleed-leaking-session-tokens-with-cve-2023-4966
 - https://github.com/assetnote/exploits/tree/main/citrix/CVE-2023-4966
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +52,8 @@ tags:
   atomic_guid: []
   confidence: 90
   impact: 100
-  message: Possible exploitation of Citrix Bleed vulnerability against $dest$ fron $src$.
+  message: Possible exploitation of Citrix Bleed vulnerability against $dest$ fron
+    $src$.
   mitre_attack_id:
   - T1190
   observable:
@@ -46,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/cve-2023-4966-citrix.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/cve-2023-4966-citrix.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/citrix_adc_exploitation_cve_2023_3519.yml b/detections/web/citrix_adc_exploitation_cve_2023_3519.yml
index 5308ee63ed..3eb44d688f 100644
--- a/detections/web/citrix_adc_exploitation_cve_2023_3519.yml
+++ b/detections/web/citrix_adc_exploitation_cve_2023_3519.yml
@@ -7,10 +7,23 @@ status: production
 type: Hunting
 data_source:
 - Palo Alto Network Threat
-description: The following analytic identifies potential exploitation attempts against Citrix ADC related to CVE-2023-3519. It detects POST requests to specific web endpoints associated with this vulnerability by leveraging the Web datamodel. This activity is significant as CVE-2023-3519 involves a SAML processing overflow issue that can lead to memory corruption, posing a high risk. If confirmed malicious, attackers could exploit this to execute arbitrary code, escalate privileges, or disrupt services, making it crucial for SOC analysts to monitor and investigate these alerts promptly.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/saml/login","/cgi/samlauth","*/saml/activelogin","/cgi/samlart?samlart=*","*/cgi/logout","/gwtest/formssso?event=start&target=*","/netscaler/ns_gui/vpn/*")  Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `citrix_adc_exploitation_cve_2023_3519_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: False positives may be present based on organization use of SAML utilities. Filter, or restrict the analytic to Citrix devices only.
+description: The following analytic identifies potential exploitation attempts against
+  Citrix ADC related to CVE-2023-3519. It detects POST requests to specific web endpoints
+  associated with this vulnerability by leveraging the Web datamodel. This activity
+  is significant as CVE-2023-3519 involves a SAML processing overflow issue that can
+  lead to memory corruption, posing a high risk. If confirmed malicious, attackers
+  could exploit this to execute arbitrary code, escalate privileges, or disrupt services,
+  making it crucial for SOC analysts to monitor and investigate these alerts promptly.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/saml/login","/cgi/samlauth","*/saml/activelogin","/cgi/samlart?samlart=*","*/cgi/logout","/gwtest/formssso?event=start&target=*","/netscaler/ns_gui/vpn/*")  Web.http_method=POST
+  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src,
+  Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `citrix_adc_exploitation_cve_2023_3519_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk
+  for Palo Alto.
+known_false_positives: False positives may be present based on organization use of
+  SAML utilities. Filter, or restrict the analytic to Citrix devices only.
 references:
 - https://blog.assetnote.io/2023/07/21/citrix-CVE-2023-3519-analysis/
 - https://support.citrix.com/article/CTX561482/citrix-adc-and-citrix-gateway-security-bulletin-for-cve20233519-cve20233466-cve20233467
@@ -38,17 +51,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve20233519.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve20233519.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml b/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml
index 002ba3e6b0..b9c479a9cf 100644
--- a/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml
+++ b/detections/web/citrix_sharefile_exploitation_cve_2023_24489.yml
@@ -7,10 +7,29 @@ status: production
 type: Hunting
 data_source:
 - Suricata
-description: The following analytic detects potentially malicious file upload attempts to Citrix ShareFile via specific suspicious URLs and the HTTP POST method. It leverages the Web datamodel to identify URL patterns such as "/documentum/upload.aspx?parentid=", "/documentum/upload.aspx?filename=", and "/documentum/upload.aspx?uploadId=*", combined with the HTTP POST method. This activity is significant for a SOC as it may indicate an attempt to upload harmful scripts or content, potentially compromising the Documentum application. If confirmed malicious, this could lead to unauthorized access, data breaches, and operational disruptions.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="/documentum/upload.aspx?*" AND Web.url IN ("*parentid=*","*filename=*","*uploadId=*") AND Web.url IN ("*unzip=*", "*raw=*") Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `citrix_sharefile_exploitation_cve_2023_24489_filter`'
-how_to_implement: Dependent upon the placement of the ShareFile application, ensure the latest Technology Add-On is eneabled. This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. The ShareFile application is IIS based, therefore ingesting IIS logs and reviewing for the same pattern would identify this activity, successful or not.
-known_false_positives: False positives may be present, filtering may be needed. Also, restricting to known web servers running IIS or ShareFile will change this from Hunting to TTP.
+description: The following analytic detects potentially malicious file upload attempts
+  to Citrix ShareFile via specific suspicious URLs and the HTTP POST method. It leverages
+  the Web datamodel to identify URL patterns such as "/documentum/upload.aspx?parentid=",
+  "/documentum/upload.aspx?filename=", and "/documentum/upload.aspx?uploadId=*", combined
+  with the HTTP POST method. This activity is significant for a SOC as it may indicate
+  an attempt to upload harmful scripts or content, potentially compromising the Documentum
+  application. If confirmed malicious, this could lead to unauthorized access, data
+  breaches, and operational disruptions.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url="/documentum/upload.aspx?*" AND Web.url IN ("*parentid=*","*filename=*","*uploadId=*")
+  AND Web.url IN ("*unzip=*", "*raw=*") Web.http_method=POST by Web.http_user_agent,
+  Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|
+  `citrix_sharefile_exploitation_cve_2023_24489_filter`'
+how_to_implement: Dependent upon the placement of the ShareFile application, ensure
+  the latest Technology Add-On is eneabled. This detection requires the Web datamodel
+  to be populated from a supported Technology Add-On like Suricata, Splunk for Apache,
+  Splunk for Nginx, or Splunk for Palo Alto. The ShareFile application is IIS based,
+  therefore ingesting IIS logs and reviewing for the same pattern would identify this
+  activity, successful or not.
+known_false_positives: False positives may be present, filtering may be needed. Also,
+  restricting to known web servers running IIS or ShareFile will change this from
+  Hunting to TTP.
 references:
 - https://blog.assetnote.io/2023/07/04/citrix-sharefile-rce/
 tags:
@@ -34,17 +53,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve_2023_24489.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/citrix/citrix-cve_2023_24489.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml b/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml
index 8c53699d83..e8e554521f 100644
--- a/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml
+++ b/detections/web/confluence_cve_2023_22515_trigger_vulnerability.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies potential exploitation attempts of the Confluence CVE-2023-22515 vulnerability. It detects successful accesses (HTTP status 200) to specific vulnerable endpoints by analyzing web logs within the Splunk 'Web' Data Model. This activity is significant for a SOC as it indicates possible privilege escalation attempts in Confluence. If confirmed malicious, attackers could gain unauthorized access or create accounts with escalated privileges, leading to potential data breaches or further exploitation within the environment.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false*","*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0&*") Web.http_method=GET Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_cve_2023_22515_trigger_vulnerability_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. Tested with Suricata and nginx:plus:kv.
-known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to Confluence servers.
+description: The following analytic identifies potential exploitation attempts of
+  the Confluence CVE-2023-22515 vulnerability. It detects successful accesses (HTTP
+  status 200) to specific vulnerable endpoints by analyzing web logs within the Splunk
+  'Web' Data Model. This activity is significant for a SOC as it indicates possible
+  privilege escalation attempts in Confluence. If confirmed malicious, attackers could
+  gain unauthorized access or create accounts with escalated privileges, leading to
+  potential data breaches or further exploitation within the environment.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=false*","*/server-info.action?bootstrapStatusProvider.applicationConfig.setupComplete=0&*")
+  Web.http_method=GET Web.status=200 by Web.http_user_agent, Web.status Web.http_method,
+  Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_cve_2023_22515_trigger_vulnerability_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+  Tested with Suricata and nginx:plus:kv.
+known_false_positives: False positives may be present with legitimate applications.
+  Attempt to filter by dest IP or use Asset groups to restrict to Confluence servers.
 references:
 - https://github.com/Chocapikk/CVE-2023-22515/blob/main/exploit.py
 - https://x.com/Shadowserver/status/1712378833536741430?s=20
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +49,8 @@ tags:
   atomic_guid: []
   confidence: 80
   impact: 90
-  message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$.
+  message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence
+    detected. The source IP is $src$ and the destination hostname is $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -47,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/confluence_vuln_trigger_cve-2023-22515.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/confluence_vuln_trigger_cve-2023-22515.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/confluence_data_center_and_server_privilege_escalation.yml b/detections/web/confluence_data_center_and_server_privilege_escalation.yml
index eeef10aa21..8784e448b6 100644
--- a/detections/web/confluence_data_center_and_server_privilege_escalation.yml
+++ b/detections/web/confluence_data_center_and_server_privilege_escalation.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Nginx Access
-description: The following analytic identifies potential exploitation attempts on a known vulnerability in Atlassian Confluence, specifically targeting the /setup/*.action* URL pattern. It leverages web logs within the Splunk 'Web' Data Model, filtering for successful accesses (HTTP status 200) to these endpoints. This activity is significant as it suggests attackers might be exploiting a privilege escalation flaw in Confluence. If confirmed malicious, it could result in unauthorized access or account creation with escalated privileges, leading to potential data breaches or further exploitation within the environment.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/setup/setupadministrator.action*", "*/setup/finishsetup.action*", "*/json/setup-restore-local.action*", "*/json/setup-restore-progress.action*", "*/json/setup-restore.action*", "*/bootstrap/selectsetupstep.action*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_data_center_and_server_privilege_escalation_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
-known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to confluence servers.
+description: The following analytic identifies potential exploitation attempts on
+  a known vulnerability in Atlassian Confluence, specifically targeting the /setup/*.action*
+  URL pattern. It leverages web logs within the Splunk 'Web' Data Model, filtering
+  for successful accesses (HTTP status 200) to these endpoints. This activity is significant
+  as it suggests attackers might be exploiting a privilege escalation flaw in Confluence.
+  If confirmed malicious, it could result in unauthorized access or account creation
+  with escalated privileges, leading to potential data breaches or further exploitation
+  within the environment.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/setup/setupadministrator.action*", "*/setup/finishsetup.action*",
+  "*/json/setup-restore-local.action*", "*/json/setup-restore-progress.action*", "*/json/setup-restore.action*",
+  "*/bootstrap/selectsetupstep.action*") Web.status=200 by Web.http_user_agent, Web.status
+  Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_data_center_and_server_privilege_escalation_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+known_false_positives: False positives may be present with legitimate applications.
+  Attempt to filter by dest IP or use Asset groups to restrict to confluence servers.
 references:
 - https://confluence.atlassian.com/security/cve-2023-22515-privilege-escalation-vulnerability-in-confluence-data-center-and-server-1295682276.html
 - https://confluence.atlassian.com/security/cve-2023-22518-improper-authorization-vulnerability-in-confluence-data-center-and-server-1311473907.html
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +54,8 @@ tags:
   atomic_guid: []
   confidence: 80
   impact: 90
-  message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$.
+  message: Potential exploitation attempts on a known vulnerability in Atlassian Confluence
+    detected. The source IP is $src$ and the destination hostname is $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -51,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: Nginx Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_plus_kv_confluence.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_plus_kv_confluence.log
     source: nginx:plus:kv
     sourcetype: nginx:plus:kv
diff --git a/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml b/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml
index e337feaab0..00ed877e59 100644
--- a/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml
+++ b/detections/web/confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies attempts to exploit a critical template injection vulnerability (CVE-2023-22527) in outdated Confluence Data Center and Server versions. It detects POST requests to the "/template/aui/text-inline.vm" endpoint with HTTP status codes 200 or 202, indicating potential OGNL injection attacks. This activity is significant as it allows unauthenticated attackers to execute arbitrary code remotely. If confirmed malicious, attackers could gain full control over the affected Confluence instance, leading to data breaches, system compromise, and further network infiltration. Immediate patching is essential to mitigate this threat.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="*/template/aui/text-inline.vm*" Web.http_method=POST Web.status IN (200, 202) by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
-known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to confluence servers.
+description: The following analytic identifies attempts to exploit a critical template
+  injection vulnerability (CVE-2023-22527) in outdated Confluence Data Center and
+  Server versions. It detects POST requests to the "/template/aui/text-inline.vm"
+  endpoint with HTTP status codes 200 or 202, indicating potential OGNL injection
+  attacks. This activity is significant as it allows unauthenticated attackers to
+  execute arbitrary code remotely. If confirmed malicious, attackers could gain full
+  control over the affected Confluence instance, leading to data breaches, system
+  compromise, and further network infiltration. Immediate patching is essential to
+  mitigate this threat.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url="*/template/aui/text-inline.vm*" Web.http_method=POST Web.status IN
+  (200, 202) by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_pre_auth_rce_via_ognl_injection_cve_2023_22527_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+known_false_positives: False positives may be present with legitimate applications.
+  Attempt to filter by dest IP or use Asset groups to restrict to confluence servers.
 references:
 - https://github.com/cleverg0d/CVE-2023-22527
 - https://confluence.atlassian.com/security/cve-2023-22527-rce-remote-code-execution-vulnerability-in-confluence-data-center-and-confluence-server-1333990257.html
@@ -20,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +50,8 @@ tags:
   atomic_guid: []
   confidence: 90
   impact: 90
-  message: Exploitation attempts on a known vulnerability in Atlassian Confluence detected. The source IP is $src$ and the destination hostname is $dest$.
+  message: Exploitation attempts on a known vulnerability in Atlassian Confluence
+    detected. The source IP is $src$ and the destination hostname is $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -48,16 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
-  - Web.status
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/suricata_confluence_cve-2023-22527.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/suricata_confluence_cve-2023-22527.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml b/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml
index f027a73ed4..c3ede00011 100644
--- a/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml
+++ b/detections/web/confluence_unauthenticated_remote_code_execution_cve_2022_26134.yml
@@ -5,12 +5,30 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to exploit CVE-2022-26134, an unauthenticated remote code execution vulnerability in Confluence. It leverages the Web datamodel to analyze network and CIM-compliant web logs, identifying suspicious URL patterns and parameters indicative of exploitation attempts. This activity is significant as it allows attackers to execute arbitrary code on the Confluence server without authentication, potentially leading to full system compromise. If confirmed malicious, this could result in unauthorized access, data exfiltration, and further lateral movement within the network. Immediate investigation and remediation are crucial to prevent extensive damage.
+description: The following analytic detects attempts to exploit CVE-2022-26134, an
+  unauthenticated remote code execution vulnerability in Confluence. It leverages
+  the Web datamodel to analyze network and CIM-compliant web logs, identifying suspicious
+  URL patterns and parameters indicative of exploitation attempts. This activity is
+  significant as it allows attackers to execute arbitrary code on the Confluence server
+  without authentication, potentially leading to full system compromise. If confirmed
+  malicious, this could result in unauthorized access, data exfiltration, and further
+  lateral movement within the network. Immediate investigation and remediation are
+  crucial to prevent extensive damage.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*${*", "*%2F%7B*") (Web.url="*org.apache.commons.io.IOUtils*" Web.url="*java.lang.Runtime@getRuntime().exec*") OR (Web.url="*java.lang.Runtime%40getRuntime%28%29.exec*") OR (Web.url="*getEngineByName*" AND Web.url="*nashorn*" AND Web.url="*ProcessBuilder*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `confluence_unauthenticated_remote_code_execution_cve_2022_26134_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx. In addition, network based logs or event data like PAN Threat.
-known_false_positives: Tune based on assets if possible, or restrict to known Confluence servers. Remove the ${ for a more broad query. To identify more exec, remove everything up to the last parameter (Runtime().exec) for a broad query.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*${*", "*%2F%7B*") (Web.url="*org.apache.commons.io.IOUtils*"
+  Web.url="*java.lang.Runtime@getRuntime().exec*") OR (Web.url="*java.lang.Runtime%40getRuntime%28%29.exec*")
+  OR (Web.url="*getEngineByName*" AND Web.url="*nashorn*" AND Web.url="*ProcessBuilder*")
+  by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest
+  sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `confluence_unauthenticated_remote_code_execution_cve_2022_26134_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache or Splunk for Nginx. In addition,
+  network based logs or event data like PAN Threat.
+known_false_positives: Tune based on assets if possible, or restrict to known Confluence
+  servers. Remove the ${ for a more broad query. To identify more exec, remove everything
+  up to the last parameter (Runtime().exec) for a broad query.
 references:
 - https://confluence.atlassian.com/doc/confluence-security-advisory-2022-06-02-1130377146.html
 - https://www.splunk.com/en_us/blog/security/atlassian-confluence-vulnerability-cve-2022-26134.html
@@ -22,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +57,8 @@ tags:
   cve:
   - CVE-2022-26134
   impact: 100
-  message: A URL was requested related to CVE-2022-26134, a unauthenticated remote code execution vulnerability,  on $dest$ by $src$.
+  message: A URL was requested related to CVE-2022-26134, a unauthenticated remote
+    code execution vulnerability,  on $dest$ by $src$.
   mitre_attack_id:
   - T1505
   - T1190
@@ -52,18 +76,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/confluence.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/confluence.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/connectwise_screenconnect_authentication_bypass.yml b/detections/web/connectwise_screenconnect_authentication_bypass.yml
index 936d9a08af..489bf79190 100644
--- a/detections/web/connectwise_screenconnect_authentication_bypass.yml
+++ b/detections/web/connectwise_screenconnect_authentication_bypass.yml
@@ -7,10 +7,31 @@ data_source:
 - Suricata
 type: TTP
 status: production
-description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via an alternate path or channel. It leverages web request logs to identify access to the SetupWizard.aspx page, indicating potential exploitation. This activity is significant as it can lead to unauthorized administrative access and remote code execution. If confirmed malicious, attackers could create administrative users and gain full control over the affected system, posing severe security risks. Immediate remediation by updating to version 23.9.8 or above is recommended.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/SetupWizard.aspx/*","*/SetupWizard/") Web.status=200 Web.http_method=POST by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status, Web.http_method, sourcetype, source | rex field=Web.url "/SetupWizard.aspx/(?<randomPath>.+)" | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_authentication_bypass_filter`'
-how_to_implement: To implement this analytic, ensure proper logging is occurring with IIS, Apache, or a Proxy server and that these logs are being ingested into Splunk. The analytic was written against Suricata. The proper TA will need to be enabled and should be mapped to CIM and the Web datamodel. Ingestion of the data source is required to utilize this detection. In addition, if it is not mapped to the datamodel, modify the query for your application logs to look for requests the same URI and investigate further.
-known_false_positives: False positives are not expected, as the detection is based on the presence of web requests to the SetupWizard.aspx page, which is not a common page to be accessed by legitimate users. Note that the analytic is limited to HTTP POST and a status of 200 to reduce false positives. Modify the query as needed to reduce false positives or hunt for additional indicators of compromise.
+description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect
+  CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via
+  an alternate path or channel. It leverages web request logs to identify access to
+  the SetupWizard.aspx page, indicating potential exploitation. This activity is significant
+  as it can lead to unauthorized administrative access and remote code execution.
+  If confirmed malicious, attackers could create administrative users and gain full
+  control over the affected system, posing severe security risks. Immediate remediation
+  by updating to version 23.9.8 or above is recommended.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/SetupWizard.aspx/*","*/SetupWizard/") Web.status=200 Web.http_method=POST
+  by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status, Web.http_method,
+  sourcetype, source | rex field=Web.url "/SetupWizard.aspx/(?<randomPath>.+)" | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `connectwise_screenconnect_authentication_bypass_filter`'
+how_to_implement: To implement this analytic, ensure proper logging is occurring with
+  IIS, Apache, or a Proxy server and that these logs are being ingested into Splunk.
+  The analytic was written against Suricata. The proper TA will need to be enabled
+  and should be mapped to CIM and the Web datamodel. Ingestion of the data source
+  is required to utilize this detection. In addition, if it is not mapped to the datamodel,
+  modify the query for your application logs to look for requests the same URI and
+  investigate further.
+known_false_positives: False positives are not expected, as the detection is based
+  on the presence of web requests to the SetupWizard.aspx page, which is not a common
+  page to be accessed by legitimate users. Note that the analytic is limited to HTTP
+  POST and a status of 200 to reduce false positives. Modify the query as needed to
+  reduce false positives or hunt for additional indicators of compromise.
 references:
 - https://www.huntress.com/blog/a-catastrophe-for-control-understanding-the-screenconnect-authentication-bypass
 - https://www.huntress.com/blog/detection-guidance-for-connectwise-cwe-288-2
@@ -21,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +56,8 @@ tags:
   asset_type: Web Server
   confidence: 100
   impact: 100
-  message: An authentication bypass attempt against ScreenConnect has been detected on $dest$.
+  message: An authentication bypass attempt against ScreenConnect has been detected
+    on $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -42,13 +69,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
-  - Web.status
-  - Web.http_method
   security_domain: network
   cve:
   - CVE-2024-1708
@@ -56,6 +76,7 @@ tags:
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/connectwise_auth_suricata.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/connectwise_auth_suricata.log
     sourcetype: suricata
     source: suricata
diff --git a/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml b/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml
index 381d4edf33..a50ef31f22 100644
--- a/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml
+++ b/detections/web/detect_attackers_scanning_for_vulnerable_jboss_servers.yml
@@ -5,11 +5,24 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies specific GET or HEAD requests to web servers that indicate reconnaissance attempts to find vulnerable JBoss servers. It leverages data from the Web data model, focusing on HTTP methods and URLs associated with JBoss management interfaces. This activity is significant because it often precedes exploitation attempts using tools like JexBoss, which can compromise the server. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, or escalate privileges, leading to potential data breaches and system compromise.
+description: The following analytic identifies specific GET or HEAD requests to web
+  servers that indicate reconnaissance attempts to find vulnerable JBoss servers.
+  It leverages data from the Web data model, focusing on HTTP methods and URLs associated
+  with JBoss management interfaces. This activity is significant because it often
+  precedes exploitation attempts using tools like JexBoss, which can compromise the
+  server. If confirmed malicious, attackers could gain unauthorized access, execute
+  arbitrary code, or escalate privileges, leading to potential data breaches and system
+  compromise.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`'
-how_to_implement: You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model.
-known_false_positives: It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD")
+  AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*"
+  OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`'
+how_to_implement: You must be ingesting data from the web server or network traffic
+  that contains web specific information, and populating the Web data model.
+known_false_positives: It's possible for legitimate HTTP requests to be made to URLs
+  containing the suspicious paths.
 references: []
 tags:
   analytic_story:
@@ -31,10 +44,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.src
-  - Web.dest
   security_domain: network
diff --git a/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml b/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml
index 1ece9d6aa5..16d96360e1 100644
--- a/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml
+++ b/detections/web/detect_f5_tmui_rce_cve_2020_5902.yml
@@ -5,10 +5,23 @@ date: '2024-10-17'
 author: Shannon Davis, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies remote code execution (RCE) attempts targeting F5 BIG-IP, BIG-IQ, and Traffix SDC devices, specifically exploiting CVE-2020-5902. It uses regex to detect patterns in syslog data that match known exploit strings such as "hsqldb;" and directory traversal sequences. This activity is significant because successful exploitation can allow attackers to execute arbitrary commands on the affected devices, leading to full system compromise. If confirmed malicious, this could result in unauthorized access, data exfiltration, or further lateral movement within the network.
+description: The following analytic identifies remote code execution (RCE) attempts
+  targeting F5 BIG-IP, BIG-IQ, and Traffix SDC devices, specifically exploiting CVE-2020-5902.
+  It uses regex to detect patterns in syslog data that match known exploit strings
+  such as "hsqldb;" and directory traversal sequences. This activity is significant
+  because successful exploitation can allow attackers to execute arbitrary commands
+  on the affected devices, leading to full system compromise. If confirmed malicious,
+  this could result in unauthorized access, data exfiltration, or further lateral
+  movement within the network.
 data_source: []
 search: '`f5_bigip_rogue` | regex _raw="(hsqldb;|.*\\.\\.;.*)" | search `detect_f5_tmui_rce_cve_2020_5902_filter`'
-how_to_implement: To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog.  As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it.  I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;).
+how_to_implement: To consistently detect exploit attempts on F5 devices using the
+  vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs
+  via syslog.  As many BIG-IP devices will have SSL enabled on their management interfaces,
+  detections via wire data may not pick anything up unless you are decrypting SSL
+  traffic in order to inspect it.  I am using a regex string from a Cloudflare mitigation
+  technique to try and always catch the offending string (..;), along with the other
+  exploit of using (hsqldb;).
 known_false_positives: unknown
 references:
 - https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/
@@ -33,6 +46,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
   security_domain: network
diff --git a/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml b/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml
index 07b8f1f3b0..17e7190b16 100644
--- a/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml
+++ b/detections/web/detect_malicious_requests_to_exploit_jboss_servers.yml
@@ -5,10 +5,24 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies malicious HTTP requests targeting the jmx-console in JBoss servers. It detects unusually long URLs, indicative of embedded payloads, by analyzing web server logs for GET or HEAD requests with specific URL patterns and lengths. This activity is significant as it may indicate an attempt to exploit JBoss vulnerabilities, potentially leading to unauthorized remote code execution. If confirmed malicious, attackers could gain control over the server, escalate privileges, and compromise sensitive data, posing a severe threat to the organization's security.
+description: The following analytic identifies malicious HTTP requests targeting the
+  jmx-console in JBoss servers. It detects unusually long URLs, indicative of embedded
+  payloads, by analyzing web server logs for GET or HEAD requests with specific URL
+  patterns and lengths. This activity is significant as it may indicate an attempt
+  to exploit JBoss vulnerabilities, potentially leading to unauthorized remote code
+  execution. If confirmed malicious, attackers could gain control over the server,
+  escalate privileges, and compromise sensitive data, posing a severe threat to the
+  organization's security.
 data_source: []
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter`'
-how_to_implement: You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD")
+  by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*"
+  AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime,
+  lastTime | `detect_malicious_requests_to_exploit_jboss_servers_filter`'
+how_to_implement: You must ingest data from the web server or capture network data
+  that contains web specific information with solutions such as Bro or Splunk Stream,
+  and populating the Web data model
 known_false_positives: No known false positives for this detection.
 references: []
 tags:
@@ -28,11 +42,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: network
diff --git a/detections/web/detect_remote_access_software_usage_url.yml b/detections/web/detect_remote_access_software_usage_url.yml
index 9022f25782..39473d6a2a 100644
--- a/detections/web/detect_remote_access_software_usage_url.yml
+++ b/detections/web/detect_remote_access_software_usage_url.yml
@@ -5,12 +5,35 @@ date: '2024-09-30'
 author: Steven Dick
 status: production
 type: Anomaly
-description: The following analytic detects the execution of known remote access software within the environment. It leverages network logs mapped to the Web data model, identifying specific URLs and user agents associated with remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries often use these utilities to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or further compromise the network, posing a severe security risk.
+description: The following analytic detects the execution of known remote access software
+  within the environment. It leverages network logs mapped to the Web data model,
+  identifying specific URLs and user agents associated with remote access tools like
+  AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries
+  often use these utilities to maintain unauthorized remote access. If confirmed malicious,
+  this could allow attackers to control systems remotely, exfiltrate data, or further
+  compromise the network, posing a severe security risk.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime latest(Web.http_method) as http_method latest(Web.http_user_agent) as http_user_agent latest(Web.url) as url latest(Web.user) as user latest(Web.dest) as dest from datamodel=Web by Web.action Web.src Web.category Web.url_domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name("Web")` | lookup remote_access_software remote_domain AS url_domain OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = True | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_url_filter`'
-how_to_implement: The detection is based on data that originates from network logs. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the network logs. The logs must also be mapped to the `Web` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections.
-known_false_positives: It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime latest(Web.http_method)
+  as http_method latest(Web.http_user_agent) as http_user_agent latest(Web.url) as
+  url latest(Web.user) as user latest(Web.dest) as dest from datamodel=Web by Web.action
+  Web.src Web.category Web.url_domain | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `drop_dm_object_name("Web")` | lookup remote_access_software remote_domain AS
+  url_domain OUTPUT isutility, description as signature, comment_reference as desc,
+  category | search isutility = True | `remote_access_software_usage_exceptions` |
+  `detect_remote_access_software_usage_url_filter`'
+how_to_implement: The detection is based on data that originates from network logs.
+  These logs must be processed using the appropriate Splunk Technology Add-ons that
+  are specific to the network logs. The logs must also be mapped to the `Web` data
+  model. Use the Splunk Common Information Model (CIM) to normalize the field names
+  and speed up the data modeling process. The "exceptions" macro leverages both an
+  Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions"
+  that lets you track and maintain device- based exceptions for this set of detections.
+known_false_positives: It is possible that legitimate remote access software is used
+  within the environment. Ensure that the lookup is reviewed and updated with any
+  additional remote access software that is used within the environment. Known false
+  positives can be added to the remote_access_software_usage_exception.csv lookup
+  to globally suppress these situations across all remote access content
 references:
 - https://attack.mitre.org/techniques/T1219/
 - https://thedfirreport.com/2022/08/08/bumblebee-roasts-its-way-to-domain-admin/
@@ -21,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +61,8 @@ tags:
   asset_type: Network
   confidence: 50
   impact: 50
-  message: A domain for a known remote access software $url_domain$ was contacted by $src$.
+  message: A domain for a known remote access software $url_domain$ was contacted
+    by $src$.
   mitre_attack_id:
   - T1219
   observable:
@@ -53,17 +82,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.action
-  - Web.src
-  - Web.category
-  - Web.url_domain
   security_domain: network
   manual_test: This detection uses A&I lookups from Enterprise Security.
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1219/screenconnect/screenconnect_palo.log
     source: screenconnect_palo
     sourcetype: pan:threat
diff --git a/detections/web/exploit_public_facing_application_via_apache_commons_text.yml b/detections/web/exploit_public_facing_application_via_apache_commons_text.yml
index 5e1aec50aa..6e691b85f8 100644
--- a/detections/web/exploit_public_facing_application_via_apache_commons_text.yml
+++ b/detections/web/exploit_public_facing_application_via_apache_commons_text.yml
@@ -5,12 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects attempts to exploit the CVE-2022-42889 vulnerability in the Apache Commons Text Library, known as Text4Shell. It leverages the Web datamodel to identify suspicious HTTP requests containing specific lookup keys (url, dns, script) that can lead to Remote Code Execution (RCE). This activity is significant as it targets a critical vulnerability that can allow attackers to execute arbitrary code on the server. If confirmed malicious, this could lead to full system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects attempts to exploit the CVE-2022-42889
+  vulnerability in the Apache Commons Text Library, known as Text4Shell. It leverages
+  the Web datamodel to identify suspicious HTTP requests containing specific lookup
+  keys (url, dns, script) that can lead to Remote Code Execution (RCE). This activity
+  is significant as it targets a critical vulnerability that can allow attackers to
+  execute arbitrary code on the server. If confirmed malicious, this could lead to
+  full system compromise, data exfiltration, or further lateral movement within the
+  network.
 data_source:
 - Nginx Access
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.http_method IN (POST, GET) by Web.src Web.status Web.uri_path Web.dest Web.http_method Web.uri_query Web.http_user_agent | `drop_dm_object_name("Web")` | eval utf=if(like(lower(uri_query),"%:utf-8:http%"),2,0) | eval lookup = if(like(lower(uri_query), "%url%") OR like(lower(uri_query), "%dns%") OR like(lower(uri_query), "%script%"),2,0) | eval other_lookups = if(like(lower(uri_query), "%env%") OR like(lower(uri_query), "%file%") OR like(lower(uri_query), "%getRuntime%") OR like(lower(uri_query), "%java%") OR like(lower(uri_query), "%localhost%") OR like(lower(uri_query), "%properties%") OR like(lower(uri_query), "%resource%") OR like(lower(uri_query), "%sys%") OR like(lower(uri_query), "%xml%") OR like(lower(uri_query), "%base%"),1,0) | addtotals fieldname=Score utf lookup other_lookups | fields Score, src, dest, status, uri_query, uri_path, http_method, http_user_agent firstTime lastTime | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | where Score >= 3 | `exploit_public_facing_application_via_apache_commons_text_filter`'
-how_to_implement: To implement, one must be collecting network traffic that is normalized in CIM and able to be queried via the Web datamodel. Or, take the chunks out needed and tie to a specific network source type to hunt in. Tune as needed, or remove the other_lookups statement.
-known_false_positives: False positives are present when the values are set to 1 for utf and lookup. It's possible to raise this to TTP (direct notable) if removal of other_lookups occur and Score is raised to 2 (down from 4).
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web where Web.http_method IN (POST, GET) by Web.src Web.status
+  Web.uri_path Web.dest Web.http_method Web.uri_query Web.http_user_agent | `drop_dm_object_name("Web")`
+  | eval utf=if(like(lower(uri_query),"%:utf-8:http%"),2,0) | eval lookup = if(like(lower(uri_query),
+  "%url%") OR like(lower(uri_query), "%dns%") OR like(lower(uri_query), "%script%"),2,0)
+  | eval other_lookups = if(like(lower(uri_query), "%env%") OR like(lower(uri_query),
+  "%file%") OR like(lower(uri_query), "%getRuntime%") OR like(lower(uri_query), "%java%")
+  OR like(lower(uri_query), "%localhost%") OR like(lower(uri_query), "%properties%")
+  OR like(lower(uri_query), "%resource%") OR like(lower(uri_query), "%sys%") OR like(lower(uri_query),
+  "%xml%") OR like(lower(uri_query), "%base%"),1,0) | addtotals fieldname=Score utf
+  lookup other_lookups | fields Score, src, dest, status, uri_query, uri_path, http_method,
+  http_user_agent firstTime lastTime | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | where Score >= 3 | `exploit_public_facing_application_via_apache_commons_text_filter`'
+how_to_implement: To implement, one must be collecting network traffic that is normalized
+  in CIM and able to be queried via the Web datamodel. Or, take the chunks out needed
+  and tie to a specific network source type to hunt in. Tune as needed, or remove
+  the other_lookups statement.
+known_false_positives: False positives are present when the values are set to 1 for
+  utf and lookup. It's possible to raise this to TTP (direct notable) if removal of
+  other_lookups occur and Score is raised to 2 (down from 4).
 references:
 - https://sysdig.com/blog/cve-2022-42889-text4shell/
 - https://nvd.nist.gov/vuln/detail/CVE-2022-42889
@@ -24,7 +48,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -54,19 +83,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.status
-  - Web.uri_query
-  - Web.uri_path
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/text4shell/text4shell_nginx.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/text4shell/text4shell_nginx.log
     source: nginx:plus:kv
     sourcetype: nginx:plus:kv
diff --git a/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml b/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml
index 9ed79183f9..1a0192fa4a 100644
--- a/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml
+++ b/detections/web/exploit_public_facing_fortinet_fortinac_cve_2022_39952.yml
@@ -5,12 +5,25 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to exploit the Fortinet FortiNAC CVE-2022-39952 vulnerability. It identifies HTTP POST requests to the URI configWizard/keyUpload.jsp with a payload.zip file. The detection leverages the Web datamodel, analyzing fields such as URL, HTTP method, and user agent. This activity is significant as it indicates an attempt to exploit a known vulnerability, potentially leading to remote code execution. If confirmed malicious, attackers could gain control over the affected system, schedule malicious tasks, and establish persistent access via a remote command and control (C2) server.
+description: The following analytic detects attempts to exploit the Fortinet FortiNAC
+  CVE-2022-39952 vulnerability. It identifies HTTP POST requests to the URI configWizard/keyUpload.jsp
+  with a payload.zip file. The detection leverages the Web datamodel, analyzing fields
+  such as URL, HTTP method, and user agent. This activity is significant as it indicates
+  an attempt to exploit a known vulnerability, potentially leading to remote code
+  execution. If confirmed malicious, attackers could gain control over the affected
+  system, schedule malicious tasks, and establish persistent access via a remote command
+  and control (C2) server.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*configWizard/keyUpload.jsp*") by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exploit_public_facing_fortinet_fortinac_cve_2022_39952_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: False positives may be present. Modify the query as needed to POST, or add additional filtering (based on log source).
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*configWizard/keyUpload.jsp*") by Web.http_user_agent, Web.status
+  Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `exploit_public_facing_fortinet_fortinac_cve_2022_39952_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache, Splunk for Nginx, or Splunk
+  for Palo Alto.
+known_false_positives: False positives may be present. Modify the query as needed
+  to POST, or add additional filtering (based on log source).
 references:
 - https://github.com/horizon3ai/CVE-2022-39952
 - https://www.horizon3.ai/fortinet-fortinac-cve-2022-39952-deep-dive-and-iocs/
@@ -21,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +50,8 @@ tags:
   cve:
   - CVE-2022-39952
   impact: 80
-  message: Potential CVE-2022-39952 against a Fortinet NAC may be occurring against $dest$.
+  message: Potential CVE-2022-39952 against a Fortinet NAC may be occurring against
+    $dest$.
   mitre_attack_id:
   - T1190
   - T1133
@@ -45,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/web_fortinetnac.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/web_fortinetnac.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/f5_tmui_authentication_bypass.yml b/detections/web/f5_tmui_authentication_bypass.yml
index 97c467e59b..e43aef8156 100644
--- a/detections/web/f5_tmui_authentication_bypass.yml
+++ b/detections/web/f5_tmui_authentication_bypass.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects attempts to exploit the CVE-2023-46747 vulnerability, an authentication bypass flaw in F5 BIG-IP's Configuration utility (TMUI). It identifies this activity by monitoring for specific URI paths such as "*/mgmt/tm/auth/user/*" with the PATCH method and a 200 status code. This behavior is significant for a SOC as it indicates potential unauthorized access attempts, leading to remote code execution. If confirmed malicious, an attacker could gain unauthorized access, execute arbitrary code, steal data, disrupt systems, or conduct further malicious activities within the network.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/mgmt/tm/auth/user/*") Web.http_method=PATCH Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `f5_tmui_authentication_bypass_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relevant for traffic into the `Web` datamodel.
-known_false_positives: False positives should be limited to as this is strict to active exploitation. Reduce noise by filtering to F5 devices with TMUI enabled or filter data as needed.
+description: The following analytic detects attempts to exploit the CVE-2023-46747
+  vulnerability, an authentication bypass flaw in F5 BIG-IP's Configuration utility
+  (TMUI). It identifies this activity by monitoring for specific URI paths such as
+  "*/mgmt/tm/auth/user/*" with the PATCH method and a 200 status code. This behavior
+  is significant for a SOC as it indicates potential unauthorized access attempts,
+  leading to remote code execution. If confirmed malicious, an attacker could gain
+  unauthorized access, execute arbitrary code, steal data, disrupt systems, or conduct
+  further malicious activities within the network.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/mgmt/tm/auth/user/*") Web.http_method=PATCH Web.status=200
+  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src,
+  Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| `f5_tmui_authentication_bypass_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relevant for traffic into the `Web` datamodel.
+known_false_positives: False positives should be limited to as this is strict to active
+  exploitation. Reduce noise by filtering to F5 devices with TMUI enabled or filter
+  data as needed.
 references:
 - https://www.praetorian.com/blog/refresh-compromising-f5-big-ip-with-request-smuggling-cve-2023-46747/
 - https://github.com/projectdiscovery/nuclei-templates/blob/3b0bb71bd627c6c3139e1d06c866f8402aa228ae/http/cves/2023/CVE-2023-46747.yaml
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +51,8 @@ tags:
   - CVE-2023-46747
   confidence: 90
   impact: 100
-  message: Potential CVE-2023-46747 F5 TMUI Authentication Bypass may be occurring against $dest$ from $src$.
+  message: Potential CVE-2023-46747 F5 TMUI Authentication Bypass may be occurring
+    against $dest$ from $src$.
   observable:
   - name: dest
     type: Hostname
@@ -46,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: endpoint
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5_tmui.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/f5/f5_tmui.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/fortinet_appliance_auth_bypass.yml b/detections/web/fortinet_appliance_auth_bypass.yml
index b10816899d..470fed8970 100644
--- a/detections/web/fortinet_appliance_auth_bypass.yml
+++ b/detections/web/fortinet_appliance_auth_bypass.yml
@@ -5,12 +5,29 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to exploit CVE-2022-40684, a Fortinet appliance authentication bypass vulnerability. It identifies REST API requests to the /api/v2/ endpoint using various HTTP methods (GET, POST, PUT, DELETE) that may indicate unauthorized modifications, such as adding SSH keys or creating new users. This detection leverages the Web datamodel to monitor specific URL patterns and HTTP methods. This activity is significant as it can lead to unauthorized access and control over the appliance. If confirmed malicious, attackers could gain persistent access, reroute network traffic, or capture sensitive information.
+description: The following analytic detects attempts to exploit CVE-2022-40684, a
+  Fortinet appliance authentication bypass vulnerability. It identifies REST API requests
+  to the /api/v2/ endpoint using various HTTP methods (GET, POST, PUT, DELETE) that
+  may indicate unauthorized modifications, such as adding SSH keys or creating new
+  users. This detection leverages the Web datamodel to monitor specific URL patterns
+  and HTTP methods. This activity is significant as it can lead to unauthorized access
+  and control over the appliance. If confirmed malicious, attackers could gain persistent
+  access, reroute network traffic, or capture sensitive information.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/api/v2/cmdb/system/admin*")  Web.http_method IN ("GET", "PUT") by Web.http_user_agent, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `fortinet_appliance_auth_bypass_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache. Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: GET requests will be noisy and need to be filtered out or removed from the query based on volume. Restrict analytic to known publically facing Fortigates, or run analytic as a Hunt until properly tuned. It is also possible the user agent may be filtered on Report Runner or Node.js only for the exploit, however, it is unknown at this if other user agents may be used.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/api/v2/cmdb/system/admin*")  Web.http_method IN ("GET", "PUT")
+  by Web.http_user_agent, Web.http_method, Web.url, Web.url_length, Web.src, Web.dest,
+  sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `fortinet_appliance_auth_bypass_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache. Splunk for Nginx, or Splunk
+  for Palo Alto.
+known_false_positives: GET requests will be noisy and need to be filtered out or removed
+  from the query based on volume. Restrict analytic to known publically facing Fortigates,
+  or run analytic as a Hunt until properly tuned. It is also possible the user agent
+  may be filtered on Report Runner or Node.js only for the exploit, however, it is
+  unknown at this if other user agents may be used.
 references:
 - https://www.wordfence.com/blog/2022/10/threat-advisory-cve-2022-40684-fortinet-appliance-auth-bypass/
 - https://www.horizon3.ai/fortios-fortiproxy-and-fortiswitchmanager-authentication-bypass-technical-deep-dive-cve-2022-40684/
@@ -24,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -35,7 +57,8 @@ tags:
   cve:
   - CVE-2022-40684
   impact: 90
-  message: Potential CVE-2022-40684 against a Fortinet appliance may be occurring against $dest$.
+  message: Potential CVE-2022-40684 against a Fortinet appliance may be occurring
+    against $dest$.
   mitre_attack_id:
   - T1190
   - T1133
@@ -48,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/fortinetcve202240684.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/fortigate/fortinetcve202240684.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/high_volume_of_bytes_out_to_url.yml b/detections/web/high_volume_of_bytes_out_to_url.yml
index 7cd735eb49..bcb3c8d52c 100644
--- a/detections/web/high_volume_of_bytes_out_to_url.yml
+++ b/detections/web/high_volume_of_bytes_out_to_url.yml
@@ -7,10 +7,26 @@ data_source:
 - Nginx Access
 type: Anomaly
 status: production
-description: The following analytic detects a high volume of outbound web traffic, specifically over 1GB of data sent to a URL within a 2-minute window. It leverages the Web data model to identify significant uploads by analyzing the sum of bytes out. This activity is significant as it may indicate potential data exfiltration by malware or malicious insiders. If confirmed as malicious, this behavior could lead to unauthorized data transfer, resulting in data breaches and loss of sensitive information. Immediate investigation is required to determine the legitimacy of the transfer and mitigate any potential threats.
-search: '| tstats  `security_content_summariesonly` count sum(Web.bytes_out) as sum_bytes_out values(Web.user) as user values(Web.app) as app values(Web.dest) as dest from datamodel=Web by _time span=2m Web.url Web.src sourcetype | search sum_bytes_out > 1070000000 | `drop_dm_object_name("Web")`| `high_volume_of_bytes_out_to_url_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel. Please adjust the threshold for the sum of bytes out as per your environment and user behavior.
-known_false_positives: This search may trigger false positives if there is a legitimate reason for a high volume of bytes out to a URL. We recommend to investigate these findings. Consider updating the filter macro to exclude the applications that are relevant to your environment.
+description: The following analytic detects a high volume of outbound web traffic,
+  specifically over 1GB of data sent to a URL within a 2-minute window. It leverages
+  the Web data model to identify significant uploads by analyzing the sum of bytes
+  out. This activity is significant as it may indicate potential data exfiltration
+  by malware or malicious insiders. If confirmed as malicious, this behavior could
+  lead to unauthorized data transfer, resulting in data breaches and loss of sensitive
+  information. Immediate investigation is required to determine the legitimacy of
+  the transfer and mitigate any potential threats.
+search: '| tstats  `security_content_summariesonly` count sum(Web.bytes_out) as sum_bytes_out
+  values(Web.user) as user values(Web.app) as app values(Web.dest) as dest from datamodel=Web
+  by _time span=2m Web.url Web.src sourcetype | search sum_bytes_out > 1070000000
+  | `drop_dm_object_name("Web")`| `high_volume_of_bytes_out_to_url_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+  Please adjust the threshold for the sum of bytes out as per your environment and
+  user behavior.
+known_false_positives: This search may trigger false positives if there is a legitimate
+  reason for a high volume of bytes out to a URL. We recommend to investigate these
+  findings. Consider updating the filter macro to exclude the applications that are
+  relevant to your environment.
 references:
 - https://attack.mitre.org/techniques/T1567/
 - https://www.trendmicro.com/en_us/research/20/l/pawn-storm-lack-of-sophistication-as-a-strategy.html
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +51,8 @@ tags:
   asset_type: Endpoint
   confidence: 30
   impact: 30
-  message: A high volume of bytes out to a URL $url$ was detected from src $src$ to dest $dest$.
+  message: A high volume of bytes out to a URL $url$ was detected from src $src$ to
+    dest $dest$.
   mitre_attack_id:
   - T1567
   observable:
@@ -46,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/web_upload_nginx/web_upload_nginx.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1567/web_upload_nginx/web_upload_nginx.log
     source: /var/log/nginx/access.log
     sourcetype: nginx:plus:kv
diff --git a/detections/web/hunting_for_log4shell.yml b/detections/web/hunting_for_log4shell.yml
index 12cde1629b..0c133d6377 100644
--- a/detections/web/hunting_for_log4shell.yml
+++ b/detections/web/hunting_for_log4shell.yml
@@ -5,12 +5,36 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic detects potential exploitation attempts of the Log4Shell vulnerability (CVE-2021-44228) by analyzing HTTP headers for specific patterns. It leverages the Web Datamodel and evaluates various indicators such as the presence of `{jndi:`, environment variables, and common URI paths. This detection is significant as Log4Shell allows remote code execution, posing a severe threat to systems. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and potentially compromise sensitive data, leading to extensive damage and data breaches.
+description: The following analytic detects potential exploitation attempts of the
+  Log4Shell vulnerability (CVE-2021-44228) by analyzing HTTP headers for specific
+  patterns. It leverages the Web Datamodel and evaluates various indicators such as
+  the presence of `{jndi:`, environment variables, and common URI paths. This detection
+  is significant as Log4Shell allows remote code execution, posing a severe threat
+  to systems. If confirmed malicious, attackers could gain unauthorized access, execute
+  arbitrary code, and potentially compromise sensitive data, leading to extensive
+  damage and data breaches.
 data_source:
 - Nginx Access
-search: '| from datamodel Web.Web | eval jndi=if(match(_raw, "(\{|%7B)[jJnNdDiI]{4}:"),4,0) | eval jndi_fastmatch=if(match(_raw, "[jJnNdDiI]{4}"),2,0) | eval jndi_proto=if(match(_raw,"(?i)jndi:(ldap[s]?|rmi|dns|nis|iiop|corba|nds|http|https):"),5,0) | eval all_match = if(match(_raw, "(?i)(%(25){0,}20|\s)*(%(25){0,}24|\$)(%(25){0,}20|\s)*(%(25){0,}7B|{)(%(25){0,}20|\s)*(%(25){0,}(6A|4A)|J)(%(25){0,}(6E|4E)|N)(%(25){0,}(64|44)|D)(%(25){0,}(69|49)|I)(%(25){0,}20|\s)*(%(25){0,}3A|:)[\w\%]+(%(25){1,}3A|:)(%(25){1,}2F|\/)[^\n]+"),5,0) | eval env_var = if(match(_raw, "env:") OR match(_raw, "env:AWS_ACCESS_KEY_ID") OR match(_raw, "env:AWS_SECRET_ACCESS_KEY"),5,0) | eval uridetect = if(match(_raw, "(?i)Basic\/Command\/Base64|Basic\/ReverseShell|Basic\/TomcatMemshell|Basic\/JBossMemshell|Basic\/WebsphereMemshell|Basic\/SpringMemshell|Basic\/Command|Deserialization\/CommonsCollectionsK|Deserialization\/CommonsBeanutils|Deserialization\/Jre8u20\/TomcatMemshell|Deserialization\/CVE_2020_2555\/WeblogicMemshell|TomcatBypass|GroovyBypass|WebsphereBypass"),4,0) | eval keywords = if(match(_raw,"(?i)\$\{ctx\:loginId\}|\$\{map\:type\}|\$\{filename\}|\$\{date\:MM-dd-yyyy\}|\$\{docker\:containerId\}|\$\{docker\:containerName\}|\$\{docker\:imageName\}|\$\{env\:USER\}|\$\{event\:Marker\}|\$\{mdc\:UserId\}|\$\{java\:runtime\}|\$\{java\:vm\}|\$\{java\:os\}|\$\{jndi\:logging/context-name\}|\$\{hostName\}|\$\{docker\:containerId\}|\$\{k8s\:accountName\}|\$\{k8s\:clusterName\}|\$\{k8s\:containerId\}|\$\{k8s\:containerName\}|\$\{k8s\:host\}|\$\{k8s\:labels.app\}|\$\{k8s\:labels.podTemplateHash\}|\$\{k8s\:masterUrl\}|\$\{k8s\:namespaceId\}|\$\{k8s\:namespaceName\}|\$\{k8s\:podId\}|\$\{k8s\:podIp\}|\$\{k8s\:podName\}|\$\{k8s\:imageId\}|\$\{k8s\:imageName\}|\$\{log4j\:configLocation\}|\$\{log4j\:configParentLocation\}|\$\{spring\:spring.application.name\}|\$\{main\:myString\}|\$\{main\:0\}|\$\{main\:1\}|\$\{main\:2\}|\$\{main\:3\}|\$\{main\:4\}|\$\{main\:bar\}|\$\{name\}|\$\{marker\}|\$\{marker\:name\}|\$\{spring\:profiles.active[0]|\$\{sys\:logPath\}|\$\{web\:rootDir\}|\$\{sys\:user.name\}"),4,0) | eval obf = if(match(_raw, "(\$|%24)[^ /]*({|%7b)[^ /]*(j|%6a)[^ /]*(n|%6e)[^ /]*(d|%64)[^ /]*(i|%69)[^ /]*(:|%3a)[^ /]*(:|%3a)[^ /]*(/|%2f)"),5,0) | eval lookups = if(match(_raw, "(?i)({|%7b)(main|sys|k8s|spring|lower|upper|env|date|sd)"),4,0)  | addtotals fieldname=Score, jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, obf, lookups | where Score > 2 | stats values(Score) by  jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, lookups, obf, dest, src, http_method, _raw | `hunting_for_log4shell_filter`'
-how_to_implement: Out of the box, the Web datamodel is required to be pre-filled. However, tested was performed against raw httpd access logs. Change the first line to any dataset to pass the regex's against.
-known_false_positives: It is highly possible you will find false positives, however, the base score is set to 2 for _any_ jndi found in raw logs. tune and change as needed, include any filtering.
+search: '| from datamodel Web.Web | eval jndi=if(match(_raw, "(\{|%7B)[jJnNdDiI]{4}:"),4,0)
+  | eval jndi_fastmatch=if(match(_raw, "[jJnNdDiI]{4}"),2,0) | eval jndi_proto=if(match(_raw,"(?i)jndi:(ldap[s]?|rmi|dns|nis|iiop|corba|nds|http|https):"),5,0)
+  | eval all_match = if(match(_raw, "(?i)(%(25){0,}20|\s)*(%(25){0,}24|\$)(%(25){0,}20|\s)*(%(25){0,}7B|{)(%(25){0,}20|\s)*(%(25){0,}(6A|4A)|J)(%(25){0,}(6E|4E)|N)(%(25){0,}(64|44)|D)(%(25){0,}(69|49)|I)(%(25){0,}20|\s)*(%(25){0,}3A|:)[\w\%]+(%(25){1,}3A|:)(%(25){1,}2F|\/)[^\n]+"),5,0)
+  | eval env_var = if(match(_raw, "env:") OR match(_raw, "env:AWS_ACCESS_KEY_ID")
+  OR match(_raw, "env:AWS_SECRET_ACCESS_KEY"),5,0) | eval uridetect = if(match(_raw,
+  "(?i)Basic\/Command\/Base64|Basic\/ReverseShell|Basic\/TomcatMemshell|Basic\/JBossMemshell|Basic\/WebsphereMemshell|Basic\/SpringMemshell|Basic\/Command|Deserialization\/CommonsCollectionsK|Deserialization\/CommonsBeanutils|Deserialization\/Jre8u20\/TomcatMemshell|Deserialization\/CVE_2020_2555\/WeblogicMemshell|TomcatBypass|GroovyBypass|WebsphereBypass"),4,0)
+  | eval keywords = if(match(_raw,"(?i)\$\{ctx\:loginId\}|\$\{map\:type\}|\$\{filename\}|\$\{date\:MM-dd-yyyy\}|\$\{docker\:containerId\}|\$\{docker\:containerName\}|\$\{docker\:imageName\}|\$\{env\:USER\}|\$\{event\:Marker\}|\$\{mdc\:UserId\}|\$\{java\:runtime\}|\$\{java\:vm\}|\$\{java\:os\}|\$\{jndi\:logging/context-name\}|\$\{hostName\}|\$\{docker\:containerId\}|\$\{k8s\:accountName\}|\$\{k8s\:clusterName\}|\$\{k8s\:containerId\}|\$\{k8s\:containerName\}|\$\{k8s\:host\}|\$\{k8s\:labels.app\}|\$\{k8s\:labels.podTemplateHash\}|\$\{k8s\:masterUrl\}|\$\{k8s\:namespaceId\}|\$\{k8s\:namespaceName\}|\$\{k8s\:podId\}|\$\{k8s\:podIp\}|\$\{k8s\:podName\}|\$\{k8s\:imageId\}|\$\{k8s\:imageName\}|\$\{log4j\:configLocation\}|\$\{log4j\:configParentLocation\}|\$\{spring\:spring.application.name\}|\$\{main\:myString\}|\$\{main\:0\}|\$\{main\:1\}|\$\{main\:2\}|\$\{main\:3\}|\$\{main\:4\}|\$\{main\:bar\}|\$\{name\}|\$\{marker\}|\$\{marker\:name\}|\$\{spring\:profiles.active[0]|\$\{sys\:logPath\}|\$\{web\:rootDir\}|\$\{sys\:user.name\}"),4,0)
+  | eval obf = if(match(_raw, "(\$|%24)[^ /]*({|%7b)[^ /]*(j|%6a)[^ /]*(n|%6e)[^ /]*(d|%64)[^
+  /]*(i|%69)[^ /]*(:|%3a)[^ /]*(:|%3a)[^ /]*(/|%2f)"),5,0) | eval lookups = if(match(_raw,
+  "(?i)({|%7b)(main|sys|k8s|spring|lower|upper|env|date|sd)"),4,0)  | addtotals fieldname=Score,
+  jndi, jndi_proto, env_var, uridetect, all_match, jndi_fastmatch, keywords, obf,
+  lookups | where Score > 2 | stats values(Score) by  jndi, jndi_proto, env_var, uridetect,
+  all_match, jndi_fastmatch, keywords, lookups, obf, dest, src, http_method, _raw
+  | `hunting_for_log4shell_filter`'
+how_to_implement: Out of the box, the Web datamodel is required to be pre-filled.
+  However, tested was performed against raw httpd access logs. Change the first line
+  to any dataset to pass the regex's against.
+known_false_positives: It is highly possible you will find false positives, however,
+  the base score is set to 2 for _any_ jndi found in raw logs. tune and change as
+  needed, include any filtering.
 references:
 - https://gist.github.com/olafhartong/916ebc673ba066537740164f7e7e1d72
 - https://gist.github.com/Neo23x0/e4c8b03ff8cdf1fa63b7d15db6e3860b#gistcomment-3994449
@@ -45,13 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - _raw
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/log4shell-nginx.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/log4shell-nginx.log
     source: /var/log/nginx/access.log
     sourcetype: nginx:plus:kv
diff --git a/detections/web/ivanti_connect_secure_command_injection_attempts.yml b/detections/web/ivanti_connect_secure_command_injection_attempts.yml
index 1ca016f75b..201313cd3c 100644
--- a/detections/web/ivanti_connect_secure_command_injection_attempts.yml
+++ b/detections/web/ivanti_connect_secure_command_injection_attempts.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies attempts to exploit the CVE-2023-46805 and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects POST requests to specific URIs that leverage command injection to execute arbitrary commands. The detection uses the Web datamodel to monitor for these requests and checks for a 200 OK response, indicating a successful exploit attempt. This activity is significant as it can lead to unauthorized command execution on the server. If confirmed malicious, attackers could gain control over the system, leading to potential data breaches or further network compromise.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN("*/api/v1/totp/user-backup-code/../../system/maintenance/archiving/cloud-server-test-connection*","*/api/v1/totp/user-backup-code/../../license/keys-status/*") Web.http_method IN ("POST", "GET") Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.http_method, Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_connect_secure_command_injection_attempts_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
+description: The following analytic identifies attempts to exploit the CVE-2023-46805
+  and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects POST requests
+  to specific URIs that leverage command injection to execute arbitrary commands.
+  The detection uses the Web datamodel to monitor for these requests and checks for
+  a 200 OK response, indicating a successful exploit attempt. This activity is significant
+  as it can lead to unauthorized command execution on the server. If confirmed malicious,
+  attackers could gain control over the system, leading to potential data breaches
+  or further network compromise.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN("*/api/v1/totp/user-backup-code/../../system/maintenance/archiving/cloud-server-test-connection*","*/api/v1/totp/user-backup-code/../../license/keys-status/*")
+  Web.http_method IN ("POST", "GET") Web.status=200 by Web.src, Web.dest, Web.http_user_agent,
+  Web.url, Web.http_method, Web.status | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `ivanti_connect_secure_command_injection_attempts_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx,
+  or Splunk for Palo Alto.
+known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary.
+  False positives may occur if the URI path is IP-restricted or externally blocked.
+  It's recommended to review the context of the alerts and adjust the analytic parameters
+  to better fit the specific environment.
 references:
 - https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse
 - https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml
@@ -24,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -50,15 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_exploitphase.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_exploitphase.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml b/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml
index 625a98f383..1a1ccce1aa 100644
--- a/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml
+++ b/detections/web/ivanti_connect_secure_ssrf_in_saml_component.yml
@@ -7,10 +7,26 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies POST requests targeting endpoints vulnerable to the SSRF issue (CVE-2024-21893) in Ivanti's products. It leverages the Web data model, focusing on endpoints such as /dana-ws/saml20.ws, /dana-ws/saml.ws, /dana-ws/samlecp.ws, and /dana-na/auth/saml-logout.cgi. The detection filters for POST requests that received an HTTP 200 OK response, indicating successful execution. This activity is significant as it may indicate an attempt to exploit SSRF vulnerabilities, potentially allowing attackers to access internal services or sensitive data. If confirmed malicious, this could lead to unauthorized access and data exfiltration.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/dana-ws/saml20.ws*","*/dana-ws/saml.ws*","*/dana-ws/samlecp.ws*","*/dana-na/auth/saml-logout.cgi/*") Web.http_method=POST Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status, Web.http_method | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_connect_secure_ssrf_in_saml_component_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the HTTP Status is removed, as most failed attempts result in a 301. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
+description: The following analytic identifies POST requests targeting endpoints vulnerable
+  to the SSRF issue (CVE-2024-21893) in Ivanti's products. It leverages the Web data
+  model, focusing on endpoints such as /dana-ws/saml20.ws, /dana-ws/saml.ws, /dana-ws/samlecp.ws,
+  and /dana-na/auth/saml-logout.cgi. The detection filters for POST requests that
+  received an HTTP 200 OK response, indicating successful execution. This activity
+  is significant as it may indicate an attempt to exploit SSRF vulnerabilities, potentially
+  allowing attackers to access internal services or sensitive data. If confirmed malicious,
+  this could lead to unauthorized access and data exfiltration.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/dana-ws/saml20.ws*","*/dana-ws/saml.ws*","*/dana-ws/samlecp.ws*","*/dana-na/auth/saml-logout.cgi/*")
+  Web.http_method=POST Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url,
+  Web.status, Web.http_method | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `ivanti_connect_secure_ssrf_in_saml_component_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx,
+  or Splunk for Palo Alto.
+known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary.
+  False positives may occur if the HTTP Status is removed, as most failed attempts
+  result in a 301. It's recommended to review the context of the alerts and adjust
+  the analytic parameters to better fit the specific environment.
 references:
 - https://attackerkb.com/topics/FGlK1TVnB2/cve-2024-21893/rapid7-analysis
 - https://www.assetnote.io/resources/research/ivantis-pulse-connect-secure-auth-bypass-round-two
@@ -20,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,17 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
-  - Web.status
-  - Web.http_method
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_saml.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_saml.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml b/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml
index 059c005bbe..79b84375b2 100644
--- a/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml
+++ b/detections/web/ivanti_connect_secure_system_information_access_via_auth_bypass.yml
@@ -7,10 +7,26 @@ status: production
 type: Anomaly
 data_source:
 - Suricata
-description: The following analytic identifies attempts to exploit the CVE-2023-46805 and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects GET requests to the /api/v1/totp/user-backup-code/../../system/system-information URI, which leverage an authentication bypass to access system information. The detection uses the Web datamodel to identify requests with a 200 OK response, indicating a successful exploit attempt. This activity is significant as it reveals potential unauthorized access to sensitive system information. If confirmed malicious, attackers could gain critical insights into the system, facilitating further exploitation and compromise.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="*/api/v1/totp/user-backup-code/../../system/system-information*" Web.http_method=GET Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_connect_secure_system_information_access_via_auth_bypass_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto.
-known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary. False positives may occur if the URI path is IP-restricted or externally blocked. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
+description: The following analytic identifies attempts to exploit the CVE-2023-46805
+  and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects GET requests
+  to the /api/v1/totp/user-backup-code/../../system/system-information URI, which
+  leverage an authentication bypass to access system information. The detection uses
+  the Web datamodel to identify requests with a 200 OK response, indicating a successful
+  exploit attempt. This activity is significant as it reveals potential unauthorized
+  access to sensitive system information. If confirmed malicious, attackers could
+  gain critical insights into the system, facilitating further exploitation and compromise.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url="*/api/v1/totp/user-backup-code/../../system/system-information*"
+  Web.http_method=GET Web.status=200 by Web.src, Web.dest, Web.http_user_agent, Web.url
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `ivanti_connect_secure_system_information_access_via_auth_bypass_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx,
+  or Splunk for Palo Alto.
+known_false_positives: This analytic is limited to HTTP Status 200; adjust as necessary.
+  False positives may occur if the URI path is IP-restricted or externally blocked.
+  It's recommended to review the context of the alerts and adjust the analytic parameters
+  to better fit the specific environment.
 references:
 - https://github.com/RootUp/PersonalStuff/blob/master/http-vuln-cve2023-46805_2024_21887.nse
 - https://github.com/projectdiscovery/nuclei-templates/blob/c6b351e71b0fb0e40e222e97038f1fe09ac58194/http/misconfiguration/ivanti/CVE-2023-46085-CVE-2024-21887-mitigation-not-applied.yaml
@@ -21,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,15 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_checkphase.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_secure_connect_checkphase.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml b/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml
index af9be5b8f9..2dc447ed0b 100644
--- a/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml
+++ b/detections/web/ivanti_epm_sql_injection_remote_code_execution.yml
@@ -7,10 +7,36 @@ type: TTP
 status: production
 data_source:
 - Suricata
-description: This detection identifies potential exploitation of a critical SQL injection vulnerability in Ivanti Endpoint Manager (EPM), identified as CVE-2024-29824. The vulnerability, which has a CVSS score of 9.8, allows for remote code execution through the `RecordGoodApp` function in the `PatchBiz.dll` file. An attacker can exploit this vulnerability by manipulating the `goodApp.md5` value in an HTTP POST request to the `/WSStatusEvents/EventHandler.asmx` endpoint, leading to unauthorized command execution on the server. Monitoring for unusual SQL commands and HTTP requests to this endpoint can help identify exploitation attempts. Note that, the detection is focused on the URI path, HTTP method and status code of 200, indicating potential exploitation. To properly identify if this was successful, TLS inspection and additional network traffic analysis is required as the xp_cmdshell comes in via the request body.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/WSStatusEvents/EventHandler.asmx") Web.http_method=POST Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_epm_sql_injection_remote_code_execution_filter`'
-how_to_implement: The detection is based on monitoring HTTP POST requests to the `/WSStatusEvents/EventHandler.asmx` endpoint with a status code of 200. The detection is focused on the URI path, HTTP method, and status code, which can indicate potential exploitation of the CVE-2024-29824 vulnerability. To implement this detection, ensure that you have enabled the necessary data sources and are ingesting HTTP traffic data. The detection can be implemented using Splunk Enterprise Security and Splunk Cloud with the provided search query. The search query should be scheduled to run at regular intervals to detect potential exploitation attempts. Additionally, consider implementing TLS inspection or network traffic analysis (IDS/IPS) to identify successful exploitation attempts.
-known_false_positives: False positives are not expected, as this detection is based on monitoring HTTP POST requests to a specific endpoint with a status code of 200. However, ensure that legitimate requests to the `/WSStatusEvents/EventHandler.asmx` endpoint are accounted for in the environment to avoid false positives.
+description: This detection identifies potential exploitation of a critical SQL injection
+  vulnerability in Ivanti Endpoint Manager (EPM), identified as CVE-2024-29824. The
+  vulnerability, which has a CVSS score of 9.8, allows for remote code execution through
+  the `RecordGoodApp` function in the `PatchBiz.dll` file. An attacker can exploit
+  this vulnerability by manipulating the `goodApp.md5` value in an HTTP POST request
+  to the `/WSStatusEvents/EventHandler.asmx` endpoint, leading to unauthorized command
+  execution on the server. Monitoring for unusual SQL commands and HTTP requests to
+  this endpoint can help identify exploitation attempts. Note that, the detection
+  is focused on the URI path, HTTP method and status code of 200, indicating potential
+  exploitation. To properly identify if this was successful, TLS inspection and additional
+  network traffic analysis is required as the xp_cmdshell comes in via the request
+  body.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/WSStatusEvents/EventHandler.asmx") Web.http_method=POST Web.status=200
+  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src,
+  Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `ivanti_epm_sql_injection_remote_code_execution_filter`'
+how_to_implement: The detection is based on monitoring HTTP POST requests to the `/WSStatusEvents/EventHandler.asmx`
+  endpoint with a status code of 200. The detection is focused on the URI path, HTTP
+  method, and status code, which can indicate potential exploitation of the CVE-2024-29824
+  vulnerability. To implement this detection, ensure that you have enabled the necessary
+  data sources and are ingesting HTTP traffic data. The detection can be implemented
+  using Splunk Enterprise Security and Splunk Cloud with the provided search query.
+  The search query should be scheduled to run at regular intervals to detect potential
+  exploitation attempts. Additionally, consider implementing TLS inspection or network
+  traffic analysis (IDS/IPS) to identify successful exploitation attempts.
+known_false_positives: False positives are not expected, as this detection is based
+  on monitoring HTTP POST requests to a specific endpoint with a status code of 200.
+  However, ensure that legitimate requests to the `/WSStatusEvents/EventHandler.asmx`
+  endpoint are accounted for in the environment to avoid false positives.
 references:
 - https://www.horizon3.ai/attack-research/attack-blogs/cve-2024-29824-deep-dive-ivanti-epm-sql-injection-remote-code-execution-vulnerability/
 - https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-29824
@@ -21,7 +47,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +61,8 @@ tags:
   asset_type: Web Server
   confidence: 80
   impact: 100
-  message: Potential exploitation of a critical SQL injection vulnerability in Ivanti Endpoint Manager (EPM), identified as CVE-2024-29824 against $dest$.
+  message: Potential exploitation of a critical SQL injection vulnerability in Ivanti
+    Endpoint Manager (EPM), identified as CVE-2024-29824 against $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -42,21 +74,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
   cve:
   - CVE-2024-29824
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_epm.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_epm.log
     sourcetype: suricata
     source: suricata
diff --git a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml
index 416f12e8cd..a7b8f67ea4 100644
--- a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml
+++ b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects attempts to exploit CVE-2023-35078, a vulnerability in Ivanti Endpoint Manager Mobile (EPMM) versions up to 11.4. It identifies HTTP requests to the endpoint "/mifs/aad/api/v2/authorized/users?*" with a status code of 200 in web logs. This activity is significant as it indicates unauthorized remote access to restricted functionalities or resources. If confirmed malicious, this could lead to data theft, unauthorized modifications, or further system compromise, necessitating immediate action to mitigate potential severe impacts.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/mifs/aad/api/v2/authorized/users?*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078_filter`'
-how_to_implement: To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products.
-known_false_positives: The Proof of Concept exploit script indicates that status=200 is required for successful exploitation of the vulnerability. False positives may be present if status=200 is removed from the search.  If it is removed,then the search also alert on status=301 and status=404 which indicates unsuccessful exploitation attempts.  Analysts may find it useful to hunt for these status codes as well, but it is likely to produce a significant number of alerts as this is a widespread vulnerability.
+description: The following analytic detects attempts to exploit CVE-2023-35078, a
+  vulnerability in Ivanti Endpoint Manager Mobile (EPMM) versions up to 11.4. It identifies
+  HTTP requests to the endpoint "/mifs/aad/api/v2/authorized/users?*" with a status
+  code of 200 in web logs. This activity is significant as it indicates unauthorized
+  remote access to restricted functionalities or resources. If confirmed malicious,
+  this could lead to data theft, unauthorized modifications, or further system compromise,
+  necessitating immediate action to mitigate potential severe impacts.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/mifs/aad/api/v2/authorized/users?*") Web.status=200 by Web.http_user_agent,
+  Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35078_filter`'
+how_to_implement: To implement this analytic, a network product similar to Suricata
+  or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work
+  with your products.
+known_false_positives: The Proof of Concept exploit script indicates that status=200
+  is required for successful exploitation of the vulnerability. False positives may
+  be present if status=200 is removed from the search.  If it is removed,then the
+  search also alert on status=301 and status=404 which indicates unsuccessful exploitation
+  attempts.  Analysts may find it useful to hunt for these status codes as well, but
+  it is likely to produce a significant number of alerts as this is a widespread vulnerability.
 references:
 - https://forums.ivanti.com/s/article/CVE-2023-35078-Remote-unauthenticated-API-access-vulnerability?language=en_US
 - https://github.com/vchan-in/CVE-2023-35078-Exploit-POC/blob/main/cve_2023_35078_poc.py
@@ -20,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -45,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335078.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335078.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml
index 782a025e21..cc0caee118 100644
--- a/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml
+++ b/detections/web/ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082.yml
@@ -7,10 +7,29 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects potential unauthorized access attempts exploiting CVE-2023-35082 within Ivanti's software products. It identifies access to the specific URI path /mifs/asfV3/api/v2/ with an HTTP 200 response code in web access logs, indicating successful unauthorized access. This activity is significant for a SOC as it highlights potential security breaches that could lead to unauthorized data access or system modifications. If confirmed malicious, an attacker could gain unbridled access to sensitive organizational data or modify systems maliciously, posing severe security risks.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/mifs/asfV3/api/v2/*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082_filter`'
-how_to_implement: To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products.
-known_false_positives: Similar to CVE-2023-35078, the path for exploitation indicates that status=200 is required for successful exploitation of the vulnerability. False positives may be present if status=200 is removed from the search.  If it is removed,then the search also alert on status=301 and status=404 which indicates unsuccessful exploitation attempts.  Analysts may find it useful to hunt for these status codes as well, but it is likely to produce a significant number of alerts as this is a widespread vulnerability.
+description: The following analytic detects potential unauthorized access attempts
+  exploiting CVE-2023-35082 within Ivanti's software products. It identifies access
+  to the specific URI path /mifs/asfV3/api/v2/ with an HTTP 200 response code in web
+  access logs, indicating successful unauthorized access. This activity is significant
+  for a SOC as it highlights potential security breaches that could lead to unauthorized
+  data access or system modifications. If confirmed malicious, an attacker could gain
+  unbridled access to sensitive organizational data or modify systems maliciously,
+  posing severe security risks.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/mifs/asfV3/api/v2/*") Web.status=200 by Web.http_user_agent,
+  Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `ivanti_epmm_remote_unauthenticated_api_access_cve_2023_35082_filter`'
+how_to_implement: To implement this analytic, a network product similar to Suricata
+  or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work
+  with your products.
+known_false_positives: Similar to CVE-2023-35078, the path for exploitation indicates
+  that status=200 is required for successful exploitation of the vulnerability. False
+  positives may be present if status=200 is removed from the search.  If it is removed,then
+  the search also alert on status=301 and status=404 which indicates unsuccessful
+  exploitation attempts.  Analysts may find it useful to hunt for these status codes
+  as well, but it is likely to produce a significant number of alerts as this is a
+  widespread vulnerability.
 references:
 - https://forums.ivanti.com/s/article/CVE-2023-35082-Remote-Unauthenticated-API-Access-Vulnerability-in-MobileIron-Core-11-2-and-older?language=en_US
 - https://github.com/vchan-in/CVE-2023-35078-Exploit-POC/blob/main/cve_2023_35078_poc.py
@@ -21,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,17 +70,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335082.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/suricata_ivanti_CVE202335082.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/ivanti_sentry_authentication_bypass.yml b/detections/web/ivanti_sentry_authentication_bypass.yml
index 9ebcb8b9e1..0785e8239f 100644
--- a/detections/web/ivanti_sentry_authentication_bypass.yml
+++ b/detections/web/ivanti_sentry_authentication_bypass.yml
@@ -7,10 +7,27 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic identifies unauthenticated access attempts to the System Manager Portal in Ivanti Sentry, exploiting CVE-2023-38035. It detects this activity by monitoring HTTP requests to specific endpoints ("/mics/services/configservice/*", "/mics/services/*", "/mics/services/MICSLogService*") with a status code of 200. This behavior is significant for a SOC as it indicates potential unauthorized access, which could lead to OS command execution as root. If confirmed malicious, this activity could result in significant system compromise and data breaches, especially if port 8443 is exposed to the internet.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ivanti_sentry_authentication_bypass_filter`'
-how_to_implement: To implement this analytic, a network product similar to Suricata or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work with your products.
-known_false_positives: It is important to note that false positives may occur if the search criteria are expanded beyond the HTTP status code 200. In other words, if the search includes other HTTP status codes, the likelihood of encountering false positives increases. This is due to the fact that HTTP status codes other than 200 may not necessarily indicate a successful exploitation attempt.
+description: The following analytic identifies unauthenticated access attempts to
+  the System Manager Portal in Ivanti Sentry, exploiting CVE-2023-38035. It detects
+  this activity by monitoring HTTP requests to specific endpoints ("/mics/services/configservice/*",
+  "/mics/services/*", "/mics/services/MICSLogService*") with a status code of 200.
+  This behavior is significant for a SOC as it indicates potential unauthorized access,
+  which could lead to OS command execution as root. If confirmed malicious, this activity
+  could result in significant system compromise and data breaches, especially if port
+  8443 is exposed to the internet.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/mics/services/configservice/*", "/mics/services/*","/mics/services/MICSLogService*")
+  Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length,
+  Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `ivanti_sentry_authentication_bypass_filter`'
+how_to_implement: To implement this analytic, a network product similar to Suricata
+  or Palo Alto needs to be mapped to the Web datamodel. Modify accordingly to work
+  with your products.
+known_false_positives: It is important to note that false positives may occur if the
+  search criteria are expanded beyond the HTTP status code 200. In other words, if
+  the search includes other HTTP status codes, the likelihood of encountering false
+  positives increases. This is due to the fact that HTTP status codes other than 200
+  may not necessarily indicate a successful exploitation attempt.
 references:
 - https://github.com/horizon3ai/CVE-2023-38035/blob/main/CVE-2023-38035.py
 - https://www.horizon3.ai/ivanti-sentry-authentication-bypass-cve-2023-38035-deep-dive/
@@ -21,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,19 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_sentry_CVE_2023_38035.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ivanti/ivanti_sentry_CVE_2023_38035.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/java_class_file_download_by_java_user_agent.yml b/detections/web/java_class_file_download_by_java_user_agent.yml
index d4ee8b6561..673dcd2066 100644
--- a/detections/web/java_class_file_download_by_java_user_agent.yml
+++ b/detections/web/java_class_file_download_by_java_user_agent.yml
@@ -5,13 +5,19 @@ date: '2024-10-16'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies a Java user agent performing a GET request for a .class file from a remote site. It leverages web or proxy logs within the Web Datamodel to detect this activity. This behavior is significant as it may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). If confirmed malicious, an attacker could exploit vulnerabilities in the Java application, potentially leading to remote code execution and further compromise of the affected system.
+description: The following analytic identifies a Java user agent performing a GET
+  request for a .class file from a remote site. It leverages web or proxy logs within
+  the Web Datamodel to detect this activity. This behavior is significant as it may
+  indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell).
+  If confirmed malicious, an attacker could exploit vulnerabilities in the Java application,
+  potentially leading to remote code execution and further compromise of the affected
+  system.
 data_source:
 - Splunk Stream HTTP
-search: '| tstats  `security_content_summariesonly` count from datamodel=Web where Web.http_user_agent="*Java*" Web.http_method="GET"
-  Web.url="*.class*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length
-  Web.src, Web.dest | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
-  | `security_content_ctime(lastTime)` | `java_class_file_download_by_java_user_agent_filter`'
+search: '| tstats  `security_content_summariesonly` count from datamodel=Web where
+  Web.http_user_agent="*Java*" Web.http_method="GET" Web.url="*.class*" by Web.http_user_agent
+  Web.http_method, Web.url,Web.url_length Web.src, Web.dest | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `java_class_file_download_by_java_user_agent_filter`'
 how_to_implement: To successfully implement this search, you need to be ingesting
   web or proxy logs, or ensure it is being filled by a proxy like device, into the
   Web Datamodel. For additional filtering, allow list private IP space or restrict
@@ -25,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +47,8 @@ tags:
   cve:
   - CVE-2021-44228
   impact: 80
-  message: A Java user agent $http_user_agent$ was performing a $http_method$ to retrieve a remote class file.
+  message: A Java user agent $http_user_agent$ was performing a $http_method$ to retrieve
+    a remote class file.
   mitre_attack_id:
   - T1190
   observable:
@@ -52,18 +64,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/java/java.log
     source: stream:http
     sourcetype: stream:http
diff --git a/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml b/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml
index 880fe3fe9c..70a21af677 100644
--- a/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml
+++ b/detections/web/jenkins_arbitrary_file_read_cve_2024_23897.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Nginx Access
-description: The following analytic identifies attempts to exploit Jenkins Arbitrary File Read CVE-2024-23897. It detects HTTP POST requests to Jenkins URLs containing "*/cli?remoting=false*" with a 200 status code. This activity is significant as it indicates potential unauthorized access to sensitive files on the Jenkins server, such as credentials and private keys. If confirmed malicious, this could lead to severe data breaches, unauthorized access, and further exploitation within the environment.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url="*/cli?remoting=false*" Web.status=200 Web.http_method=POST by Web.src, Web.dest, Web.http_user_agent, Web.url Web.status, Web.http_method | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jenkins_arbitrary_file_read_cve_2024_23897_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx, or Splunk for Palo Alto. If unable to utilize the Web datamodel, modify query to your data source.
-known_false_positives: False positives should be limited as this detection is based on a specific URL path and HTTP status code. Adjust the search as necessary to fit the environment.
+description: The following analytic identifies attempts to exploit Jenkins Arbitrary
+  File Read CVE-2024-23897. It detects HTTP POST requests to Jenkins URLs containing
+  "*/cli?remoting=false*" with a 200 status code. This activity is significant as
+  it indicates potential unauthorized access to sensitive files on the Jenkins server,
+  such as credentials and private keys. If confirmed malicious, this could lead to
+  severe data breaches, unauthorized access, and further exploitation within the environment.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url="*/cli?remoting=false*" Web.status=200 Web.http_method=POST by Web.src,
+  Web.dest, Web.http_user_agent, Web.url Web.status, Web.http_method | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jenkins_arbitrary_file_read_cve_2024_23897_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Suricata, Splunk for Apache, Splunk for Nginx,
+  or Splunk for Palo Alto. If unable to utilize the Web datamodel, modify query to
+  your data source.
+known_false_positives: False positives should be limited as this detection is based
+  on a specific URL path and HTTP status code. Adjust the search as necessary to fit
+  the environment.
 references:
 - https://github.com/projectdiscovery/nuclei-templates/pull/9025
 - https://github.com/jenkinsci-cert/SECURITY-3314-3315
@@ -25,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -53,17 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
-  - Web.status
-  - Web.http_method
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jenkins/nginx_jenkins_cve_2023_23897.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jenkins/nginx_jenkins_cve_2023_23897.log
     source: nginx:plus:kv
     sourcetype: nginx:plus:kv
diff --git a/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml b/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml
index 975e1ff580..a80160821c 100644
--- a/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml
+++ b/detections/web/jetbrains_teamcity_authentication_bypass_cve_2024_27198.yml
@@ -7,10 +7,27 @@ data_source:
 - Suricata
 type: TTP
 status: production
-description: The following analytic identifies attempts to exploit the JetBrains TeamCity Authentication Bypass vulnerability (CVE-2024-27198). It detects suspicious POST requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` endpoints, which are indicative of attempts to create new administrator users or generate admin access tokens without authentication. This detection leverages the Web datamodel and CIM-compliant log sources, such as Nginx or TeamCity logs. This activity is significant as it can lead to full control over the TeamCity server, including all projects, builds, agents, and artifacts. If confirmed malicious, attackers could gain unauthorized administrative access, leading to severe security breaches.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where ((Web.url="*?jsp=*" AND Web.url="*;.jsp*") Web.status=200 Web.http_method=POST) OR (Web.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*") Web.status=200 Web.http_method=POST ) by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.status, Web.http_method, sourcetype, source | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `jetbrains_teamcity_authentication_bypass_cve_2024_27198_filter`'
-how_to_implement: The detection relies on the Web datamodel and a CIM compliant log source, that may include Nginx, TeamCity logs, or other web server logs.
-known_false_positives: False positives are not expected, as this detection is based on the presence of specific URI paths and HTTP methods that are indicative of the CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based on organization log sources.
+description: The following analytic identifies attempts to exploit the JetBrains TeamCity
+  Authentication Bypass vulnerability (CVE-2024-27198). It detects suspicious POST
+  requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` endpoints, which
+  are indicative of attempts to create new administrator users or generate admin access
+  tokens without authentication. This detection leverages the Web datamodel and CIM-compliant
+  log sources, such as Nginx or TeamCity logs. This activity is significant as it
+  can lead to full control over the TeamCity server, including all projects, builds,
+  agents, and artifacts. If confirmed malicious, attackers could gain unauthorized
+  administrative access, leading to severe security breaches.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where ((Web.url="*?jsp=*" AND Web.url="*;.jsp*") Web.status=200 Web.http_method=POST)
+  OR (Web.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*")
+  Web.status=200 Web.http_method=POST ) by Web.src, Web.dest, Web.http_user_agent,
+  Web.url, Web.status, Web.http_method, sourcetype, source | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `jetbrains_teamcity_authentication_bypass_cve_2024_27198_filter`'
+how_to_implement: The detection relies on the Web datamodel and a CIM compliant log
+  source, that may include Nginx, TeamCity logs, or other web server logs.
+known_false_positives: False positives are not expected, as this detection is based
+  on the presence of specific URI paths and HTTP methods that are indicative of the
+  CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based
+  on organization log sources.
 references:
 - https://github.com/projectdiscovery/nuclei-templates/pull/9279/files
 - https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/
@@ -23,7 +40,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +54,8 @@ tags:
   asset_type: Web Server
   confidence: 90
   impact: 90
-  message: Possible JetBrains TeamCity Authentication Bypass CVE-2024-27198 Attempt against $dest$ from $src$.
+  message: Possible JetBrains TeamCity Authentication Bypass CVE-2024-27198 Attempt
+    against $dest$ from $src$.
   mitre_attack_id:
   - T1190
   observable:
@@ -48,21 +71,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
-  - Web.status
-  - Web.http_method
-  - sourcetype
-  - source
   security_domain: network
   cve:
   - CVE-2024-27198
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log
     sourcetype: suricata
     source: suricata
diff --git a/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml b/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml
index 379ee36fb1..d3fd727b51 100644
--- a/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml
+++ b/detections/web/jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198.yml
@@ -7,10 +7,26 @@ data_source:
 - Suricata
 type: TTP
 status: production
-description: The following analytic detects attempts to exploit the CVE-2024-27198 vulnerability in JetBrains TeamCity on-premises servers, which allows attackers to bypass authentication mechanisms. It leverages Suricata HTTP traffic logs to identify suspicious POST requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens` endpoints. This activity is significant because it can lead to unauthorized administrative access, enabling attackers to gain full control over the TeamCity server, including projects, builds, agents, and artifacts. If confirmed malicious, this could result in severe security breaches and compromise the integrity of the development environment.
-search: '`suricata` ((http.url="*?jsp=*" AND http.url="*;.jsp*") http.status=200 http_method=POST) OR (http.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*") http.status=200 http_method=POST ) | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, http.http_user_agent, http.url, http.status,http_method | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198_filter`'
-how_to_implement: The following detection relies on the Suricata TA and ensuring it is properly configured to monitor HTTP traffic. Modify the query for your environment and log sources as needed.
-known_false_positives: False positives are not expected, as this detection is based on the presence of specific URI paths and HTTP methods that are indicative of the CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based on organization log sources.
+description: The following analytic detects attempts to exploit the CVE-2024-27198
+  vulnerability in JetBrains TeamCity on-premises servers, which allows attackers
+  to bypass authentication mechanisms. It leverages Suricata HTTP traffic logs to
+  identify suspicious POST requests to the `/app/rest/users` and `/app/rest/users/id:1/tokens`
+  endpoints. This activity is significant because it can lead to unauthorized administrative
+  access, enabling attackers to gain full control over the TeamCity server, including
+  projects, builds, agents, and artifacts. If confirmed malicious, this could result
+  in severe security breaches and compromise the integrity of the development environment.
+search: '`suricata` ((http.url="*?jsp=*" AND http.url="*;.jsp*") http.status=200 http_method=POST)
+  OR (http.url IN ("*jsp=/app/rest/users;.jsp","*?jsp=/app/rest/users;.jsp","*?jsp=.*/app/rest/users/id:*/tokens;*")
+  http.status=200 http_method=POST ) | stats count min(_time) as firstTime max(_time)
+  as lastTime by src, dest, http.http_user_agent, http.url, http.status,http_method
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| `jetbrains_teamcity_authentication_bypass_suricata_cve_2024_27198_filter`'
+how_to_implement: The following detection relies on the Suricata TA and ensuring it
+  is properly configured to monitor HTTP traffic. Modify the query for your environment
+  and log sources as needed.
+known_false_positives: False positives are not expected, as this detection is based
+  on the presence of specific URI paths and HTTP methods that are indicative of the
+  CVE-2024-27198 vulnerability exploitation. Monitor, filter and tune as needed based
+  on organization log sources.
 references:
 - https://github.com/projectdiscovery/nuclei-templates/pull/9279/files
 - https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/
@@ -22,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +52,8 @@ tags:
   asset_type: Web Server
   confidence: 90
   impact: 90
-  message: Possible JetBrains TeamCity Authentication Bypass Attempt against $dest$ from $src$.
+  message: Possible JetBrains TeamCity Authentication Bypass Attempt against $dest$
+    from $src$.
   mitre_attack_id:
   - T1190
   observable:
@@ -47,19 +69,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src
-  - dest
-  - http.http_user_agent
-  - http.url
-  - http.status
-  - http_method
   security_domain: network
   cve:
   - CVE-2024-27198
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27198.log
     sourcetype: suricata
     source: suricata
diff --git a/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml b/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml
index 1c51d2876d..371f241173 100644
--- a/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml
+++ b/detections/web/jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199.yml
@@ -7,10 +7,31 @@ data_source:
 - Suricata
 type: TTP
 status: production
-description: The following analytic identifies attempts to exploit CVE-2024-27199, a critical vulnerability in JetBrains TeamCity web server, allowing unauthenticated access to specific endpoints. It detects unusual access patterns to vulnerable paths such as /res/, /update/, and /.well-known/acme-challenge/ by monitoring HTTP traffic logs via Suricata. This activity is significant as it could indicate an attacker bypassing authentication to access or modify system settings. If confirmed malicious, this could lead to unauthorized changes, disclosure of sensitive information, or uploading of malicious certificates, severely compromising the server's security.
-search: '`suricata` http.url IN ("*../admin/diagnostic.jsp*", "*../app/https/settings/*", "*../app/pipeline*", "*../app/oauth/space/createBuild.html*", "*../res/*", "*../update/*", "*../.well-known/acme-challenge/*", "*../app/availableRunners*", "*../app/https/settings/setPort*", "*../app/https/settings/certificateInfo*", "*../app/https/settings/defaultHttpsPort*", "*../app/https/settings/fetchFromAcme*", "*../app/https/settings/removeCertificate*", "*../app/https/settings/uploadCertificate*", "*../app/https/settings/termsOfService*", "*../app/https/settings/triggerAcmeChallenge*", "*../app/https/settings/cancelAcmeChallenge*", "*../app/https/settings/getAcmeOrder*", "*../app/https/settings/setRedirectStrategy*") http.status=200 http_method=GET | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, http_user_agent, http.url, http.status, http_method | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199_filter`'
-how_to_implement: The following detection relies on the Suricata TA and ensuring it is properly configured to monitor HTTP traffic. Modify the query for your environment and log sources as needed.
-known_false_positives: False positives are not expected, however, monitor, filter, and tune as needed based on organization log sources. The analytic is restricted to 200 and GET requests to specific URI paths, which should limit false positives.
+description: The following analytic identifies attempts to exploit CVE-2024-27199,
+  a critical vulnerability in JetBrains TeamCity web server, allowing unauthenticated
+  access to specific endpoints. It detects unusual access patterns to vulnerable paths
+  such as /res/, /update/, and /.well-known/acme-challenge/ by monitoring HTTP traffic
+  logs via Suricata. This activity is significant as it could indicate an attacker
+  bypassing authentication to access or modify system settings. If confirmed malicious,
+  this could lead to unauthorized changes, disclosure of sensitive information, or
+  uploading of malicious certificates, severely compromising the server's security.
+search: '`suricata` http.url IN ("*../admin/diagnostic.jsp*", "*../app/https/settings/*",
+  "*../app/pipeline*", "*../app/oauth/space/createBuild.html*", "*../res/*", "*../update/*",
+  "*../.well-known/acme-challenge/*", "*../app/availableRunners*", "*../app/https/settings/setPort*",
+  "*../app/https/settings/certificateInfo*", "*../app/https/settings/defaultHttpsPort*",
+  "*../app/https/settings/fetchFromAcme*", "*../app/https/settings/removeCertificate*",
+  "*../app/https/settings/uploadCertificate*", "*../app/https/settings/termsOfService*",
+  "*../app/https/settings/triggerAcmeChallenge*", "*../app/https/settings/cancelAcmeChallenge*",
+  "*../app/https/settings/getAcmeOrder*", "*../app/https/settings/setRedirectStrategy*")
+  http.status=200 http_method=GET | stats count min(_time) as firstTime max(_time)
+  as lastTime by src, dest, http_user_agent, http.url, http.status, http_method |
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jetbrains_teamcity_limited_auth_bypass_suricata_cve_2024_27199_filter`'
+how_to_implement: The following detection relies on the Suricata TA and ensuring it
+  is properly configured to monitor HTTP traffic. Modify the query for your environment
+  and log sources as needed.
+known_false_positives: False positives are not expected, however, monitor, filter,
+  and tune as needed based on organization log sources. The analytic is restricted
+  to 200 and GET requests to specific URI paths, which should limit false positives.
 references:
 - https://github.com/projectdiscovery/nuclei-templates/blob/f644ec82dfe018890c6aa308967424d26c0f1522/http/cves/2024/CVE-2024-27199.yaml
 - https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/
@@ -22,7 +43,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +57,8 @@ tags:
   asset_type: Web Server
   confidence: 70
   impact: 90
-  message: Possible JetBrains TeamCity Limited Authentication Bypass Attempt against $dest$ from $src$.
+  message: Possible JetBrains TeamCity Limited Authentication Bypass Attempt against
+    $dest$ from $src$.
   mitre_attack_id:
   - T1190
   observable:
@@ -47,19 +74,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - src
-  - dest
-  - http_user_agent
-  - http.url
-  - http.status
-  - http_method
   security_domain: network
   cve:
   - CVE-2024-27199
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27199.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity_cve_2024_27199.log
     sourcetype: suricata
     source: suricata
diff --git a/detections/web/jetbrains_teamcity_rce_attempt.yml b/detections/web/jetbrains_teamcity_rce_attempt.yml
index 05e5eb3741..c1caf56ebf 100644
--- a/detections/web/jetbrains_teamcity_rce_attempt.yml
+++ b/detections/web/jetbrains_teamcity_rce_attempt.yml
@@ -7,10 +7,23 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects attempts to exploit the CVE-2023-42793 vulnerability in JetBrains TeamCity On-Premises. It identifies suspicious POST requests to /app/rest/users/id:1/tokens/RPC2, leveraging the Web datamodel to monitor specific URL patterns and HTTP methods. This activity is significant as it may indicate an unauthenticated attacker attempting to gain administrative access via Remote Code Execution (RCE). If confirmed malicious, this could allow the attacker to execute arbitrary code, potentially compromising the entire TeamCity environment and leading to further unauthorized access and data breaches.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/app/rest/users/id:1/tokens/RPC2*") Web.status=200 Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `jetbrains_teamcity_rce_attempt_filter`'
-how_to_implement: The following analytic requires the Web datamodel. Ensure data source is mapped correctly or modify and tune for your data source.
-known_false_positives: If TeamCity is not in use, this analytic will not return results. Monitor and tune for your environment.
+description: The following analytic detects attempts to exploit the CVE-2023-42793
+  vulnerability in JetBrains TeamCity On-Premises. It identifies suspicious POST requests
+  to /app/rest/users/id:1/tokens/RPC2, leveraging the Web datamodel to monitor specific
+  URL patterns and HTTP methods. This activity is significant as it may indicate an
+  unauthenticated attacker attempting to gain administrative access via Remote Code
+  Execution (RCE). If confirmed malicious, this could allow the attacker to execute
+  arbitrary code, potentially compromising the entire TeamCity environment and leading
+  to further unauthorized access and data breaches.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/app/rest/users/id:1/tokens/RPC2*") Web.status=200 Web.http_method=POST
+  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src,
+  Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `jetbrains_teamcity_rce_attempt_filter`'
+how_to_implement: The following analytic requires the Web datamodel. Ensure data source
+  is mapped correctly or modify and tune for your data source.
+known_false_positives: If TeamCity is not in use, this analytic will not return results.
+  Monitor and tune for your environment.
 references:
 - https://blog.jetbrains.com/teamcity/2023/09/critical-security-issue-affecting-teamcity-on-premises-update-to-2023-05-4-now/
 - https://www.sonarsource.com/blog/teamcity-vulnerability/
@@ -22,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -36,7 +54,8 @@ tags:
   atomic_guid: []
   confidence: 90
   impact: 90
-  message: Potential JetBrains TeamCity RCE Attempt detected against URL $url$ on $dest$.
+  message: Potential JetBrains TeamCity RCE Attempt detected against URL $url$ on
+    $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -52,18 +71,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/jetbrains/teamcity.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml b/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml
index 9aa7637609..f1a11ffa06 100644
--- a/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml
+++ b/detections/web/juniper_networks_remote_code_execution_exploit_detection.yml
@@ -7,10 +7,28 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects attempts to exploit a remote code execution vulnerability in Juniper Networks devices. It identifies requests to /webauth_operation.php?PHPRC=*, which are indicative of uploading and executing malicious PHP files. This detection leverages the Web data model, focusing on specific URL patterns and HTTP status codes. This activity is significant because it signals an attempt to gain unauthorized access and execute arbitrary code on the device. If confirmed malicious, the attacker could gain control over the device, leading to data theft, network compromise, or other severe consequences.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/webauth_operation.php?PHPRC=*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `juniper_networks_remote_code_execution_exploit_detection_filter`'
-how_to_implement: To implement this search, ensure that the Web data model is populated. The search is activated when the Web data model is accelerated. Network products, such as Suricata or Palo Alto, need to be mapped to the Web data model. Adjust the mapping as necessary to suit your specific products.
-known_false_positives: Be aware of potential false positives - legitimate uses of the /webauth_operation.php endpoint may cause benign activities to be flagged.The URL in the analytic is specific to a successful attempt to exploit the vulnerability. Review contents of the HTTP body to determine if the request is malicious. If the request is benign, add the URL to the whitelist or continue to monitor.
+description: The following analytic detects attempts to exploit a remote code execution
+  vulnerability in Juniper Networks devices. It identifies requests to /webauth_operation.php?PHPRC=*,
+  which are indicative of uploading and executing malicious PHP files. This detection
+  leverages the Web data model, focusing on specific URL patterns and HTTP status
+  codes. This activity is significant because it signals an attempt to gain unauthorized
+  access and execute arbitrary code on the device. If confirmed malicious, the attacker
+  could gain control over the device, leading to data theft, network compromise, or
+  other severe consequences.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/webauth_operation.php?PHPRC=*") Web.status=200 by Web.http_user_agent,
+  Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `juniper_networks_remote_code_execution_exploit_detection_filter`'
+how_to_implement: To implement this search, ensure that the Web data model is populated.
+  The search is activated when the Web data model is accelerated. Network products,
+  such as Suricata or Palo Alto, need to be mapped to the Web data model. Adjust the
+  mapping as necessary to suit your specific products.
+known_false_positives: Be aware of potential false positives - legitimate uses of
+  the /webauth_operation.php endpoint may cause benign activities to be flagged.The
+  URL in the analytic is specific to a successful attempt to exploit the vulnerability.
+  Review contents of the HTTP body to determine if the request is malicious. If the
+  request is benign, add the URL to the whitelist or continue to monitor.
 references:
 - https://supportportal.juniper.net/s/article/2023-08-Out-of-Cycle-Security-Bulletin-Junos-OS-SRX-Series-and-EX-Series-Multiple-vulnerabilities-in-J-Web-can-be-combined-to-allow-a-preAuth-Remote-Code-Execution?language=en_US
 - https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2023/CVE-2023-36844.yaml
@@ -24,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -39,7 +62,9 @@ tags:
   atomic_guid: []
   confidence: 80
   impact: 90
-  message: This analytic has identified a potential exploitation of a remote code execution vulnerability in Juniper Networks devices on $dest$ on the URL $url$ used for the exploit.
+  message: This analytic has identified a potential exploitation of a remote code
+    execution vulnerability in Juniper Networks devices on $dest$ on the URL $url$
+    used for the exploit.
   observable:
   - name: dest
     type: Hostname
@@ -57,19 +82,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/juniper/suricata_junos_cvemegazord.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/juniper/suricata_junos_cvemegazord.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/log4shell_jndi_payload_injection_attempt.yml b/detections/web/log4shell_jndi_payload_injection_attempt.yml
index d9e077848e..c1750a7c48 100644
--- a/detections/web/log4shell_jndi_payload_injection_attempt.yml
+++ b/detections/web/log4shell_jndi_payload_injection_attempt.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Jose Hernandez
 status: production
 type: Anomaly
-description: The following analytic identifies attempts to inject Log4Shell JNDI payloads via web calls. It leverages the Web datamodel and uses regex to detect patterns like `${jndi:ldap://` in raw web event data, including HTTP headers. This activity is significant because it targets vulnerabilities in Java web applications using Log4j, such as Apache Struts and Solr. If confirmed malicious, this could allow attackers to execute arbitrary code, potentially leading to full system compromise. Immediate investigation is required to determine if the attempt was successful and to mitigate any potential exploitation.
+description: The following analytic identifies attempts to inject Log4Shell JNDI payloads
+  via web calls. It leverages the Web datamodel and uses regex to detect patterns
+  like `${jndi:ldap://` in raw web event data, including HTTP headers. This activity
+  is significant because it targets vulnerabilities in Java web applications using
+  Log4j, such as Apache Struts and Solr. If confirmed malicious, this could allow
+  attackers to execute arbitrary code, potentially leading to full system compromise.
+  Immediate investigation is required to determine if the attempt was successful and
+  to mitigate any potential exploitation.
 data_source:
 - Nginx Access
-search: '| from datamodel Web.Web | regex _raw="[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)\w+(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?" | fillnull | stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user | `log4shell_jndi_payload_injection_attempt_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx.
-known_false_positives: If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives.
+search: '| from datamodel Web.Web | regex _raw="[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)\w+(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?"
+  | fillnull | stats count by action, category, dest, dest_port, http_content_type,
+  http_method, http_referrer, http_user_agent, site, src, url, url_domain, user |
+  `log4shell_jndi_payload_injection_attempt_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache or Splunk for Nginx.
+known_false_positives: If there is a vulnerablility scannner looking for log4shells
+  this will trigger, otherwise likely to have low false positives.
 references:
 - https://www.lunasec.io/docs/blog/log4j-zero-day/
 drilldown_searches:
@@ -19,7 +31,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -49,24 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - category
-  - dest
-  - dest_port
-  - http_content_type
-  - http_method
-  - http_referrer
-  - http_user_agent
-  - site
-  - src
-  - url
-  - url_domain
-  - user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log
     source: nginx
     sourcetype: nginx:plus:kv
diff --git a/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml b/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml
index 39709bbe05..5f20f94aa7 100644
--- a/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml
+++ b/detections/web/log4shell_jndi_payload_injection_with_outbound_connection.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: Jose Hernandez
 status: production
 type: Anomaly
-description: The following analytic detects Log4Shell JNDI payload injections via outbound connections. It identifies suspicious LDAP lookup functions in web logs, such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic to known malicious IP addresses. This detection leverages the Web and Network_Traffic data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities in Java web applications using log4j, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise sensitive data within the affected environment.
+description: The following analytic detects Log4Shell JNDI payload injections via
+  outbound connections. It identifies suspicious LDAP lookup functions in web logs,
+  such as `${jndi:ldap://PAYLOAD_INJECTED}`, and correlates them with network traffic
+  to known malicious IP addresses. This detection leverages the Web and Network_Traffic
+  data models in Splunk. Monitoring this activity is crucial as it targets vulnerabilities
+  in Java web applications using log4j, potentially leading to remote code execution.
+  If confirmed malicious, attackers could gain unauthorized access, execute arbitrary
+  code, and compromise sensitive data within the affected environment.
 data_source: []
-search: '| from datamodel Web.Web | rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)(?<proto>\w+)(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?<affected_host>[a-zA-Z0-9\.\-\_\$]+)" | join affected_host type=inner [| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic by All_Traffic.dest | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rename dest AS affected_host] | fillnull | stats count by action, category, dest, dest_port, http_content_type, http_method, http_referrer, http_user_agent, site, src, url, url_domain, user | `log4shell_jndi_payload_injection_with_outbound_connection_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Apache or Splunk for Nginx.
-known_false_positives: If there is a vulnerablility scannner looking for log4shells this will trigger, otherwise likely to have low false positives.
+search: '| from datamodel Web.Web | rex field=_raw max_match=0 "[jJnNdDiI]{4}(\:|\%3A|\/|\%2F)(?<proto>\w+)(\:\/\/|\%3A\%2F\%2F)(\$\{.*?\}(\.)?)?(?<affected_host>[a-zA-Z0-9\.\-\_\$]+)"
+  | join affected_host type=inner [| tstats `security_content_summariesonly` count
+  min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic.All_Traffic
+  by All_Traffic.dest | `drop_dm_object_name(All_Traffic)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | rename dest AS affected_host] | fillnull
+  | stats count by action, category, dest, dest_port, http_content_type, http_method,
+  http_referrer, http_user_agent, site, src, url, url_domain, user | `log4shell_jndi_payload_injection_with_outbound_connection_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Apache or Splunk for Nginx.
+known_false_positives: If there is a vulnerablility scannner looking for log4shells
+  this will trigger, otherwise likely to have low false positives.
 references:
 - https://www.lunasec.io/docs/blog/log4j-zero-day/
 drilldown_searches:
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$user$" and "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$",
+    "$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,27 +67,15 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - category
-  - dest
-  - dest_port
-  - http_content_type
-  - http_method
-  - http_referrer
-  - http_user_agent
-  - site
-  - src
-  - url
-  - url_domain
-  - user
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_proxy_logs/log4j_proxy_logs.log
     source: nginx
     sourcetype: nginx:plus:kv
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_network_logs/log4j_network_logs.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/log4j_network_logs/log4j_network_logs.log
     source: stream:Splunk_IP
     sourcetype: stream:ip
diff --git a/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml b/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml
index 20c134c316..a5a6f2bbb5 100644
--- a/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml
+++ b/detections/web/microsoft_sharepoint_server_elevation_of_privilege.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects potential exploitation attempts against Microsoft SharePoint Server vulnerability CVE-2023-29357. It leverages the Web datamodel to monitor for specific API calls and HTTP methods indicative of privilege escalation attempts. This activity is significant as it may indicate an attacker is trying to gain unauthorized privileged access to the SharePoint environment. If confirmed malicious, the impact could include unauthorized access to sensitive data, potential data theft, and further compromise of the SharePoint server, leading to a broader security breach.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/_api/web/siteusers*","/_api/web/currentuser*") Web.status=200 Web.http_method=GET by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `microsoft_sharepoint_server_elevation_of_privilege_filter`'
-how_to_implement: This detection requires the Web datamodel to be populated from a supported Technology Add-On like Splunk for Microsoft SharePoint.
-known_false_positives: False positives may occur if there are legitimate activities that mimic the exploitation pattern. It's recommended to review the context of the alerts and adjust the analytic parameters to better fit the specific environment.
+description: The following analytic detects potential exploitation attempts against
+  Microsoft SharePoint Server vulnerability CVE-2023-29357. It leverages the Web datamodel
+  to monitor for specific API calls and HTTP methods indicative of privilege escalation
+  attempts. This activity is significant as it may indicate an attacker is trying
+  to gain unauthorized privileged access to the SharePoint environment. If confirmed
+  malicious, the impact could include unauthorized access to sensitive data, potential
+  data theft, and further compromise of the SharePoint server, leading to a broader
+  security breach.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/_api/web/siteusers*","/_api/web/currentuser*") Web.status=200
+  Web.http_method=GET by Web.http_user_agent, Web.status Web.http_method, Web.url,
+  Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `microsoft_sharepoint_server_elevation_of_privilege_filter`'
+how_to_implement: This detection requires the Web datamodel to be populated from a
+  supported Technology Add-On like Splunk for Microsoft SharePoint.
+known_false_positives: False positives may occur if there are legitimate activities
+  that mimic the exploitation pattern. It's recommended to review the context of the
+  alerts and adjust the analytic parameters to better fit the specific environment.
 references:
 - https://socradar.io/microsoft-sharepoint-server-elevation-of-privilege-vulnerability-exploit-cve-2023-29357/
 - https://github.com/LuemmelSec/CVE-2023-29357/blob/main/CVE-2023-29357/Program.cs
@@ -20,7 +34,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -48,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.status
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/sharepointeop.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/sharepoint/sharepointeop.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/monitor_web_traffic_for_brand_abuse.yml b/detections/web/monitor_web_traffic_for_brand_abuse.yml
index a8b891a97a..0016b874fb 100644
--- a/detections/web/monitor_web_traffic_for_brand_abuse.yml
+++ b/detections/web/monitor_web_traffic_for_brand_abuse.yml
@@ -5,10 +5,22 @@ date: '2024-10-17'
 author: David Dorsey, Splunk
 status: experimental
 type: TTP
-description: The following analytic identifies web requests to domains that closely resemble your monitored brand's domain, indicating potential brand abuse. It leverages data from web traffic sources, such as web proxies or network traffic analysis tools, and cross-references these with known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. This activity is significant as it can indicate phishing attempts or other malicious activities targeting your brand. If confirmed malicious, attackers could deceive users, steal credentials, or distribute malware, leading to significant reputational and financial damage.
+description: The following analytic identifies web requests to domains that closely
+  resemble your monitored brand's domain, indicating potential brand abuse. It leverages
+  data from web traffic sources, such as web proxies or network traffic analysis tools,
+  and cross-references these with known domain permutations generated by the "ESCU
+  - DNSTwist Domain Names" search. This activity is significant as it can indicate
+  phishing attempts or other malicious activities targeting your brand. If confirmed
+  malicious, attackers could deceive users, steal credentials, or distribute malware,
+  leading to significant reputational and financial damage.
 data_source: []
-search: '| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter`'
-how_to_implement: You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
+search: '| tstats `security_content_summariesonly` values(Web.url) as urls min(_time)
+  as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `brand_abuse_web` | `monitor_web_traffic_for_brand_abuse_filter`'
+how_to_implement: You need to ingest data from your web traffic. This can be accomplished
+  by indexing data from a web proxy, or using a network traffic analysis tool, such
+  as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain
+  Names", which creates the permutations of the domain that will be checked for.
 known_false_positives: None at this time
 references: []
 tags:
@@ -27,8 +39,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.url
-  - Web.src
   security_domain: network
diff --git a/detections/web/multiple_archive_files_http_post_traffic.yml b/detections/web/multiple_archive_files_http_post_traffic.yml
index 2533ef967c..a7542fe245 100644
--- a/detections/web/multiple_archive_files_http_post_traffic.yml
+++ b/detections/web/multiple_archive_files_http_post_traffic.yml
@@ -5,11 +5,25 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: "The following analytic detects the high-frequency exfiltration of archive files via HTTP POST requests. It leverages HTTP stream logs to identify specific archive file headers within the request body. This activity is significant as it often indicates data exfiltration by APTs or trojan spyware after data collection. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive data to an attacker\u2019s command and control server, potentially resulting in severe data breaches and loss of confidential information."
+description: The following analytic detects the high-frequency exfiltration of archive
+  files via HTTP POST requests. It leverages HTTP stream logs to identify specific
+  archive file headers within the request body. This activity is significant as it
+  often indicates data exfiltration by APTs or trojan spyware after data collection.
+  If confirmed malicious, this behavior could lead to the unauthorized transfer of
+  sensitive data to an attacker’s command and control server, potentially resulting
+  in severe data breaches and loss of confidential information.
 data_source:
 - Splunk Stream HTTP
-search: '`stream_http` http_method=POST |eval archive_hdr1=substr(form_data,1,2) | eval archive_hdr2 = substr(form_data,1,4) |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by src_ip dest_ip http_method http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 |where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `multiple_archive_files_http_post_traffic_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled in stream http configuration.
+search: '`stream_http` http_method=POST |eval archive_hdr1=substr(form_data,1,2) |
+  eval archive_hdr2 = substr(form_data,1,4) |stats values(form_data) as http_request_body
+  min(_time) as firstTime max(_time) as lastTime count by src_ip dest_ip http_method
+  http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 |where
+  count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!")
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `multiple_archive_files_http_post_traffic_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the stream HTTP logs or network logs that catch network traffic. Make
+  sure that the http-request-body, payload, or request field is enabled in stream
+  http configuration.
 known_false_positives: Normal archive transfer via HTTP protocol may trip this detection.
 references:
 - https://attack.mitre.org/techniques/T1560/001/
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +50,8 @@ tags:
   asset_type: Endpoint
   confidence: 50
   impact: 50
-  message: A http post $http_method$ sending packet with possible archive bytes header in uri path $uri_path$
+  message: A http post $http_method$ sending packet with possible archive bytes header
+    in uri path $uri_path$
   mitre_attack_id:
   - T1048.003
   - T1048
@@ -48,21 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_method
-  - http_user_agent
-  - uri_path
-  - url
-  - bytes_in
-  - bytes_out
-  - archive_hdr1
-  - archive_hdr2
-  - form_data
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/archive_http_post/stream_http_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/archive_http_post/stream_http_events.log
     source: stream
     sourcetype: stream:http
diff --git a/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml b/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml
index 613124ed19..cd9a81d3d9 100644
--- a/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml
+++ b/detections/web/nginx_connectwise_screenconnect_authentication_bypass.yml
@@ -7,10 +7,28 @@ data_source:
 - Nginx Access
 type: TTP
 status: production
-description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via alternate paths or channels. It leverages Nginx access logs to identify web requests to the SetupWizard.aspx page, indicating potential exploitation. This activity is significant as it can lead to unauthorized administrative access and remote code execution. If confirmed malicious, attackers could create administrative users and gain full control over the affected ScreenConnect instance, posing severe security risks. Immediate remediation by updating to version 23.9.8 or above is recommended.
-search: '`nginx_access_logs` uri_path IN ("*/SetupWizard.aspx/*","*/SetupWizard/") status=200 http_method=POST  | stats count min(_time) as firstTime max(_time) as lastTime by src, dest, http_user_agent, url, uri_path, status, http_method, sourcetype, source | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `nginx_connectwise_screenconnect_authentication_bypass_filter`'
-how_to_implement: To implement this analytic, ensure proper logging is occurring with Nginx, access.log and error.log, and that these logs are being ingested into Splunk. STRT utilizes this nginx.conf https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec to properly log as much data with Nginx.
-known_false_positives: False positives are not expected, as the detection is based on the presence of web requests to the SetupWizard.aspx page, which is not a common page to be accessed by legitimate users. Note that the analytic is limited to HTTP POST and a status of 200 to reduce false positives. Modify the query as needed to reduce false positives or hunt for additional indicators of compromise.
+description: The following analytic detects attempts to exploit the ConnectWise ScreenConnect
+  CVE-2024-1709 vulnerability, which allows attackers to bypass authentication via
+  alternate paths or channels. It leverages Nginx access logs to identify web requests
+  to the SetupWizard.aspx page, indicating potential exploitation. This activity is
+  significant as it can lead to unauthorized administrative access and remote code
+  execution. If confirmed malicious, attackers could create administrative users and
+  gain full control over the affected ScreenConnect instance, posing severe security
+  risks. Immediate remediation by updating to version 23.9.8 or above is recommended.
+search: '`nginx_access_logs` uri_path IN ("*/SetupWizard.aspx/*","*/SetupWizard/")
+  status=200 http_method=POST  | stats count min(_time) as firstTime max(_time) as
+  lastTime by src, dest, http_user_agent, url, uri_path, status, http_method, sourcetype,
+  source | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `nginx_connectwise_screenconnect_authentication_bypass_filter`'
+how_to_implement: To implement this analytic, ensure proper logging is occurring with
+  Nginx, access.log and error.log, and that these logs are being ingested into Splunk.
+  STRT utilizes this nginx.conf https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec
+  to properly log as much data with Nginx.
+known_false_positives: False positives are not expected, as the detection is based
+  on the presence of web requests to the SetupWizard.aspx page, which is not a common
+  page to be accessed by legitimate users. Note that the analytic is limited to HTTP
+  POST and a status of 200 to reduce false positives. Modify the query as needed to
+  reduce false positives or hunt for additional indicators of compromise.
 references:
 - https://docs.splunk.com/Documentation/AddOns/released/NGINX/Sourcetypes
 - https://gist.github.com/MHaggis/26f59108b04da8f1d870c9cc3a3c8eec
@@ -23,7 +41,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +55,8 @@ tags:
   asset_type: Web Proxy
   confidence: 100
   impact: 100
-  message: An authentication bypass attempt against ScreenConnect has been detected on $dest$.
+  message: An authentication bypass attempt against ScreenConnect has been detected
+    on $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -44,17 +68,6 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - src
-  - dest
-  - http_user_agent
-  - url
-  - uri_path
-  - status
-  - http_method
-  - sourcetype
-  - source
   security_domain: network
   cve:
   - CVE-2024-1708
@@ -62,6 +75,7 @@ tags:
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/nginx_screenconnect.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/screenconnect/nginx_screenconnect.log
     sourcetype: nginx:plus:kv
     source: nginx:plus:kv
diff --git a/detections/web/papercut_ng_remote_web_access_attempt.yml b/detections/web/papercut_ng_remote_web_access_attempt.yml
index 444473a694..cb56df0bff 100644
--- a/detections/web/papercut_ng_remote_web_access_attempt.yml
+++ b/detections/web/papercut_ng_remote_web_access_attempt.yml
@@ -7,9 +7,24 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects potential exploitation attempts on publicly accessible PaperCut NG servers. It identifies connections from public IP addresses to the server, specifically monitoring URI paths commonly used in proof-of-concept scripts for exploiting PaperCut NG vulnerabilities. This detection leverages web traffic data from the `Web` datamodel, focusing on specific URI paths and excluding internal IP ranges. This activity is significant as it may indicate an attempt to exploit known vulnerabilities in PaperCut NG, potentially leading to unauthorized access or control of the server. If confirmed malicious, attackers could gain administrative access, leading to data breaches or further network compromise.
-search: '| tstats count from datamodel=Web where Web.url IN ("/app?service=page/SetupCompleted", "/app", "/app?service=page/PrinterList", "/app?service=direct/1/PrinterList/selectPrinter&sp=*", "/app?service=direct/1/PrinterDetails/printerOptionsTab.tab") NOT (src IN ("10.*.*.*","172.16.*.*", "192.168.*.*", "169.254.*.*", "127.*.*.*", "fc00::*", "fd00::*", "fe80::*")) by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest Web.dest_port sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `papercut_ng_remote_web_access_attempt_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+description: The following analytic detects potential exploitation attempts on publicly
+  accessible PaperCut NG servers. It identifies connections from public IP addresses
+  to the server, specifically monitoring URI paths commonly used in proof-of-concept
+  scripts for exploiting PaperCut NG vulnerabilities. This detection leverages web
+  traffic data from the `Web` datamodel, focusing on specific URI paths and excluding
+  internal IP ranges. This activity is significant as it may indicate an attempt to
+  exploit known vulnerabilities in PaperCut NG, potentially leading to unauthorized
+  access or control of the server. If confirmed malicious, attackers could gain administrative
+  access, leading to data breaches or further network compromise.
+search: '| tstats count from datamodel=Web where Web.url IN ("/app?service=page/SetupCompleted",
+  "/app", "/app?service=page/PrinterList", "/app?service=direct/1/PrinterList/selectPrinter&sp=*",
+  "/app?service=direct/1/PrinterDetails/printerOptionsTab.tab") NOT (src IN ("10.*.*.*","172.16.*.*",
+  "192.168.*.*", "169.254.*.*", "127.*.*.*", "fc00::*", "fd00::*", "fe80::*")) by
+  Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest Web.dest_port
+  sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `papercut_ng_remote_web_access_attempt_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
 known_false_positives: False positives may be present, filter as needed.
 references:
 - https://www.cisa.gov/news-events/alerts/2023/05/11/cisa-and-fbi-release-joint-advisory-response-active-exploitation-papercut-vulnerability
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -46,19 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_user_agent
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-suricata.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/papercut/papercutng-suricata.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/plain_http_post_exfiltrated_data.yml b/detections/web/plain_http_post_exfiltrated_data.yml
index f72015cc5a..1445e4e25f 100644
--- a/detections/web/plain_http_post_exfiltrated_data.yml
+++ b/detections/web/plain_http_post_exfiltrated_data.yml
@@ -5,11 +5,24 @@ date: '2024-09-30'
 author: Teoderick Contreras, Splunk
 status: production
 type: TTP
-description: The following analytic detects potential data exfiltration using plain HTTP POST requests. It leverages network traffic logs, specifically monitoring the `stream_http` data source for POST methods containing suspicious form data such as "wermgr.exe" or "svchost.exe". This activity is significant because it is commonly associated with malware like Trickbot, trojans, keyloggers, or APT adversaries, which use plain text HTTP POST requests to communicate with remote C2 servers. If confirmed malicious, this activity could lead to unauthorized data exfiltration, compromising sensitive information and potentially leading to further network infiltration.
+description: The following analytic detects potential data exfiltration using plain
+  HTTP POST requests. It leverages network traffic logs, specifically monitoring the
+  `stream_http` data source for POST methods containing suspicious form data such
+  as "wermgr.exe" or "svchost.exe". This activity is significant because it is commonly
+  associated with malware like Trickbot, trojans, keyloggers, or APT adversaries,
+  which use plain text HTTP POST requests to communicate with remote C2 servers. If
+  confirmed malicious, this activity could lead to unauthorized data exfiltration,
+  compromising sensitive information and potentially leading to further network infiltration.
 data_source:
 - Splunk Stream HTTP
-search: '`stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") |stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by src_ip dest_ip http_method http_user_agent uri_path url bytes_in bytes_out | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `plain_http_post_exfiltrated_data_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled.
+search: '`stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*",
+  "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") |stats values(form_data)
+  as http_request_body min(_time) as firstTime max(_time) as lastTime count by src_ip
+  dest_ip http_method http_user_agent uri_path url bytes_in bytes_out | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `plain_http_post_exfiltrated_data_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the stream HTTP logs or network logs that catch network traffic. Make
+  sure that the http-request-body, payload, or request field is enabled.
 known_false_positives: unknown
 references:
 - https://blog.talosintelligence.com/2020/03/trickbot-primer.html
@@ -19,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src_ip$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +47,8 @@ tags:
   asset_type: Endpoint
   confidence: 90
   impact: 70
-  message: A http post $http_method$ sending packet with plain text of information in uri path $uri_path$
+  message: A http post $http_method$ sending packet with plain text of information
+    in uri path $uri_path$
   mitre_attack_id:
   - T1048.003
   - T1048
@@ -42,18 +61,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_method
-  - http_user_agent
-  - uri_path
-  - url
-  - bytes_in
-  - bytes_out
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/plain_exfil_data/stream_http_events.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/plain_exfil_data/stream_http_events.log
     source: stream
     sourcetype: stream:http
diff --git a/detections/web/proxyshell_proxynotshell_behavior_detected.yml b/detections/web/proxyshell_proxynotshell_behavior_detected.yml
index d785550fe6..57a2f07624 100644
--- a/detections/web/proxyshell_proxynotshell_behavior_detected.yml
+++ b/detections/web/proxyshell_proxynotshell_behavior_detected.yml
@@ -5,11 +5,36 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Correlation
-description: The following analytic identifies potential exploitation of Windows Exchange servers via ProxyShell or ProxyNotShell vulnerabilities, followed by post-exploitation activities such as running nltest, Cobalt Strike, Mimikatz, and adding new users. It leverages data from multiple analytic stories, requiring at least five distinct sources to trigger, thus reducing noise. This activity is significant as it indicates a high likelihood of an active compromise, potentially leading to unauthorized access, privilege escalation, and persistent threats within the environment. If confirmed malicious, attackers could gain control over the Exchange server, exfiltrate data, and maintain long-term access.
+description: The following analytic identifies potential exploitation of Windows Exchange
+  servers via ProxyShell or ProxyNotShell vulnerabilities, followed by post-exploitation
+  activities such as running nltest, Cobalt Strike, Mimikatz, and adding new users.
+  It leverages data from multiple analytic stories, requiring at least five distinct
+  sources to trigger, thus reducing noise. This activity is significant as it indicates
+  a high likelihood of an active compromise, potentially leading to unauthorized access,
+  privilege escalation, and persistent threats within the environment. If confirmed
+  malicious, attackers could gain control over the Exchange server, exfiltrate data,
+  and maintain long-term access.
 data_source: []
-search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.analyticstories) as analyticstories values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count dc(All_Risk.analyticstories) as dc_analyticstories from datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") OR (All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") AND All_Risk.analyticstories="Cobalt Strike") All_Risk.risk_object_type="system" by _time span=1h All_Risk.risk_object All_Risk.risk_object_type | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`| where source_count >=5 | `proxyshell_proxynotshell_behavior_detected_filter`'
-how_to_implement: To implement this correlation, you will need to enable ProxyShell, ProxyNotShell and Cobalt Strike analytic stories (the anaytics themselves) and ensure proper data is being collected for Web and Endpoint datamodels. Run the correlation rule seperately to validate it is not triggering too much or generating incorrectly. Validate by running ProxyShell POC code and Cobalt Strike behavior.
-known_false_positives: False positives will be limited, however tune or modify the query as needed.
+search: '| tstats `security_content_summariesonly` min(_time) as firstTime max(_time)
+  as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score)
+  as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as
+  annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id)
+  as mitre_tactic_id_count, values(All_Risk.analyticstories) as analyticstories values(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id)
+  as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source,
+  dc(source) as source_count dc(All_Risk.analyticstories) as dc_analyticstories from
+  datamodel=Risk.All_Risk where All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell")
+  OR (All_Risk.analyticstories IN ("ProxyNotShell","ProxyShell") AND All_Risk.analyticstories="Cobalt
+  Strike") All_Risk.risk_object_type="system" by _time span=1h All_Risk.risk_object
+  All_Risk.risk_object_type | `drop_dm_object_name(All_Risk)` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)`| where source_count >=5 | `proxyshell_proxynotshell_behavior_detected_filter`'
+how_to_implement: To implement this correlation, you will need to enable ProxyShell,
+  ProxyNotShell and Cobalt Strike analytic stories (the anaytics themselves) and ensure
+  proper data is being collected for Web and Endpoint datamodels. Run the correlation
+  rule seperately to validate it is not triggering too much or generating incorrectly.
+  Validate by running ProxyShell POC code and Cobalt Strike behavior.
+known_false_positives: False positives will be limited, however tune or modify the
+  query as needed.
 references:
 - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html
 - https://msrc-blog.microsoft.com/2022/09/29/customer-guidance-for-reported-zero-day-vulnerabilities-in-microsoft-exchange-server/
@@ -19,7 +44,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$risk_object$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -43,16 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - All_Risk.analyticstories
-  - All_Risk.risk_object_type
-  - All_Risk.risk_object
-  - All_Risk.annotations.mitre_attack.mitre_tactic
-  - source
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell-risk.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell-risk.log
     source: proxyshell
     sourcetype: stash
diff --git a/detections/web/spring4shell_payload_url_request.yml b/detections/web/spring4shell_payload_url_request.yml
index cc3d8d3bd1..9fa60a9b5b 100644
--- a/detections/web/spring4shell_payload_url_request.yml
+++ b/detections/web/spring4shell_payload_url_request.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects attempts to exploit the Spring4Shell vulnerability (CVE-2022-22963) by identifying specific URL patterns associated with web shell payloads. It leverages web traffic data, focusing on HTTP GET requests with URLs containing indicators like "tomcatwar.jsp," "poc.jsp," and "shell.jsp." This activity is significant as it suggests an attacker is trying to deploy a web shell, which can lead to remote code execution. If confirmed malicious, this could allow the attacker to gain persistent access, execute arbitrary commands, and potentially escalate privileges within the compromised environment.
+description: The following analytic detects attempts to exploit the Spring4Shell vulnerability
+  (CVE-2022-22963) by identifying specific URL patterns associated with web shell
+  payloads. It leverages web traffic data, focusing on HTTP GET requests with URLs
+  containing indicators like "tomcatwar.jsp," "poc.jsp," and "shell.jsp." This activity
+  is significant as it suggests an attacker is trying to deploy a web shell, which
+  can lead to remote code execution. If confirmed malicious, this could allow the
+  attacker to gain persistent access, execute arbitrary commands, and potentially
+  escalate privileges within the compromised environment.
 data_source:
 - Nginx Access
-search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url IN ("*tomcatwar.jsp*","*poc.jsp*","*shell.jsp*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spring4shell_payload_url_request_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
-known_false_positives: The jsp file names are static names used in current proof of concept code. =
+search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url
+  IN ("*tomcatwar.jsp*","*poc.jsp*","*shell.jsp*") by Web.http_user_agent Web.http_method,
+  Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `spring4shell_payload_url_request_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+known_false_positives: The jsp file names are static names used in current proof of
+  concept code. =
 references:
 - https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/
 - https://github.com/TheGejr/SpringShell
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -51,18 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log
     source: /var/log/nginx/access.log
     sourcetype: nginx:plus:kv
diff --git a/detections/web/sql_injection_with_long_urls.yml b/detections/web/sql_injection_with_long_urls.yml
index 767904ba41..1b2d6e7d47 100644
--- a/detections/web/sql_injection_with_long_urls.yml
+++ b/detections/web/sql_injection_with_long_urls.yml
@@ -5,11 +5,36 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects long URLs containing multiple SQL commands, indicating a potential SQL injection attack. This detection leverages web traffic data, specifically targeting web server destinations with URLs longer than 1024 characters or HTTP user agents longer than 200 characters. SQL injection is significant as it allows attackers to manipulate a web application's database, potentially leading to unauthorized data access or modification. If confirmed malicious, this activity could result in data breaches, unauthorized access, and complete system compromise. Immediate investigation and validation of alerts are crucial to mitigate these risks.
+description: The following analytic detects long URLs containing multiple SQL commands,
+  indicating a potential SQL injection attack. This detection leverages web traffic
+  data, specifically targeting web server destinations with URLs longer than 1024
+  characters or HTTP user agents longer than 200 characters. SQL injection is significant
+  as it allows attackers to manipulate a web application's database, potentially leading
+  to unauthorized data access or modification. If confirmed malicious, this activity
+  could result in data breaches, unauthorized access, and complete system compromise.
+  Immediate investigation and validation of alerts are crucial to mitigate these risks.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval url=lower(url) | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter`'
-how_to_implement: To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table.
-known_false_positives: It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate.
+search: '| tstats `security_content_summariesonly` count from datamodel=Web where
+  Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length
+  > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")`
+  | eval url=lower(url) | eval num_sql_cmds=mvcount(split(url, "alter%20table")) +
+  mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url,
+  "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url,
+  "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database"))
+  + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url,
+  "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url,
+  "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join"))
+  + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url,
+  "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct"))
+  + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url,
+  "xp_cmdshell")) - 24 | where num_sql_cmds > 3 | `sql_injection_with_long_urls_filter`'
+how_to_implement: To successfully implement this search, you need to be monitoring
+  network communications to your web servers or ingesting your HTTP logs and populating
+  the Web data model. You must also identify your web servers in the Enterprise Security
+  assets table.
+known_false_positives: It's possible that legitimate traffic will have long URLs or
+  long user agent strings and that common SQL commands may be found within the URL.
+  Please investigate as appropriate.
 references: []
 tags:
   analytic_story:
@@ -29,13 +54,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.dest_category
-  - Web.url_length
-  - Web.http_user_agent_length
-  - Web.src
-  - Web.dest
-  - Web.url
-  - Web.http_user_agent
   security_domain: network
diff --git a/detections/web/supernova_webshell.yml b/detections/web/supernova_webshell.yml
index f4b693cf30..e849f6e543 100644
--- a/detections/web/supernova_webshell.yml
+++ b/detections/web/supernova_webshell.yml
@@ -5,11 +5,26 @@ date: '2024-10-17'
 author: John Stoner, Splunk
 status: experimental
 type: TTP
-description: The following analytic detects the presence of the Supernova webshell, used in the SUNBURST attack, by identifying specific patterns in web URLs. The detection leverages Splunk to search for URLs containing "*logoimagehandler.ashx*codes*", "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". This activity is significant as it indicates potential unauthorized access and arbitrary code execution on a compromised system. If confirmed malicious, this could lead to data theft, ransomware deployment, or other severe outcomes. Immediate steps include reviewing the web URLs, inspecting on-disk artifacts, and analyzing concurrent processes and network connections.
+description: The following analytic detects the presence of the Supernova webshell,
+  used in the SUNBURST attack, by identifying specific patterns in web URLs. The detection
+  leverages Splunk to search for URLs containing "*logoimagehandler.ashx*codes*",
+  "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*".
+  This activity is significant as it indicates potential unauthorized access and arbitrary
+  code execution on a compromised system. If confirmed malicious, this could lead
+  to data theft, ransomware deployment, or other severe outcomes. Immediate steps
+  include reviewing the web URLs, inspecting on-disk artifacts, and analyzing concurrent
+  processes and network connections.
 data_source: []
-search: '| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s | `supernova_webshell_filter`'
-how_to_implement: To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.
-known_false_positives: There might be false positives associted with this detection since items like args as a web argument is pretty generic.
+search: '| tstats `security_content_summariesonly` count from datamodel=Web.Web where
+  web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR
+  Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by
+  Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s
+  | `supernova_webshell_filter`'
+how_to_implement: To successfully implement this search, you need to be monitoring
+  web traffic to your Solarwinds Orion. The logs should be ingested into splunk and
+  populating/mapped to the Web data model.
+known_false_positives: There might be false positives associted with this detection
+  since items like args as a web argument is pretty generic.
 references:
 - https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html
 - https://www.guidepointsecurity.com/blog/supernova-solarwinds-net-webshell-analysis/
@@ -36,12 +51,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.url
-  - Web.src
-  - Web.dest
-  - Web.vendor_product
-  - Web.user
-  - Web.http_user_agent
   security_domain: network
diff --git a/detections/web/unusually_long_content_type_length.yml b/detections/web/unusually_long_content_type_length.yml
index bfb251a46b..0a4b0c0443 100644
--- a/detections/web/unusually_long_content_type_length.yml
+++ b/detections/web/unusually_long_content_type_length.yml
@@ -5,17 +5,23 @@ date: '2024-10-17'
 author: Bhavin Patel, Splunk
 status: experimental
 type: Anomaly
-description: The following analytic identifies unusually long strings in the Content-Type HTTP header sent by the client to the server. It uses data from the Stream:HTTP source, specifically evaluating the length of the `cs_content_type` field. This activity is significant because excessively long Content-Type headers can indicate attempts to exploit vulnerabilities or evade detection mechanisms. If confirmed malicious, this behavior could allow attackers to execute code, manipulate data, or bypass security controls, potentially leading to unauthorized access or data breaches.
+description: The following analytic identifies unusually long strings in the Content-Type
+  HTTP header sent by the client to the server. It uses data from the Stream:HTTP
+  source, specifically evaluating the length of the `cs_content_type` field. This
+  activity is significant because excessively long Content-Type headers can indicate
+  attempts to exploit vulnerabilities or evade detection mechanisms. If confirmed
+  malicious, this behavior could allow attackers to execute code, manipulate data,
+  or bypass security controls, potentially leading to unauthorized access or data
+  breaches.
 data_source: []
 search: >-
-  | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web by Web.src Web.dest Web.url Web.http_user_agent Web.http_content_type
-  | `drop_dm_object_name("Web")` 
-  | eval http_content_type_length = len(http_content_type) 
-  | where http_content_type_length > 100
-  | table firstTime lastTime src dest http_content_type_length http_content_type url http_user_agent
-  | `security_content_ctime(firstTime)` 
-  | `security_content_ctime(lastTime)` 
-  | `unusually_long_content_type_length_filter`
+  | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web by Web.src Web.dest Web.url Web.http_user_agent Web.http_content_type
+  | `drop_dm_object_name("Web")`  | eval http_content_type_length = len(http_content_type)  |
+  where http_content_type_length > 100
+  | table firstTime lastTime src dest http_content_type_length http_content_type url
+  http_user_agent
+  | `security_content_ctime(firstTime)`  | `security_content_ctime(lastTime)`  | `unusually_long_content_type_length_filter`
 how_to_implement: This particular search leverages data extracted from Stream:HTTP.
   You must configure the http stream using the Splunk Stream App on your Splunk Stream
   deployment server to extract the cs_content_type field.
@@ -28,7 +34,8 @@ tags:
   asset_type: Web Server
   confidence: 50
   impact: 50
-  message: Unusually Long Content-Type Length ($http_content_type_length$ characters) In Web Request from $src$
+  message: Unusually Long Content-Type Length ($http_content_type_length$ characters)
+    In Web Request from $src$
   observable:
   - name: dest
     type: Hostname
@@ -42,11 +49,4 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - cs_content_type
-  - endtime
-  - src_ip
-  - dest_ip
-  - url
   security_domain: network
diff --git a/detections/web/vmware_aria_operations_exploit_attempt.yml b/detections/web/vmware_aria_operations_exploit_attempt.yml
index b9ae90ba69..92d47fd622 100644
--- a/detections/web/vmware_aria_operations_exploit_attempt.yml
+++ b/detections/web/vmware_aria_operations_exploit_attempt.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Palo Alto Network Threat
-description: The following analytic detects potential exploitation attempts against VMWare vRealize Network Insight, specifically targeting the CVE-2023-20887 vulnerability. It monitors web traffic for HTTP POST requests directed at the vulnerable endpoint "/saas./resttosaasservlet." This detection leverages web traffic data, focusing on specific URL patterns and HTTP methods. Identifying this behavior is crucial for a SOC as it indicates an active exploit attempt. If confirmed malicious, the attacker could execute arbitrary code, leading to unauthorized access, data theft, or further network compromise.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/saas./resttosaasservlet*")  Web.http_method=POST Web.status IN ("unknown", "200") by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_aria_operations_exploit_attempt_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. Restrict to specific dest assets to reduce false positives.
-known_false_positives: False positives will be present based on gateways in use, modify the status field as needed.
+description: The following analytic detects potential exploitation attempts against
+  VMWare vRealize Network Insight, specifically targeting the CVE-2023-20887 vulnerability.
+  It monitors web traffic for HTTP POST requests directed at the vulnerable endpoint
+  "/saas./resttosaasservlet." This detection leverages web traffic data, focusing
+  on specific URL patterns and HTTP methods. Identifying this behavior is crucial
+  for a SOC as it indicates an active exploit attempt. If confirmed malicious, the
+  attacker could execute arbitrary code, leading to unauthorized access, data theft,
+  or further network compromise.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*/saas./resttosaasservlet*")  Web.http_method=POST Web.status
+  IN ("unknown", "200") by Web.http_user_agent, Web.status Web.http_method, Web.url,
+  Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `vmware_aria_operations_exploit_attempt_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  web or proxy logs, or ensure it is being filled by a proxy like device, into the
+  Web Datamodel. Restrict to specific dest assets to reduce false positives.
+known_false_positives: False positives will be present based on gateways in use, modify
+  the status field as needed.
 references:
 - https://nvd.nist.gov/vuln/detail/CVE-2023-20887
 - https://viz.greynoise.io/tag/vmware-aria-operations-for-networks-rce-attempt?days=30
@@ -22,7 +36,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +53,8 @@ tags:
   atomic_guid: []
   confidence: 80
   impact: 90
-  message: An exploitation attempt has occurred against $dest$ from $src$ related to CVE-2023-20887
+  message: An exploitation attempt has occurred against $dest$ from $src$ related
+    to CVE-2023-20887
   mitre_attack_id:
   - T1133
   - T1190
@@ -53,17 +73,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_aria.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_aria.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/vmware_server_side_template_injection_hunt.yml b/detections/web/vmware_server_side_template_injection_hunt.yml
index 81257504a2..25aa31c436 100644
--- a/detections/web/vmware_server_side_template_injection_hunt.yml
+++ b/detections/web/vmware_server_side_template_injection_hunt.yml
@@ -5,12 +5,27 @@ date: '2024-10-17'
 author: Michael Haag, Splunk
 status: production
 type: Hunting
-description: The following analytic identifies potential server-side template injection attempts related to CVE-2022-22954. It detects suspicious URL patterns containing "deviceudid" and keywords like "java.lang.ProcessBuilder" or "freemarker.template.utility.ObjectConstructor" using web or proxy logs within the Web Datamodel. This activity is significant as it may indicate an attempt to exploit a known vulnerability in VMware, potentially leading to remote code execution. If confirmed malicious, attackers could gain unauthorized access, execute arbitrary code, and compromise the affected system, posing a severe security risk.
+description: The following analytic identifies potential server-side template injection
+  attempts related to CVE-2022-22954. It detects suspicious URL patterns containing
+  "deviceudid" and keywords like "java.lang.ProcessBuilder" or "freemarker.template.utility.ObjectConstructor"
+  using web or proxy logs within the Web Datamodel. This activity is significant as
+  it may indicate an attempt to exploit a known vulnerability in VMware, potentially
+  leading to remote code execution. If confirmed malicious, attackers could gain unauthorized
+  access, execute arbitrary code, and compromise the affected system, posing a severe
+  security risk.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*deviceudid=*" AND Web.url IN ("*java.lang.ProcessBuilder*","*freemarker.template.utility.ObjectConstructor*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_server_side_template_injection_hunt_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
-known_false_positives: False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed.
+search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*deviceudid=*"
+  AND Web.url IN ("*java.lang.ProcessBuilder*","*freemarker.template.utility.ObjectConstructor*")
+  by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest
+  sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `vmware_server_side_template_injection_hunt_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  web or proxy logs, or ensure it is being filled by a proxy like device, into the
+  Web Datamodel. For additional filtering, allow list private IP space or restrict
+  by known good.
+known_false_positives: False positives may be present if the activity is blocked or
+  was not successful. Filter known vulnerablity scanners. Filter as needed.
 references:
 - https://www.cisa.gov/uscert/ncas/alerts/aa22-138b
 - https://github.com/wvu/metasploit-framework/blob/master/modules/exploits/linux/http/vmware_workspace_one_access_cve_2022_22954.rb
@@ -26,7 +41,8 @@ tags:
   cve:
   - CVE-2022-22954
   impact: 70
-  message: An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on $dest$ has occurred.
+  message: An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on
+    $dest$ has occurred.
   mitre_attack_id:
   - T1190
   - T1133
@@ -39,17 +55,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml b/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml
index 9d60802bcc..2e35c89963 100644
--- a/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml
+++ b/detections/web/vmware_workspace_one_freemarker_server_side_template_injection.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: Anomaly
-description: The following analytic detects server-side template injection attempts related to CVE-2022-22954 in VMware Workspace ONE. It leverages web or proxy logs to identify HTTP GET requests to the endpoint catalog-portal/ui/oauth/verify with the freemarker.template.utility.Execute command. This activity is significant as it indicates potential exploitation attempts that could lead to remote code execution. If confirmed malicious, an attacker could execute arbitrary commands on the server, leading to full system compromise, data exfiltration, or further lateral movement within the network.
+description: The following analytic detects server-side template injection attempts
+  related to CVE-2022-22954 in VMware Workspace ONE. It leverages web or proxy logs
+  to identify HTTP GET requests to the endpoint catalog-portal/ui/oauth/verify with
+  the freemarker.template.utility.Execute command. This activity is significant as
+  it indicates potential exploitation attempts that could lead to remote code execution.
+  If confirmed malicious, an attacker could execute arbitrary commands on the server,
+  leading to full system compromise, data exfiltration, or further lateral movement
+  within the network.
 data_source:
 - Palo Alto Network Threat
-search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*/catalog-portal/ui/oauth/verify?error=&deviceudid=*" AND Web.url="*freemarker.template.utility.Execute*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_workspace_one_freemarker_server_side_template_injection_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
-known_false_positives: False positives may be present if the activity is blocked or was not successful. Filter known vulnerablity scanners. Filter as needed.
+search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url="*/catalog-portal/ui/oauth/verify?error=&deviceudid=*"
+  AND Web.url="*freemarker.template.utility.Execute*" by Web.http_user_agent Web.http_method,
+  Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `vmware_workspace_one_freemarker_server_side_template_injection_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  web or proxy logs, or ensure it is being filled by a proxy like device, into the
+  Web Datamodel. For additional filtering, allow list private IP space or restrict
+  by known good.
+known_false_positives: False positives may be present if the activity is blocked or
+  was not successful. Filter known vulnerablity scanners. Filter as needed.
 references:
 - https://www.cisa.gov/uscert/ncas/alerts/aa22-138b
 - https://github.com/wvu/metasploit-framework/blob/master/modules/exploits/linux/http/vmware_workspace_one_access_cve_2022_22954.rb
@@ -23,7 +37,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -34,7 +53,8 @@ tags:
   cve:
   - CVE-2022-22954
   impact: 70
-  message: An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on $dest$ has occurred.
+  message: An attempt to exploit a VMware Server Side Injection CVE-2022-22954 on
+    $dest$ has occurred.
   mitre_attack_id:
   - T1190
   - T1133
@@ -47,17 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/vmware/vmware_scanning_pan_threat.log
     source: pan:threat
     sourcetype: pan:threat
diff --git a/detections/web/web_jsp_request_via_url.yml b/detections/web/web_jsp_request_via_url.yml
index 2a85b005a1..0178c3a26f 100644
--- a/detections/web/web_jsp_request_via_url.yml
+++ b/detections/web/web_jsp_request_via_url.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies URL requests associated with CVE-2022-22965 (Spring4Shell) exploitation attempts, specifically targeting webshell access on a remote webserver. It detects HTTP GET requests with URLs containing ".jsp?cmd=" or "j&cmd=" patterns. This activity is significant as it indicates potential webshell deployment, which can lead to unauthorized remote command execution. If confirmed malicious, attackers could gain control over the webserver, execute arbitrary commands, and potentially escalate privileges, leading to severe data breaches and system compromise.
+description: The following analytic identifies URL requests associated with CVE-2022-22965
+  (Spring4Shell) exploitation attempts, specifically targeting webshell access on
+  a remote webserver. It detects HTTP GET requests with URLs containing ".jsp?cmd="
+  or "j&cmd=" patterns. This activity is significant as it indicates potential webshell
+  deployment, which can lead to unauthorized remote command execution. If confirmed
+  malicious, attackers could gain control over the webserver, execute arbitrary commands,
+  and potentially escalate privileges, leading to severe data breaches and system
+  compromise.
 data_source:
 - Nginx Access
-search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url IN ("*.jsp?cmd=*","*j&cmd=*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_jsp_request_via_url_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
-known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to servers.
+search: '| tstats count from datamodel=Web where Web.http_method IN ("GET") Web.url
+  IN ("*.jsp?cmd=*","*j&cmd=*") by Web.http_user_agent Web.http_method, Web.url,Web.url_length
+  Web.src, Web.dest sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `web_jsp_request_via_url_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+known_false_positives: False positives may be present with legitimate applications.
+  Attempt to filter by dest IP or use Asset groups to restrict to servers.
 references:
 - https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/
 - https://github.com/TheGejr/SpringShell
@@ -21,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +49,8 @@ tags:
   cve:
   - CVE-2022-22965
   impact: 90
-  message: A suspicious URL has been requested against $dest$ by $src$, related to web shell activity.
+  message: A suspicious URL has been requested against $dest$ by $src$, related to
+    web shell activity.
   mitre_attack_id:
   - T1505.003
   - T1505
@@ -51,18 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/spring4shell_nginx.log
     source: /var/log/nginx/access.log
     sourcetype: nginx:plus:kv
diff --git a/detections/web/web_remote_shellservlet_access.yml b/detections/web/web_remote_shellservlet_access.yml
index 300a723b01..da90f5f926 100644
--- a/detections/web/web_remote_shellservlet_access.yml
+++ b/detections/web/web_remote_shellservlet_access.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Nginx Access
-description: The following analytic identifies attempts to access the Remote ShellServlet on a web server, specifically targeting Confluence servers vulnerable to CVE-2023-22518 and CVE-2023-22515. It leverages web data to detect URLs containing "*plugins/servlet/com.jsos.shell/*" with a status code of 200. This activity is significant as it is commonly associated with web shells and other malicious behaviors, potentially leading to unauthorized command execution. If confirmed malicious, attackers could gain remote code execution capabilities, compromising the server and potentially the entire network.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*plugins/servlet/com.jsos.shell/*") Web.status=200 by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_remote_shellservlet_access_filter`'
-how_to_implement: This analytic necessitates the collection of web data, which can be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web Server. No additional configuration is required for this analytic.
-known_false_positives: False positives may occur depending on the web server's configuration. If the web server is intentionally configured to utilize the Remote ShellServlet, then the detections by this analytic would not be considered true positives.
+description: The following analytic identifies attempts to access the Remote ShellServlet
+  on a web server, specifically targeting Confluence servers vulnerable to CVE-2023-22518
+  and CVE-2023-22515. It leverages web data to detect URLs containing "*plugins/servlet/com.jsos.shell/*"
+  with a status code of 200. This activity is significant as it is commonly associated
+  with web shells and other malicious behaviors, potentially leading to unauthorized
+  command execution. If confirmed malicious, attackers could gain remote code execution
+  capabilities, compromising the server and potentially the entire network.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("*plugins/servlet/com.jsos.shell/*") Web.status=200 by Web.http_user_agent,
+  Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype
+  | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `web_remote_shellservlet_access_filter`'
+how_to_implement: This analytic necessitates the collection of web data, which can
+  be achieved through Splunk Stream or by utilizing the Splunk Add-on for Apache Web
+  Server. No additional configuration is required for this analytic.
+known_false_positives: False positives may occur depending on the web server's configuration.
+  If the web server is intentionally configured to utilize the Remote ShellServlet,
+  then the detections by this analytic would not be considered true positives.
 references:
 - http://www.servletsuite.com/servlets/shell.htm
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -29,7 +48,8 @@ tags:
   atomic_guid: []
   confidence: 90
   impact: 90
-  message: An attempt to access the Remote ShellServlet on a web server was detected. The source IP is $src$ and the destination hostname is $dest$.
+  message: An attempt to access the Remote ShellServlet on a web server was detected.
+    The source IP is $src$ and the destination hostname is $dest$.
   mitre_attack_id:
   - T1190
   observable:
@@ -45,19 +65,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_shellservlet.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/confluence/nginx_shellservlet.log
     source: /var/log/nginx/access.log
     sourcetype: nginx:plus:kv
diff --git a/detections/web/web_spring4shell_http_request_class_module.yml b/detections/web/web_spring4shell_http_request_class_module.yml
index 50c2c55ef2..589fd6f67f 100644
--- a/detections/web/web_spring4shell_http_request_class_module.yml
+++ b/detections/web/web_spring4shell_http_request_class_module.yml
@@ -5,12 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic detects HTTP requests containing payloads related to the Spring4Shell vulnerability (CVE-2022-22965). It leverages Splunk Stream HTTP data to inspect the HTTP request body and form data for specific fields such as "class.module.classLoader.resources.context.parent.pipeline.first". This activity is significant as it indicates an attempt to exploit a critical vulnerability in Spring Framework, potentially leading to remote code execution. If confirmed malicious, this could allow attackers to gain unauthorized access, execute arbitrary code, and compromise the affected system.
+description: The following analytic detects HTTP requests containing payloads related
+  to the Spring4Shell vulnerability (CVE-2022-22965). It leverages Splunk Stream HTTP
+  data to inspect the HTTP request body and form data for specific fields such as
+  "class.module.classLoader.resources.context.parent.pipeline.first". This activity
+  is significant as it indicates an attempt to exploit a critical vulnerability in
+  Spring Framework, potentially leading to remote code execution. If confirmed malicious,
+  this could allow attackers to gain unauthorized access, execute arbitrary code,
+  and compromise the affected system.
 data_source:
 - Splunk Stream HTTP
-search: '`stream_http` http_method IN ("POST") | stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by src dest http_method http_user_agent uri_path url bytes_in bytes_out | search http_request_body IN ("*class.module.classLoader.resources.context.parent.pipeline.first.fileDateFormat=_*", "*class.module.classLoader.resources.context.parent.pipeline.first.pattern*","*suffix=.jsp*") | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_spring4shell_http_request_class_module_filter`'
-how_to_implement: To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled.
-known_false_positives: False positives may occur and filtering may be required. Restrict analytic to asset type.
+search: '`stream_http` http_method IN ("POST") | stats values(form_data) as http_request_body
+  min(_time) as firstTime max(_time) as lastTime count by src dest http_method http_user_agent
+  uri_path url bytes_in bytes_out | search http_request_body IN ("*class.module.classLoader.resources.context.parent.pipeline.first.fileDateFormat=_*",
+  "*class.module.classLoader.resources.context.parent.pipeline.first.pattern*","*suffix=.jsp*")
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_spring4shell_http_request_class_module_filter`'
+how_to_implement: To successfully implement this search, you need to be ingesting
+  logs with the stream HTTP logs or network logs that catch network traffic. Make
+  sure that the http-request-body, payload, or request field is enabled.
+known_false_positives: False positives may occur and filtering may be required. Restrict
+  analytic to asset type.
 references:
 - https://github.com/DDuarte/springshell-rce-poc/blob/master/poc.py
 drilldown_searches:
@@ -19,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -30,7 +49,8 @@ tags:
   cve:
   - CVE-2022-22965
   impact: 90
-  message: A http body request related to Spring4Shell has been sent to $dest$ by $src$.
+  message: A http body request related to Spring4Shell has been sent to $dest$ by
+    $src$.
   mitre_attack_id:
   - T1190
   - T1133
@@ -47,19 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - http_request_body
-  - http_method
-  - http_user_agent
-  - uri_path
-  - url
-  - bytes_in
-  - bytes_out
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/http_request_body_streams.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/http_request_body_streams.log
     source: stream:http
     sourcetype: stream:http
diff --git a/detections/web/web_spring_cloud_function_functionrouter.yml b/detections/web/web_spring_cloud_function_functionrouter.yml
index 1ea2a671cf..e957f9c047 100644
--- a/detections/web/web_spring_cloud_function_functionrouter.yml
+++ b/detections/web/web_spring_cloud_function_functionrouter.yml
@@ -5,12 +5,24 @@ date: '2024-09-30'
 author: Michael Haag, Splunk
 status: production
 type: TTP
-description: The following analytic identifies HTTP POST requests to the Spring Cloud Function endpoint containing "functionRouter" in the URL. It leverages the Web data model to detect these requests based on specific fields such as http_method, url, and http_user_agent. This activity is significant because it targets CVE-2022-22963, a known vulnerability in Spring Cloud Function, which has multiple proof-of-concept exploits available. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to unauthorized access, data exfiltration, or further compromise of the affected system.
+description: The following analytic identifies HTTP POST requests to the Spring Cloud
+  Function endpoint containing "functionRouter" in the URL. It leverages the Web data
+  model to detect these requests based on specific fields such as http_method, url,
+  and http_user_agent. This activity is significant because it targets CVE-2022-22963,
+  a known vulnerability in Spring Cloud Function, which has multiple proof-of-concept
+  exploits available. If confirmed malicious, this activity could allow attackers
+  to execute arbitrary code, potentially leading to unauthorized access, data exfiltration,
+  or further compromise of the affected system.
 data_source:
 - Splunk Stream HTTP
-search: '| tstats count from datamodel=Web where Web.http_method IN ("POST") Web.url="*/functionRouter*" by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest Web.status sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_spring_cloud_function_functionrouter_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic that include fields relavent for traffic into the `Web` datamodel.
-known_false_positives: False positives may be present with legitimate applications. Attempt to filter by dest IP or use Asset groups to restrict to servers.
+search: '| tstats count from datamodel=Web where Web.http_method IN ("POST") Web.url="*/functionRouter*"
+  by Web.http_user_agent Web.http_method, Web.url,Web.url_length Web.src, Web.dest
+  Web.status sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `web_spring_cloud_function_functionrouter_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic that include fields relavent for traffic into the `Web` datamodel.
+known_false_positives: False positives may be present with legitimate applications.
+  Attempt to filter by dest IP or use Asset groups to restrict to servers.
 references:
 - https://github.com/rapid7/metasploit-framework/pull/16395
 - https://github.com/hktalent/spring-spel-0day-poc
@@ -20,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -31,7 +48,8 @@ tags:
   cve:
   - CVE-2022-22963
   impact: 70
-  message: A suspicious URL has been requested against $dest$ by $src$, related to a vulnerability in Spring Cloud.
+  message: A suspicious URL has been requested against $dest$ by $src$, related to
+    a vulnerability in Spring Cloud.
   mitre_attack_id:
   - T1190
   - T1133
@@ -48,18 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/all_functionrouter_http_streams.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/spring4shell/all_functionrouter_http_streams.log
     source: stream:http
     sourcetype: stream:http
diff --git a/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml b/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml
index a4ce0844bc..464c3f5e76 100644
--- a/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml
+++ b/detections/web/windows_exchange_autodiscover_ssrf_abuse.yml
@@ -5,11 +5,26 @@ date: '2024-09-30'
 author: Michael Haag, Nathaniel Stearns, Splunk
 status: production
 type: TTP
-description: The following analytic detects potential abuse of the ProxyShell or ProxyNotShell vulnerabilities in Microsoft Exchange via Server Side Request Forgery (SSRF). It leverages the Web datamodel to identify suspicious POST requests with specific URI paths and queries related to autodiscover, powershell, and mapi. This activity is significant as it may indicate an attempt to exploit Exchange server vulnerabilities to access internal services or sensitive data. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the network.
+description: The following analytic detects potential abuse of the ProxyShell or ProxyNotShell
+  vulnerabilities in Microsoft Exchange via Server Side Request Forgery (SSRF). It
+  leverages the Web datamodel to identify suspicious POST requests with specific URI
+  paths and queries related to autodiscover, powershell, and mapi. This activity is
+  significant as it may indicate an attempt to exploit Exchange server vulnerabilities
+  to access internal services or sensitive data. If confirmed malicious, this could
+  lead to unauthorized access, data exfiltration, or further compromise of the network.
 data_source:
 - Windows IIS
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.status=200 OR Web.status=302 OR Web.status=401) AND Web.http_method=POST by Web.src Web.status Web.uri_path Web.dest Web.http_method Web.uri_query | `drop_dm_object_name("Web")` | eval is_autodiscover=if(like(lower(uri_path),"%autodiscover%"),1,0) | eval powershell = if(match(lower(uri_query),"powershell"), "1",0) | eval mapi=if(like(uri_query,"%/mapi/%"),1,0) | addtotals fieldname=Score is_autodiscover, powershell, mapi | fields Score, src,dest, status, uri_query,uri_path,http_method | where Score >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_exchange_autodiscover_ssrf_abuse_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node. In addition, confirm the latest CIM App 4.20 or higher is installed.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web where (Web.status=200 OR Web.status=302 OR Web.status=401)
+  AND Web.http_method=POST by Web.src Web.status Web.uri_path Web.dest Web.http_method
+  Web.uri_query | `drop_dm_object_name("Web")` | eval is_autodiscover=if(like(lower(uri_path),"%autodiscover%"),1,0)
+  | eval powershell = if(match(lower(uri_query),"powershell"), "1",0) | eval mapi=if(like(uri_query,"%/mapi/%"),1,0)
+  | addtotals fieldname=Score is_autodiscover, powershell, mapi | fields Score, src,dest,
+  status, uri_query,uri_path,http_method | where Score >= 2 | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `windows_exchange_autodiscover_ssrf_abuse_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node.
+  In addition, confirm the latest CIM App 4.20 or higher is installed.
 known_false_positives: False positives are limited.
 references:
 - https://www.gteltsc.vn/blog/warning-new-attack-campaign-utilized-a-new-0day-rce-vulnerability-on-microsoft-exchange-server-12715.html
@@ -27,7 +42,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -44,7 +64,8 @@ tags:
   - CVE-2022-41040
   - CVE-2022-41082
   impact: 90
-  message: Activity related to ProxyShell or ProxyNotShell has been identified on $dest$. Review events and take action accordingly.
+  message: Activity related to ProxyShell or ProxyNotShell has been identified on
+    $dest$. Review events and take action accordingly.
   mitre_attack_id:
   - T1190
   - T1133
@@ -57,18 +78,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.src
-  - Web.status
-  - Web.uri_path
-  - Web.dest
-  - Web.http_method
-  - Web.uri_query
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/proxyshell/proxyshell.log
     source: ms:iis:splunk
     sourcetype: ms:iis:splunk
diff --git a/detections/web/windows_iis_server_pswa_console_access.yml b/detections/web/windows_iis_server_pswa_console_access.yml
index 049a760fee..e27f8a42e2 100644
--- a/detections/web/windows_iis_server_pswa_console_access.yml
+++ b/detections/web/windows_iis_server_pswa_console_access.yml
@@ -7,10 +7,24 @@ data_source:
 - Windows IIS
 type: Hunting
 status: production
-description: This analytic detects access attempts to the PowerShell Web Access (PSWA) console on Windows IIS servers. It monitors web traffic for requests to PSWA-related URIs, which could indicate legitimate administrative activity or potential unauthorized access attempts. By tracking source IP, HTTP status, URI path, and HTTP method, it helps identify suspicious patterns or brute-force attacks targeting PSWA. This detection is crucial for maintaining the security of remote PowerShell management interfaces and preventing potential exploitation of this powerful administrative tool.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.dest IN ("/pswa/*") by Web.src Web.status Web.uri_path Web.dest Web.http_method Web.uri_query | `drop_dm_object_name("Web")`| `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_server_pswa_console_access_filter`'
-how_to_implement: To successfully implement this search you need to be ingesting information on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node. In addition, confirm the latest CIM App 4.20 or higher is installed.
-known_false_positives: False positives may occur if legitimate PSWA processes are used for administrative tasks. Careful review of the logs is recommended to distinguish between legitimate and malicious activity.
+description: This analytic detects access attempts to the PowerShell Web Access (PSWA)
+  console on Windows IIS servers. It monitors web traffic for requests to PSWA-related
+  URIs, which could indicate legitimate administrative activity or potential unauthorized
+  access attempts. By tracking source IP, HTTP status, URI path, and HTTP method,
+  it helps identify suspicious patterns or brute-force attacks targeting PSWA. This
+  detection is crucial for maintaining the security of remote PowerShell management
+  interfaces and preventing potential exploitation of this powerful administrative
+  tool.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web where Web.dest IN ("/pswa/*") by Web.src Web.status
+  Web.uri_path Web.dest Web.http_method Web.uri_query | `drop_dm_object_name("Web")`|
+  `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_iis_server_pswa_console_access_filter`'
+how_to_implement: To successfully implement this search you need to be ingesting information
+  on Web traffic, Exchange OR IIS logs, mapped to `Web` datamodel in the `Web` node.
+  In addition, confirm the latest CIM App 4.20 or higher is installed.
+known_false_positives: False positives may occur if legitimate PSWA processes are
+  used for administrative tasks. Careful review of the logs is recommended to distinguish
+  between legitimate and malicious activity.
 references:
 - https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-241a
 tags:
@@ -35,19 +49,12 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.src
-  - Web.status
-  - Web.uri_path
-  - Web.dest
-  - Web.http_method
-  - Web.uri_query
   security_domain: network
   cve: []
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/iis_pswaaccess.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/pswa/iis_pswaaccess.log
     sourcetype: ms:iis:splunk
     source: ms:iis:splunk
diff --git a/detections/web/wordpress_bricks_builder_plugin_rce.yml b/detections/web/wordpress_bricks_builder_plugin_rce.yml
index dc09d50d34..f9928ac190 100644
--- a/detections/web/wordpress_bricks_builder_plugin_rce.yml
+++ b/detections/web/wordpress_bricks_builder_plugin_rce.yml
@@ -7,10 +7,25 @@ data_source:
 - Nginx Access
 type: TTP
 status: production
-description: The following analytic identifies potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability. It detects HTTP POST requests to the URL path "/wp-json/bricks/v1/render_element" with a status code of 200, leveraging the Web datamodel. This activity is significant as it indicates an attempt to exploit CVE-2024-25600, a known vulnerability that allows remote code execution. If confirmed malicious, an attacker could execute arbitrary commands on the target server, leading to potential full system compromise and unauthorized access to sensitive data.
-search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("*/wp-json/bricks/v1/render_element") Web.status=200 Web.http_method=POST by Web.src, Web.dest, Web.http_user_agent, Web.url, Web.uri_path, Web.status, Web.http_method, sourcetype, source | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wordpress_bricks_builder_plugin_rce_filter`'
-how_to_implement: The search is based on data in the Web datamodel and was modeled from NGINX logs. Ensure that the Web datamodel is accelerated and that the data source for the Web datamodel is properly configured. If using other web sources, modify they query, or review the data, as needed.
-known_false_positives: False positives may be possible, however we restricted it to HTTP Status 200 and POST requests, based on the POC. Upon investigation review the POST body for the actual payload - or command - being executed.
+description: The following analytic identifies potential exploitation of the WordPress
+  Bricks Builder plugin RCE vulnerability. It detects HTTP POST requests to the URL
+  path "/wp-json/bricks/v1/render_element" with a status code of 200, leveraging the
+  Web datamodel. This activity is significant as it indicates an attempt to exploit
+  CVE-2024-25600, a known vulnerability that allows remote code execution. If confirmed
+  malicious, an attacker could execute arbitrary commands on the target server, leading
+  to potential full system compromise and unauthorized access to sensitive data.
+search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
+  as lastTime from datamodel=Web where Web.url IN ("*/wp-json/bricks/v1/render_element")
+  Web.status=200 Web.http_method=POST by Web.src, Web.dest, Web.http_user_agent, Web.url,
+  Web.uri_path, Web.status, Web.http_method, sourcetype, source | `drop_dm_object_name("Web")`
+  | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `wordpress_bricks_builder_plugin_rce_filter`'
+how_to_implement: The search is based on data in the Web datamodel and was modeled
+  from NGINX logs. Ensure that the Web datamodel is accelerated and that the data
+  source for the Web datamodel is properly configured. If using other web sources,
+  modify they query, or review the data, as needed.
+known_false_positives: False positives may be possible, however we restricted it to
+  HTTP Status 200 and POST requests, based on the POC. Upon investigation review the
+  POST body for the actual payload - or command - being executed.
 references:
 - https://attack.mitre.org/techniques/T1190
 - https://github.com/Tornad0007/CVE-2024-25600-Bricks-Builder-plugin-for-WordPress/blob/main/exploit.py
@@ -23,7 +38,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -32,7 +52,8 @@ tags:
   asset_type: Web Server
   confidence: 100
   impact: 100
-  message: Potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability on $dest$ by $src$.
+  message: Potential exploitation of the WordPress Bricks Builder plugin RCE vulnerability
+    on $dest$ by $src$.
   mitre_attack_id:
   - T1190
   observable:
@@ -48,22 +69,13 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - _time
-  - Web.src
-  - Web.dest
-  - Web.http_user_agent
-  - Web.url
-  - Web.status
-  - Web.http_method
-  - sourcetype
-  - source
   security_domain: network
   cve:
   - CVE-2024-25600
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/wordpress/bricks_cve_2024_25600.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/wordpress/bricks_cve_2024_25600.log
     source: nginx:plus:kv
     sourcetype: nginx:plus:kv
diff --git a/detections/web/ws_ftp_remote_code_execution.yml b/detections/web/ws_ftp_remote_code_execution.yml
index ed10071fc6..4ca64d58f5 100644
--- a/detections/web/ws_ftp_remote_code_execution.yml
+++ b/detections/web/ws_ftp_remote_code_execution.yml
@@ -7,10 +7,24 @@ status: production
 type: TTP
 data_source:
 - Suricata
-description: The following analytic detects potential Remote Code Execution (RCE) attempts exploiting CVE-2023-40044 in WS_FTP software. It identifies HTTP POST requests to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status code of 200. This detection leverages the Web datamodel to monitor specific URL patterns and HTTP status codes. This activity is significant as it may indicate an exploitation attempt, potentially allowing an attacker to execute arbitrary code on the server. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or further compromise of the affected system.
-search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where Web.url IN ("/AHT/AhtApiService.asmx/AuthUser") Web.status=200 Web.http_method=POST by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src, Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ws_ftp_remote_code_execution_filter`'
-how_to_implement: The following analytic requires the Web datamodel. Ensure data source is mapped correctly or modify and tune for your data source.
-known_false_positives: If WS_FTP Server is not in use, this analytic will not return results. Monitor and tune for your environment. Note the MetaSploit module is focused on only hitting /AHT/ and not the full /AHT/AhtApiService.asmx/AuthUser URL.
+description: The following analytic detects potential Remote Code Execution (RCE)
+  attempts exploiting CVE-2023-40044 in WS_FTP software. It identifies HTTP POST requests
+  to the "/AHT/AhtApiService.asmx/AuthUser" URL with a status code of 200. This detection
+  leverages the Web datamodel to monitor specific URL patterns and HTTP status codes.
+  This activity is significant as it may indicate an exploitation attempt, potentially
+  allowing an attacker to execute arbitrary code on the server. If confirmed malicious,
+  this could lead to unauthorized access, data exfiltration, or further compromise
+  of the affected system.
+search: '| tstats count min(_time) as firstTime max(_time) as lastTime from datamodel=Web
+  where Web.url IN ("/AHT/AhtApiService.asmx/AuthUser") Web.status=200 Web.http_method=POST
+  by Web.http_user_agent, Web.status Web.http_method, Web.url, Web.url_length, Web.src,
+  Web.dest, sourcetype | `drop_dm_object_name("Web")` | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | `ws_ftp_remote_code_execution_filter`'
+how_to_implement: The following analytic requires the Web datamodel. Ensure data source
+  is mapped correctly or modify and tune for your data source.
+known_false_positives: If WS_FTP Server is not in use, this analytic will not return
+  results. Monitor and tune for your environment. Note the MetaSploit module is focused
+  on only hitting /AHT/ and not the full /AHT/AhtApiService.asmx/AuthUser URL.
 references:
 - https://github.com/projectdiscovery/nuclei-templates/pull/8296/files
 - https://www.assetnote.io/resources/research/rce-in-progress-ws-ftp-ad-hoc-via-iis-http-modules-cve-2023-40044
@@ -21,7 +35,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$dest$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$")
+    starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime
+    values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories)
+    as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic)
+    as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)`
+    | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -33,7 +52,8 @@ tags:
   atomic_guid: []
   confidence: 80
   impact: 90
-  message: Potential WS FTP Remote Code Execution detected against URL $url$ on $dest$ from $src$
+  message: Potential WS FTP Remote Code Execution detected against URL $url$ on $dest$
+    from $src$
   mitre_attack_id:
   - T1190
   observable:
@@ -49,19 +69,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - Web.http_user_agent
-  - Web.status
-  - Web.http_method
-  - Web.url
-  - Web.url_length
-  - Web.src
-  - Web.dest
-  - sourcetype
   security_domain: network
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ws_ftp/wsftpweb.log
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1190/ws_ftp/wsftpweb.log
     source: suricata
     sourcetype: suricata
diff --git a/detections/web/zscaler_adware_activities_threat_blocked.yml b/detections/web/zscaler_adware_activities_threat_blocked.yml
index 6e02290d65..973d20b128 100644
--- a/detections/web/zscaler_adware_activities_threat_blocked.yml
+++ b/detections/web/zscaler_adware_activities_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies potential adware activity blocked by Zscaler. It leverages web proxy logs to detect blocked actions associated with adware threats. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant as adware can degrade system performance, lead to unwanted advertisements, and potentially expose users to further malicious content. If confirmed malicious, it could indicate an attempt to compromise user systems, necessitating further investigation and remediation to prevent potential data breaches or system exploitation.
-search: '`zscaler_proxy` action=blocked threatname=*adware* | stats count min(_time) as firstTime max(_time) as lastTime  by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_adware_activities_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies potential adware activity blocked by
+  Zscaler. It leverages web proxy logs to detect blocked actions associated with adware
+  threats. Key data points such as device owner, user, URL category, destination URL,
+  and IP are analyzed. This activity is significant as adware can degrade system performance,
+  lead to unwanted advertisements, and potentially expose users to further malicious
+  content. If confirmed malicious, it could indicate an attempt to compromise user
+  systems, necessitating further investigation and remediation to prevent potential
+  data breaches or system exploitation.
+search: '`zscaler_proxy` action=blocked threatname=*adware* | stats count min(_time)
+  as firstTime max(_time) as lastTime  by action deviceowner user urlcategory url
+  src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_adware_activities_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_behavior_analysis_threat_blocked.yml b/detections/web/zscaler_behavior_analysis_threat_blocked.yml
index 70662bb8c6..9b67ffa0f6 100644
--- a/detections/web/zscaler_behavior_analysis_threat_blocked.yml
+++ b/detections/web/zscaler_behavior_analysis_threat_blocked.yml
@@ -6,9 +6,23 @@ author: Rod Soto, Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies threats blocked by the Zscaler proxy based on behavior analysis. It leverages web proxy logs to detect entries where actions are blocked and threat names and classes are specified. This detection is significant as it highlights potential malicious activities that were intercepted by Zscaler's behavior analysis, providing early indicators of threats. If confirmed malicious, these blocked threats could indicate attempted breaches or malware infections, helping security teams to understand and mitigate potential risks in their environment.
-search: '`zscaler_proxy` action=blocked threatname!="None" threatclass="Behavior Analysis" | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user threatname url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_behavior_analysis_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies threats blocked by the Zscaler proxy
+  based on behavior analysis. It leverages web proxy logs to detect entries where
+  actions are blocked and threat names and classes are specified. This detection is
+  significant as it highlights potential malicious activities that were intercepted
+  by Zscaler's behavior analysis, providing early indicators of threats. If confirmed
+  malicious, these blocked threats could indicate attempted breaches or malware infections,
+  helping security teams to understand and mitigate potential risks in their environment.
+search: '`zscaler_proxy` action=blocked threatname!="None" threatclass="Behavior Analysis"
+  | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner
+  user threatname url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_behavior_analysis_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscalar configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +46,8 @@ tags:
   asset_type: Web Server
   confidence: 80
   impact: 10
-  message: Potential Adware Behavior Analysis Threat from dest -[$dest$] on $src$ for user-[$user$].
+  message: Potential Adware Behavior Analysis Threat from dest -[$dest$] on $src$
+    for user-[$user$].
   mitre_attack_id:
   - T1566
   observable:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml b/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml
index 0af14a4625..af583516df 100644
--- a/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml
+++ b/detections/web/zscaler_cryptominer_downloaded_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies attempts to download cryptomining software that are blocked by Zscaler. It leverages web proxy logs to detect blocked actions associated with cryptominer threats, analyzing key data points such as device owner, user, URL category, destination URL, and IP. This activity is significant for a SOC as it helps in early identification and mitigation of cryptomining activities, which can compromise network integrity and resource availability. If confirmed malicious, this activity could lead to unauthorized use of network resources for cryptomining, potentially degrading system performance and increasing operational costs.
-search: '`zscaler_proxy` action=blocked threatname=*miner* | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_cryptominer_downloaded_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies attempts to download cryptomining software
+  that are blocked by Zscaler. It leverages web proxy logs to detect blocked actions
+  associated with cryptominer threats, analyzing key data points such as device owner,
+  user, URL category, destination URL, and IP. This activity is significant for a
+  SOC as it helps in early identification and mitigation of cryptomining activities,
+  which can compromise network integrity and resource availability. If confirmed malicious,
+  this activity could lead to unauthorized use of network resources for cryptomining,
+  potentially degrading system performance and increasing operational costs.
+search: '`zscaler_proxy` action=blocked threatname=*miner* | stats count min(_time)
+  as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_cryptominer_downloaded_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +47,8 @@ tags:
   asset_type: Web Server
   confidence: 80
   impact: 40
-  message: Potential CryptoMiner Downloaded Threat from dest -[$dest$] on $src$ for user-[$user$].
+  message: Potential CryptoMiner Downloaded Threat from dest -[$dest$] on $src$ for
+    user-[$user$].
   mitre_attack_id:
   - T1566
   observable:
@@ -47,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_employment_search_web_activity.yml b/detections/web/zscaler_employment_search_web_activity.yml
index 619bddefba..8738dd0547 100644
--- a/detections/web/zscaler_employment_search_web_activity.yml
+++ b/detections/web/zscaler_employment_search_web_activity.yml
@@ -6,9 +6,24 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies web activity related to employment searches within a network. It leverages Zscaler web proxy logs, focusing on entries categorized as 'Job/Employment Search'. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This detection is significant for SOCs as it helps monitor potential insider threats by identifying users who may be seeking new employment. If confirmed malicious, this activity could indicate a risk of data exfiltration or other insider threats, potentially leading to sensitive information leakage or other security breaches.
-search: '`zscaler_proxy` urlsupercategory="Job/Employment Search" | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_employment_search_web_activity_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies web activity related to employment
+  searches within a network. It leverages Zscaler web proxy logs, focusing on entries
+  categorized as 'Job/Employment Search'. Key data points such as device owner, user,
+  URL category, destination URL, and IP are analyzed. This detection is significant
+  for SOCs as it helps monitor potential insider threats by identifying users who
+  may be seeking new employment. If confirmed malicious, this activity could indicate
+  a risk of data exfiltration or other insider threats, potentially leading to sensitive
+  information leakage or other security breaches.
+search: '`zscaler_proxy` urlsupercategory="Job/Employment Search" | stats count min(_time)
+  as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_employment_search_web_activity_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -27,7 +47,8 @@ tags:
   asset_type: Web Server
   confidence: 80
   impact: 5
-  message: Potential Employment Search Web Activity from dest -[$dest$] on $src$ for user-[$user$].
+  message: Potential Employment Search Web Activity from dest -[$dest$] on $src$ for
+    user-[$user$].
   mitre_attack_id:
   - T1566
   observable:
@@ -47,20 +68,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_exploit_threat_blocked.yml b/detections/web/zscaler_exploit_threat_blocked.yml
index 06ebbb524c..f3c254d236 100644
--- a/detections/web/zscaler_exploit_threat_blocked.yml
+++ b/detections/web/zscaler_exploit_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Rod Soto, Gowthamaraj Rajendran, Splunk
 status: production
 type: TTP
 data_source: []
-description: The following analytic identifies potential exploit attempts involving command and script interpreters blocked by Zscaler. It leverages web proxy logs to detect incidents where actions are blocked due to exploit references. The detection compiles statistics by user, threat name, URL, hostname, file class, and filename. This activity is significant as it helps identify and mitigate exploit attempts, which are critical for maintaining security. If confirmed malicious, such activity could lead to unauthorized code execution, privilege escalation, or persistent access within the environment, posing a severe threat to organizational security.
-search: '`zscaler_proxy` action=blocked threatname=*exploit* | stats count min(_time) as firstTime max(_time) as lastTime  by user threatname src hostname fileclass filename url dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_exploit_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies potential exploit attempts involving
+  command and script interpreters blocked by Zscaler. It leverages web proxy logs
+  to detect incidents where actions are blocked due to exploit references. The detection
+  compiles statistics by user, threat name, URL, hostname, file class, and filename.
+  This activity is significant as it helps identify and mitigate exploit attempts,
+  which are critical for maintaining security. If confirmed malicious, such activity
+  could lead to unauthorized code execution, privilege escalation, or persistent access
+  within the environment, posing a severe threat to organizational security.
+search: '`zscaler_proxy` action=blocked threatname=*exploit* | stats count min(_time)
+  as firstTime max(_time) as lastTime  by user threatname src hostname fileclass filename
+  url dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_exploit_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_legal_liability_threat_blocked.yml b/detections/web/zscaler_legal_liability_threat_blocked.yml
index 34798bfdf2..cb29789bc6 100644
--- a/detections/web/zscaler_legal_liability_threat_blocked.yml
+++ b/detections/web/zscaler_legal_liability_threat_blocked.yml
@@ -6,9 +6,23 @@ author: Rod Soto, Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies significant legal liability threats blocked by the Zscaler web proxy. It uses web proxy logs to track destinations, device owners, users, URL categories, and actions associated with legal liability. By leveraging statistics on unique fields, it ensures a precise focus on these threats. This activity is significant for SOC as it helps enforce legal compliance and risk management. If confirmed malicious, it could indicate attempts to access legally sensitive or restricted content, potentially leading to legal repercussions and compliance violations.
-search: '`zscaler_proxy` urlclass="Legal Liability" |  stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | dedup urlcategory | `zscaler_legal_liability_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies significant legal liability threats
+  blocked by the Zscaler web proxy. It uses web proxy logs to track destinations,
+  device owners, users, URL categories, and actions associated with legal liability.
+  By leveraging statistics on unique fields, it ensures a precise focus on these threats.
+  This activity is significant for SOC as it helps enforce legal compliance and risk
+  management. If confirmed malicious, it could indicate attempts to access legally
+  sensitive or restricted content, potentially leading to legal repercussions and
+  compliance violations.
+search: '`zscaler_proxy` urlclass="Legal Liability" |  stats count min(_time) as firstTime
+  max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)`
+  | `security_content_ctime(lastTime)` | dedup urlcategory | `zscaler_legal_liability_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_malware_activity_threat_blocked.yml b/detections/web/zscaler_malware_activity_threat_blocked.yml
index 10445d094f..cf5726630d 100644
--- a/detections/web/zscaler_malware_activity_threat_blocked.yml
+++ b/detections/web/zscaler_malware_activity_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Rod Soto, Gowthamaraj Rajendran, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies potential malware activities within a network that are blocked by Zscaler. It leverages web proxy logs to filter for blocked actions associated with malware, aggregating occurrences by user, URL, and threat category. This detection is significant for SOC as it highlights attempts to access malicious content, indicating potential compromise or targeted attacks. If confirmed malicious, this activity could signify an ongoing attempt to infiltrate the network, necessitating immediate investigation to prevent further threats and ensure network integrity.
-search: '`zscaler_proxy` action=blocked threatname=*malware* threatcategory!=None | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_malware_activity_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies potential malware activities within
+  a network that are blocked by Zscaler. It leverages web proxy logs to filter for
+  blocked actions associated with malware, aggregating occurrences by user, URL, and
+  threat category. This detection is significant for SOC as it highlights attempts
+  to access malicious content, indicating potential compromise or targeted attacks.
+  If confirmed malicious, this activity could signify an ongoing attempt to infiltrate
+  the network, necessitating immediate investigation to prevent further threats and
+  ensure network integrity.
+search: '`zscaler_proxy` action=blocked threatname=*malware* threatcategory!=None
+  | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner
+  user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_malware_activity_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscalar configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_phishing_activity_threat_blocked.yml b/detections/web/zscaler_phishing_activity_threat_blocked.yml
index 0775ba6e46..c6722c5fe9 100644
--- a/detections/web/zscaler_phishing_activity_threat_blocked.yml
+++ b/detections/web/zscaler_phishing_activity_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies potential phishing attempts blocked by Zscaler within a network. It leverages web proxy logs to detect actions tagged as HTML.Phish. The detection method involves analyzing critical data points such as user, threat name, URL, and hostname. This activity is significant for a SOC as it serves as an early warning system for phishing threats, enabling prompt investigation and mitigation. If confirmed malicious, this activity could indicate an attempt to deceive users into divulging sensitive information, potentially leading to data breaches or credential theft.
-search: '`zscaler_proxy` action=blocked threatname="HTML.Phish*" | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user threatname url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_phishing_activity_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies potential phishing attempts blocked
+  by Zscaler within a network. It leverages web proxy logs to detect actions tagged
+  as HTML.Phish. The detection method involves analyzing critical data points such
+  as user, threat name, URL, and hostname. This activity is significant for a SOC
+  as it serves as an early warning system for phishing threats, enabling prompt investigation
+  and mitigation. If confirmed malicious, this activity could indicate an attempt
+  to deceive users into divulging sensitive information, potentially leading to data
+  breaches or credential theft.
+search: '`zscaler_proxy` action=blocked threatname="HTML.Phish*" | stats count min(_time)
+  as firstTime max(_time) as lastTime by action deviceowner user threatname url src
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_phishing_activity_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscalar configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_potentially_abused_file_download.yml b/detections/web/zscaler_potentially_abused_file_download.yml
index 8089c4ac5c..784d2d3e7e 100644
--- a/detections/web/zscaler_potentially_abused_file_download.yml
+++ b/detections/web/zscaler_potentially_abused_file_download.yml
@@ -6,9 +6,23 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies the download of potentially malicious file types, such as .scr, .dll, .bat, and .lnk, within a network. It leverages web proxy logs from Zscaler, focusing on blocked actions and analyzing fields like deviceowner, user, urlcategory, url, dest, and filename. This activity is significant as these file types are often used to spread malware, posing a threat to network security. If confirmed malicious, this activity could lead to malware execution, data compromise, or further network infiltration.
-search: '`zscaler_proxy` url IN ("*.scr", "*.dll", "*.bat", "*.lnk") | stats count min(_time) as firstTime max(_time) as lastTime by deviceowner user urlcategory url src filename dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_potentially_abused_file_download_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies the download of potentially malicious
+  file types, such as .scr, .dll, .bat, and .lnk, within a network. It leverages web
+  proxy logs from Zscaler, focusing on blocked actions and analyzing fields like deviceowner,
+  user, urlcategory, url, dest, and filename. This activity is significant as these
+  file types are often used to spread malware, posing a threat to network security.
+  If confirmed malicious, this activity could lead to malware execution, data compromise,
+  or further network infiltration.
+search: '`zscaler_proxy` url IN ("*.scr", "*.dll", "*.bat", "*.lnk") | stats count
+  min(_time) as firstTime max(_time) as lastTime by deviceowner user urlcategory url
+  src filename dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_potentially_abused_file_download_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml b/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml
index 4b4bc567f4..6b1a6cbb99 100644
--- a/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml
+++ b/detections/web/zscaler_privacy_risk_destinations_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies blocked destinations within a network that are deemed privacy risks by Zscaler. It leverages web proxy logs, focusing on entries marked as "Privacy Risk." Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant for a SOC as it helps monitor and manage privacy risks, ensuring a secure network environment. If confirmed malicious, this activity could indicate attempts to access or exfiltrate sensitive information, posing a significant threat to data privacy and security.
-search: '`zscaler_proxy` action=blocked urlclass="Privacy Risk" |  stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | dedup urlcategory | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_privacy_risk_destinations_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies blocked destinations within a network
+  that are deemed privacy risks by Zscaler. It leverages web proxy logs, focusing
+  on entries marked as "Privacy Risk." Key data points such as device owner, user,
+  URL category, destination URL, and IP are analyzed. This activity is significant
+  for a SOC as it helps monitor and manage privacy risks, ensuring a secure network
+  environment. If confirmed malicious, this activity could indicate attempts to access
+  or exfiltrate sensitive information, posing a significant threat to data privacy
+  and security.
+search: '`zscaler_proxy` action=blocked urlclass="Privacy Risk" |  stats count min(_time)
+  as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src
+  dest | dedup urlcategory | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_privacy_risk_destinations_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_scam_destinations_threat_blocked.yml b/detections/web/zscaler_scam_destinations_threat_blocked.yml
index d5810096fc..c3d5dad232 100644
--- a/detections/web/zscaler_scam_destinations_threat_blocked.yml
+++ b/detections/web/zscaler_scam_destinations_threat_blocked.yml
@@ -6,9 +6,23 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies blocked scam-related activities detected by Zscaler within a network. It leverages web proxy logs to examine actions flagged as scam threats, focusing on data points such as device owner, user, URL category, destination URL, and IP. This detection is significant for SOC as it helps in the early identification and mitigation of scam activities, ensuring network safety. If confirmed malicious, this activity could indicate attempts to deceive users, potentially leading to data theft or financial loss.
-search: '`zscaler_proxy` action=blocked threatname=*scam* | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_scam_destinations_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies blocked scam-related activities detected
+  by Zscaler within a network. It leverages web proxy logs to examine actions flagged
+  as scam threats, focusing on data points such as device owner, user, URL category,
+  destination URL, and IP. This detection is significant for SOC as it helps in the
+  early identification and mitigation of scam activities, ensuring network safety.
+  If confirmed malicious, this activity could indicate attempts to deceive users,
+  potentially leading to data theft or financial loss.
+search: '`zscaler_proxy` action=blocked threatname=*scam* | stats count min(_time)
+  as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src
+  dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_scam_destinations_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +32,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +66,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/detections/web/zscaler_virus_download_threat_blocked.yml b/detections/web/zscaler_virus_download_threat_blocked.yml
index e51131ecef..72cac71d2b 100644
--- a/detections/web/zscaler_virus_download_threat_blocked.yml
+++ b/detections/web/zscaler_virus_download_threat_blocked.yml
@@ -6,9 +6,24 @@ author: Gowthamaraj Rajendran, Rod Soto, Splunk
 status: production
 type: Anomaly
 data_source: []
-description: The following analytic identifies attempts to download viruses that were blocked by Zscaler within a network. It leverages web proxy logs to detect blocked actions indicative of virus download attempts. Key data points such as device owner, user, URL category, destination URL, and IP are analyzed. This activity is significant as it helps in early detection and remediation of potential virus threats, enhancing network security. If confirmed malicious, this activity could indicate an attempt to compromise the network, potentially leading to data breaches or further malware infections.
-search: '`zscaler_proxy` action=blocked threatname!="None" threatclass=Virus | stats count min(_time) as firstTime max(_time) as lastTime by action deviceowner user urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `zscaler_virus_download_threat_blocked_filter`'
-how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase. You must be ingesting Zscaler events into your Splunk environment through an ingester. This analytic was written to be used with the "zscalernss-web" sourcetype leveraging the Zscaler proxy data. This enables the integration with Splunk Enterprise Security. Security teams are encouraged to adjust the detection parameters, ensuring the detection is tailored to their specific environment.
+description: The following analytic identifies attempts to download viruses that were
+  blocked by Zscaler within a network. It leverages web proxy logs to detect blocked
+  actions indicative of virus download attempts. Key data points such as device owner,
+  user, URL category, destination URL, and IP are analyzed. This activity is significant
+  as it helps in early detection and remediation of potential virus threats, enhancing
+  network security. If confirmed malicious, this activity could indicate an attempt
+  to compromise the network, potentially leading to data breaches or further malware
+  infections.
+search: '`zscaler_proxy` action=blocked threatname!="None" threatclass=Virus | stats
+  count min(_time) as firstTime max(_time) as lastTime by action deviceowner user
+  urlcategory url src dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`
+  | `zscaler_virus_download_threat_blocked_filter`'
+how_to_implement: You must install the latest version of Zscaler Add-on from Splunkbase.
+  You must be ingesting Zscaler events into your Splunk environment through an ingester.
+  This analytic was written to be used with the "zscalernss-web" sourcetype leveraging
+  the Zscaler proxy data. This enables the integration with Splunk Enterprise Security.
+  Security teams are encouraged to adjust the detection parameters, ensuring the detection
+  is tailored to their specific environment.
 known_false_positives: False positives are limited to Zscaler configuration.
 references:
 - https://help.zscaler.com/zia/nss-feed-output-format-web-logs
@@ -18,7 +33,12 @@ drilldown_searches:
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 - name: View risk events for the last 7 days for - "$src$" and "$user$"
-  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
+  search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$",
+    "$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time)
+    as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk
+    Message" values(analyticstories) as "Analytic Stories" values(annotations._all)
+    as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics"
+    by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
   earliest_offset: $info_min_time$
   latest_offset: $info_max_time$
 tags:
@@ -47,20 +67,11 @@ tags:
   - Splunk Enterprise
   - Splunk Enterprise Security
   - Splunk Cloud
-  required_fields:
-  - action
-  - threatname
-  - deviceowner
-  - user
-  - urlcategory
-  - url
-  - dest
-  - dest_ip
-  - action
   security_domain: threat
 tests:
 - name: True Positive Test
   attack_data:
-  - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
+  - data: 
+      https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566/zscalar_web_proxy/zscalar_web_proxy.json
     source: zscaler
     sourcetype: zscalernss-web
diff --git a/investigations/all_backup_logs_for_host.yml b/investigations/all_backup_logs_for_host.yml
index b5a7e557c2..fb5ceeefa9 100644
--- a/investigations/all_backup_logs_for_host.yml
+++ b/investigations/all_backup_logs_for_host.yml
@@ -16,7 +16,4 @@ tags:
   - Monitor Backup Solution
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - dest
   security_domain: endpoint
diff --git a/investigations/amazon_eks_kubernetes_activity_by_src_ip.yml b/investigations/amazon_eks_kubernetes_activity_by_src_ip.yml
index 2291090569..549b3eec93 100644
--- a/investigations/amazon_eks_kubernetes_activity_by_src_ip.yml
+++ b/investigations/amazon_eks_kubernetes_activity_by_src_ip.yml
@@ -21,12 +21,4 @@ tags:
   - Kubernetes Scanning Activity
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - sourceIPs{}
-  - user.username
-  - requestURI
-  - verb
-  - userAgent
-  - annotations.authorization.k8s.io/decision
   security_domain: network
diff --git a/investigations/aws_investigate_security_hub_alerts_by_dest.yml b/investigations/aws_investigate_security_hub_alerts_by_dest.yml
index f17354b263..c000401848 100644
--- a/investigations/aws_investigate_security_hub_alerts_by_dest.yml
+++ b/investigations/aws_investigate_security_hub_alerts_by_dest.yml
@@ -23,15 +23,4 @@ tags:
   - AWS Suspicious Provisioning Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - findings{}.Resources{}.Type
-  - findings{}.Resources{}.Id
-  - instance
-  - Remediation.Recommendation.Text
-  - Title
-  - ProductArn
-  - Description
-  - FirstObservedAt
-  - RecordState
   security_domain: network
diff --git a/investigations/aws_investigate_user_activities_by_accesskeyid.yml b/investigations/aws_investigate_user_activities_by_accesskeyid.yml
index 00dd750a51..52a16896d9 100644
--- a/investigations/aws_investigate_user_activities_by_accesskeyid.yml
+++ b/investigations/aws_investigate_user_activities_by_accesskeyid.yml
@@ -20,13 +20,4 @@ tags:
   product:
   - Splunk Phantom
   - Splunk Security Analytics for AWS
-  required_fields:
-  - _time
-  - userIdentity.accessKeyId
-  - userIdentity.arn
-  - sourceIPAddress
-  - awsRegion
-  - eventName
-  - errorCode
-  - errorMessage
   security_domain: network
diff --git a/investigations/aws_investigate_user_activities_by_arn.yml b/investigations/aws_investigate_user_activities_by_arn.yml
index 780fab63dc..6c928258e1 100644
--- a/investigations/aws_investigate_user_activities_by_arn.yml
+++ b/investigations/aws_investigate_user_activities_by_arn.yml
@@ -32,15 +32,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - user
-  - userIdentity.type
-  - userIdentity.userName
-  - userIdentity.arn
-  - aws_account_id
-  - src
-  - awsRegion
-  - eventName
-  - eventType
   security_domain: network
diff --git a/investigations/aws_network_acl_details_from_id.yml b/investigations/aws_network_acl_details_from_id.yml
index 4dae0c54f7..2e54e32512 100644
--- a/investigations/aws_network_acl_details_from_id.yml
+++ b/investigations/aws_network_acl_details_from_id.yml
@@ -20,10 +20,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - id
-  - account_id
-  - vpc_id
-  - network_acl_entries{}.*
   security_domain: network
diff --git a/investigations/aws_network_interface_details_via_resourceid.yml b/investigations/aws_network_interface_details_via_resourceid.yml
index 2912346a4d..7ed717720b 100644
--- a/investigations/aws_network_interface_details_via_resourceid.yml
+++ b/investigations/aws_network_interface_details_via_resourceid.yml
@@ -23,13 +23,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - resourceId
-  - ARN
-  - relationships{}.resourceType
-  - relationships{}.name
-  - relationships{}.resourceId
-  - configuration.privateIpAddresses{}.privateIpAddress
-  - configuration.privateIpAddresses{}.association.publicIp
   security_domain: network
diff --git a/investigations/aws_s3_bucket_details_via_bucketname.yml b/investigations/aws_s3_bucket_details_via_bucketname.yml
index 9882bd43c7..875ea0b095 100644
--- a/investigations/aws_s3_bucket_details_via_bucketname.yml
+++ b/investigations/aws_s3_bucket_details_via_bucketname.yml
@@ -21,13 +21,4 @@ tags:
   - Suspicious AWS S3 Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - resourceId
-  - bucketName
-  - resourceCreationTime
-  - vendor_region
-  - action
-  - aws_account_id
-  - supplementaryConfiguration.AccessControlList
   security_domain: network
diff --git a/investigations/gcp_kubernetes_activity_by_src_ip.yml b/investigations/gcp_kubernetes_activity_by_src_ip.yml
index 5c345586d4..df57d09804 100644
--- a/investigations/gcp_kubernetes_activity_by_src_ip.yml
+++ b/investigations/gcp_kubernetes_activity_by_src_ip.yml
@@ -25,14 +25,4 @@ tags:
   - Kubernetes Scanning Activity
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - data.protoPayload.requestMetadata.callerIp
-  - data.protoPayload.methodName
-  - data.protoPayload.resourceName
-  - data.protoPayload.requestMetadata.callerSuppliedUserAgent
-  - data.protoPayload.authenticationInfo.principalEmail
-  - data.protoPayload.status.message
-  - data.resource.labels.cluster_name
-  - data.resource.type
   security_domain: network
diff --git a/investigations/get_all_aws_activity_from_city.yml b/investigations/get_all_aws_activity_from_city.yml
index 9dab76c00c..80cc059499 100644
--- a/investigations/get_all_aws_activity_from_city.yml
+++ b/investigations/get_all_aws_activity_from_city.yml
@@ -22,13 +22,4 @@ tags:
   - AWS Suspicious Provisioning Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - sourceIPAddress
-  - userIdentity.arn
-  - userIdentity.userName
-  - userIdentity.type
-  - awsRegion
-  - eventName
-  - errorCode
   security_domain: network
diff --git a/investigations/get_all_aws_activity_from_country.yml b/investigations/get_all_aws_activity_from_country.yml
index 18175ab6d5..e7b9c1124a 100644
--- a/investigations/get_all_aws_activity_from_country.yml
+++ b/investigations/get_all_aws_activity_from_country.yml
@@ -23,13 +23,4 @@ tags:
   - AWS Suspicious Provisioning Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - sourceIPAddress
-  - userIdentity.arn
-  - userIdentity.userName
-  - userIdentity.type
-  - awsRegion
-  - eventName
-  - errorCode
   security_domain: network
diff --git a/investigations/get_all_aws_activity_from_ip_address.yml b/investigations/get_all_aws_activity_from_ip_address.yml
index c6dd6f1f83..ec81104fc7 100644
--- a/investigations/get_all_aws_activity_from_ip_address.yml
+++ b/investigations/get_all_aws_activity_from_ip_address.yml
@@ -27,13 +27,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - sourceIPAddress
-  - userIdentity.arn
-  - userIdentity.userName
-  - userIdentity.type
-  - awsRegion
-  - eventName
-  - errorCode
   security_domain: network
diff --git a/investigations/get_all_aws_activity_from_region.yml b/investigations/get_all_aws_activity_from_region.yml
index 9b2a4cdc1a..a6e53b8bde 100644
--- a/investigations/get_all_aws_activity_from_region.yml
+++ b/investigations/get_all_aws_activity_from_region.yml
@@ -22,13 +22,4 @@ tags:
   - AWS Suspicious Provisioning Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - sourceIPAddress
-  - userIdentity.arn
-  - userIdentity.userName
-  - userIdentity.type
-  - awsRegion
-  - eventName
-  - errorCode
   security_domain: network
diff --git a/investigations/get_backup_logs_for_endpoint.yml b/investigations/get_backup_logs_for_endpoint.yml
index b43bff6418..c1bd52ccac 100644
--- a/investigations/get_backup_logs_for_endpoint.yml
+++ b/investigations/get_backup_logs_for_endpoint.yml
@@ -17,8 +17,4 @@ tags:
   - SamSam Ransomware
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - COMPUTERNAME
-  - MESSAGE
   security_domain: endpoint
diff --git a/investigations/get_certificate_logs_for_a_domain.yml b/investigations/get_certificate_logs_for_a_domain.yml
index fe6d4acb96..932ed57c66 100644
--- a/investigations/get_certificate_logs_for_a_domain.yml
+++ b/investigations/get_certificate_logs_for_a_domain.yml
@@ -23,11 +23,4 @@ tags:
   - Common Phishing Frameworks
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - All_Certificates.SSL.ssl_subject_common_name
-  - All_Certificates.dest
-  - All_Certificates.src
-  - All_Certificates.SSL.ssl_issuer_common_name
-  - All_Certificates.SSL.ssl_hash
   security_domain: network
diff --git a/investigations/get_dns_server_history_for_a_host.yml b/investigations/get_dns_server_history_for_a_host.yml
index 0365ad4c8b..53ebad3fa0 100644
--- a/investigations/get_dns_server_history_for_a_host.yml
+++ b/investigations/get_dns_server_history_for_a_host.yml
@@ -29,9 +29,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - src_ip
-  - dest_port
-  - dest_ip
   security_domain: network
diff --git a/investigations/get_dns_traffic_ratio.yml b/investigations/get_dns_traffic_ratio.yml
index 939418d5ab..b9da20a5e3 100644
--- a/investigations/get_dns_traffic_ratio.yml
+++ b/investigations/get_dns_traffic_ratio.yml
@@ -4,11 +4,11 @@ version: 2
 date: '2024-09-24'
 author: Bhavin Patel, Splunk
 type: Investigation
-description: 'This search calculates the ratio of DNS traffic originating and coming
+description: This search calculates the ratio of DNS traffic originating and coming
   from a host to a list of DNS servers over the last 24 hours. A high value of this
   ratio could be very useful to quickly understand if a src_ip (host) is sending a
   high volume of data out via port 53, could be an indicator of data exfiltration
-  via DNS.'
+  via DNS.
 search: '| tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out"
   sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic
   All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest| `drop_dm_object_name(All_Traffic)`
@@ -28,11 +28,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - All_Traffic.bytes_out
-  - All_Traffic.bytes_in
-  - All_Traffic.dest_port
-  - All_Traffic.src
-  - All_Traffic.dest
   security_domain: network
diff --git a/investigations/get_ec2_instance_details_by_instanceid.yml b/investigations/get_ec2_instance_details_by_instanceid.yml
index 08342d3dcb..41faacccf5 100644
--- a/investigations/get_ec2_instance_details_by_instanceid.yml
+++ b/investigations/get_ec2_instance_details_by_instanceid.yml
@@ -28,17 +28,4 @@ tags:
   - AWS Security Hub Alerts
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - id
-  - ip_address
-  - tags
-  - aws_account_id
-  - placement
-  - instance_type
-  - key_name
-  - launch_time
-  - state
-  - vpc_id
-  - subnet_id
   security_domain: network
diff --git a/investigations/get_ec2_launch_details.yml b/investigations/get_ec2_launch_details.yml
index dca1fb110e..9eced4ac58 100644
--- a/investigations/get_ec2_launch_details.yml
+++ b/investigations/get_ec2_launch_details.yml
@@ -23,13 +23,4 @@ tags:
   - AWS Security Hub Alerts
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - dest
-  - userIdentity.arn
-  - responseElements.instancesSet.items{}.instanceId
-  - responseElements.instancesSet.items{}.privateIpAddress
-  - responseElements.instancesSet.items{}.imageId
-  - responseElements.instancesSet.items{}.architecture
-  - responseElements.instancesSet.items{}.keyName
   security_domain: network
diff --git a/investigations/get_email_info.yml b/investigations/get_email_info.yml
index b2d937be65..778409bb43 100644
--- a/investigations/get_email_info.yml
+++ b/investigations/get_email_info.yml
@@ -17,7 +17,4 @@ tags:
   - Suspicious Emails
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - message
   security_domain: network
diff --git a/investigations/get_emails_from_specific_sender.yml b/investigations/get_emails_from_specific_sender.yml
index be43e0e708..109bbb8bf7 100644
--- a/investigations/get_emails_from_specific_sender.yml
+++ b/investigations/get_emails_from_specific_sender.yml
@@ -19,7 +19,4 @@ tags:
   - Web Fraud Detection
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - src_user
   security_domain: network
diff --git a/investigations/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml b/investigations/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml
index e6e389499e..12c8480ae1 100644
--- a/investigations/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml
+++ b/investigations/get_first_occurrence_and_last_occurrence_of_a_mac_address.yml
@@ -21,11 +21,4 @@ tags:
   - Asset Tracking
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - All_Sessions.DHCP
-  - All_Sessions.signature
-  - All_Sessions.src_mac
-  - All_Sessions.src_ip
-  - All_Sessions.user
   security_domain: network
diff --git a/investigations/get_history_of_email_sources.yml b/investigations/get_history_of_email_sources.yml
index f8331110dc..b5ab421254 100644
--- a/investigations/get_history_of_email_sources.yml
+++ b/investigations/get_history_of_email_sources.yml
@@ -28,9 +28,4 @@ tags:
   - SamSam Ransomware
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - All_Email.dest
-  - All_Email.recipient
-  - All_Email.src
   security_domain: network
diff --git a/investigations/get_logon_rights_modifications_for_endpoint.yml b/investigations/get_logon_rights_modifications_for_endpoint.yml
index c5d4327192..62a1483ba5 100644
--- a/investigations/get_logon_rights_modifications_for_endpoint.yml
+++ b/investigations/get_logon_rights_modifications_for_endpoint.yml
@@ -18,9 +18,4 @@ tags:
   - AWS Cryptomining
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - signature_id
-  - dest
-  - user
   security_domain: endpoint
diff --git a/investigations/get_logon_rights_modifications_for_user.yml b/investigations/get_logon_rights_modifications_for_user.yml
index 91bcf366be..5fcf3325d2 100644
--- a/investigations/get_logon_rights_modifications_for_user.yml
+++ b/investigations/get_logon_rights_modifications_for_user.yml
@@ -18,9 +18,4 @@ tags:
   - AWS Cryptomining
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - signature_id
-  - dest
-  - user
   security_domain: endpoint
diff --git a/investigations/get_notable_history.yml b/investigations/get_notable_history.yml
index 15044c80de..01a9d592f6 100644
--- a/investigations/get_notable_history.yml
+++ b/investigations/get_notable_history.yml
@@ -83,6 +83,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
   security_domain: endpoint
diff --git a/investigations/get_outbound_emails_to_hidden_cobra_threat_actors.yml b/investigations/get_outbound_emails_to_hidden_cobra_threat_actors.yml
index d636bb71c7..ff887cae70 100644
--- a/investigations/get_outbound_emails_to_hidden_cobra_threat_actors.yml
+++ b/investigations/get_outbound_emails_to_hidden_cobra_threat_actors.yml
@@ -21,10 +21,4 @@ tags:
   - Hidden Cobra Malware
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - recipient
-  - src_user
-  - dest
-  - sec
   security_domain: network
diff --git a/investigations/get_parent_process_info.yml b/investigations/get_parent_process_info.yml
index cfa1fdf6d3..0d909b9d3e 100644
--- a/investigations/get_parent_process_info.yml
+++ b/investigations/get_parent_process_info.yml
@@ -49,10 +49,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
diff --git a/investigations/get_process_file_activity.yml b/investigations/get_process_file_activity.yml
index 67b8d9aa54..a1acf702dd 100644
--- a/investigations/get_process_file_activity.yml
+++ b/investigations/get_process_file_activity.yml
@@ -22,11 +22,4 @@ tags:
   - Suspicious Zoom Child Processes
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Filesystem.file_name
-  - Filesystem.dest
-  - Filesystem.process_name
-  - Filesystem.file_path
-  - Filesystem.action
   security_domain: endpoint
diff --git a/investigations/get_process_info.yml b/investigations/get_process_info.yml
index 988afa1a3e..7dfe4d72ec 100644
--- a/investigations/get_process_info.yml
+++ b/investigations/get_process_info.yml
@@ -50,10 +50,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.parent_process_name
-  - Processes.process_name
-  - Processes.dest
   security_domain: endpoint
diff --git a/investigations/get_process_information_for_port_activity.yml b/investigations/get_process_information_for_port_activity.yml
index ee370623d1..95b0e406a4 100644
--- a/investigations/get_process_information_for_port_activity.yml
+++ b/investigations/get_process_information_for_port_activity.yml
@@ -32,13 +32,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
-  - Ports.process_id
-  - Ports.src
-  - Ports.dest_port
   security_domain: endpoint
diff --git a/investigations/get_process_responsible_for_the_dns_traffic.yml b/investigations/get_process_responsible_for_the_dns_traffic.yml
index f90064f6a7..1ffd93df83 100644
--- a/investigations/get_process_responsible_for_the_dns_traffic.yml
+++ b/investigations/get_process_responsible_for_the_dns_traffic.yml
@@ -32,14 +32,4 @@ tags:
   - Command And Control
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Processes.user
-  - Processes.process_id
-  - Processes.process_name
-  - Processes.dest
-  - Processes.parent_process
-  - Ports.process_id
-  - Ports.src
-  - Ports.dest_port
   security_domain: endpoint
diff --git a/investigations/get_sysmon_wmi_activity_for_host.yml b/investigations/get_sysmon_wmi_activity_for_host.yml
index 556b5ccc45..61e769e8b9 100644
--- a/investigations/get_sysmon_wmi_activity_for_host.yml
+++ b/investigations/get_sysmon_wmi_activity_for_host.yml
@@ -19,15 +19,4 @@ tags:
   - Suspicious WMI Use
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - EventCode
-  - user
-  - Name
-  - Operation
-  - EventType
-  - Type
-  - Query
-  - Consumer
-  - Filter
   security_domain: endpoint
diff --git a/investigations/get_web_session_information_via_session_id.yml b/investigations/get_web_session_information_via_session_id.yml
index d495908670..86854006a5 100644
--- a/investigations/get_web_session_information_via_session_id.yml
+++ b/investigations/get_web_session_information_via_session_id.yml
@@ -20,10 +20,4 @@ tags:
   - Web Fraud Detection
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - session_id
-  - http_user_agent
-  - src_ip
-  - status
   security_domain: network
diff --git a/investigations/investigate_aws_activities_via_region_name.yml b/investigations/investigate_aws_activities_via_region_name.yml
index 38be596002..0eaf244531 100644
--- a/investigations/investigate_aws_activities_via_region_name.yml
+++ b/investigations/investigate_aws_activities_via_region_name.yml
@@ -22,10 +22,4 @@ tags:
   - Suspicious AWS S3 Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - vendor_region
-  - requestParameters.instancesSet.items{}.instanceId
-  - eventName
-  - user
   security_domain: network
diff --git a/investigations/investigate_aws_user_activities_by_user_field.yml b/investigations/investigate_aws_user_activities_by_user_field.yml
index a2c8113502..ad753be950 100644
--- a/investigations/investigate_aws_user_activities_by_user_field.yml
+++ b/investigations/investigate_aws_user_activities_by_user_field.yml
@@ -21,15 +21,4 @@ tags:
   - Suspicious Cloud Authentication Activities
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - user
-  - userIdentity.type
-  - userIdentity.userName
-  - userIdentity.arn
-  - aws_account_id
-  - src
-  - awsRegion
-  - eventName
-  - eventType
   security_domain: network
diff --git a/investigations/investigate_failed_logins_for_multiple_destinations.yml b/investigations/investigate_failed_logins_for_multiple_destinations.yml
index 2818d7ce28..6047c78962 100644
--- a/investigations/investigate_failed_logins_for_multiple_destinations.yml
+++ b/investigations/investigate_failed_logins_for_multiple_destinations.yml
@@ -20,10 +20,4 @@ tags:
   - Credential Dumping
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Authentication.dest
-  - Authentication.app
-  - Authentication.action
-  - Authentication.user
   security_domain: endpoint
diff --git a/investigations/investigate_network_traffic_from_src_ip.yml b/investigations/investigate_network_traffic_from_src_ip.yml
index 2018c3554a..560f4f52e3 100644
--- a/investigations/investigate_network_traffic_from_src_ip.yml
+++ b/investigations/investigate_network_traffic_from_src_ip.yml
@@ -16,7 +16,4 @@ tags:
   - ColdRoot MacOS RAT
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - src_ip
   security_domain: network
diff --git a/investigations/investigate_okta_activity_by_app.yml b/investigations/investigate_okta_activity_by_app.yml
index c231a76d36..875e9467c5 100644
--- a/investigations/investigate_okta_activity_by_app.yml
+++ b/investigations/investigate_okta_activity_by_app.yml
@@ -16,15 +16,4 @@ tags:
   - Suspicious Okta Activity
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - app
-  - client.geographicalContext.country
-  - client.geographicalContext.state
-  - client.geographicalContext.city
-  - user
-  - displayMessage
-  - src_ip
-  - result
-  - outcome.reason
   security_domain: network
diff --git a/investigations/investigate_okta_activity_by_ip_address.yml b/investigations/investigate_okta_activity_by_ip_address.yml
index 6fe23ba8e7..07b5763759 100644
--- a/investigations/investigate_okta_activity_by_ip_address.yml
+++ b/investigations/investigate_okta_activity_by_ip_address.yml
@@ -16,15 +16,4 @@ tags:
   - Suspicious Okta Activity
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - app
-  - client.geographicalContext.country
-  - client.geographicalContext.state
-  - client.geographicalContext.city
-  - user
-  - displayMessage
-  - src_ip
-  - result
-  - outcome.reason
   security_domain: network
diff --git a/investigations/investigate_pass_the_hash_attempts.yml b/investigations/investigate_pass_the_hash_attempts.yml
index e22d2d1c65..58dd9550af 100644
--- a/investigations/investigate_pass_the_hash_attempts.yml
+++ b/investigations/investigate_pass_the_hash_attempts.yml
@@ -22,11 +22,4 @@ tags:
   - Credential Dumping
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - EventCode
-  - Logon_Type
-  - AuthenticationPackageName
-  - src_user
-  - dest
   security_domain: endpoint
diff --git a/investigations/investigate_pass_the_ticket_attempts.yml b/investigations/investigate_pass_the_ticket_attempts.yml
index bd59f4dc5f..6fe8ef1f33 100644
--- a/investigations/investigate_pass_the_ticket_attempts.yml
+++ b/investigations/investigate_pass_the_ticket_attempts.yml
@@ -22,9 +22,4 @@ tags:
   - Credential Dumping
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - EventCode
-  - user
-  - dest
   security_domain: endpoint
diff --git a/investigations/investigate_previous_unseen_user.yml b/investigations/investigate_previous_unseen_user.yml
index ccb2bbf513..861bd4b1e4 100644
--- a/investigations/investigate_previous_unseen_user.yml
+++ b/investigations/investigate_previous_unseen_user.yml
@@ -24,10 +24,4 @@ tags:
   - Credential Dumping
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Authentication.dest
-  - Authentication.app
-  - Authentication.action
-  - Authentication.user
   security_domain: endpoint
diff --git a/investigations/investigate_successful_remote_desktop_authentications.yml b/investigations/investigate_successful_remote_desktop_authentications.yml
index 0910a8ec35..f763bc9c43 100644
--- a/investigations/investigate_successful_remote_desktop_authentications.yml
+++ b/investigations/investigate_successful_remote_desktop_authentications.yml
@@ -4,9 +4,9 @@ version: 2
 date: '2024-09-24'
 author: Jose Hernandez, Splunk
 type: Investigation
-description: 'This search returns the source, destination, and user for all successful
+description: This search returns the source, destination, and user for all successful
   remote-desktop authentications. A successful authentication after a brute-force
-  attack on a destination machine is suspicious behavior.'
+  attack on a destination machine is suspicious behavior.
 search: '| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
   as lastTime from datamodel=Authentication where Authentication.signature_id=4624
   Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app
@@ -25,13 +25,4 @@ tags:
   - SamSam Ransomware
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Authentication.signature_id
-  - Authentication.app
-  - Authentication.src
-  - Authentication.dest
-  - Authentication.user
-  - Authentication.signature
-  - Authentication.src_nt_domain
   security_domain: endpoint
diff --git a/investigations/investigate_suspicious_strings_in_http_header.yml b/investigations/investigate_suspicious_strings_in_http_header.yml
index 5d285345d8..9dffaedc6b 100644
--- a/investigations/investigate_suspicious_strings_in_http_header.yml
+++ b/investigations/investigate_suspicious_strings_in_http_header.yml
@@ -26,10 +26,4 @@ tags:
   - Apache Struts Vulnerability
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - src_ip
-  - dest_ip
-  - cs_content_type
-  - url
   security_domain: network
diff --git a/investigations/investigate_user_activities_in_okta.yml b/investigations/investigate_user_activities_in_okta.yml
index db977a57fc..5e0af3da3d 100644
--- a/investigations/investigate_user_activities_in_okta.yml
+++ b/investigations/investigate_user_activities_in_okta.yml
@@ -16,14 +16,4 @@ tags:
   - Suspicious Okta Activity
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - client.geographicalContext.country
-  - client.geographicalContext.state
-  - client.geographicalContext.city
-  - user
-  - displayMessage
-  - src_ip
-  - result
-  - outcome.reason
   security_domain: network
diff --git a/investigations/investigate_web_posts_from_src.yml b/investigations/investigate_web_posts_from_src.yml
index 39e55b37cf..267c3fdb22 100644
--- a/investigations/investigate_web_posts_from_src.yml
+++ b/investigations/investigate_web_posts_from_src.yml
@@ -4,9 +4,9 @@ version: 2
 date: '2024-09-24'
 author: Jose Hernandez, Splunk
 type: Investigation
-description: 'This investigative search retrieves POST requests from a specified source
+description: This investigative search retrieves POST requests from a specified source
   IP or hostname. Identifying the POST requests, as well as their associated destination
-  URLs and user agent(s), may help you scope and characterize the suspicious traffic.'
+  URLs and user agent(s), may help you scope and characterize the suspicious traffic.
 search: '| tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web
   by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| search
   http_method, "POST" | search src=$src$'
@@ -19,10 +19,4 @@ tags:
   - Apache Struts Vulnerability
   product:
   - Splunk Phantom
-  required_fields:
-  - _time
-  - Web.url
-  - Web.src
-  - Web.http_user_agent
-  - Web.http_method
   security_domain: network